Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning
Commercial jerky counterfeiting is widespread in the market. This study combined visible-near-infrared and short-wave-near-infrared hyperspectral imaging along with multiple machine learning algorithms for non-destructive identification of five types of commercial jerky products, and explored the im...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Food Chemistry: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590157525001403 |
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| _version_ | 1850079751275806720 |
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| author | Yuanxi Han Liang Li Siyuan Jiang Pengpeng Sun Wenliang Wu Zhendong Liu |
| author_facet | Yuanxi Han Liang Li Siyuan Jiang Pengpeng Sun Wenliang Wu Zhendong Liu |
| author_sort | Yuanxi Han |
| collection | DOAJ |
| description | Commercial jerky counterfeiting is widespread in the market. This study combined visible-near-infrared and short-wave-near-infrared hyperspectral imaging along with multiple machine learning algorithms for non-destructive identification of five types of commercial jerky products, and explored the impact of different spectral bands, algorithm selection, and optimization methods on identification performance. After data preprocessing, all models' accuracies and stability improved. Specifically, the logistic regression model was best for jerky identification, with 85.78 %–100.00 % accuracy. With hyperparameter optimization, Support Vector Machine with linear kernel had highest accuracy (89.29 % and 95.29 % in two bands). Additionally, the artificial neural network with the hyperbolic tangent activation function had optimal training performance, exceeding 90.00 % accuracy. The findings demonstrate short-wave-near-infrared hyperspectral imaging combined with linear models (logistic regression and Support Vector Machine with linear kernel parameter settings) is better for identifying the types of jerky. This study provides reference for the band, model selection, and optimization of jerky type identification. |
| format | Article |
| id | doaj-art-b2964538b3a744efae216d6c4e77c3a0 |
| institution | DOAJ |
| issn | 2590-1575 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Food Chemistry: X |
| spelling | doaj-art-b2964538b3a744efae216d6c4e77c3a02025-08-20T02:45:07ZengElsevierFood Chemistry: X2590-15752025-02-012610229310.1016/j.fochx.2025.102293Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learningYuanxi Han0Liang Li1Siyuan Jiang2Pengpeng Sun3Wenliang Wu4Zhendong Liu5Food Science College, Xizang Agriculture & Animal Husbandry University, R&D Center of Agricultural Products with Xizang Plateau Characteristics, The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Xizang Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, ChinaFood Science College, Xizang Agriculture & Animal Husbandry University, R&D Center of Agricultural Products with Xizang Plateau Characteristics, The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Xizang Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, ChinaFood Science College, Xizang Agriculture & Animal Husbandry University, R&D Center of Agricultural Products with Xizang Plateau Characteristics, The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Xizang Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, ChinaCollege of Information Engineering, Northwest A&F University, Shaanxi, Xianyang, 712100, ChinaCollege of Information Engineering, Northwest A&F University, Shaanxi, Xianyang, 712100, China; Corresponding authors.Food Science College, Xizang Agriculture & Animal Husbandry University, R&D Center of Agricultural Products with Xizang Plateau Characteristics, The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Xizang Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China; Corresponding authors.Commercial jerky counterfeiting is widespread in the market. This study combined visible-near-infrared and short-wave-near-infrared hyperspectral imaging along with multiple machine learning algorithms for non-destructive identification of five types of commercial jerky products, and explored the impact of different spectral bands, algorithm selection, and optimization methods on identification performance. After data preprocessing, all models' accuracies and stability improved. Specifically, the logistic regression model was best for jerky identification, with 85.78 %–100.00 % accuracy. With hyperparameter optimization, Support Vector Machine with linear kernel had highest accuracy (89.29 % and 95.29 % in two bands). Additionally, the artificial neural network with the hyperbolic tangent activation function had optimal training performance, exceeding 90.00 % accuracy. The findings demonstrate short-wave-near-infrared hyperspectral imaging combined with linear models (logistic regression and Support Vector Machine with linear kernel parameter settings) is better for identifying the types of jerky. This study provides reference for the band, model selection, and optimization of jerky type identification.http://www.sciencedirect.com/science/article/pii/S2590157525001403Commodity jerkyHyperspectral imagingMachine learningSpectral band optimizationType identification |
| spellingShingle | Yuanxi Han Liang Li Siyuan Jiang Pengpeng Sun Wenliang Wu Zhendong Liu Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning Food Chemistry: X Commodity jerky Hyperspectral imaging Machine learning Spectral band optimization Type identification |
| title | Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning |
| title_full | Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning |
| title_fullStr | Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning |
| title_full_unstemmed | Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning |
| title_short | Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning |
| title_sort | non destructive identification of commercial jerky types based on multi band hyperspectral imaging with machine learning |
| topic | Commodity jerky Hyperspectral imaging Machine learning Spectral band optimization Type identification |
| url | http://www.sciencedirect.com/science/article/pii/S2590157525001403 |
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