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

Abstract: 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.