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

被引:0
作者
Shao S. [1 ]
Liu M. [1 ]
Shi Y. [1 ]
Hao C. [1 ]
Han Z. [1 ]
Zhang W. [2 ]
Chen D. [2 ]
机构
[1] School of Safety Science and Engineering, Civil Aviation University of China, Tianjin
[2] Key Laboratory of Civil Aviation Thermal Hazards Prevention and Emergency Response, Civil Aviation University of China, Tianjin
来源
Shipin Kexue/Food Science | 2022年 / 43卷 / 14期
关键词
Convolutional neural network; Milk powder adulterants; Raman spectroscopy; Spectral classification; Spectral preprocessing;
D O I
10.7506/spkx1002-6630-20210922-246
中图分类号
学科分类号
摘要
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. © 2022, China Food Publishing Company. All right reserved.
引用
收藏
页码:296 / 301
页数:5
相关论文
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