基于复合神经网络的食品添加剂太赫兹光谱分类识别方法  

Classification and Identification Method of Food Additives Using Terahertz Spectroscopy Based on Composite Neural Networks

作　　者：刘洋硕 燕芳[1] 李文文[1] 赵渺钰 LIU Yang-shuo;YAN Fang;LI Wen-wen;ZHAO Miao-yu(School of Automation and Electrical Engineering,Inner Mongolia University of Science and Technology,Key Laboratory of Synthetical Automation for Process Industries at Universities of Inner Mongolia Autonomous Region,Baotou 014010,China)

机构地区：[1]内蒙古科技大学自动化与电气工程学院,内蒙古自治区高等学校流程工业综合自动化重点实验室,内蒙古包头014010

出　　处：《分析测试学报》2024年第12期1883-1892,共10页Journal of Instrumental Analysis

基　　金：国家自然科学基金项目(62161042);内蒙古自治区关键技术攻关计划项目(2021GG0361);内蒙古自治区直属高校基本科研业务费项目资助-支持地方高校改革发展资金(学科建设)。

摘　　要：该研究利用太赫兹时域光谱技术,对苯甲酸、山梨酸、木糖醇、L-丙氨酸、三聚氰胺和苏丹红Ⅰ号6种食品添加剂进行光谱检测,并计算得到其在0.4~2.4 THz频段的太赫兹实验谱。采用Savitzky-Golay(S-G)平滑方法对实验谱数据进行校正与预处理,结合主成分分析法(PCA)对光谱数据降维,并分别将卷积神经网络(CNN)和长短时记忆网络(LSTM)应用于食品添加剂的定性分类识别模型。结果表明,CNN模型和LSTM模型的分类准确率分别为93.8%和92.7%,而引入Attention机制建立的复合神经网络(CNN-LSTM-Attention)模型的分类识别准确率得到大幅提升,达到99.5%。为了构建更客观以及更丰富的评价体系综合评价上述3种模型,采用F1分数作为评价指标,经对比发现,CNN模型和LSTM模型的F1分数分别为0.91和0.88,而CNN-LSTM-Attention模型的F1分数为0.95,明显优于上述两种模型。将3种模型应用于食品添加剂混合物的定性分析,结果显示,CNN-LSTM-Attention模型在对混合物的识别中表现出明显优势,识别准确率为90.0%,F1分数为0.87,优于CNN与LSTM模型,在食品添加剂混合物的定性识别中具有明显优势。研究结果表明,相比于CNN和LSTM模型,使用复合神经网络CNN-LSTM-Attention建立的定性分类模型在准确率、F1分数方面均为最优。该研究为食品添加剂的快速、准确、无损检测提供了理论支撑,有着极大的应用价值和潜在应用前景。This study utilized terahertz time-domain spectroscopy(THz-TDS) to conduct spectral detection on six food additives,namely benzoic acid,sorbic acid,xylitol,L-alanine,melamine,and Sudan I,obtaining their terahertz experimental spectra in the range of 0.4-2.4 THz.The experimental spectra data were corrected and preprocessed using the Savitzky-Golay(S-G) smoothing method,followed by dimensionality reduction of the spectral data through principal component analysis(PCA).This paper applied convolutional neural networks(CNN) and long short-term memory(LSTM) networks to construct qualitative classification recognition models for food additives.The results indicated that the classification accuracies of the CNN model and the LSTM model were 93.8% and 92.7%,respectively.To further enhance the classification accuracy and the feature extraction capability of the data,as well as to better integrate information from different modalities,this study introduced the Attention mechanism and established a composite neural network(CNN-LSTM-Attention) model.The experimental results demonstrated a significant improvement in the classification accuracy of the constructed CNN-LSTM-Attention model,reaching 99.5%.To create a more objective and comprehensive evaluation system for the aforementioned three models,this paper employed the F1 score as an evaluation metric.Comparatively,the F1 scores of the CNN and LSTM models were 0.91 and 0.88,respectively,while the CNN-LSTM-Attention model achieved a F1 score of 0.95,significantly outperforming the other two models.Furthermore,this paper applied the three models to the qualitative analysis of food additive mixtures,specifically preparing samples of melamine mixed with L-alanine and sorbic acid mixed with benzoic acid.The CNN-LSTM-Attention model demonstrated a distinct advantage in identifying these two mixtures,with a recognition accuracy of 90.0% and an F1 score of 0.87,both superior to the CNN and LSTM models.The results indicate that the CNN-LSTM-Attention model still possesses a clear ad

关 键 词：太赫兹时域光谱技术 食品添加剂 神经网络 CNN-LSTM-Attention 

分 类 号：O657.3[理学—分析化学] TS202.3[理学—化学]