School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China.
National Research and Development Center for Matcha Processing Technology, Jiangsu Xinpin Tea Co., Ltd, Changzhou, 213254, PR China; Tea Industry Research Institute, Changzhou Academy of Modern Agricultural Sciences, Changzhou, 213254, PR China.
Food Chem. 2023 Jun 30;412:135505. doi: 10.1016/j.foodchem.2023.135505. Epub 2023 Jan 28.
Monitoring chlorophyll during Tencha (the raw ingredient for matcha) processing is critical for determining the matcha's color and quality. The purpose of this study is to explore the mechanism of chlorophyll changes during Tencha processing and evaluate the viability of predicting its content by a portable near-infrared (NIR) spectrometer. The Tencha samples' spectral data were first preprocessed using various preprocessing techniques. Subsequently, iteratively variable subset optimization (IVSO), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS) were used to optimize and build partial least square (PLS) models. The CARS-PLS models achieved the best predictive accuracy, with correlation coefficients of prediction (R) = 0.9204 for chlorophyll a, R = 0.9282 for chlorophyll b, and R = 0.9385 for total chlorophyll. These findings suggest that NIR spectroscopy could be used as a surrogate for immediate quantification and monitoring of chlorophyll during Tencha processing.
监测抹茶(抹茶粉的原料)加工过程中的叶绿素含量对于确定抹茶的颜色和质量至关重要。本研究旨在探讨叶绿素在抹茶加工过程中的变化机制,并评估便携式近红外(NIR)光谱仪预测其含量的可行性。首先,对抹茶样品的光谱数据进行了各种预处理技术的预处理。随后,采用迭代变量子集优化(IVSO)、引导软收缩(BOSS)和竞争自适应重加权采样(CARS)对偏最小二乘(PLS)模型进行优化和构建。CARS-PLS 模型取得了最佳的预测准确性,叶绿素 a 的预测相关系数(R)为 0.9204,叶绿素 b 的 R 为 0.9282,总叶绿素的 R 为 0.9385。这些发现表明,近红外光谱技术可用于替代抹茶加工过程中对叶绿素的即时定量和监测。