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Near-Infrared Spectroscopy Measurement of Contrastive Variational Autoencoder and Its Application in the Detection of Liquid Sample

文献类型： 外文期刊

作者： Yuan Zhuang 1 ; Dong Da-ming 2 ;

作者机构： 1.Guangxi Univ, Nanning 530004, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

关键词： Near-infrared; cVAE; Data dimensionality reduction; Feature extraction

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.609； 五年影响因子：0.516 ）

ISSN： 1000-0593

年卷期： 2022 年 42 卷 11 期

页码：

收录情况： SCI

摘要： Near-infrared spectroscopy is one of the popular food detection methods. Analyzing such high-dimensional spectral data often requires using data dimensionality reduction algorithms to extract features. However, most algorithms can only analyze a single data set. Although the contrastive principal component analysis based on contrastive learning has been successfully applied to the near-infrared spectroscopy detection of pesticide residues on the surface of different fruits, this method can only linearly combine the original features, and the feature extraction effect has limitations and needs to be adjusted. Comparing parameters to control the influence of the background set requires more time and cost. cVAE (contrastive variational autoencoder) is an improved algorithm based on contrastive learning and variational autoencoder, which is used in image denoising and RNA sequence analysis. It still has the characteristics of analyzing multiple data sets, and at the same time, it can extract nonlinear hidden features because of the combination of the probability generation model of the neural network. The cVAE algorithm is applied to near-infrared spectroscopy analysis, and an accurate dimensionality reduction model of near-infrared spectroscopy data is established. In actual verification, the cVAE algorithm was used to detect melamine adulteration in pure milk purchased by different brands and batches. The results show that when analyzing whether melamine is adulterated in pure milk purchased by different brands and batches, the VAE algorithm can only distinguish between different brands and batches of pure milk, and the important information about whether or not melamine is adulterated is not available. When using the cVAE algorithm for data analysis, because the background data set is added to separate irrelevant variables, it is possible to classify samples with or without adulterated melamine clearly. This shows that cVAE has the advantages of cPCA (contrastive principle component analysis) in dimensionality reduction of near-infrared spectroscopy data and can extract nonlinear features without the need to adjust variable parameters. The dimensionality reduction model of the near-infrared spectroscopy can be established more conveniently.

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