食品科学 ›› 2022, Vol. 43 ›› Issue (14): 296-301.doi: 10.7506/spkx1002-6630-20210922-246

• 安全检测 • 上一篇    

基于卷积神经网络的乳粉掺杂物拉曼光谱分类方法

邵帅斌,刘美含,石宇晴,郝朝龙,韩宙,张伟,陈达   

  1. (1.中国民航大学安全科学与工程学院,天津 300300;2.中国民航大学 民航热灾害防控和应急重点实验室,天津 300300)
  • 发布日期:2022-07-28
  • 基金资助:
    国家自然科学基金面上项目(21973111;61378048);国家自然科学基金青年科学基金项目(61801519); 中国民航大学研究生科研创新项目(2020YJS052)

Raman Spectroscopic Classification of Adulterants in Milk Powder Samples Using Convolutional Neural Network

SHAO Shuaibin, LIU Meihan, SHI Yuqing, HAO Chaolong, HAN Zhou, ZHANG Wei, CHEN Da   

  1. (1. School of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China; 2. Key Laboratory of Civil Aviation Thermal Hazards Prevention and Emergency Response, Civil Aviation University of China, Tianjin 300300, China)
  • Published:2022-07-28

摘要: 提出一种基于卷积神经网络的乳粉掺杂物拉曼光谱分类方法。首先利用拉曼高光谱成像平台采集足量乳粉样品的原始光谱,然后利用离散小波变换对原始光谱进行预处理,将预处理后的光谱信号作为卷积神经网络输入构建模型,并分别比较光谱预处理前后的建模效果。结果表明,不合适的光谱预处理反而会降低卷积神经网络的分类效果,而原始拉曼光谱就能被卷积神经网络精准识别,所构建的原始光谱模型对实际未知样品的识别准确率为95.5%。结果表明,卷积神经网络具备光谱预处理与建模的一体化功能,可极大简化拉曼光谱分类识别的计算过程,对乳粉质量安全筛查具有重要意义。

关键词: 拉曼光谱;乳粉掺杂物;光谱预处理;光谱分类;卷积神经网络

Abstract: This work develops a Raman spectral classification method using convolutional neural network (CNN-Raman) for detecting milk powder adulterants. Using a Raman hyperspectral imaging platform, the raw spectra of sufficient milk powder samples were collected and preprocessed by discrete wavelet transform (DWT). Subsequently, the DWT-filtered spectra were used as the input of CNN to construct a multivariate model. The classification results before and after spectral preprocessing were investigated. Unexpectedly, inappropriate spectral preprocessing worsened the classification performance of the CNN model, while the raw Raman spectra were accurately identified by the CNN. The CNN model based on the raw Raman spectra was capable of identifying an unknown sample accurately with a recognition rate of 95.5%. These results reveal that CNN can be combined with spectral preprocessing and modeling to greatly simplify the calculation process of Raman spectral classification. The CNN-Raman method represents a promising tool for quality and safety inspection of milk powder samples.

Key words: Raman spectroscopy; milk powder adulterants; spectral preprocessing; spectral classification; convolutional neural network

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