分析化学2025，Vol.53Issue(5)：842-851,10.DOI:10.19756/j.issn.0253-3820.241385
基于残差注意力卷积神经网络的鲜茶叶成分含量检测方法研究
Research on Detection Method for Constituent Content of Fresh Tea Leaf Based on Residual Attention Convolutional Neural Network
摘要
Abstract
The rapid and non-destructive detection of constituent content of fresh tea leaves shows an important reference value for quality identification of tea.Visible near infrared(Vis-NIR)spectroscopy has been used for qualitative and quantitative analysis of chemical components in plant samples with the advantages such as simple,rapid and non-destructive detection.In this study,residual attention convolutional neural network(RACNN)was used to predict the internal constituent content of fresh tea leaves.Firstly,the reflectance spectral data of the samples in the Vis-NIR band range and the constituent contents of gallic acid(GA),gallocatechin(GC),epigallocatechin(EGC),and epigallocatechin gallate(ECG)in fresh tea leaves were collected.Based on the preprocessing of the spectral data,the contents of the four components were predicted using a partial least squares regression(PLSR)model,and the optimal preprocessing was determined.Subsequently,the characteristic bands were extracted using the random forest(RF)algorithm.Finally,the performances of PLSR,convolutional neural network(CNN)and RACNN models were compared.The results showed that for GA,the RACNN model worked best with a validation set coefficient of determination(R2)of 0.946 and a root mean square error of the prediction set(RMSEP)of 1.173;for GC,the RACNN model works best with a validation set R2 of 0.928 and RMSEP of 6.081;for EGC,the RACNN model works best with a validation set R2 of 0.891 and a RMSEP of 15.197;for ECG,the RACNN model worked best with a validation set R2 of 0.878 and a RMSEP of 7.837.The RACNN model established by Vis-NIR spectroscopy combined with chemometrics could realize the accurate detection of the contents of components in fresh tea.关键词
可见-近红外光谱/茶叶/偏最小二乘回归/随机森林/残差注意力卷积神经网络Key words
Visible near-infrared spectroscopy/Tea/Partial least squares regression/Random forest/Residual attention convolutional neural network引用本文复制引用
章海亮,周燕,罗微,詹白勺,张晶,刘雪梅..基于残差注意力卷积神经网络的鲜茶叶成分含量检测方法研究[J].分析化学,2025,53(5):842-851,10.基金项目
国家自然科学基金项目(Nos.32260622,62265007)和江西省自然科学基金项目(Nos.20232ACB205026,20224BAB212007)资助. Supported by the National Natural Science Foundation of China(Nos.32260622,62265007)and the Natural Science Foundation of Jiangxi Province(Nos.20232ACB205026,20224BAB212007). （Nos.32260622,62265007）
