Revilla Isabel, Hernández Jiménez Miriam, Martínez-Martín Iván, Valderrama Patricia, Rodríguez-Fernández Marta, Vivar-Quintana Ana M
Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain.
Department of Chemistry, Universidade Tecnológica Federal do Paraná (UTFPR), Via Rosalina Maria dos Santos 1233, Campo Mourão 87301-899, Paraná, Brazil.
Foods. 2024 Jan 31;13(3):450. doi: 10.3390/foods13030450.
The following study analyzed the potential of Near Infrared Spectroscopy (NIRS) to predict the metal composition (Al, Pb, As, Hg and Cu) of tea and for establishing discriminant models for pure teas (green, red, and black) and their different blends. A total of 322 samples of pure black, red, and green teas and binary blends were analyzed. The results showed that pure red teas had the highest content of As and Pb, green teas were the only ones containing Hg, and black teas showed higher levels of Cu. NIRS allowed to predict the content of Al, Pb, As, Hg, and Cu with ratio performance deviation values > 3 for all of them. Additionally, it was possible to discriminate pure samples from their respective blends with an accuracy of 98.3% in calibration and 92.3% in validation. However, when the samples were discriminated according to the percentage of blending (>95%, 95-85%, 85-75%, or 75-50% of pure tea) 100% of the samples of 10 out of 12 groups were correctly classified in calibration, but only the groups with a level of pure tea of >95% showed 100% of the samples as being correctly classified as to validation.
以下研究分析了近红外光谱（NIRS）预测茶叶金属成分（铝、铅、砷、汞和铜）的潜力，以及建立纯茶（绿茶、红茶和黑茶）及其不同混合茶判别模型的潜力。共分析了322个纯黑茶、红茶、绿茶及二元混合茶样本。结果表明，纯红茶中砷和铅的含量最高，绿茶是唯一含汞的茶，黑茶中铜的含量较高。近红外光谱能够预测铝、铅、砷、汞和铜的含量，其比率性能偏差值均大于3。此外，有可能将纯样本与各自的混合样本区分开来，在校准中的准确率为98.3%，在验证中的准确率为92.3%。然而，当根据混合比例（纯茶的>95%、95 - 85%、85 - 75%或75 - 50%）对样本进行区分时，12组中有10组的100%样本在校准中被正确分类，但只有纯茶含量>95%的组在验证中显示100%的样本被正确分类。
