Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm
The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared h...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/11/15/2278 |
| _version_ | 1797413877122596864 |
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| author | Binbin Fan Rongguang Zhu Dongyu He Shichang Wang Xiaomin Cui Xuedong Yao |
| author_facet | Binbin Fan Rongguang Zhu Dongyu He Shichang Wang Xiaomin Cui Xuedong Yao |
| author_sort | Binbin Fan |
| collection | DOAJ |
| description | The evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R<sub>P</sub><sup>2</sup> of 0.9304 and an RMSEP of 0.0458 g·g<sup>−1</sup>. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions. |
| first_indexed | 2024-03-09T05:25:20Z |
| format | Article |
| id | doaj.art-717c605ad9af4ab89d1232c353c5c353 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-09T05:25:20Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-717c605ad9af4ab89d1232c353c5c3532023-12-03T12:37:31ZengMDPI AGFoods2304-81582022-07-011115227810.3390/foods11152278Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search AlgorithmBinbin Fan0Rongguang Zhu1Dongyu He2Shichang Wang3Xiaomin Cui4Xuedong Yao5College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaThe evaluation of mutton adulteration faces new challenges because of mutton flavour essence, which achieves a similar flavour between the adulterant and mutton. Hence, methods for classifying and quantifying the adulterated mutton under the effect of mutton flavour essence, based on near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm) combined with machine learning (ML) and sparrow search algorithm (SSA), were proposed in this study. After spectral preprocessing via first derivative combined with multiple scattering correction (1D + MSC), classification and quantification models were established using back propagation neural network (BP), extreme learning machine (ELM) and support vector machine/regression (SVM/SVR). SSA was further used to explore the global optimal parameters of these models. Results showed that the performance of models improves after optimisation via the SSA. SSA-SVM achieved the optimal discrimination result, with an accuracy of 99.79% in the prediction set; SSA-SVR achieved the optimal prediction result, with an R<sub>P</sub><sup>2</sup> of 0.9304 and an RMSEP of 0.0458 g·g<sup>−1</sup>. Hence, NIR-HSI combined with ML and SSA is feasible for classification and quantification of mutton adulteration under the effect of mutton flavour essence. This study can provide a theoretical and practical reference for the evaluation and supervision of food quality under complex conditions.https://www.mdpi.com/2304-8158/11/15/2278food additivemutton adulterationnear-infrared hyperspectral imagingsparrow search algorithmmachine learning |
| spellingShingle | Binbin Fan Rongguang Zhu Dongyu He Shichang Wang Xiaomin Cui Xuedong Yao Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm Foods food additive mutton adulteration near-infrared hyperspectral imaging sparrow search algorithm machine learning |
| title | Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm |
| title_full | Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm |
| title_fullStr | Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm |
| title_full_unstemmed | Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm |
| title_short | Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm |
| title_sort | evaluation of mutton adulteration under the effect of mutton flavour essence using hyperspectral imaging combined with machine learning and sparrow search algorithm |
| topic | food additive mutton adulteration near-infrared hyperspectral imaging sparrow search algorithm machine learning |
| url | https://www.mdpi.com/2304-8158/11/15/2278 |
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