Quantitative Analysis Model of Near Infrared Spectroscopy Based on Multi-Scale Dilated Convolutional Neural Networks
10.19756/j.issn.0253-3820.241388
- VernacularTitle:基于多尺度扩张卷积神经网络的近红外光谱定量分析模型研究
- Author:
Qiang LI
1
;
Bei CHEN
;
Fang ZHANG
Author Information
1. 陕西科技大学电气与控制工程学院,西安 710021
- Keywords:
Near infrared spectroscopy;
Quantitative analysis;
Dilated convolution;
Multi-scale feature fusion
- From:
Chinese Journal of Analytical Chemistry
2025;53(3):451-463
- CountryChina
- Language:Chinese
-
Abstract:
Near infrared spectroscopy(NIRS)technology has been widely applied in quantitative analysis of pharmaceuticals,food,and chemical industries.In this study,a NIRS quantitative analysis model(MDCSpecNet)based on a multi-scale dilated convolutional neural network was proposed.The model consisted of a one-dimensional convolutional layer,a batch normalization layer,a max-pooling layer,a multi-scale dilated convolutional neural network,and a full-connected layer.Among which,the one-dimensional convolutional layer and the max-pooling layer performed preliminary featured extraction and dimensionality reduction on the original spectra,the batch normalization layer accelerated the convergence of the model,the multi-scale dilated convolutional neural network extracted and fused spectral features,and the fully-connected layer linearly represented the feature information,enhancing the model's prediction accuracy and generalization ability.MDCSpecNet prediction models were established using publicly available NIRS datasets of pharmaceuticals,grains,wheat,milk,and gasoline.The prediction results were compared and analyzed with those of one dimensional convolution neural network(1D-CNN),partial least squares(PLS),support vector regression(SVR),and extreme learning machine regression(ELM)modeling methods.The results showed that,in prediction of the content of active pharmaceutical ingredient(API)in pharmaceuticals,the glucose content in grains,the lactate content in grains,the moisture content in grains,the protein content in wheat,the octane number in gasoline and the cloud point of melamine,the accuracy of the MDCSpecNet model increased by 16%,36.7%,25.1%,22.6%,34.2%,15.2%and 22.6%compared to 1D-CNN,46.9%,66.7%,73.2%,65.8%,16.6%,15.9%and 13.7%compared to PLS,68.1%,70.6%,81.7%,73.9%,69.2%,77.9%and 56%compared to SVR,and 62%,20.4%,48.9%,85.6%,50.4%,13%and 44.6%compared to ELM,respectively.The MDCSpecNet model based on the multi-scale dilated convolutional neural network addressed the issues of low accuracy and poor generalization ability of traditional NIRS modeling methods,and it was feasible to use the MDCSpecNet model for quantitative analysis of NIRS of various substances.
