基于内在品质参数的乌龙茶等级判别模型建立  

Building the Oolong Tea Grade Judgement Model Based on Interior Quality Parameters

作　　者：刘翠玲[1] 秦冬 凌彩金[2] 郜礼阳 周巧仪 孙晓荣[3] 吴静珠[3] 昝佳睿 LIU Cuiling;QIN Dong;LING Caijin;GAO Liyang;ZHOU Qiaoyi;SUN Xiaorong;WU Jingzhu;ZAN Jiarui(Key Laboratory of Industry Industrial Internet and Big Data,China National Light Industry,Beijing Technology and Business University,Beijing 100048,China;Tea Research Institute,Guangdong Academy of Agricultural Sciences,Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation and Utilization,Guangzhou 510640,China;Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University,Beijing 100048,China)

机构地区：[1]北京工商大学,中国轻工业工业互联网与大数据重点实验室,北京100048 [2]广东省农业科学院茶叶研究所,广东省茶树种质资源创新利用重点实验室,广东广州510640 [3]北京工商大学,北京市食品安全大数据技术重点实验室,北京100048

出　　处：《食品工业科技》2023年第12期308-318,共11页Science and Technology of Food Industry

基　　金：北京市自然科学基金项目(4222043);2021年度中国轻工业工业互联网与大数据重点实验室开放课题(IIBD-2021-KF09);以农产品为单元的广东省现代农业产业技术体系创新团队建设项目(茶叶)(2022KJ120);清远市科技计划项目(2022KJJH065);2020年广东省农业科学院院长基金项目(202032);2022年研究生科研能力提升计划项目资助。

摘　　要：茶叶等级评价是检测茶叶品质的重要技术手段,科学建立茶叶等级评价模型具有重要意义。本文以102个乌龙茶为研究对象,采用多种特征值筛选方法结合支持向量机算法建立基于特征内在品质参数的乌龙茶等级评价模型。同时,采用高光谱技术结合化学计量学,对特征品质参数建立基于特征波长的粒子群算法优化反向误差神经网络神经网络(PSO-BP)和麻雀搜索算法优化最小二乘支持向量机(SSA-LSSVM)的定量预测模型,最后对定量预测的化学值模型验证。结果表明,当参数组合酯型儿茶素、简单儿茶素、茶多酚、水浸出物、咖啡碱、表没食子儿茶素没食子酸酯(EGCG)六种化学值时的乌龙茶等级模型判别准确率最高,训练集的准确率为97.22%,预测集准确率为93.33%。基于特征波长的麻雀搜索算法优化最小二乘支持向量机(SSA-LSSVM)定量预测模型的预测精度更高且均方根误差更低,预测集的决定系数R^(2)均在0.93~0.99之间。随机抽取30个乌龙茶样本六种化学值的最佳预测值,其判别准确率达90%。综上所述,基于内在品质参数组合对不同等级的乌龙茶准确判别是可行的,且基于高光谱技术的预测模型可以快速精准的获得其化学值大小,预测的化学值也能准确的判别不同乌龙茶品质等级,同时为科学判别茶叶品质等级领域提供了新的分析思路和应用实例。Tea grade evaluation is an important technical method to test the quality of tea,and scientifically building the tea grade evaluation model has an important significance.This paper took 102 Oolong teas as research object and built the Oolong tea grade evaluation model based on interior quality parameters with various characteristic value screening methods in combination with support vector machine algorithm.Meanwhile,by combining hyperspectral technology with chemometrics,this paper built the quantitative forecast model of particle swarm optimization back propagation neural network(PSO-BP)based on characteristic wavelength and sparrow search algorithm optimization least squares support vector machine(SSA-LSSVM)for characteristic quality parameters,and finally verified the chemical value model of quantitative forecast.The results showed that in case of parameter combination of ester catechin,simple catechin,tea polyphenol,aqueous extract,caffeine and epigallocatechin gallate(EGCG),the Oolong tea judgement model had the highest accuracy,the accuracy of training set was 97.22%,and the accuracy of forecast set was 93.33%.The sparrow search algorithm optimization least squares support vector machine(SSA-LSSVM)quantitative forecast model had higher forecast accuracy and lower root mean square error,and the determination coefficient of forecast set R^(2) ranged from 0.93 to 0.99.By randomly selecting the optimal six forecasted chemical values of 30 Oolong tea samples,the judgement accuracy reached up to 90%.In conclusion,it was feasible to accurately judge different grades of Oolong tea based on interior quality parameter combination,the forecast model based on hyperspectral technology could rapidly and accurately obtain the chemical value,and the forecasted chemical value could accurately judge different Oolong tea grades,which would provide a new analysis method and application example for scientifically judging tea quality and grade.

关 键 词：乌龙茶品质参数 高光谱技术 特征提取 反向误差神经网络 最小二乘支持向量机 

分 类 号：TS272.7[农业科学—茶叶生产加工]