基于EGCG,ECG和咖啡碱含量的乌龙茶产地鉴定

Identification of the Geographical Origins of Oolong Tea Based on EGCG, ECG and Caffeine Contents

提出一种量化判定乌龙茶产地的方法。共收集主要乌龙茶产区闽南、闽北、广东和台湾等地的代表性乌龙茶样品130个,用高效液相色谱方法检测其没食子酸、儿茶素、咖啡碱和茶氨酸等理化成分的含量。用遗传算法和连续投影算法筛选出重要的化合物,基于这些化合物指标分别用支持向量机、反向传播人工神经网络及随机森林模型对闽南、闽北、广东和台湾4个产区的乌龙茶进行分类和预测。结果表明,用遗传算法筛选的3个化合物(咖啡碱、EGCG和ECG)结合反向传播人工神经网络模型BPNN能够实现对4个产区乌龙茶的高效判别,且训练集和预测集的判别率分别为97.13%和98.34%。该研究结果能为乌龙茶产地的鉴别提供科学依据。

A quantitative method to discriminate the geographical origins of Oolong teas was proposed to promote the fair of tea trade. A total of 130 Oolong tea samples were collected across China, and the chemical compositions including gallic acid, catechins, caffeine and theanine were qu antified by high performance liquid chromatography. Genetic algorithm and successive projections algorithm were applied to identify important compounds, and then support vector machine, back propagation artificial neural networks and random forest models w ere used to classify and predict Oolong tea samples from Minnan, Minbei, Guangdong and Taiwan based on the selected compounds. The overall results indicated that compounds selected by genetic algorithm（caffeine, EGCG and ECG） combined with back propagation artificial neural networks could achieve a high efficiency in identifying Oolong tea samples from four origins, and the total identification rate in the training and prediction sets were 97.13% and 98.34%. The results provided scientific credibility to identify Oolong tea origins.