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Article

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

1
School of Information Engineering, Southwest University of Science and Technology, No. 59 Qinglong Road, Mianyang 621010, China
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School of Automation & Information Engineering, Sichuan University of Science & Engineering, No. 1 Baita Road, Yibin 644000, China
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Artificial Intelligence Key Laboratory of Sichuan Province, No. 1 Baita Road, Yibin 644000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Michael Kontominas and Anastasia Badeka
Sensors 2022, 22(4), 1654; https://doi.org/10.3390/s22041654
Received: 13 January 2022 / Revised: 16 February 2022 / Accepted: 18 February 2022 / Published: 20 February 2022
(This article belongs to the Topic Smart Technologies in Food Packaging and Sensors)
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. View Full-Text
Keywords: multi-component; near-infrared spectroscopy; characteristic extraction; difference spectrum; qualitative analysis multi-component; near-infrared spectroscopy; characteristic extraction; difference spectrum; qualitative analysis
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MDPI and ACS Style

Zhang, G.; Tuo, X.; Zhai, S.; Zhu, X.; Luo, L.; Zeng, X. Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM. Sensors 2022, 22, 1654. https://doi.org/10.3390/s22041654

AMA Style

Zhang G, Tuo X, Zhai S, Zhu X, Luo L, Zeng X. Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM. Sensors. 2022; 22(4):1654. https://doi.org/10.3390/s22041654

Chicago/Turabian Style

Zhang, Guiyu, Xianguo Tuo, Shuang Zhai, Xuemei Zhu, Lin Luo, and Xianglin Zeng. 2022. "Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM" Sensors 22, no. 4: 1654. https://doi.org/10.3390/s22041654

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