Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM

Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors, and its results are inaccurate and unstable. Thi...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-02, Vol.22 (4), p.1654
Main Authors: Zhang, Guiyu, Tuo, Xianguo, Zhai, Shuang, Zhu, Xuemei, Luo, Lin, Zeng, Xianglin
Format: Article
Language:English
Subjects:
Accuracy
Alcohol
Algorithms
characteristic extraction
Chromatography
Classification
Detectors
difference spectrum
Discriminant analysis
Humans
Infrared analysis
Infrared spectra
Least-Squares Analysis
Liquor
Methods
Model accuracy
multi-component
Near infrared radiation
near-infrared spectroscopy
Noise
Pattern recognition
Principal Component Analysis
Principal components analysis
Process controls
Qualitative analysis
Quality assessment
Quality Control
Quantitative analysis
Spectroscopy, Near-Infrared - methods
Support Vector Machine
Support vector machines
Three dimensional analysis
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Summary:Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors, and its results are inaccurate and unstable. This study developed a near-infrared (NIR) spectral characteristic extraction method based on a three-dimensional analysis space and establishes a high-accuracy qualitative identification model. First, the Norris derivative filtering algorithm was used in the pre-processing of the NIR spectrum to obtain a smooth main absorption peak. Then, the third-order tensor robust principal component analysis (TRPCA) algorithm was used for characteristic extraction, which effectively reduced the dimensionality of the raw NIR spectral data. Finally, on this basis, a qualitative identification model based on support vector machines (SVM) was constructed, and the classification accuracy reached 98.94%. Therefore, it is possible to develop a non-destructive, rapid qualitative detection system based on NIR spectroscopy to mine the subtle differences between classes and to use low-dimensional characteristic wavebands to detect the quality of complex multi-component mixtures. This method can be a key component of automatic quality control in the production of multi-component products.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22041654