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class="data">1</p></div></a><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="254614243" href="https://www.academia.edu/Documents/in/Muiscas"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/AntonellaLozano1","location":"/AntonellaLozano1","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/AntonellaLozano1","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" 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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/95601773/Statistics_and_Food_Quality"><img alt="Research paper thumbnail of Statistics and Food Quality" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/95601773/Statistics_and_Food_Quality">Statistics and Food Quality</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601773"><a 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spectroscopy" class="work-thumbnail" src="https://attachments.academia-assets.com/97738910/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601772/Determination_of_ochratoxin_A_in_coffee_and_tea_samples_by_coupling_second_order_multivariate_calibration_and_fluorescence_spectroscopy">Determination of ochratoxin A in coffee and tea samples by coupling second-order multivariate calibration and fluorescence spectroscopy</a></div><div class="wp-workCard_item"><span>Talanta</span><span>, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="865e3be059e4bdc586c1e2c29f7cf4dd" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601770"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601770/Comparative_chemometric_analysis_of_fluorescence_and_near_infrared_spectroscopies_for_authenticity_confirmation_and_geographical_origin_of_Argentinean_extra_virgin_olive_oils"><img alt="Research paper thumbnail of Comparative chemometric analysis of fluorescence and near infrared spectroscopies for authenticity confirmation and geographical origin of Argentinean extra virgin olive oils" class="work-thumbnail" src="https://attachments.academia-assets.com/97738908/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601770/Comparative_chemometric_analysis_of_fluorescence_and_near_infrared_spectroscopies_for_authenticity_confirmation_and_geographical_origin_of_Argentinean_extra_virgin_olive_oils">Comparative chemometric analysis of fluorescence and near infrared spectroscopies for authenticity confirmation and geographical origin of Argentinean extra virgin olive oils</a></div><div class="wp-workCard_item"><span>Food Control</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4deab0d4eee20ca712ac1a8fe7708b3c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738908,"asset_id":95601770,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97738908/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601770"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601770"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601770; 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This work shows the results of applying near infrared (NIR) and fluorescence excitation-emission matrix spectroscopies, coupled to chemometric tools, to authenticate and validate the geographic origin of Argentinean EVOO samples. For each spectral data set, principal component analysis (PCA) was applied to both first-order NIR and second-order fluorescence data, partial least squares-discriminant analysis (PLS1-DA) to NIR data, and the multidimensional version of the latter (NPLS-DA) to fluorescence data. The results of the study of sixty EVOO samples of known and unknown registered designation of origin (RDO), as well as artificial samples adulterated with other edible oils, showed that: (1) fluorescence spectroscopy was unable to determine the RDO of all EVOO samples, in contrast to NIR (100% classified correctly), and (2) fluorescence data provide only slightly better results than NIR spectroscopy to detect EVOO adulterations with other vegetable edible oils.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Food 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/></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601769/Simultaneous_determination_of_urea_herbicides_in_water_and_soil_samples_based_on_second_order_photoinduced_fluorescence_data">Simultaneous determination of urea herbicides in water and soil samples based on second-order photoinduced fluorescence data</a></div><div class="wp-workCard_item"><span>Analytical Methods</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">An eco-friendly method is developed for the simultaneous quantification of four urea-derivative h...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">An eco-friendly method is developed for the simultaneous quantification of four urea-derivative herbicides in water and soil samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2a3461b8d72f57a3b8f497bace7315a5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738907,"asset_id":95601769,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97738907/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601769"><a 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"profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601768"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601768/A_second_order_fluorimetric_approach_based_on_a_boron_dipyrromethene_tetraamide_derivative_for_Hg_ii_chemosensing_in_water_and_fish_samples"><img alt="Research paper thumbnail of A second-order fluorimetric approach based on a boron dipyrromethene tetraamide derivative for Hg( ii ) chemosensing in water and fish samples" class="work-thumbnail" src="https://attachments.academia-assets.com/97738915/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" 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Methods</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new fluorimetric method is described for the determination of Hg(ii), based on the selectivity ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A new fluorimetric method is described for the determination of Hg(ii), based on the selectivity of a boron dipyrromethene tetraamide derivative towards this ion, in combination with second-order chemometric analysis, to deal with matrix interferents.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e02606506467dfbcd7e16638ae812b95" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738915,"asset_id":95601768,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97738915/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601768"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601768"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601768; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601768]").text(description); $(".js-view-count[data-work-id=95601768]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601768; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601768']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601768, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e02606506467dfbcd7e16638ae812b95" } } $('.js-work-strip[data-work-id=95601768]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601768,"title":"A second-order fluorimetric approach based on a boron dipyrromethene tetraamide derivative for Hg( ii ) chemosensing in water and fish samples","translated_title":"","metadata":{"abstract":"A new fluorimetric method is described for the determination of Hg(ii), based on the selectivity of a boron dipyrromethene tetraamide derivative towards this ion, in combination with second-order chemometric analysis, to deal with matrix interferents.","publisher":"Royal Society of Chemistry (RSC)","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Anal. 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The new model involves creating an augmented three-way array in the elution time direction, containing data for the calibration sample set and for each of the test samples, subsequently analyzed with an Augmented PARAFAC version. To test the properties of this approach, chromatographic data were simulated with different degrees of overlapping and misalignment among the chromatographic peaks. Additionally, experimental data from olive oil samples were tested with the new model, aimed at the quantitation of the level of chlorophylls and pheophytins. The results were compared with those obtained by data processing with MCR-ALS. Relative prediction errors (%) were: Augmented PARAFAC, 9.7, 21.0, 14.7 and 9.3, and MCR-ALS, 5.9, 14.5, 20.0 and 14.7 for Chl a, Chl b, Phe a Phe b, respectively, for concentrations in the range 0.00-1.00 μg mL −1. Both MCR-ALS and Augmented PARAFAC allow one to obtain a detailed and realistic description of the analyzed samples, in terms of pure elution time, excitation and emission spectral profiles, which can be independently retrieved for every component.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Chemometrics and Intelligent Laboratory Systems","grobid_abstract_attachment_id":97738913},"translated_abstract":null,"internal_url":"https://www.academia.edu/95601767/Novel_augmented_parallel_factor_model_for_four_way_calibration_of_high_performance_liquid_chromatography_fluorescence_excitation_emission_data","translated_internal_url":"","created_at":"2023-01-24T05:44:28.256-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97738913,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97738913/thumbnails/1.jpg","file_name":"j.chemolab.2014.11.01320230124-1-1fousa8.pdf","download_url":"https://www.academia.edu/attachments/97738913/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Novel_augmented_parallel_factor_model_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97738913/j.chemolab.2014.11.01320230124-1-1fousa8-libre.pdf?1674568217=\u0026response-content-disposition=attachment%3B+filename%3DNovel_augmented_parallel_factor_model_fo.pdf\u0026Expires=1732459817\u0026Signature=YeW-QYdZegplN8XSP5xmBsdydIACb7B-IWjyzA9Zrc~Nsp9fkAAhblR7Y-wPiZpFyHToDLJv554HSe9r-7FShWUevTvw8xU-dHQGzn0Itic-pvMY41ZNLmpoTY-HKlF5boDIWXlFkE2WjKnWN5~Bju1lhTybnUrw5jrl5Bj8lPndsjuJNyY6L06KnOx33QFFnGYDO8C~rWJl8FVximgxLqTtEU5Rm7F74iL2rlfJs7VS~1cnwXV3bVeWW~KCiDyPbXQO-dwGfdq2h1VBdbEm911m70g9TJc8zBbt1srIOcFpp2shOTTqC3eVBfa~EJyICpYi9Qd7dgMvcv4kEskLEw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Novel_augmented_parallel_factor_model_for_four_way_calibration_of_high_performance_liquid_chromatography_fluorescence_excitation_emission_data","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[{"id":97738913,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97738913/thumbnails/1.jpg","file_name":"j.chemolab.2014.11.01320230124-1-1fousa8.pdf","download_url":"https://www.academia.edu/attachments/97738913/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Novel_augmented_parallel_factor_model_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97738913/j.chemolab.2014.11.01320230124-1-1fousa8-libre.pdf?1674568217=\u0026response-content-disposition=attachment%3B+filename%3DNovel_augmented_parallel_factor_model_fo.pdf\u0026Expires=1732459817\u0026Signature=YeW-QYdZegplN8XSP5xmBsdydIACb7B-IWjyzA9Zrc~Nsp9fkAAhblR7Y-wPiZpFyHToDLJv554HSe9r-7FShWUevTvw8xU-dHQGzn0Itic-pvMY41ZNLmpoTY-HKlF5boDIWXlFkE2WjKnWN5~Bju1lhTybnUrw5jrl5Bj8lPndsjuJNyY6L06KnOx33QFFnGYDO8C~rWJl8FVximgxLqTtEU5Rm7F74iL2rlfJs7VS~1cnwXV3bVeWW~KCiDyPbXQO-dwGfdq2h1VBdbEm911m70g9TJc8zBbt1srIOcFpp2shOTTqC3eVBfa~EJyICpYi9Qd7dgMvcv4kEskLEw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":4656,"name":"Chromatography","url":"https://www.academia.edu/Documents/in/Chromatography"},{"id":7698,"name":"Fluorescence","url":"https://www.academia.edu/Documents/in/Fluorescence"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":903559,"name":"Enseñanza - Aprendizaje Ciencias Naturales Y Exactas","url":"https://www.academia.edu/Documents/in/Ensenanza_-_Aprendizaje_Ciencias_Naturales_Y_Exactas"},{"id":1587407,"name":"Química Analítica","url":"https://www.academia.edu/Documents/in/Qu%C3%ADmica_Anal%C3%ADtica"},{"id":3188247,"name":"Elution","url":"https://www.academia.edu/Documents/in/Elution"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[{"id":28350237,"url":"https://api.elsevier.com/content/article/PII:S0169743914002469?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601766"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601766/Green_analytical_determination_of_emerging_pollutants_in_environmental_waters_using_excitation_emission_photoinduced_fluorescence_data_and_multivariate_calibration"><img alt="Research paper thumbnail of Green analytical determination of emerging pollutants in environmental waters using excitation–emission photoinduced fluorescence data and multivariate calibration" class="work-thumbnail" src="https://attachments.academia-assets.com/97738911/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601766/Green_analytical_determination_of_emerging_pollutants_in_environmental_waters_using_excitation_emission_photoinduced_fluorescence_data_and_multivariate_calibration">Green analytical determination of emerging pollutants in environmental waters using excitation–emission photoinduced fluorescence data and multivariate calibration</a></div><div class="wp-workCard_item"><span>Talanta</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4c93e61150e491388ebc30f0ecae73a1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738911,"asset_id":95601766,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97738911/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601766"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601766"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601766; 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The proposed analytical method, which involves photochemically induced fluorescence matrix data combined with second-order chemometric analysis, was used for the determination of carbamazepine, ofloxacin and piroxicam in water samples of different complexity without the need of chromatographic separation. Excitation-emission photoinduced fluorescence matrices were obtained after UV irradiation, and processed with second-order algorithms. Only one of the tested algorithms was able to overcome the strong spectral overlapping among the studied pollutants and allowed their successful quantitation in very interferent media. The method sensitivity in superficial and underground water samples was enhanced by a simple solid-phase extraction with C18 membranes, which was successful for the extraction/preconcentration of the pollutants at trace levels. Detection limits in preconcentrated (1:125) real water samples ranged from 0.04 to 0.3 ng mL-1. Relative prediction errors around 10 % were achieved. 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The algorithm employed, multivariate curve resolution-alternating least-squares, is one of the few methodologies which permit the achievement of the second-order advantage in the presence of a high degree of overlapping between the time decay profiles for the analyte and the interferent complexes. Furthermore, the presence of analyte-background interactions makes it necessary to employ the standard addition method for successful quantitation. Both simulations and experiments showed that the modified standard addition method was suitable for this purpose, in which the test data matrix was subtracted from the standard addition matrices, and quantitation proceeded using classical external calibration procedure. T...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a149bd6731ef3635e224433bdbb76cca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738940,"asset_id":95601765,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97738940/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601765"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601765"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601765; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601765]").text(description); $(".js-view-count[data-work-id=95601765]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601765; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601765']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601765, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a149bd6731ef3635e224433bdbb76cca" } } $('.js-work-strip[data-work-id=95601765]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601765,"title":"Standard addition analysis of fluoroquinolones in human serum in the presence of the interferent salicylate using lanthanide-sensitized excitation-time decay luminescence data and multivariate curve resolution","translated_title":"","metadata":{"abstract":"Three fluoroquinolone antibiotics (ciprofloxacin, norfloxacin and danofloxacin) have been determined in human serum in the presence of the potential interferent salicylate, by processing lanthanide-sensitized excitation-time decay matrix data for their terbium (III) complexes. The algorithm employed, multivariate curve resolution-alternating least-squares, is one of the few methodologies which permit the achievement of the second-order advantage in the presence of a high degree of overlapping between the time decay profiles for the analyte and the interferent complexes. Furthermore, the presence of analyte-background interactions makes it necessary to employ the standard addition method for successful quantitation. Both simulations and experiments showed that the modified standard addition method was suitable for this purpose, in which the test data matrix was subtracted from the standard addition matrices, and quantitation proceeded using classical external calibration procedure. T...","publisher":"ncbi.nlm.nih.gov","publication_date":{"day":15,"month":1,"year":2009,"errors":{}},"publication_name":"Talanta"},"translated_abstract":"Three fluoroquinolone antibiotics (ciprofloxacin, norfloxacin and danofloxacin) have been determined in human serum in the presence of the potential interferent salicylate, by processing lanthanide-sensitized excitation-time decay matrix data for their terbium (III) complexes. The algorithm employed, multivariate curve resolution-alternating least-squares, is one of the few methodologies which permit the achievement of the second-order advantage in the presence of a high degree of overlapping between the time decay profiles for the analyte and the interferent complexes. Furthermore, the presence of analyte-background interactions makes it necessary to employ the standard addition method for successful quantitation. Both simulations and experiments showed that the modified standard addition method was suitable for this purpose, in which the test data matrix was subtracted from the standard addition matrices, and quantitation proceeded using classical external calibration procedure. T...","internal_url":"https://www.academia.edu/95601765/Standard_addition_analysis_of_fluoroquinolones_in_human_serum_in_the_presence_of_the_interferent_salicylate_using_lanthanide_sensitized_excitation_time_decay_luminescence_data_and_multivariate_curve_resolution","translated_internal_url":"","created_at":"2023-01-24T05:44:27.886-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97738940,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97738940/thumbnails/1.jpg","file_name":"j.talanta.2008.10.02020230124-1-10dy8j4.pdf","download_url":"https://www.academia.edu/attachments/97738940/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Standard_addition_analysis_of_fluoroquin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97738940/j.talanta.2008.10.02020230124-1-10dy8j4-libre.pdf?1674568207=\u0026response-content-disposition=attachment%3B+filename%3DStandard_addition_analysis_of_fluoroquin.pdf\u0026Expires=1732459817\u0026Signature=KJG6pwwiBMPKx8dFxnetvWVbb~TnwuyQNY~DPerPeBXRE-ou8z8C-K9yBFW9GXFyhdzaBLGbApQLtQUNdGSDK4D7nqFINXP85Z9hZAKhiHO6bKAy1YLfFqciK8pyyLLSZIs6wtbr5j38xoSeSBGTU7Sw6ox~OiuROH~cajqjnZtzguF4jwEQOCq~vqbr~PXC~K33I1f9Nm3oPRMfjqtvc3Aw6Znnn8niOGkmYyfeXFDByI9ywjIKwgKqBxSVQQ0Ht47D8iBTU3eSYdXtIe8zuDqW1os5Fh96a2kavptmeFCb5Yh88YKCJJ7qfkFCK0anvAVfpmU6dmB58Nt36jWq~A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Standard_addition_analysis_of_fluoroquinolones_in_human_serum_in_the_presence_of_the_interferent_salicylate_using_lanthanide_sensitized_excitation_time_decay_luminescence_data_and_multivariate_curve_resolution","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella 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href="https://www.academia.edu/95601764/Four_way_multivariate_calibration_using_ultra_fast_high_performance_liquid_chromatography_with_fluorescence_excitation_emission_detection_Application_to_the_direct_analysis_of_chlorophylls_a_and_b_and_pheophytins_a_and_b_in_olive_oils"><img alt="Research paper thumbnail of Four-way multivariate calibration using ultra-fast high-performance liquid chromatography with fluorescence excitation–emission detection. Application to the direct analysis of chlorophylls a and b and pheophytins a and b in olive oils" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601764/Four_way_multivariate_calibration_using_ultra_fast_high_performance_liquid_chromatography_with_fluorescence_excitation_emission_detection_Application_to_the_direct_analysis_of_chlorophylls_a_and_b_and_pheophytins_a_and_b_in_olive_oils">Four-way multivariate calibration using ultra-fast high-performance liquid chromatography with fluorescence excitation–emission detection. Application to the direct analysis of chlorophylls a and b and pheophytins a and b in olive oils</a></div><div class="wp-workCard_item"><span>Chemometrics and Intelligent Laboratory Systems</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A four-way multivariate calibration approach, based on the combination of ultra-fasthigh...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A four-way multivariate calibration approach, based on the combination of ultra-fasthigh-performance liquid chromatographic data and four-way algorithms, is described for the first time. To achieve this goal, several emission wavelength–elution time matrices (ETMs) were recorded as a function of the excitation wavelength. Each sample was injected into the chromatograph eight times, in sequential mode, each time exciting at a different wavelength across the excitation spectra of the compounds of interest, and the emission spectra were recorded along the full chromatogram using a fast scanning fluorescence detector. The data were obtained in a very short time with an ultrafast chromatographic system operating in gradient mode. The three-wayETM data thus obtained for the calibration sample set and for each of the test samples were joined into a single four-way array, which was subsequently analyzed with parallel factor analysis (PARAFAC), unfolded partial least-squares with residual trilinearization (U-PLS/RTL) and multi-way partial least-squares with residual trilinearization (N-PLS/RTL) multivariate calibration algorithms. Best results were found when either U-PLS/RTL or N-PLS/RTL algorithms were used to perform the multivariate calibration. The method was applied to the direct determination of chlorophylls a and b and pheophytins a and b in olive oil samples, in the presence of other interfering fluorescent compounds, and without previous sample treatment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601764"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601764"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601764; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601764]").text(description); $(".js-view-count[data-work-id=95601764]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601764; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601764']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601764, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601764]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601764,"title":"Four-way multivariate calibration using ultra-fast high-performance liquid chromatography with fluorescence excitation–emission detection. Application to the direct analysis of chlorophylls a and b and pheophytins a and b in olive oils","translated_title":"","metadata":{"abstract":"ABSTRACT A four-way multivariate calibration approach, based on the combination of ultra-fasthigh-performance liquid chromatographic data and four-way algorithms, is described for the first time. To achieve this goal, several emission wavelength–elution time matrices (ETMs) were recorded as a function of the excitation wavelength. Each sample was injected into the chromatograph eight times, in sequential mode, each time exciting at a different wavelength across the excitation spectra of the compounds of interest, and the emission spectra were recorded along the full chromatogram using a fast scanning fluorescence detector. The data were obtained in a very short time with an ultrafast chromatographic system operating in gradient mode. The three-wayETM data thus obtained for the calibration sample set and for each of the test samples were joined into a single four-way array, which was subsequently analyzed with parallel factor analysis (PARAFAC), unfolded partial least-squares with residual trilinearization (U-PLS/RTL) and multi-way partial least-squares with residual trilinearization (N-PLS/RTL) multivariate calibration algorithms. Best results were found when either U-PLS/RTL or N-PLS/RTL algorithms were used to perform the multivariate calibration. The method was applied to the direct determination of chlorophylls a and b and pheophytins a and b in olive oil samples, in the presence of other interfering fluorescent compounds, and without previous sample treatment.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Chemometrics and Intelligent Laboratory Systems"},"translated_abstract":"ABSTRACT A four-way multivariate calibration approach, based on the combination of ultra-fasthigh-performance liquid chromatographic data and four-way algorithms, is described for the first time. To achieve this goal, several emission wavelength–elution time matrices (ETMs) were recorded as a function of the excitation wavelength. Each sample was injected into the chromatograph eight times, in sequential mode, each time exciting at a different wavelength across the excitation spectra of the compounds of interest, and the emission spectra were recorded along the full chromatogram using a fast scanning fluorescence detector. The data were obtained in a very short time with an ultrafast chromatographic system operating in gradient mode. The three-wayETM data thus obtained for the calibration sample set and for each of the test samples were joined into a single four-way array, which was subsequently analyzed with parallel factor analysis (PARAFAC), unfolded partial least-squares with residual trilinearization (U-PLS/RTL) and multi-way partial least-squares with residual trilinearization (N-PLS/RTL) multivariate calibration algorithms. Best results were found when either U-PLS/RTL or N-PLS/RTL algorithms were used to perform the multivariate calibration. The method was applied to the direct determination of chlorophylls a and b and pheophytins a and b in olive oil samples, in the presence of other interfering fluorescent compounds, and without previous sample treatment.","internal_url":"https://www.academia.edu/95601764/Four_way_multivariate_calibration_using_ultra_fast_high_performance_liquid_chromatography_with_fluorescence_excitation_emission_detection_Application_to_the_direct_analysis_of_chlorophylls_a_and_b_and_pheophytins_a_and_b_in_olive_oils","translated_internal_url":"","created_at":"2023-01-24T05:44:27.702-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Four_way_multivariate_calibration_using_ultra_fast_high_performance_liquid_chromatography_with_fluorescence_excitation_emission_detection_Application_to_the_direct_analysis_of_chlorophylls_a_and_b_and_pheophytins_a_and_b_in_olive_oils","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":7698,"name":"Fluorescence","url":"https://www.academia.edu/Documents/in/Fluorescence"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":358917,"name":"Partial Least Squares Regression","url":"https://www.academia.edu/Documents/in/Partial_Least_Squares_Regression"},{"id":903559,"name":"Enseñanza - Aprendizaje Ciencias Naturales Y Exactas","url":"https://www.academia.edu/Documents/in/Ensenanza_-_Aprendizaje_Ciencias_Naturales_Y_Exactas"},{"id":1587407,"name":"Química Analítica","url":"https://www.academia.edu/Documents/in/Qu%C3%ADmica_Anal%C3%ADtica"},{"id":3030532,"name":"Residual","url":"https://www.academia.edu/Documents/in/Residual"},{"id":3188247,"name":"Elution","url":"https://www.academia.edu/Documents/in/Elution"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[{"id":28350234,"url":"https://api.elsevier.com/content/article/PII:S0169743913000622?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601763"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601763/A_novel_nylon_membrane_rhodamine_6G_spirocyclic_phenylthiosemicarbazide_derivative_system_as_a_fluorimetric_probe_for_mercury_ii_ion"><img alt="Research paper thumbnail of A novel nylon membrane–rhodamine 6G spirocyclic phenylthiosemicarbazide derivative system as a fluorimetric probe for mercury(ii) ion" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601763/A_novel_nylon_membrane_rhodamine_6G_spirocyclic_phenylthiosemicarbazide_derivative_system_as_a_fluorimetric_probe_for_mercury_ii_ion">A novel nylon membrane–rhodamine 6G spirocyclic phenylthiosemicarbazide derivative system as a fluorimetric probe for mercury(ii) ion</a></div><div class="wp-workCard_item"><span>Analytical Methods</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A highly sensitive and selective probe for the fluorimetric determination of mercury ion...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A highly sensitive and selective probe for the fluorimetric determination of mercury ion traces in aqueous solution is proposed. The probe is based on the mercury-promoted ring opening of the spirolactam moiety of a rhodamine 6G spirocyclic phenylthiosemicarbazide derivative (FC1) retained in nylon membranes. It is demonstrated that the chemodosimeter preserves its sensor ability, displaying intense fluorescence in the presence of Hg(II) after being immobilized on the nylon surface and reacting with the mercury ion solution via a simple syringe procedure. The advantages of this proposal are: (1) the use of an easily affordable solid support which is able to immobilize the FC1 molecular probe without involving a covalent bond, (2) the consumption of a very small volume of organic reagent, dramatically reducing the environmental impact, and (3) the development of a solid phase system potentially useful as a main component for designing chemical sensors capable of providing continuous real-time information. In order to obtain higher and stable fluorescence signals, both experimental and instrumental variables were optimized. Thus, a simple and sensitive fluorescence method for the determination of mercury ion was established. The limit of detection calculated according to 1995 IUPAC Recommendations was 0.4 ng mL−1 (lower than the toxic levels in drinking water for human consumption, established by several regulatory agencies), the relative standard deviation was 2.3% (n = 6) at a level of 3.5 ng mL−1, and the sampling rate was about 15 samples per hour. The study of the potential interference from common cations demonstrated a remarkable selectivity for the investigated metal ion. The viability of determining Hg(II) ion residues in real water samples was successfully evaluated through the recovery study of several spiked environmental water samples from different locations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601763"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601763"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601763; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601763]").text(description); $(".js-view-count[data-work-id=95601763]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601763; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601763']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601763, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601763]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601763,"title":"A novel nylon membrane–rhodamine 6G spirocyclic phenylthiosemicarbazide derivative system as a fluorimetric probe for mercury(ii) ion","translated_title":"","metadata":{"abstract":"ABSTRACT A highly sensitive and selective probe for the fluorimetric determination of mercury ion traces in aqueous solution is proposed. The probe is based on the mercury-promoted ring opening of the spirolactam moiety of a rhodamine 6G spirocyclic phenylthiosemicarbazide derivative (FC1) retained in nylon membranes. It is demonstrated that the chemodosimeter preserves its sensor ability, displaying intense fluorescence in the presence of Hg(II) after being immobilized on the nylon surface and reacting with the mercury ion solution via a simple syringe procedure. The advantages of this proposal are: (1) the use of an easily affordable solid support which is able to immobilize the FC1 molecular probe without involving a covalent bond, (2) the consumption of a very small volume of organic reagent, dramatically reducing the environmental impact, and (3) the development of a solid phase system potentially useful as a main component for designing chemical sensors capable of providing continuous real-time information. In order to obtain higher and stable fluorescence signals, both experimental and instrumental variables were optimized. Thus, a simple and sensitive fluorescence method for the determination of mercury ion was established. The limit of detection calculated according to 1995 IUPAC Recommendations was 0.4 ng mL−1 (lower than the toxic levels in drinking water for human consumption, established by several regulatory agencies), the relative standard deviation was 2.3% (n = 6) at a level of 3.5 ng mL−1, and the sampling rate was about 15 samples per hour. The study of the potential interference from common cations demonstrated a remarkable selectivity for the investigated metal ion. The viability of determining Hg(II) ion residues in real water samples was successfully evaluated through the recovery study of several spiked environmental water samples from different locations.","publisher":"Royal Society of Chemistry (RSC)","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"Analytical Methods"},"translated_abstract":"ABSTRACT A highly sensitive and selective probe for the fluorimetric determination of mercury ion traces in aqueous solution is proposed. The probe is based on the mercury-promoted ring opening of the spirolactam moiety of a rhodamine 6G spirocyclic phenylthiosemicarbazide derivative (FC1) retained in nylon membranes. It is demonstrated that the chemodosimeter preserves its sensor ability, displaying intense fluorescence in the presence of Hg(II) after being immobilized on the nylon surface and reacting with the mercury ion solution via a simple syringe procedure. The advantages of this proposal are: (1) the use of an easily affordable solid support which is able to immobilize the FC1 molecular probe without involving a covalent bond, (2) the consumption of a very small volume of organic reagent, dramatically reducing the environmental impact, and (3) the development of a solid phase system potentially useful as a main component for designing chemical sensors capable of providing continuous real-time information. In order to obtain higher and stable fluorescence signals, both experimental and instrumental variables were optimized. Thus, a simple and sensitive fluorescence method for the determination of mercury ion was established. The limit of detection calculated according to 1995 IUPAC Recommendations was 0.4 ng mL−1 (lower than the toxic levels in drinking water for human consumption, established by several regulatory agencies), the relative standard deviation was 2.3% (n = 6) at a level of 3.5 ng mL−1, and the sampling rate was about 15 samples per hour. The study of the potential interference from common cations demonstrated a remarkable selectivity for the investigated metal ion. The viability of determining Hg(II) ion residues in real water samples was successfully evaluated through the recovery study of several spiked environmental water samples from different locations.","internal_url":"https://www.academia.edu/95601763/A_novel_nylon_membrane_rhodamine_6G_spirocyclic_phenylthiosemicarbazide_derivative_system_as_a_fluorimetric_probe_for_mercury_ii_ion","translated_internal_url":"","created_at":"2023-01-24T05:44:27.570-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_novel_nylon_membrane_rhodamine_6G_spirocyclic_phenylthiosemicarbazide_derivative_system_as_a_fluorimetric_probe_for_mercury_ii_ion","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":7698,"name":"Fluorescence","url":"https://www.academia.edu/Documents/in/Fluorescence"},{"id":242298,"name":"Membrane","url":"https://www.academia.edu/Documents/in/Membrane"},{"id":615457,"name":"Analytical Methods","url":"https://www.academia.edu/Documents/in/Analytical_Methods"},{"id":903559,"name":"Enseñanza - Aprendizaje Ciencias Naturales Y Exactas","url":"https://www.academia.edu/Documents/in/Ensenanza_-_Aprendizaje_Ciencias_Naturales_Y_Exactas"},{"id":1587407,"name":"Química Analítica","url":"https://www.academia.edu/Documents/in/Qu%C3%ADmica_Anal%C3%ADtica"},{"id":2783994,"name":"Spectrofluorimetry","url":"https://www.academia.edu/Documents/in/Spectrofluorimetry"},{"id":3502884,"name":"Rhodamine","url":"https://www.academia.edu/Documents/in/Rhodamine"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601762"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601762/Second_Order_Analyte_Quantitation_under_Identical_Profiles_in_One_Data_Dimension_A_Dependency_Adapted_Partial_Least_Squares_Residual_Bilinearization_Method"><img alt="Research paper thumbnail of Second-Order Analyte Quantitation under Identical Profiles in One Data Dimension. 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src="https://attachments.academia-assets.com/97738916/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601761/Second_order_advantage_with_excitation_emission_photoinduced_fluorimetry_for_the_determination_of_the_antiepileptic_carbamazepine_in_environmental_waters">Second-order advantage with excitation–emission photoinduced fluorimetry for the determination of the antiepileptic carbamazepine in environmental waters</a></div><div class="wp-workCard_item"><span>Analytica Chimica Acta</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7fbca09354782c61755b4fe628a4b161" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7fbca09354782c61755b4fe628a4b161" } } $('.js-work-strip[data-work-id=95601761]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601761,"title":"Second-order advantage with excitation–emission photoinduced fluorimetry for the determination of the antiepileptic carbamazepine in environmental waters","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"A photochemically-induced fluorescence system combined with second-order chemometric analysis for the determination of the anticonvulsant carbamazepine (CBZ) is presented. CBZ is a widely used drug for the treatment of epilepsy and is included in the group of emerging contaminant present in the aquatic environment. CBZ is not fluorescent in solution but can be converted into a fluorescent compound through a photochemical reaction in a strong acid medium. The determination is carried out by measuring excitation-emission photoinduced fluorescence matrices of the products formed upon ultraviolet light irradiation in a laboratoryconstructed reactor constituted by two simple 4 W germicidal tubes. Working conditions related to both the reaction medium and the photoreactor geometry are optimized by an experimental design. The developed approach enabled the determination of CBZ at trace levels without the necessity of applying separation steps, and in the presence of uncalibrated interferences which also display photoinduced fluorescence and may be potentially present in the investigated samples. Different second-order algorithms were tested and successful resolution was achieved using multivariate curve resolution-alternating least-squares (MCR-ALS). The study is employed for the discussion of the scopes and yields of each of the applied second-order chemometric tools. The quality of the proposed method is probed through the determination of the studied emerging pollutant in both environmental and drinking water samples. After a pre-concentration step on a C18 membrane using 50.0 mL of real water samples, a prediction relative error of 2 % and limits of detection and quantification of 0.2 and 0.6 ng mL-1 were respectively obtained.","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Analytica Chimica 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wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601760/Second_order_advantage_from_kinetic_spectroscopic_data_matrices_in_the_presence_of_extreme_spectral_overlapping">Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping</a></div><div class="wp-workCard_item"><span>Analytica Chimica Acta</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601760"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601760"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601760; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601760]").text(description); $(".js-view-count[data-work-id=95601760]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601760; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601760']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601760, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601760]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601760,"title":"Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping","translated_title":"","metadata":{"abstract":"Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Analytica Chimica Acta"},"translated_abstract":"Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.","internal_url":"https://www.academia.edu/95601760/Second_order_advantage_from_kinetic_spectroscopic_data_matrices_in_the_presence_of_extreme_spectral_overlapping","translated_internal_url":"","created_at":"2023-01-24T05:44:27.065-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Second_order_advantage_from_kinetic_spectroscopic_data_matrices_in_the_presence_of_extreme_spectral_overlapping","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":4987,"name":"Kinetics","url":"https://www.academia.edu/Documents/in/Kinetics"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":41482,"name":"Multivariate Analysis","url":"https://www.academia.edu/Documents/in/Multivariate_Analysis"},{"id":85877,"name":"Alternating least squares","url":"https://www.academia.edu/Documents/in/Alternating_least_squares"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":160656,"name":"Potassium","url":"https://www.academia.edu/Documents/in/Potassium"},{"id":323652,"name":"Interference","url":"https://www.academia.edu/Documents/in/Interference"},{"id":347272,"name":"Second Order","url":"https://www.academia.edu/Documents/in/Second_Order"},{"id":386276,"name":"Cerium","url":"https://www.academia.edu/Documents/in/Cerium"},{"id":477077,"name":"Bromate","url":"https://www.academia.edu/Documents/in/Bromate"},{"id":563382,"name":"Oxidation","url":"https://www.academia.edu/Documents/in/Oxidation"},{"id":924470,"name":"Methyl Orange","url":"https://www.academia.edu/Documents/in/Methyl_Orange"},{"id":1505380,"name":"Multivariate Curve Resolution","url":"https://www.academia.edu/Documents/in/Multivariate_Curve_Resolution"},{"id":1587407,"name":"Química Analítica","url":"https://www.academia.edu/Documents/in/Qu%C3%ADmica_Anal%C3%ADtica"},{"id":1600695,"name":"Potassium Bromate","url":"https://www.academia.edu/Documents/in/Potassium_Bromate"},{"id":2525232,"name":"Multivariate Calibration","url":"https://www.academia.edu/Documents/in/Multivariate_Calibration"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[{"id":28350231,"url":"https://api.elsevier.com/content/article/PII:S0003267008004819?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601759"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601759/Three_way_partial_least_squares_residual_bilinearization_study_of_second_order_lanthanide_sensitized_luminescence_excitation_time_decay_data"><img alt="Research paper thumbnail of Three-way partial least-squares/residual bilinearization study of second-order lanthanide-sensitized luminescence excitation-time decay data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601759/Three_way_partial_least_squares_residual_bilinearization_study_of_second_order_lanthanide_sensitized_luminescence_excitation_time_decay_data">Three-way partial least-squares/residual bilinearization study of second-order lanthanide-sensitized luminescence excitation-time decay data</a></div><div class="wp-workCard_item"><span>Analytica Chimica Acta</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the second-order advantage using as chemometric algorithms parallel factor analysis (PARAFAC) and multidimensional partial least-squares with residual bilinearization (N-PLS/RBL). The second-order data were measured for a calibration set of samples containing the analyte benzoic acid in the concentration range from 0.00 to 5.00 mg L(-1), for a validation set containing the analyte and the potential interferent saccharin (in the range 0.00-6.00 mg L(-1)), and for real samples of beverages containing benzoic acid as preservant, saccharin, and other potentially interfering compounds. All samples were treated with terbium(III), trioctylphosphine oxide as a synergistic ligand, and contained a suitable imidazol buffer, in order to ensure maximum intensity of the luminescence signals. The results indicate a slightly better predictive ability of the newly introduced N-PLS/RBL procedure over standard PARAFAC, both in what concerns the comparison with nominal analyte concentrations in the validation sample set and with results provided by the reference high-performance liquid chromatographic technique for the real sample set.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601759"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601759"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601759; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601759]").text(description); $(".js-view-count[data-work-id=95601759]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601759; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601759']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601759, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601759]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601759,"title":"Three-way partial least-squares/residual bilinearization study of second-order lanthanide-sensitized luminescence excitation-time decay data","translated_title":"","metadata":{"abstract":"Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the second-order advantage using as chemometric algorithms parallel factor analysis (PARAFAC) and multidimensional partial least-squares with residual bilinearization (N-PLS/RBL). The second-order data were measured for a calibration set of samples containing the analyte benzoic acid in the concentration range from 0.00 to 5.00 mg L(-1), for a validation set containing the analyte and the potential interferent saccharin (in the range 0.00-6.00 mg L(-1)), and for real samples of beverages containing benzoic acid as preservant, saccharin, and other potentially interfering compounds. All samples were treated with terbium(III), trioctylphosphine oxide as a synergistic ligand, and contained a suitable imidazol buffer, in order to ensure maximum intensity of the luminescence signals. The results indicate a slightly better predictive ability of the newly introduced N-PLS/RBL procedure over standard PARAFAC, both in what concerns the comparison with nominal analyte concentrations in the validation sample set and with results provided by the reference high-performance liquid chromatographic technique for the real sample set.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Analytica Chimica Acta"},"translated_abstract":"Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the second-order advantage using as chemometric algorithms parallel factor analysis (PARAFAC) and multidimensional partial least-squares with residual bilinearization (N-PLS/RBL). The second-order data were measured for a calibration set of samples containing the analyte benzoic acid in the concentration range from 0.00 to 5.00 mg L(-1), for a validation set containing the analyte and the potential interferent saccharin (in the range 0.00-6.00 mg L(-1)), and for real samples of beverages containing benzoic acid as preservant, saccharin, and other potentially interfering compounds. All samples were treated with terbium(III), trioctylphosphine oxide as a synergistic ligand, and contained a suitable imidazol buffer, in order to ensure maximum intensity of the luminescence signals. The results indicate a slightly better predictive ability of the newly introduced N-PLS/RBL procedure over standard PARAFAC, both in what concerns the comparison with nominal analyte concentrations in the validation sample set and with results provided by the reference high-performance liquid chromatographic technique for the real sample set.","internal_url":"https://www.academia.edu/95601759/Three_way_partial_least_squares_residual_bilinearization_study_of_second_order_lanthanide_sensitized_luminescence_excitation_time_decay_data","translated_internal_url":"","created_at":"2023-01-24T05:44:26.896-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Three_way_partial_least_squares_residual_bilinearization_study_of_second_order_lanthanide_sensitized_luminescence_excitation_time_decay_data","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms"},{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis"},{"id":4467,"name":"Chemometrics","url":"https://www.academia.edu/Documents/in/Chemometrics"},{"id":16216,"name":"Lanthanide","url":"https://www.academia.edu/Documents/in/Lanthanide"},{"id":23890,"name":"Comparative Study","url":"https://www.academia.edu/Documents/in/Comparative_Study"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":60585,"name":"Factor analysis","url":"https://www.academia.edu/Documents/in/Factor_analysis"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":158914,"name":"Luminescence","url":"https://www.academia.edu/Documents/in/Luminescence"},{"id":246560,"name":"High Pressure Liquid Chromatography","url":"https://www.academia.edu/Documents/in/High_Pressure_Liquid_Chromatography"},{"id":302971,"name":"Beverages","url":"https://www.academia.edu/Documents/in/Beverages"},{"id":323652,"name":"Interference","url":"https://www.academia.edu/Documents/in/Interference"},{"id":358917,"name":"Partial Least Squares Regression","url":"https://www.academia.edu/Documents/in/Partial_Least_Squares_Regression"},{"id":469092,"name":"Benzoic Acid","url":"https://www.academia.edu/Documents/in/Benzoic_Acid"},{"id":537404,"name":"Least Squares","url":"https://www.academia.edu/Documents/in/Least_Squares"},{"id":1343804,"name":"Partial Least Square","url":"https://www.academia.edu/Documents/in/Partial_Least_Square"},{"id":3881467,"name":"HPLC Chromatography","url":"https://www.academia.edu/Documents/in/HPLC_Chromatography"}],"urls":[{"id":28350230,"url":"https://api.elsevier.com/content/article/PII:S0003267008001049?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601758"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601758/A_novel_second_order_standard_addition_analytical_method_based_on_data_processing_with_multidimensional_partial_least_squares_and_residual_bilinearization"><img alt="Research paper thumbnail of A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601758/A_novel_second_order_standard_addition_analytical_method_based_on_data_processing_with_multidimensional_partial_least_squares_and_residual_bilinearization">A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization</a></div><div class="wp-workCard_item"><span>Analytica Chimica Acta</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the presence of analyte-background interactions and a significant background signal, both seco...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601758"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601758"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601758; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601758]").text(description); $(".js-view-count[data-work-id=95601758]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601758; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601758']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601758, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601758]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601758,"title":"A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization","translated_title":"","metadata":{"abstract":"In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2009,"errors":{}},"publication_name":"Analytica Chimica Acta"},"translated_abstract":"In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.","internal_url":"https://www.academia.edu/95601758/A_novel_second_order_standard_addition_analytical_method_based_on_data_processing_with_multidimensional_partial_least_squares_and_residual_bilinearization","translated_internal_url":"","created_at":"2023-01-24T05:44:26.717-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_novel_second_order_standard_addition_analytical_method_based_on_data_processing_with_multidimensional_partial_least_squares_and_residual_bilinearization","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms"},{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":4467,"name":"Chemometrics","url":"https://www.academia.edu/Documents/in/Chemometrics"},{"id":23890,"name":"Comparative Study","url":"https://www.academia.edu/Documents/in/Comparative_Study"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":41482,"name":"Multivariate Analysis","url":"https://www.academia.edu/Documents/in/Multivariate_Analysis"},{"id":60585,"name":"Factor analysis","url":"https://www.academia.edu/Documents/in/Factor_analysis"},{"id":64568,"name":"Humans","url":"https://www.academia.edu/Documents/in/Humans"},{"id":85877,"name":"Alternating least squares","url":"https://www.academia.edu/Documents/in/Alternating_least_squares"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":323652,"name":"Interference","url":"https://www.academia.edu/Documents/in/Interference"},{"id":330839,"name":"Analytical Method","url":"https://www.academia.edu/Documents/in/Analytical_Method"},{"id":563659,"name":"Human Serum","url":"https://www.academia.edu/Documents/in/Human_Serum"},{"id":581652,"name":"Data Processing","url":"https://www.academia.edu/Documents/in/Data_Processing"},{"id":1120502,"name":"Experimental Data","url":"https://www.academia.edu/Documents/in/Experimental_Data"},{"id":1534916,"name":"Model coupling","url":"https://www.academia.edu/Documents/in/Model_coupling"},{"id":2226477,"name":"Automatic data processing","url":"https://www.academia.edu/Documents/in/Automatic_data_processing"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[{"id":28350229,"url":"https://api.elsevier.com/content/article/PII:S0003267009011404?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="16962302" id="papers"><div class="js-work-strip profile--work_container" data-work-id="95601773"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/95601773/Statistics_and_Food_Quality"><img alt="Research paper thumbnail of Statistics and Food Quality" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/95601773/Statistics_and_Food_Quality">Statistics and Food Quality</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601773"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601773"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601773; 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src="https://attachments.academia-assets.com/97738912/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601771/On_second_order_calibration_based_on_multivariate_curve_resolution_in_the_presence_of_highly_overlapped_profiles">On second-order calibration based on multivariate curve resolution in the presence of highly overlapped profiles</a></div><div class="wp-workCard_item"><span>Analytica Chimica Acta</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4319a3e4bed5c9a46ff9cccee20dcf71" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738912,"asset_id":95601771,"asset_type":"Work","button_location":"profile"}" 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class="work-thumbnail" src="https://attachments.academia-assets.com/97738908/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601770/Comparative_chemometric_analysis_of_fluorescence_and_near_infrared_spectroscopies_for_authenticity_confirmation_and_geographical_origin_of_Argentinean_extra_virgin_olive_oils">Comparative chemometric analysis of fluorescence and near infrared spectroscopies for authenticity confirmation and geographical origin of Argentinean extra virgin olive oils</a></div><div class="wp-workCard_item"><span>Food Control</span><span>, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4deab0d4eee20ca712ac1a8fe7708b3c" class="wp-workCard--action" rel="nofollow" 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oils","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"Extra virgin olive oil (EVOO) is widely used in gastronomy because of its healthy properties, and is a candidate to be adulterated with other vegetable oils to reduce costs. This work shows the results of applying near infrared (NIR) and fluorescence excitation-emission matrix spectroscopies, coupled to chemometric tools, to authenticate and validate the geographic origin of Argentinean EVOO samples. For each spectral data set, principal component analysis (PCA) was applied to both first-order NIR and second-order fluorescence data, partial least squares-discriminant analysis (PLS1-DA) to NIR data, and the multidimensional version of the latter (NPLS-DA) to fluorescence data. The results of the study of sixty EVOO samples of known and unknown registered designation of origin (RDO), as well as artificial samples adulterated with other edible oils, showed that: (1) fluorescence spectroscopy was unable to determine the RDO of all EVOO samples, in contrast to NIR (100% classified correctly), and (2) fluorescence data provide only slightly better results than NIR spectroscopy to detect EVOO adulterations with other vegetable edible oils.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Food 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/></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601769/Simultaneous_determination_of_urea_herbicides_in_water_and_soil_samples_based_on_second_order_photoinduced_fluorescence_data">Simultaneous determination of urea herbicides in water and soil samples based on second-order photoinduced fluorescence data</a></div><div class="wp-workCard_item"><span>Analytical Methods</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">An eco-friendly method is developed for the simultaneous quantification of four urea-derivative h...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">An eco-friendly method is developed for the simultaneous quantification of four urea-derivative herbicides in water and soil samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2a3461b8d72f57a3b8f497bace7315a5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738907,"asset_id":95601769,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97738907/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" 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Methods</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new fluorimetric method is described for the determination of Hg(ii), based on the selectivity ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A new fluorimetric method is described for the determination of Hg(ii), based on the selectivity of a boron dipyrromethene tetraamide derivative towards this ion, in combination with second-order chemometric analysis, to deal with matrix interferents.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e02606506467dfbcd7e16638ae812b95" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738915,"asset_id":95601768,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97738915/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601768"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601768"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601768; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601768]").text(description); $(".js-view-count[data-work-id=95601768]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601768; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601768']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601768, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e02606506467dfbcd7e16638ae812b95" } } $('.js-work-strip[data-work-id=95601768]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601768,"title":"A second-order fluorimetric approach based on a boron dipyrromethene tetraamide derivative for Hg( ii ) chemosensing in water and fish samples","translated_title":"","metadata":{"abstract":"A new fluorimetric method is described for the determination of Hg(ii), based on the selectivity of a boron dipyrromethene tetraamide derivative towards this ion, in combination with second-order chemometric analysis, to deal with matrix interferents.","publisher":"Royal Society of Chemistry (RSC)","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Anal. 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The new model involves creating an augmented three-way array in the elution time direction, containing data for the calibration sample set and for each of the test samples, subsequently analyzed with an Augmented PARAFAC version. To test the properties of this approach, chromatographic data were simulated with different degrees of overlapping and misalignment among the chromatographic peaks. Additionally, experimental data from olive oil samples were tested with the new model, aimed at the quantitation of the level of chlorophylls and pheophytins. The results were compared with those obtained by data processing with MCR-ALS. Relative prediction errors (%) were: Augmented PARAFAC, 9.7, 21.0, 14.7 and 9.3, and MCR-ALS, 5.9, 14.5, 20.0 and 14.7 for Chl a, Chl b, Phe a Phe b, respectively, for concentrations in the range 0.00-1.00 μg mL −1. Both MCR-ALS and Augmented PARAFAC allow one to obtain a detailed and realistic description of the analyzed samples, in terms of pure elution time, excitation and emission spectral profiles, which can be independently retrieved for every component.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Chemometrics and Intelligent Laboratory Systems","grobid_abstract_attachment_id":97738913},"translated_abstract":null,"internal_url":"https://www.academia.edu/95601767/Novel_augmented_parallel_factor_model_for_four_way_calibration_of_high_performance_liquid_chromatography_fluorescence_excitation_emission_data","translated_internal_url":"","created_at":"2023-01-24T05:44:28.256-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97738913,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97738913/thumbnails/1.jpg","file_name":"j.chemolab.2014.11.01320230124-1-1fousa8.pdf","download_url":"https://www.academia.edu/attachments/97738913/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Novel_augmented_parallel_factor_model_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97738913/j.chemolab.2014.11.01320230124-1-1fousa8-libre.pdf?1674568217=\u0026response-content-disposition=attachment%3B+filename%3DNovel_augmented_parallel_factor_model_fo.pdf\u0026Expires=1732459817\u0026Signature=YeW-QYdZegplN8XSP5xmBsdydIACb7B-IWjyzA9Zrc~Nsp9fkAAhblR7Y-wPiZpFyHToDLJv554HSe9r-7FShWUevTvw8xU-dHQGzn0Itic-pvMY41ZNLmpoTY-HKlF5boDIWXlFkE2WjKnWN5~Bju1lhTybnUrw5jrl5Bj8lPndsjuJNyY6L06KnOx33QFFnGYDO8C~rWJl8FVximgxLqTtEU5Rm7F74iL2rlfJs7VS~1cnwXV3bVeWW~KCiDyPbXQO-dwGfdq2h1VBdbEm911m70g9TJc8zBbt1srIOcFpp2shOTTqC3eVBfa~EJyICpYi9Qd7dgMvcv4kEskLEw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Novel_augmented_parallel_factor_model_for_four_way_calibration_of_high_performance_liquid_chromatography_fluorescence_excitation_emission_data","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[{"id":97738913,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97738913/thumbnails/1.jpg","file_name":"j.chemolab.2014.11.01320230124-1-1fousa8.pdf","download_url":"https://www.academia.edu/attachments/97738913/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Novel_augmented_parallel_factor_model_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97738913/j.chemolab.2014.11.01320230124-1-1fousa8-libre.pdf?1674568217=\u0026response-content-disposition=attachment%3B+filename%3DNovel_augmented_parallel_factor_model_fo.pdf\u0026Expires=1732459817\u0026Signature=YeW-QYdZegplN8XSP5xmBsdydIACb7B-IWjyzA9Zrc~Nsp9fkAAhblR7Y-wPiZpFyHToDLJv554HSe9r-7FShWUevTvw8xU-dHQGzn0Itic-pvMY41ZNLmpoTY-HKlF5boDIWXlFkE2WjKnWN5~Bju1lhTybnUrw5jrl5Bj8lPndsjuJNyY6L06KnOx33QFFnGYDO8C~rWJl8FVximgxLqTtEU5Rm7F74iL2rlfJs7VS~1cnwXV3bVeWW~KCiDyPbXQO-dwGfdq2h1VBdbEm911m70g9TJc8zBbt1srIOcFpp2shOTTqC3eVBfa~EJyICpYi9Qd7dgMvcv4kEskLEw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":4656,"name":"Chromatography","url":"https://www.academia.edu/Documents/in/Chromatography"},{"id":7698,"name":"Fluorescence","url":"https://www.academia.edu/Documents/in/Fluorescence"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":903559,"name":"Enseñanza - Aprendizaje Ciencias Naturales Y Exactas","url":"https://www.academia.edu/Documents/in/Ensenanza_-_Aprendizaje_Ciencias_Naturales_Y_Exactas"},{"id":1587407,"name":"Química Analítica","url":"https://www.academia.edu/Documents/in/Qu%C3%ADmica_Anal%C3%ADtica"},{"id":3188247,"name":"Elution","url":"https://www.academia.edu/Documents/in/Elution"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[{"id":28350237,"url":"https://api.elsevier.com/content/article/PII:S0169743914002469?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601766]").text(description); $(".js-view-count[data-work-id=95601766]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601766; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601766']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601766, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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The proposed analytical method, which involves photochemically induced fluorescence matrix data combined with second-order chemometric analysis, was used for the determination of carbamazepine, ofloxacin and piroxicam in water samples of different complexity without the need of chromatographic separation. Excitation-emission photoinduced fluorescence matrices were obtained after UV irradiation, and processed with second-order algorithms. Only one of the tested algorithms was able to overcome the strong spectral overlapping among the studied pollutants and allowed their successful quantitation in very interferent media. The method sensitivity in superficial and underground water samples was enhanced by a simple solid-phase extraction with C18 membranes, which was successful for the extraction/preconcentration of the pollutants at trace levels. Detection limits in preconcentrated (1:125) real water samples ranged from 0.04 to 0.3 ng mL-1. Relative prediction errors around 10 % were achieved. The proposed strategy is significantly simpler and greener than liquid chromatography-mass spectrometry methods, without compromising the analytical quality of the results.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Talanta","grobid_abstract_attachment_id":97738911},"translated_abstract":null,"internal_url":"https://www.academia.edu/95601766/Green_analytical_determination_of_emerging_pollutants_in_environmental_waters_using_excitation_emission_photoinduced_fluorescence_data_and_multivariate_calibration","translated_internal_url":"","created_at":"2023-01-24T05:44:28.080-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97738911,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97738911/thumbnails/1.jpg","file_name":"S003991401400914X.pdf","download_url":"https://www.academia.edu/attachments/97738911/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Green_analytical_determination_of_emergi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97738911/S003991401400914X-libre.pdf?1674568212=\u0026response-content-disposition=attachment%3B+filename%3DGreen_analytical_determination_of_emergi.pdf\u0026Expires=1732459817\u0026Signature=TpbOxBrxfEZwpDxHHxWzcp-pmYVSRX7wg5akfdHRr7VlRFHCtvNSjhQAUDLum2abtWiIug~fvsN1LO~GVjuIbFsQxZFH0XPqRQYuM-jDHnEj~QbkIOLRvRgVhAtRhntednOkuzud8XczQUsVLw~o0n7nwjNLwmSM2qloEDNM~pWnofsAcWMWje86U05BK4My~Y0bzS9VrTv5T-MsfaeEbnqkfPnuS2tMTAvd7yqQcGfzWmJe4zQJjnAU2Mj0nuuHc-ExIYebW0sd2-bAbn2fFFTEJy7mjwAEmEJ1F5XBPIw0mc2B9fdBbeBSIRD5wOkslXJ6-L-IvFJjZpD3OB8rFw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Green_analytical_determination_of_emerging_pollutants_in_environmental_waters_using_excitation_emission_photoinduced_fluorescence_data_and_multivariate_calibration","translated_slug":"","page_count":34,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[{"id":97738911,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97738911/thumbnails/1.jpg","file_name":"S003991401400914X.pdf","download_url":"https://www.academia.edu/attachments/97738911/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Green_analytical_determination_of_emergi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97738911/S003991401400914X-libre.pdf?1674568212=\u0026response-content-disposition=attachment%3B+filename%3DGreen_analytical_determination_of_emergi.pdf\u0026Expires=1732459817\u0026Signature=TpbOxBrxfEZwpDxHHxWzcp-pmYVSRX7wg5akfdHRr7VlRFHCtvNSjhQAUDLum2abtWiIug~fvsN1LO~GVjuIbFsQxZFH0XPqRQYuM-jDHnEj~QbkIOLRvRgVhAtRhntednOkuzud8XczQUsVLw~o0n7nwjNLwmSM2qloEDNM~pWnofsAcWMWje86U05BK4My~Y0bzS9VrTv5T-MsfaeEbnqkfPnuS2tMTAvd7yqQcGfzWmJe4zQJjnAU2Mj0nuuHc-ExIYebW0sd2-bAbn2fFFTEJy7mjwAEmEJ1F5XBPIw0mc2B9fdBbeBSIRD5wOkslXJ6-L-IvFJjZpD3OB8rFw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms"},{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":4656,"name":"Chromatography","url":"https://www.academia.edu/Documents/in/Chromatography"},{"id":7698,"name":"Fluorescence","url":"https://www.academia.edu/Documents/in/Fluorescence"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":64568,"name":"Humans","url":"https://www.academia.edu/Documents/in/Humans"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":120641,"name":"Drinking Water","url":"https://www.academia.edu/Documents/in/Drinking_Water"},{"id":382466,"name":"Carbamazepine","url":"https://www.academia.edu/Documents/in/Carbamazepine"},{"id":439435,"name":"Fresh water","url":"https://www.academia.edu/Documents/in/Fresh_water"},{"id":734759,"name":"PIROXICAM","url":"https://www.academia.edu/Documents/in/PIROXICAM"},{"id":893767,"name":"Ofloxacin","url":"https://www.academia.edu/Documents/in/Ofloxacin"},{"id":903559,"name":"Enseñanza - 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The algorithm employed, multivariate curve resolution-alternating least-squares, is one of the few methodologies which permit the achievement of the second-order advantage in the presence of a high degree of overlapping between the time decay profiles for the analyte and the interferent complexes. Furthermore, the presence of analyte-background interactions makes it necessary to employ the standard addition method for successful quantitation. Both simulations and experiments showed that the modified standard addition method was suitable for this purpose, in which the test data matrix was subtracted from the standard addition matrices, and quantitation proceeded using classical external calibration procedure. T...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a149bd6731ef3635e224433bdbb76cca" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":97738940,"asset_id":95601765,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/97738940/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601765"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601765"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601765; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601765]").text(description); $(".js-view-count[data-work-id=95601765]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601765; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601765']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601765, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a149bd6731ef3635e224433bdbb76cca" } } $('.js-work-strip[data-work-id=95601765]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601765,"title":"Standard addition analysis of fluoroquinolones in human serum in the presence of the interferent salicylate using lanthanide-sensitized excitation-time decay luminescence data and multivariate curve resolution","translated_title":"","metadata":{"abstract":"Three fluoroquinolone antibiotics (ciprofloxacin, norfloxacin and danofloxacin) have been determined in human serum in the presence of the potential interferent salicylate, by processing lanthanide-sensitized excitation-time decay matrix data for their terbium (III) complexes. The algorithm employed, multivariate curve resolution-alternating least-squares, is one of the few methodologies which permit the achievement of the second-order advantage in the presence of a high degree of overlapping between the time decay profiles for the analyte and the interferent complexes. Furthermore, the presence of analyte-background interactions makes it necessary to employ the standard addition method for successful quantitation. Both simulations and experiments showed that the modified standard addition method was suitable for this purpose, in which the test data matrix was subtracted from the standard addition matrices, and quantitation proceeded using classical external calibration procedure. T...","publisher":"ncbi.nlm.nih.gov","publication_date":{"day":15,"month":1,"year":2009,"errors":{}},"publication_name":"Talanta"},"translated_abstract":"Three fluoroquinolone antibiotics (ciprofloxacin, norfloxacin and danofloxacin) have been determined in human serum in the presence of the potential interferent salicylate, by processing lanthanide-sensitized excitation-time decay matrix data for their terbium (III) complexes. The algorithm employed, multivariate curve resolution-alternating least-squares, is one of the few methodologies which permit the achievement of the second-order advantage in the presence of a high degree of overlapping between the time decay profiles for the analyte and the interferent complexes. Furthermore, the presence of analyte-background interactions makes it necessary to employ the standard addition method for successful quantitation. Both simulations and experiments showed that the modified standard addition method was suitable for this purpose, in which the test data matrix was subtracted from the standard addition matrices, and quantitation proceeded using classical external calibration procedure. T...","internal_url":"https://www.academia.edu/95601765/Standard_addition_analysis_of_fluoroquinolones_in_human_serum_in_the_presence_of_the_interferent_salicylate_using_lanthanide_sensitized_excitation_time_decay_luminescence_data_and_multivariate_curve_resolution","translated_internal_url":"","created_at":"2023-01-24T05:44:27.886-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":97738940,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/97738940/thumbnails/1.jpg","file_name":"j.talanta.2008.10.02020230124-1-10dy8j4.pdf","download_url":"https://www.academia.edu/attachments/97738940/download_file?st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&st=MTczMjQ1NjIxNyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Standard_addition_analysis_of_fluoroquin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/97738940/j.talanta.2008.10.02020230124-1-10dy8j4-libre.pdf?1674568207=\u0026response-content-disposition=attachment%3B+filename%3DStandard_addition_analysis_of_fluoroquin.pdf\u0026Expires=1732459817\u0026Signature=KJG6pwwiBMPKx8dFxnetvWVbb~TnwuyQNY~DPerPeBXRE-ou8z8C-K9yBFW9GXFyhdzaBLGbApQLtQUNdGSDK4D7nqFINXP85Z9hZAKhiHO6bKAy1YLfFqciK8pyyLLSZIs6wtbr5j38xoSeSBGTU7Sw6ox~OiuROH~cajqjnZtzguF4jwEQOCq~vqbr~PXC~K33I1f9Nm3oPRMfjqtvc3Aw6Znnn8niOGkmYyfeXFDByI9ywjIKwgKqBxSVQQ0Ht47D8iBTU3eSYdXtIe8zuDqW1os5Fh96a2kavptmeFCb5Yh88YKCJJ7qfkFCK0anvAVfpmU6dmB58Nt36jWq~A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Standard_addition_analysis_of_fluoroquinolones_in_human_serum_in_the_presence_of_the_interferent_salicylate_using_lanthanide_sensitized_excitation_time_decay_luminescence_data_and_multivariate_curve_resolution","translated_slug":"","page_count":9,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella 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Resolution","url":"https://www.academia.edu/Documents/in/Multivariate_Curve_Resolution"},{"id":1552428,"name":"Fluoroquinolones","url":"https://www.academia.edu/Documents/in/Fluoroquinolones"},{"id":1776211,"name":"Salicylates","url":"https://www.academia.edu/Documents/in/Salicylates"},{"id":4050471,"name":"calibration curve","url":"https://www.academia.edu/Documents/in/calibration_curve"}],"urls":[{"id":28350235,"url":"http://dx.doi.org/10.1016/j.talanta.2008.10.020"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601764"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601764/Four_way_multivariate_calibration_using_ultra_fast_high_performance_liquid_chromatography_with_fluorescence_excitation_emission_detection_Application_to_the_direct_analysis_of_chlorophylls_a_and_b_and_pheophytins_a_and_b_in_olive_oils"><img alt="Research paper thumbnail of Four-way multivariate calibration using ultra-fast high-performance liquid chromatography with fluorescence excitation–emission detection. Application to the direct analysis of chlorophylls a and b and pheophytins a and b in olive oils" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601764/Four_way_multivariate_calibration_using_ultra_fast_high_performance_liquid_chromatography_with_fluorescence_excitation_emission_detection_Application_to_the_direct_analysis_of_chlorophylls_a_and_b_and_pheophytins_a_and_b_in_olive_oils">Four-way multivariate calibration using ultra-fast high-performance liquid chromatography with fluorescence excitation–emission detection. Application to the direct analysis of chlorophylls a and b and pheophytins a and b in olive oils</a></div><div class="wp-workCard_item"><span>Chemometrics and Intelligent Laboratory Systems</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A four-way multivariate calibration approach, based on the combination of ultra-fasthigh...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A four-way multivariate calibration approach, based on the combination of ultra-fasthigh-performance liquid chromatographic data and four-way algorithms, is described for the first time. To achieve this goal, several emission wavelength–elution time matrices (ETMs) were recorded as a function of the excitation wavelength. Each sample was injected into the chromatograph eight times, in sequential mode, each time exciting at a different wavelength across the excitation spectra of the compounds of interest, and the emission spectra were recorded along the full chromatogram using a fast scanning fluorescence detector. The data were obtained in a very short time with an ultrafast chromatographic system operating in gradient mode. The three-wayETM data thus obtained for the calibration sample set and for each of the test samples were joined into a single four-way array, which was subsequently analyzed with parallel factor analysis (PARAFAC), unfolded partial least-squares with residual trilinearization (U-PLS/RTL) and multi-way partial least-squares with residual trilinearization (N-PLS/RTL) multivariate calibration algorithms. Best results were found when either U-PLS/RTL or N-PLS/RTL algorithms were used to perform the multivariate calibration. The method was applied to the direct determination of chlorophylls a and b and pheophytins a and b in olive oil samples, in the presence of other interfering fluorescent compounds, and without previous sample treatment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601764"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601764"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601764; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601764]").text(description); $(".js-view-count[data-work-id=95601764]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601764; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601764']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601764, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601764]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601764,"title":"Four-way multivariate calibration using ultra-fast high-performance liquid chromatography with fluorescence excitation–emission detection. Application to the direct analysis of chlorophylls a and b and pheophytins a and b in olive oils","translated_title":"","metadata":{"abstract":"ABSTRACT A four-way multivariate calibration approach, based on the combination of ultra-fasthigh-performance liquid chromatographic data and four-way algorithms, is described for the first time. To achieve this goal, several emission wavelength–elution time matrices (ETMs) were recorded as a function of the excitation wavelength. Each sample was injected into the chromatograph eight times, in sequential mode, each time exciting at a different wavelength across the excitation spectra of the compounds of interest, and the emission spectra were recorded along the full chromatogram using a fast scanning fluorescence detector. The data were obtained in a very short time with an ultrafast chromatographic system operating in gradient mode. The three-wayETM data thus obtained for the calibration sample set and for each of the test samples were joined into a single four-way array, which was subsequently analyzed with parallel factor analysis (PARAFAC), unfolded partial least-squares with residual trilinearization (U-PLS/RTL) and multi-way partial least-squares with residual trilinearization (N-PLS/RTL) multivariate calibration algorithms. Best results were found when either U-PLS/RTL or N-PLS/RTL algorithms were used to perform the multivariate calibration. The method was applied to the direct determination of chlorophylls a and b and pheophytins a and b in olive oil samples, in the presence of other interfering fluorescent compounds, and without previous sample treatment.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Chemometrics and Intelligent Laboratory Systems"},"translated_abstract":"ABSTRACT A four-way multivariate calibration approach, based on the combination of ultra-fasthigh-performance liquid chromatographic data and four-way algorithms, is described for the first time. To achieve this goal, several emission wavelength–elution time matrices (ETMs) were recorded as a function of the excitation wavelength. Each sample was injected into the chromatograph eight times, in sequential mode, each time exciting at a different wavelength across the excitation spectra of the compounds of interest, and the emission spectra were recorded along the full chromatogram using a fast scanning fluorescence detector. The data were obtained in a very short time with an ultrafast chromatographic system operating in gradient mode. The three-wayETM data thus obtained for the calibration sample set and for each of the test samples were joined into a single four-way array, which was subsequently analyzed with parallel factor analysis (PARAFAC), unfolded partial least-squares with residual trilinearization (U-PLS/RTL) and multi-way partial least-squares with residual trilinearization (N-PLS/RTL) multivariate calibration algorithms. Best results were found when either U-PLS/RTL or N-PLS/RTL algorithms were used to perform the multivariate calibration. The method was applied to the direct determination of chlorophylls a and b and pheophytins a and b in olive oil samples, in the presence of other interfering fluorescent compounds, and without previous sample treatment.","internal_url":"https://www.academia.edu/95601764/Four_way_multivariate_calibration_using_ultra_fast_high_performance_liquid_chromatography_with_fluorescence_excitation_emission_detection_Application_to_the_direct_analysis_of_chlorophylls_a_and_b_and_pheophytins_a_and_b_in_olive_oils","translated_internal_url":"","created_at":"2023-01-24T05:44:27.702-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Four_way_multivariate_calibration_using_ultra_fast_high_performance_liquid_chromatography_with_fluorescence_excitation_emission_detection_Application_to_the_direct_analysis_of_chlorophylls_a_and_b_and_pheophytins_a_and_b_in_olive_oils","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":7698,"name":"Fluorescence","url":"https://www.academia.edu/Documents/in/Fluorescence"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":358917,"name":"Partial Least Squares Regression","url":"https://www.academia.edu/Documents/in/Partial_Least_Squares_Regression"},{"id":903559,"name":"Enseñanza - Aprendizaje Ciencias Naturales Y Exactas","url":"https://www.academia.edu/Documents/in/Ensenanza_-_Aprendizaje_Ciencias_Naturales_Y_Exactas"},{"id":1587407,"name":"Química Analítica","url":"https://www.academia.edu/Documents/in/Qu%C3%ADmica_Anal%C3%ADtica"},{"id":3030532,"name":"Residual","url":"https://www.academia.edu/Documents/in/Residual"},{"id":3188247,"name":"Elution","url":"https://www.academia.edu/Documents/in/Elution"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[{"id":28350234,"url":"https://api.elsevier.com/content/article/PII:S0169743913000622?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601763"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601763/A_novel_nylon_membrane_rhodamine_6G_spirocyclic_phenylthiosemicarbazide_derivative_system_as_a_fluorimetric_probe_for_mercury_ii_ion"><img alt="Research paper thumbnail of A novel nylon membrane–rhodamine 6G spirocyclic phenylthiosemicarbazide derivative system as a fluorimetric probe for mercury(ii) ion" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601763/A_novel_nylon_membrane_rhodamine_6G_spirocyclic_phenylthiosemicarbazide_derivative_system_as_a_fluorimetric_probe_for_mercury_ii_ion">A novel nylon membrane–rhodamine 6G spirocyclic phenylthiosemicarbazide derivative system as a fluorimetric probe for mercury(ii) ion</a></div><div class="wp-workCard_item"><span>Analytical Methods</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT A highly sensitive and selective probe for the fluorimetric determination of mercury ion...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT A highly sensitive and selective probe for the fluorimetric determination of mercury ion traces in aqueous solution is proposed. The probe is based on the mercury-promoted ring opening of the spirolactam moiety of a rhodamine 6G spirocyclic phenylthiosemicarbazide derivative (FC1) retained in nylon membranes. It is demonstrated that the chemodosimeter preserves its sensor ability, displaying intense fluorescence in the presence of Hg(II) after being immobilized on the nylon surface and reacting with the mercury ion solution via a simple syringe procedure. The advantages of this proposal are: (1) the use of an easily affordable solid support which is able to immobilize the FC1 molecular probe without involving a covalent bond, (2) the consumption of a very small volume of organic reagent, dramatically reducing the environmental impact, and (3) the development of a solid phase system potentially useful as a main component for designing chemical sensors capable of providing continuous real-time information. In order to obtain higher and stable fluorescence signals, both experimental and instrumental variables were optimized. Thus, a simple and sensitive fluorescence method for the determination of mercury ion was established. The limit of detection calculated according to 1995 IUPAC Recommendations was 0.4 ng mL−1 (lower than the toxic levels in drinking water for human consumption, established by several regulatory agencies), the relative standard deviation was 2.3% (n = 6) at a level of 3.5 ng mL−1, and the sampling rate was about 15 samples per hour. The study of the potential interference from common cations demonstrated a remarkable selectivity for the investigated metal ion. 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waters","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"A photochemically-induced fluorescence system combined with second-order chemometric analysis for the determination of the anticonvulsant carbamazepine (CBZ) is presented. CBZ is a widely used drug for the treatment of epilepsy and is included in the group of emerging contaminant present in the aquatic environment. CBZ is not fluorescent in solution but can be converted into a fluorescent compound through a photochemical reaction in a strong acid medium. The determination is carried out by measuring excitation-emission photoinduced fluorescence matrices of the products formed upon ultraviolet light irradiation in a laboratoryconstructed reactor constituted by two simple 4 W germicidal tubes. Working conditions related to both the reaction medium and the photoreactor geometry are optimized by an experimental design. The developed approach enabled the determination of CBZ at trace levels without the necessity of applying separation steps, and in the presence of uncalibrated interferences which also display photoinduced fluorescence and may be potentially present in the investigated samples. Different second-order algorithms were tested and successful resolution was achieved using multivariate curve resolution-alternating least-squares (MCR-ALS). The study is employed for the discussion of the scopes and yields of each of the applied second-order chemometric tools. The quality of the proposed method is probed through the determination of the studied emerging pollutant in both environmental and drinking water samples. 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Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":5104,"name":"Photochemistry","url":"https://www.academia.edu/Documents/in/Photochemistry"},{"id":7698,"name":"Fluorescence","url":"https://www.academia.edu/Documents/in/Fluorescence"},{"id":9813,"name":"Argentina","url":"https://www.academia.edu/Documents/in/Argentina"},{"id":12653,"name":"Rivers","url":"https://www.academia.edu/Documents/in/Rivers"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":120641,"name":"Drinking Water","url":"https://www.academia.edu/Documents/in/Drinking_Water"},{"id":376522,"name":"SVD","url":"https://www.academia.edu/Documents/in/SVD"},{"id":382466,"name":"Carbamazepine","url":"https://www.academia.edu/Documents/in/Carbamazepine"},{"id":426393,"name":"LOD","url":"https://www.academia.edu/Documents/in/LOD"},{"id":620380,"name":"IT","url":"https://www.academia.edu/Documents/in/IT"},{"id":1190911,"name":"R","url":"https://www.academia.edu/Documents/in/R"},{"id":1232950,"name":"Fluorometry","url":"https://www.academia.edu/Documents/in/Fluorometry"},{"id":1256818,"name":"SPE","url":"https://www.academia.edu/Documents/in/SPE"},{"id":1587407,"name":"Química Analítica","url":"https://www.academia.edu/Documents/in/Qu%C3%ADmica_Anal%C3%ADtica"},{"id":2525232,"name":"Multivariate Calibration","url":"https://www.academia.edu/Documents/in/Multivariate_Calibration"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[{"id":28350232,"url":"https://api.elsevier.com/content/article/PII:S000326701300531X?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601760"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601760/Second_order_advantage_from_kinetic_spectroscopic_data_matrices_in_the_presence_of_extreme_spectral_overlapping"><img alt="Research paper thumbnail of Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601760/Second_order_advantage_from_kinetic_spectroscopic_data_matrices_in_the_presence_of_extreme_spectral_overlapping">Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping</a></div><div class="wp-workCard_item"><span>Analytica Chimica Acta</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601760"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601760"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601760; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601760]").text(description); $(".js-view-count[data-work-id=95601760]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601760; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601760']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601760, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601760]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601760,"title":"Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping","translated_title":"","metadata":{"abstract":"Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Analytica Chimica Acta"},"translated_abstract":"Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.","internal_url":"https://www.academia.edu/95601760/Second_order_advantage_from_kinetic_spectroscopic_data_matrices_in_the_presence_of_extreme_spectral_overlapping","translated_internal_url":"","created_at":"2023-01-24T05:44:27.065-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":254614243,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Second_order_advantage_from_kinetic_spectroscopic_data_matrices_in_the_presence_of_extreme_spectral_overlapping","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":254614243,"first_name":"Antonella","middle_initials":null,"last_name":"Lozano","page_name":"AntonellaLozano1","domain_name":"independent","created_at":"2023-01-24T05:43:51.924-08:00","display_name":"Antonella Lozano","url":"https://independent.academia.edu/AntonellaLozano1"},"attachments":[],"research_interests":[{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry"},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry"},{"id":4987,"name":"Kinetics","url":"https://www.academia.edu/Documents/in/Kinetics"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":41482,"name":"Multivariate Analysis","url":"https://www.academia.edu/Documents/in/Multivariate_Analysis"},{"id":85877,"name":"Alternating least squares","url":"https://www.academia.edu/Documents/in/Alternating_least_squares"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration"},{"id":160656,"name":"Potassium","url":"https://www.academia.edu/Documents/in/Potassium"},{"id":323652,"name":"Interference","url":"https://www.academia.edu/Documents/in/Interference"},{"id":347272,"name":"Second Order","url":"https://www.academia.edu/Documents/in/Second_Order"},{"id":386276,"name":"Cerium","url":"https://www.academia.edu/Documents/in/Cerium"},{"id":477077,"name":"Bromate","url":"https://www.academia.edu/Documents/in/Bromate"},{"id":563382,"name":"Oxidation","url":"https://www.academia.edu/Documents/in/Oxidation"},{"id":924470,"name":"Methyl Orange","url":"https://www.academia.edu/Documents/in/Methyl_Orange"},{"id":1505380,"name":"Multivariate Curve Resolution","url":"https://www.academia.edu/Documents/in/Multivariate_Curve_Resolution"},{"id":1587407,"name":"Química Analítica","url":"https://www.academia.edu/Documents/in/Qu%C3%ADmica_Anal%C3%ADtica"},{"id":1600695,"name":"Potassium Bromate","url":"https://www.academia.edu/Documents/in/Potassium_Bromate"},{"id":2525232,"name":"Multivariate Calibration","url":"https://www.academia.edu/Documents/in/Multivariate_Calibration"},{"id":3938094,"name":"Ciencias Químicas","url":"https://www.academia.edu/Documents/in/Ciencias_Qu%C3%ADmicas"}],"urls":[{"id":28350231,"url":"https://api.elsevier.com/content/article/PII:S0003267008004819?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601759"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601759/Three_way_partial_least_squares_residual_bilinearization_study_of_second_order_lanthanide_sensitized_luminescence_excitation_time_decay_data"><img alt="Research paper thumbnail of Three-way partial least-squares/residual bilinearization study of second-order lanthanide-sensitized luminescence excitation-time decay data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601759/Three_way_partial_least_squares_residual_bilinearization_study_of_second_order_lanthanide_sensitized_luminescence_excitation_time_decay_data">Three-way partial least-squares/residual bilinearization study of second-order lanthanide-sensitized luminescence excitation-time decay data</a></div><div class="wp-workCard_item"><span>Analytica Chimica Acta</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the second-order advantage using as chemometric algorithms parallel factor analysis (PARAFAC) and multidimensional partial least-squares with residual bilinearization (N-PLS/RBL). The second-order data were measured for a calibration set of samples containing the analyte benzoic acid in the concentration range from 0.00 to 5.00 mg L(-1), for a validation set containing the analyte and the potential interferent saccharin (in the range 0.00-6.00 mg L(-1)), and for real samples of beverages containing benzoic acid as preservant, saccharin, and other potentially interfering compounds. All samples were treated with terbium(III), trioctylphosphine oxide as a synergistic ligand, and contained a suitable imidazol buffer, in order to ensure maximum intensity of the luminescence signals. The results indicate a slightly better predictive ability of the newly introduced N-PLS/RBL procedure over standard PARAFAC, both in what concerns the comparison with nominal analyte concentrations in the validation sample set and with results provided by the reference high-performance liquid chromatographic technique for the real sample set.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601759"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601759"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601759; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601759]").text(description); $(".js-view-count[data-work-id=95601759]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601759; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601759']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601759, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601759]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601759,"title":"Three-way partial least-squares/residual bilinearization study of second-order lanthanide-sensitized luminescence excitation-time decay data","translated_title":"","metadata":{"abstract":"Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the second-order advantage using as chemometric algorithms parallel factor analysis (PARAFAC) and multidimensional partial least-squares with residual bilinearization (N-PLS/RBL). The second-order data were measured for a calibration set of samples containing the analyte benzoic acid in the concentration range from 0.00 to 5.00 mg L(-1), for a validation set containing the analyte and the potential interferent saccharin (in the range 0.00-6.00 mg L(-1)), and for real samples of beverages containing benzoic acid as preservant, saccharin, and other potentially interfering compounds. All samples were treated with terbium(III), trioctylphosphine oxide as a synergistic ligand, and contained a suitable imidazol buffer, in order to ensure maximum intensity of the luminescence signals. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="95601758"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/95601758/A_novel_second_order_standard_addition_analytical_method_based_on_data_processing_with_multidimensional_partial_least_squares_and_residual_bilinearization"><img alt="Research paper thumbnail of A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/95601758/A_novel_second_order_standard_addition_analytical_method_based_on_data_processing_with_multidimensional_partial_least_squares_and_residual_bilinearization">A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization</a></div><div class="wp-workCard_item"><span>Analytica Chimica Acta</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the presence of analyte-background interactions and a significant background signal, both seco...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="95601758"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="95601758"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 95601758; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=95601758]").text(description); $(".js-view-count[data-work-id=95601758]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 95601758; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='95601758']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 95601758, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=95601758]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":95601758,"title":"A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization","translated_title":"","metadata":{"abstract":"In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. 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The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2009,"errors":{}},"publication_name":"Analytica Chimica Acta"},"translated_abstract":"In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. 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