基于近红外光谱技术的眉茶拼配比例预测方法  

Predicting the blending ratio of Mee Tea based on near infrared spectroscopy

作　　者：宋彦 汪小中 赵磊 张叶 宁井铭[2] 程福寿 Song Yan;Wang Xiaozhong;Zhao Lei;Zhang Ye;Ning Jingming;Cheng Fushou(School of Engineering,Anhui Agricultural University,Hefei 230036,China;State Key Laboratory of Tea Plant Biology and Utilization,Anhui Agricultural University,Hefei 230036,China;Huangshan Best Organic Tea Co.,Ltd.,Huangshan 245000,China;Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery,Hefei 230036,China)

机构地区：[1]安徽农业大学工学院,合肥230036 [2]安徽农业大学茶树生物学与资源利用国家重点实验室,合肥230036 [3]黄山一品有机茶业有限公司,黄山245000 [4]安徽省智能农机装备工程实验室,合肥230036

出　　处：《农业工程学报》2022年第2期307-315,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：安徽省科技重大专项(18030701153);安徽省重点研究与开发计划(201904a06020006)。

摘　　要：拼配是出口炒青绿茶精制加工过程中的一项作业,通过对不同原料茶的拼合,达到维持产品品质标准化和一致性的目的。目前仍然依靠专家人工设计茶叶拼配比例,主观性较强,工作量大。为了实现拼配比例设计的客观化、定量化,该研究以眉茶为对象,提出了一种基于近红外光谱技术的拼配比例预测方法,采用源于黄山、湖北、福建3地的4种典型原料茶拼合了不同比例的茶样,采集了其近红外光谱数据,构建了用于预测拼配比例的4种机器学习模型,分别为AE+Softmax、CNN+Softmax、PCA+Softmax及PCA+PLS,并通过对比模型预测结果与预设拼配比例评价算法性能。结果表明,基于CNN+Softmax的拼配比例预测方法精度较高,特征维度为40时,验证集决定系数为0.9643,均方根误差为0.0472,优于其他方法,经过测试集测试后的性能指标与验证集接近,说明算法具有较好的泛化能力。研究结果可为茶叶数字化、智能化拼配提供理论依据与数据支撑。A blending operation has been one of the refined processing procedures for the various types of tea.Since the quality of material teas varies greatly in different areas and seasons,it is a high demand for the standardized quality of commercial tea.There are some differences between the material teas and the commercial samples(called the standard sample).The tea blending can be widely used to maintain the product quality and increase output by blending different materials teas.Among them,the blending ratio can be determined by an experienced expert at present.A sample can be prepared after the quality evaluation on the various types of material teas,further to compare with the standard sample for an optimal blending ratio.However,the traditional blending depends mainly on the subjective senses of experts.Taking the roasted green tea(Mee tea)as the object,this study aims to rapidly and accurately predict the blending ratio using the Convolutional Neural Network(CNN).Near-infrared spectroscopy was also selected to effectively characterize the chemical components of tea.As such,the adjustment of flavor was achieved under the various ratios of raw teas.Four kinds of tea raw materials were used to prepare 25 tea samples,according to the preset ratio table,where 20 groups of sub-samples were prepared for each tea sample with different blending ratios.The spectrum of each sample was then collected.Moreover,the Standard Normal Variable(SNV)transformation was used to preprocess the spectrum.Then,four models of machine learning were constructed to predict the blending ratios.The performance of the model was evaluated to compare with the preset blending ratio.The models were:the automatic encoder with Softmax;CNN combined with Softmax;Principal Component Analysis(PCA)combined with Partial Least Squares(PLS);and PCA combined with Softmax.Subsequently,3-fold cross validation was utilized to train the model.The feature dimension was verified from 10 to 100,with the step of 10.An optimal dimensionality was achieved to select the

关 键 词：近红外光谱 机器学习 深度学习 茶叶 拼配 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]