分析测试学报2025，Vol.44Issue(6)：1043-1054,12.DOI:10.12452/j.fxcsxb.241107514
基于FT-NIR和ATR-FTIR技术结合化学计量学方法快速、准确鉴别不同地理来源的草果
Rapid and Accurate Identification of Amomum Tsaoko Geographical Origin Based on FT-NIR and ATR-FTIR Spectroscopy Combined with Chemometric Methods
摘要
Abstract
In this study,Fourier transform near-infrared spectroscopy(FT-NIR),attenuated total reflection-Fourier transform infrared spectroscopy(ATR-FTIR)and two-dimensional correlation spectroscopy(2DCOS)techniques,combined with chemometric and deep learning were adopted to establish partial least squares discriminant analysis(PLS-DA)and Residual convolution neural net-work(ResNet)discriminant models for rapid and accurate traceability of A.tsaoko samples from seven main production areas(221 samples).The results indicated that the PLS-DA model established after the second derivative(2nd)+standard normal variate(SNV)preprocessing of ATR-FTIR spectral da-ta showed the best performance(95.31%),but the optimal preprocessing for FT-NIR spectral data was 2nd.The ResNet model based on FT-NIR and ATR-FTIR synchronized 2DCOS images could achieve 100%accuracy without the need for optimal preprocessing and complex data conversion.Among them,the ResNet model established for 2DCOS images converted from FT-NIR had the least number of epochs,the shortest time consumption,and the lowest cost.This study provides a fast and accurate new method for identifying A.tsaoko from different geographical origins,laying the foun-dation for further research on the quality rating system of A.tsaoko.关键词
草果/化学计量学/机器学习/二维相关光谱/地理来源Key words
Amomum tsaoko/chemometrics/machine learning/2DCOS/geographical origin分类
化学化工引用本文复制引用
苏俊宇,杨绍兵,王元忠..基于FT-NIR和ATR-FTIR技术结合化学计量学方法快速、准确鉴别不同地理来源的草果[J].分析测试学报,2025,44(6):1043-1054,12.基金项目
云南省科技人才与平台计划(202405AD350072) （202405AD350072）
云南省农业联合专项-面上项目(202301BD070001-050) （202301BD070001-050）
