Chen Quansheng, Chen Min, Liu Yan, Wu Jizhong, Wang Xinyu, Ouyang Qin, Chen Xiaohong
1School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013 People's Republic of China.
Zhenjiang Jinshan Cuiya Tea Industry Co. Ltd, Zhenjiang, 212021 People's Republic of China.
J Food Sci Technol. 2018 Oct;55(10):4363-4368. doi: 10.1007/s13197-018-3353-1. Epub 2018 Aug 9.
Fourier transform near-infrared spectroscopy (FT-NIR) coupled to chemometric algorithms such as back propagation (BP)-AdaBoost and synergy interval partial least square (Si-PLS) were deployed for the rapid prediction taste quality and taste-related components in black tea. Eight main taste-related components were determined via chemical analysis and Pearson correlations. The achieved chemical results of the eight taste-related components in black tea infusion were predicted based on 160 tea samples obtained from different countries. Prediction results revealed BP-AdaBoost models gave superior predictions, with all the correlation coefficients of the prediction set (R) > 0.76, and the root mean square error values of the prediction set (RMSEP) < 1.7% compared with Si-PLS models (0.71 ≤ Rp ≤ 0.94, 0.08% ≤ RMSEP ≤ 1.73%). This implies that FT-NIR combined to BP-AdaBoostis capable of being deployed for the rapid evaluation of black tea taste quality and taste-related components content simultaneously.
傅里叶变换近红外光谱（FT-NIR）结合诸如反向传播（BP）-AdaBoost和协同区间偏最小二乘法（Si-PLS）等化学计量学算法，用于快速预测红茶的滋味品质和与滋味相关的成分。通过化学分析和皮尔逊相关性确定了八种主要的与滋味相关的成分。基于从不同国家获得的160个茶叶样品，对红茶茶汤中八种与滋味相关成分的化学分析结果进行了预测。预测结果表明，BP-AdaBoost模型的预测效果更佳，预测集的所有相关系数（R）>0.76，预测集的均方根误差值（RMSEP）<1.7%，而Si-PLS模型的相关系数（Rp）为0.71≤Rp≤0.94，均方根误差值（RMSEP）为0.08%≤RMSEP≤1.73%。这意味着FT-NIR结合BP-AdaBoost能够同时用于快速评估红茶的滋味品质和与滋味相关的成分含量。
