School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
Food Chem. 2021 Jul 15;350:129141. doi: 10.1016/j.foodchem.2021.129141. Epub 2021 Feb 4.
This study aimed to assess the feasibility of identifying multiple chemical constituents in matcha using visible-near infrared hyperspectral imaging (VNIR-HSI) technology. Regions of interest (ROIs) were first defined in order to calculate the representative mean spectrum of each sample. Subsequently, the standard normal variate (SNV) method was applied to correct the characteristic spectra. Competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) were used to optimize the models. They were built based on partial least squares (PLS), creating two models referred to as CARS-PLS and BOSS-PLS. The BOSS-PLS models achieved best predictive accuracy, with coefficients of determination predicted to be 0.8077 for caffeine, 0.7098 for tea polyphenols (TPs), 0.7942 for free amino acids (FAAs), 0.8314 for the ratio of TPs to FAAs, and 0.8473 for chlorophyll. These findings highlight the potential of VNIR-HSI technology as a rapid and nondestructive alternative for simultaneous quantification of chemical constituents in matcha.
本研究旨在评估使用可见-近红外高光谱成像(VNIR-HSI)技术鉴定抹茶中多种化学成分的可行性。首先定义感兴趣区域(ROI),以计算每个样本的代表性平均光谱。随后,应用标准正态变量(SNV)方法对特征光谱进行校正。竞争自适应重加权采样(CARS)和引导软收缩(BOSS)用于优化模型。它们基于偏最小二乘(PLS)建立,创建了两个称为 CARS-PLS 和 BOSS-PLS 的模型。BOSS-PLS 模型获得了最佳预测准确性,预测咖啡因的决定系数为 0.8077,茶多酚(TPs)为 0.7098,游离氨基酸(FAAs)为 0.7942,TPs 与 FAAs 的比值为 0.8314,叶绿素为 0.8473。这些发现表明,VNIR-HSI 技术作为一种快速、非破坏性的替代方法,具有同时定量抹茶中化学成分的潜力。