分析测试学报2025，Vol.44Issue(6)：1176-1182,7.DOI:10.12452/j.fxcsxb.25021897
基于CNN框架的LSTM融合优化模型用于芒果干物质的近红外光谱分析
Fusion Model of LSTM Optimization Based on CNN Framework and Its Application to NIR Spectroscopic Analysis of Mango Dry Matter
摘要
Abstract
The content of dry matter(DM)is one of the important indices to determine the quality of mango.In this paper,near-infrared spectroscopy(NIR)is used to predict the dry matter content of mango,so as to achieve rapid evaluation of mango quality.The study launched to propose the grid numericalization scheme for screening structural parameters based on the convolutional neural network(CNN)framework.The parameter optimization strategy was improved by the fusion of long short-term memory(LSTM)network,to propose the CNN-LSTM combined optimization model.In data ex-periment,a shallow CNN modeling architecture was constructed.The hyperparameters were for re-fine tuning by testing some local-scale values of the core parameters of CNN-LSTM model.Results showed that the optimal CNN model and CNN-LSTM models were obviously better than the conven-tional linear or nonlinear models in both the model training and model testing stages.In addition to identifying the most optimal models,we also provided some other appreciating less-optional models as well as their available parameter combinations.These findings are expected to be helpful in the production line of mango cultivation.The modeling framework of a shallow CNN architecture in fusion with the LSTM optimization provides chemometrics technical support for rapid detection of dry matter content in mango fruit.关键词
近红外(NIR)/芒果干物质/卷积神经网络(CNN)/长短期记忆网络(LSTM)/参数优选/网格数值化Key words
near-infrared(NIR)spectroscopy/dry matter of mango fruit/convolutional neural net-work(CNN)/long short-term memory(LSTM)/parameter optimization/grid numericalization分类
化学引用本文复制引用
林雪梅,蔡肯,黄家立,蒙芳秀,林钦永,陈华舟..基于CNN框架的LSTM融合优化模型用于芒果干物质的近红外光谱分析[J].分析测试学报,2025,44(6):1176-1182,7.基金项目
国家自然科学基金(62365008) （62365008）
广西自然科学基金(2022GXNSFAA035499) （2022GXNSFAA035499）
