College of Life Sciences, Yangtze University, Jingzhou 434025, China.
College of Life Sciences, Yangtze University, Jingzhou 434025, China.
Food Chem. 2021 Jun 15;347:128959. doi: 10.1016/j.foodchem.2020.128959. Epub 2020 Dec 30.
Laoshan green teas plucked in summer and autumn were measured by high performance liquid chromatography-diode array detector (HPLC-DAD). After baseline correction, the fingerprints data were resolved by multivariate curve resolution-alternating least squares (MCR-ALS) and a total of 57 components were acquired. Relative concentrations of these components were afterwards applied to distinguish plucking seasons using principal component analysis (PCA), support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA). For both SVM and PLS-DA models, the total recognition rates of training set, cross-validation and testing set were 100%, 91.3% and 100%, respectively. Besides, three variable selection methods were employed to determine characteristic components for the authentication of summer and autumn teas. Results showed that PLS-DA model based on three characteristic components selected by VIP possesses identical predictive ability as the original model. This study demonstrated that our proposed strategy is competent for the authentication of plucking seasons of Laoshan green tea.
采用高效液相色谱-二极管阵列检测器（HPLC-DAD）对夏秋季崂山绿茶进行测定。经基线校正后，采用多变量曲线分辨-交替最小二乘法（MCR-ALS）解析指纹图谱数据，共得到 57 个成分。应用主成分分析（PCA）、支持向量机（SVM）和偏最小二乘判别分析（PLS-DA）对这些成分的相对浓度进行区分，以区分采摘季节。对于 SVM 和 PLS-DA 模型，训练集、交叉验证集和测试集的总识别率分别为 100%、91.3%和 100%。此外，还采用了三种变量选择方法来确定特征成分，以鉴定夏茶和秋茶。结果表明，基于 VIP 选择的三个特征成分的 PLS-DA 模型具有与原始模型相同的预测能力。本研究表明，我们提出的策略适用于崂山绿茶采摘季节的鉴定。
