State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
Chongqing Academy of Agricultural Sciences Tea Research Institute, Chongqing 402160, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Feb 15;267(Pt 1):120537. doi: 10.1016/j.saa.2021.120537. Epub 2021 Oct 27.
The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality.
绿茶的产地和加工月份对其经济价值和消费者接受度有很大影响。本研究探讨了近红外高光谱成像（NIR-HSI）与化学计量学相结合用于识别绿茶的可行性。采集了 2020 年 5 月至 8 月四个月间重庆三个地区（东南部、东北部和西部）生产的绿茶样品。主成分分析（PCA）用于降低数据维度，并可视化不同类别样品的聚类。线性偏最小二乘判别分析（PLS-DA）和非线性支持向量机（SVM）算法用于建立判别模型。基于前四个和前五个主成分（PC）的 PCA-SVM 模型分别在产地和加工月份的预测集中达到了 97.5%和 95%的最佳准确率。本研究证明了 HSI 用于识别绿茶品种的可行性，为绿茶质量的评估和控制提供了一种快速、无损的方法。
