近红外光谱技术结合人工神经网络判别普洱茶发酵程度

Discriminating fermentation degree of Pu’er tea based on NIR spectroscopy and artificial neural network

为了实现对普洱茶发酵程度快速判别,该研究提出了利用近红外光谱结合人工神经网络的方法。普洱茶是中国特有的茶类,发酵是普洱熟茶品质形成的关键工序,目前对于发酵程度的评价主要依赖感官审评,缺乏客观的量化依据。试验以轻度发酵、适度发酵和过度发酵3个不同发酵程度的普洱茶为研究材料。首先对采集得到的原始光谱进行标准归一化(SNV)预处理,利用人工神经网络(ANN)模式识别方法构建普洱茶发酵程度鉴别模型,在模型建立过程中,通过交互验证的方法对模型的最佳主成分因子数(PCs)进行优化。当主成分因子数为9时,ANN模型所得到的结果最佳,模型交互验证识别率和预测识别率分别为98.9%和97.8%。研究结果表明,近红外光谱技术结合模式识别能够实现对普洱茶发酵质量的快速判别,评判结果具有较高的准确性,优于感官审评。

In order to get a rapid estimation on the fermentation degree of Pu’er tea in processing, the method of Near Infrared （NIR） spectroscopy combined with Artificial Neural Network （ANN） was first established in this study. Pu’er tea is a special tea that was processed in China only, and was favored by consumers at home and abroad with its bacteriostatic effect and its removal of grease, detoxification and other effects. Fermentation is the most critical process. The degree which is good or bad of fermentation affects the last quality of Pu’er tea directly. Fermentation is high, the beverage color may be red brown, and taste is weak. If fermentation is light, the taste is bitter and astringent, with brown leaves rather than green. Fermentation moderately can form the Pu’er ripe tea character, which is brown and red, thick in shape, and with a bright red color and mellow taste. Now, the quality of Pu’er tea on fermentation control is more dependent on sensory discrimination, there is a lack of an objective quantitative basis, which affects the stable quality of Pu’er tea. Use of different technical personnel to grasp the standard difference is very common. Because of the lack of stability of the sensory discrimination method, it is the key technical problem as to how to judge the fast and accurate fermentation degree of Pu’er tea, which affects standardization of production. Near infrared spectral analysis technology combined with a pattern recognition method has been used for the identification of the quality in wine, food, fruits, vegetables, Chinese chestnuts etc. Components analysis of tea and agricultural products has been received successfully. In this experiment, three different fermentation degrees of Pu’er tea, mild fermentation, moderate fermentation, and excessive fermentation respectively, were used as experimental targets. The original spectra data collected from the samples were firstly preprocessed by the Standard Normal Variate （SNV） method, in order to reduce the influence of the different particles of tea to the spectroscopy. The identification model for the Pu’er tea fermentation degree was constructed by the Artificial Neural Network recognition mode. In the process of model establishment, the best number of principal component factors （PCs） was optimized by a cross-validation method. The experimental results indicated that the optimum result could be obtained by an Artificial Neural Network model when the principal component factors were 9. Together, the relative discrimination rates of the Artificial Neural Network model were 98.9% and 97.8% in the training and prediction sets, respectively. The overall results proved that it was feasible to estimate Pu’er tea fermentation quality by Near Infrared spectroscopy combined with an Artificial Neural Network. The estimation results have higher veracity, and the correct rate of this estimation model was better than the sensor evaluation.