智慧农业（中英文）2025，Vol.7Issue(4)：58-70,13.DOI:10.12133/j.smartag.SA202505034
可见/短波与长波近红外光谱联用的茶树识别及茶鲜叶茶多酚含量快速检测方法
Rapid Tea Identification and Polyphenol Detection Method in Fresh Tea Leaves Using Visible/Shortwave and Longwave Near-Infrared Spectroscopy
摘要
Abstract
[Objective]Tea polyphenols,as a key indicator for evaluating tea quality,possess significant health benefits.Traditional de-tection methods are limited by poor timeliness,high cost,and destructive sampling,making them difficult to meet the demands of tea cultivar breeding and real-time monitoring of tea quality.Meanwhile,rapid identification of tea cultivars and leaf positions is critical for guiding tea production.Therefore,this study aims to develop a non-destructive detection device for quality components of fresh tea leaves based on the combined technology of visible/short-wave near-infrared and long-wave near-infrared spectroscopy,to realize rapid non-destructive detection of tea polyphenol content and rapid identification of tea cultivars and leaf positions.[Methods]A rapid non-destructive detection device for quality components of fresh tea leaves was developed by combining visible/short-wave near-infra-red spectroscopy(400～1 050 nm)and long-wave near-infrared spectroscopy(1 051～1 650 nm).The Savitzky-Golay(SG)convolution smoothing method was used for preprocessing the spectral data.The Folin-Ciocalteu method was employed to determine the tea poly-phenol content,and abnormal samples were eliminated using the interquartile range(IQR)method.Data-level and feature-level fusion methods were adopted,with the competitive adaptive reweighted sampling(CARS)algorithm used to extract characteristic wave-lengths.Prior to modeling,the Kennard-Stone algorithm was applied to partition the dataset into a training set and a prediction set at a ratio of 4∶1.Models such as principal component analysis(PCA),partial least squares-discriminant analysis(PLS-DA),least squares support vector machine(LS-SVM),extreme learning machine(ELM),and 1D convolutional neural network(1D-CNN)were con-structed for the identification of 3 cultivars(Huangdan,Tieguanyin,and Benshan)and 4 leaf positions.For predicting tea polyphenol content,models including partial least squares regression(PLSR),least squares support vector regression(LS-SVR),ELM,and 1D-CNN were established for predicting the tea polyphenol content in fresh tea leaves.[Results and Discussions]The results showed that there were significant differences in tea polyphenol contents among different cultivars and leaf positions(P<0.05).Specifically,the tea polyphenol content of Huangdan was 17.54%±1.82%,which was 1.16 times and 1.04 times that of Tieguanyin(15.04%±1.22%)and Benshan(16.81%±1.24%),respectively.For each cultivar,the tea polyphenol content generally showed a decreasing trend from the 1st to 4th leaf positions,with the highest content in the 1st leaf position.Principal component analysis(PCA)revealed that for cultivar identification,the scatter distribution of the principal components of Huangdan,Tieguanyin,and Benshan,as well as their projections in the directions of PC1 and PC2,showed a clear trend of clustering into three groups,indicating a good classification effect,although there was still some overlap among individual samples.For leaf position identification,the scatter distributions of the principal compo-nents of the 1st,2nd,3rd,and 4th leaf positions overlapped with each other,with no obvious clustering among leaf positions.Com-pared with single-source data,models based on data fusion effectively improved prediction performance.Among them,the PLS-DA model established by combining SG preprocessing with feature-level fusion achieved prediction accuracies of 100%and 87.93%for the identification of 3 tea cultivars and 4 leaf positions,respectively.Furthermore,the 1D-CNN model based on data-level fusion ex-hibited superior performance in predicting tea polyphenol content,with a coefficient of determination(R2P),root mean square error of prediction(RMSEP),and residual predictive deviation(RPD)of 0.802 0,0.636 8%,and 2.268 4,respectively,which outperformed models using only visible/short-wave near-infrared spectroscopy or long-wave near-infrared spectroscopy.[Conclusions]The devel-oped detection device combining visible/short-wave near-infrared and long-wave near-infrared spectroscopy,mainly composed of spectrometers,Y-type optical fibers,plant probes,polymer lithium batteries,DC uninterruptible power supplies,voltage conversion modules,and aluminum alloy casings,could synchronously collect multi-source spectral data of visible/short-wave near-infrared and long-wave near-infrared from fresh tea leaves.Combined with data fusion methods and machine learning algorithms,it enabled rapid detection of tea polyphenol content and efficient identification of cultivars and leaf positions in fresh tea leaves,providing new in-sights for the application of multi-source data fusion technology in elite tea cultivar breeding and non-destructive detection of fresh tea leaf quality.关键词
茶鲜叶/茶多酚/无损检测/数据融合/一维卷积神经网络(1D-CNN)Key words
fresh tea leaves/tea polyphenols/non-destructive detection/data fusion/one-dimensional convolutional neural network(1D-CNN)分类
农业科技引用本文复制引用
许金钗,李晓丽,翁海勇,何勇,朱雪松,刘鸿飞,黄镇雄,叶大鹏..可见/短波与长波近红外光谱联用的茶树识别及茶鲜叶茶多酚含量快速检测方法[J].智慧农业（中英文）,2025,7(4):58-70,13.基金项目
国家自然科学基金面上项目(31771676) （31771676）
农机智能控制与制造技术福建省高校重点实验室(武夷学院)开放基金项目(AM-ICM202402) National Natural Science Foundation of China(General Program)(31771676) （武夷学院）
Open Fund Project of Fujian Provin-cial University Key Laboratory of Agricultural Machinery Intelligent Control and Manufacturing Technology(Wuyi University)(AM-ICM202402) （Wuyi University）
