小种红茶茶多酚和咖啡碱近红外定量分析模型的建立

doi:10.13305/j.cnki.jts.2020.05.014

摘要/Abstract

摘要： 以76份有代表性的小种红茶为研究对象,采用现行国标方法测定的茶多酚和咖啡碱含量作为近红外预测模型的化学值,对应采集样品的近红外光谱值,分别建立小种红茶茶多酚和咖啡碱含量最佳偏最小二乘法（Partial least squares,PLS）模型。结果表明,所构建的茶多酚含量模型校正集决定系数（Coefficient of determination,R²）为97.59%,校正均方差（Root mean square error of calibration,RMSEC）为0.566%,验证集R²为95.06%,预测均方差（Root mean square error of prediction,RMSEP）为0.855%;咖啡碱含量模型校正集R²为96.98%,RESEC为0.110%,验证集R²为95.67%,RESEP为0.148%。茶多酚和咖啡碱含量定量分析模型效果均较好,可实现对小种红茶茶多酚和咖啡碱含量的快速检测。

关键词: 小种红茶, 近红外光谱, 茶多酚, 咖啡碱, 模型

Abstract: A total of 76 representative Souchong samples were studied. Quantitative models of tea polyphenols and caffeine of Souchong were established respectively by partial least squares (PLS). The chemical values of tea polyphenols and caffeine of 76 Souchong samples were measured by Chinese National Standard methods, combined with the near infrared spectrum. The results show that the R² and RMSEC values of tea polyphenols of calibration set were 97.59% and 0.566%. And the R² and RMSEP values of validation set were 95.06% and 0.855%. The R² and RMSEC values of caffeine of calibration set were 96.98% and 0.110%. And the R² and RMSEP values of validation set were 95.67% and 0.148%. The quantitative models had good prediction results, and could be used for rapid evaluation of the contents of tea polyphenols and caffeine in Souchong.

Key words: Souchong, near infrared spectroscopy (NIRS), tea polyphenols, caffeine, models

中图分类号:

卢莉, 程曦, 张渤, 沈小霞, 刘艳, 熊丽, 袁潇, 李远华, 黎星辉. 小种红茶茶多酚和咖啡碱近红外定量分析模型的建立[J]. 茶叶科学, 2020, 40(5): 689-695. doi: 10.13305/j.cnki.jts.2020.05.014.

LU Li, CHENG Xi, ZHANG Bo, SHEN Xiaoxia, LIU Yan, XIONG Li, YUAN Xiao, LI Yuanhua, LI Xinghui. Establishment of Predictive Model for Quantitative Analysis of Tea Polyphenols and Caffeine of Souchong by Near Infrared Spectroscopy[J]. Journal of Tea Science, 2020, 40(5): 689-695. doi: 10.13305/j.cnki.jts.2020.05.014.

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