Rapid Detection of Tea Adulteration Using FT-NIR Spectroscopy Combined with t-SNE Analysis

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dc.abstract.enTea is one of the most popular non-alcoholic beverages internationally, and it is not uncommon to find commercial tea preparations mixed with leaves and parts of other plants to increase profit and production volume, which constitutes fraud. The aim of this study was to perform Fourier transform-near-infrared spectroscopic characterization of leaves and pieces (petioles and stems) of three types of medicinal plants (Chamomile, Ginseng, and Quebra-pedras) used in the preparation of teas. Cluster analysis methods were used to evaluate the ability of Fourier transform-near-infrared to identify plant types, with t-SNE presenting the best discriminatory power. The deconvolution of the spectra showed that 15 vibration bands allow a good characterization of the samples, all with R² greater than 0.99.
dc.affiliationWydział Nauk o Żywności i Żywieniu
dc.affiliation.instituteKatedra Mleczarstwa i Inżynierii Procesowej
dc.contributor.authorLima, Clara Mariana Gonçalves
dc.contributor.authorSilveira, Paula Giarolla
dc.contributor.authorSantana, Renata Ferreira
dc.contributor.authorKhalid, Waseem
dc.contributor.authorMourão, Matheus da Silva
dc.contributor.authorBonomo, Renata Cristina Ferreira
dc.contributor.authorCoutinho, Henrique Douglas Melo
dc.contributor.authorDos Anjos, Virgílio de Carvalho
dc.contributor.authorBell, Maria José Valenzuela
dc.contributor.authorBatista, Luís Roberto
dc.contributor.authorContado, José Luís
dc.contributor.authorWawrzyniak, Jolanta
dc.contributor.authorVerruck, Silvani
dc.contributor.authorDa Rocha, Roney Alves
dc.date.access2025-06-03
dc.date.accessioned2025-07-01T07:33:16Z
dc.date.available2025-07-01T07:33:16Z
dc.date.copyright2025-04-30
dc.date.issued2025
dc.description.abstract<jats:p>Tea is one of the most popular non-alcoholic beverages internationally, and it is not uncommon to find commercial tea preparations mixed with leaves and parts of other plants to increase profit and production volume, which constitutes fraud. The aim of this study was to perform Fourier transform-near-infrared spectroscopic characterization of leaves and pieces (petioles and stems) of three types of medicinal plants (Chamomile, Ginseng, and Quebra-pedras) used in the preparation of teas. Cluster analysis methods were used to evaluate the ability of Fourier transform-near-infrared to identify plant types, with t-SNE presenting the best discriminatory power. The deconvolution of the spectra showed that 15 vibration bands allow a good characterization of the samples, all with R² greater than 0.99.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.points20
dc.description.versionfinal_published
dc.description.volume45
dc.identifier.doi10.5327/fst.00454
dc.identifier.eissn1678-457X
dc.identifier.issn0101-2061
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/3791
dc.identifier.weblinkhttps://www.fstjournal.com.br/revista/article/view/454
dc.languageen
dc.relation.ispartofFood Science and Technology
dc.relation.pagese00454
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.eninfrared
dc.subject.ennon-linear statistical method
dc.subject.enfraud
dc.subject.enmedicinal plants
dc.titleRapid Detection of Tea Adulteration Using FT-NIR Spectroscopy Combined with t-SNE Analysis
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.volume45