School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China.
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China.
Food Chem. 2023 Sep 30;421:136185. doi: 10.1016/j.foodchem.2023.136185. Epub 2023 Apr 24.
Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp = 0.9376) and P/F ratio (Rp = 0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.
消费者对抹茶的偏好深受其物理化学性质的影响。本研究采用可见-近红外（Vis-NIR）光谱技术结合多元分析，快速、非侵入式评估抹茶的粒径和茶多酚与游离氨基酸的比值（P/F 比）。比较了协同区间（Si）、变量组合种群分析（VCPA）、竞争自适应重加权采样（CARS）等多元选择算法，最终提出了 ICPA 和 CARS 杂交的变量选择策略，用于从 Vis-NIR 光谱中选择特征波长，建立偏最小二乘（PLS）模型。结果表明，基于 ICPA-CARS-PLS 模型能够很好地评估抹茶粒径（Rp=0.9376）和 P/F 比（Rp=0.9283）。因此，Vis-NIR 反射光谱与化学计量模型相结合的快速、有效、无损在线监测对于抹茶的工业生产具有重要意义。
