2025, Lima, Clara Mariana Gonçalves, Silveira, Paula Giarolla, Santana, Renata Ferreira, Khalid, Waseem, Mourão, Matheus da Silva, Bonomo, Renata Cristina Ferreira, Coutinho, Henrique Douglas Melo, Dos Anjos, Virgílio de Carvalho, Bell, Maria José Valenzuela, Batista, Luís Roberto, Contado, José Luís, Wawrzyniak, Jolanta, Verruck, Silvani, Da Rocha, Roney Alves

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.

2025, Lima, Clara Mariana Gonçalves, Silveira, Paula Giarolla, Santana, Renata Ferreira, da Piedade Edmundo Sitoe, Eugénio, Bonomo, Renata Cristina Ferreira, Coutinho, Henrique Douglas Melo, Wawrzyniak, Jolanta, de Carvalho dos Anjos, Virgílio, Bell, Maria José Valenzuela, Contado, José Luís, Zengin, Gökhan, da Rocha, Roney Alves