绿茶加工过程含水率在线检测技术研究  

On-line Detection of Moisture Content of Green Tea Processing

作　　者：李毛玉 尹旭超 张积鑫 冯晚祯 单旭江 王玉洁 宁井铭 LI Mao-yu;YIN Xu-chao;ZHANG Ji-xin;FENG Wan-zhen;SHAN Xu-jiang;WANG Yu-jie;NING Jing-ming(College of Tea and Food Science and Technology,Anhui Agricultural University,State Key Laboratory of Tea Plant Biology and Utilization,Hefei 230036,China)

机构地区：[1]安徽农业大学茶与食品科技学院,茶树生物学与资源利用国家重点实验室,安徽合肥230036

出　　处：《中国茶叶加工》2022年第3期34-39,64,共7页China Tea Processing

基　　金：国家重点研发计划课题(2021YFD1601102);安徽省高校自然科学研究项目(KJ2021A0144)。

摘　　要：实现绿茶加工过程含水率在线检测对于稳定产品质量具有重要意义。针对传统人工评判存在的准确率低、一致性差等缺陷,研究提出利用在线近红外光谱建立绿茶加工过程含水率快速、准确、量化的检测方法。以多季节、多批次的415份杀青叶样本为研究材料,采用在线光谱仪实时采集杀青叶的近红外光谱信号,随后采用传统烘干法测定杀青叶真实含水率。分别采用SG平滑(Savitzky-Golay,SG)、标准正态变量变换(Standard Normal Variate,SNV)、多元散射校正(Multiplicative Scatter Correction,MSC)、去趋势(Detrending)等预处理方法消除原始光谱中的噪声等干扰信息,比较遗传算法(Genetic Algorithms,GA)和竞争性自适应复权抽样(Competitive Adaptive Reweighted Sampling,CARS)对水分特征波长的筛选效果,并结合偏最小二乘回归(Partial Least Squares regression,PLS)算法分别建立含水率的PLS,GA-PLS和CARS-PLS定量检测模型。结果表明,光谱预处理能够降低原始信号中的噪声干扰,显著提高模型的预测精度,其中SNV的效果最优;GA和CARS均能有效降低数据维度,其中CARS取得了最优的预测性能,所建的SNV-CARS-PLS模型对预测集样本的预测精度为相关系数Rp=0.9402,预测均方根误差RMSEP=1.57%,残余预测偏差RPD=2.90。研究建立的绿茶杀青叶含水率在线检测方法,能够为杀青环节质量状况提供实时量化反馈,为实现绿茶的智能化加工提供重要基础。The on-line detection of moisture content in green tea processing is important for stabilizing product quality.To address the shortcomings of traditional manual assessment such as low accuracy and poor consistency,this study proposed the use of on-line NIR spectroscopy to establish a rapid,accurate and quantitative method for the detection of moisture content in green tea processing.415 samples of green tea leaves from multiple seasons and batches were collected as research materials,and the NIR spectral signals of green tea leaves were collected in real time by an on-line NIR spectrometer.The true moisture content of green tea leaves was subsequently determined by the traditional drying method.Preprocessing methods such as Savitzky-Golay(SG),Standard Normal Variate(SNV),Multiplicative Scatter Correction(MSC)and Detrending were used to eliminate the noise in raw spectra.The genetic algorithms(GA)and Competitive Adaptive Reweighted Sampling(CARS)were compared for the selection of moisture-related characteristic wavelengths.Partial Least Squares regression(PLS)algorithm was then employed to develop PLS,GA-PLS and CARS-PLS quantitative detection models for moisture content.The results showed that the spectral preprocessing could reduce the noise interference in the raw signal,and significantly improve the prediction accuracy of the models,among which SNV achieved the optimal performance.Both GA and CARS could effectively reduce the data dimensionality,among which CARS achieved the optimal prediction performance,and the prediction accuracy of the proposed SNV-CARS-PLS model for the samples in prediction set performed with Rp=0.9402 and RMSEP=1.57%.The research established an on-line method for the detection of moisture content of green tea during fixation,which could provide real-time quantitative feedback on the quality condition of the fixation process,and also provide an important basis for the intelligent processing of green tea.

关 键 词：绿茶杀青 含水率 近红外光谱 预测模型 

分 类 号：TS272.51[农业科学—茶叶生产加工]