Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods
Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5–50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MI...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/12/20/3856 |
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| author | Chu Chu Haitong Wang Xuelu Luo Peipei Wen Liangkang Nan Chao Du Yikai Fan Dengying Gao Dongwei Wang Zhuo Yang Guochang Yang Li Liu Yongqing Li Bo Hu Zunongjiang Abula Shujun Zhang |
| author_facet | Chu Chu Haitong Wang Xuelu Luo Peipei Wen Liangkang Nan Chao Du Yikai Fan Dengying Gao Dongwei Wang Zhuo Yang Guochang Yang Li Liu Yongqing Li Bo Hu Zunongjiang Abula Shujun Zhang |
| author_sort | Chu Chu |
| collection | DOAJ |
| description | Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5–50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning—especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)—exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and R<sub>V</sub><sup>2</sup> = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and R<sub>V</sub><sup>2</sup> = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS. |
| first_indexed | 2024-03-10T21:14:55Z |
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| id | doaj.art-9a8cbbffce9a4973b7638bdb6fda4acf |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T21:14:55Z |
| publishDate | 2023-10-01 |
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| series | Foods |
| spelling | doaj.art-9a8cbbffce9a4973b7638bdb6fda4acf2023-11-19T16:30:48ZengMDPI AGFoods2304-81582023-10-011220385610.3390/foods12203856Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning MethodsChu Chu0Haitong Wang1Xuelu Luo2Peipei Wen3Liangkang Nan4Chao Du5Yikai Fan6Dengying Gao7Dongwei Wang8Zhuo Yang9Guochang Yang10Li Liu11Yongqing Li12Bo Hu13Zunongjiang Abula14Shujun Zhang15Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaQuality Standards Institue of Animal Husbandry, Xinjiang Academy of Animal Science, Urumqi 830012, ChinaQuality Standards Institue of Animal Husbandry, Xinjiang Academy of Animal Science, Urumqi 830012, ChinaFrontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, ChinaAdulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5–50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning—especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)—exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and R<sub>V</sub><sup>2</sup> = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and R<sub>V</sub><sup>2</sup> = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.https://www.mdpi.com/2304-8158/12/20/3856mid-infrared spectroscopymachine learningmilk adulterationmilk |
| spellingShingle | Chu Chu Haitong Wang Xuelu Luo Peipei Wen Liangkang Nan Chao Du Yikai Fan Dengying Gao Dongwei Wang Zhuo Yang Guochang Yang Li Liu Yongqing Li Bo Hu Zunongjiang Abula Shujun Zhang Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods Foods mid-infrared spectroscopy machine learning milk adulteration milk |
| title | Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods |
| title_full | Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods |
| title_fullStr | Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods |
| title_full_unstemmed | Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods |
| title_short | Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods |
| title_sort | possible alternatives identifying and quantifying adulteration in buffalo goat and camel milk using mid infrared spectroscopy combined with modern statistical machine learning methods |
| topic | mid-infrared spectroscopy machine learning milk adulteration milk |
| url | https://www.mdpi.com/2304-8158/12/20/3856 |
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