Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood Indexes
Crisp grass carp products from China are becoming more prevalent in the worldwide fish market because muscle hardness is the primary desirable characteristic for consumer satisfaction of fish fillet products. Unfortunately, current instrumental methods to evaluate muscle hardness are expensive, time...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/9/11/1615 |
| _version_ | 1797548592896933888 |
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| author | Bing Fu Gen Kaneko Jun Xie Zhifei Li Jingjing Tian Wangbao Gong Kai Zhang Yun Xia Ermeng Yu Guangjun Wang |
| author_facet | Bing Fu Gen Kaneko Jun Xie Zhifei Li Jingjing Tian Wangbao Gong Kai Zhang Yun Xia Ermeng Yu Guangjun Wang |
| author_sort | Bing Fu |
| collection | DOAJ |
| description | Crisp grass carp products from China are becoming more prevalent in the worldwide fish market because muscle hardness is the primary desirable characteristic for consumer satisfaction of fish fillet products. Unfortunately, current instrumental methods to evaluate muscle hardness are expensive, time-consuming, and wasteful. This study sought to develop classification models for differentiating the muscle hardness of crisp grass carp on the basis of blood analysis. Out of the total 264 grass carp samples, 12 outliers from crisp grass carp group were removed based on muscle hardness (<9 N), and the remaining 252 samples were used for the analysis of seven blood indexes including hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), glucose 6-phosphate dehydrogenase (G6PD), malondialdehyde (MDA), glutathione (GSH/GSSH), red blood cells (RBC), platelet count (PLT), and lymphocytes (LY). Furthermore, six machine learning models were applied to predict the muscle hardness of grass carp based on the training (152) and testing (100) datasets obtained from the blood analysis: random forest (RF), naïve Bayes (NB), gradient boosting decision tree (GBDT), support vector machine (SVM), partial least squares regression (PLSR), and artificial neural network (ANN). The RF model exhibited the best prediction performance with a classification accuracy of 100%, specificity of 93.08%, and sensitivity of 100% for discriminating crisp grass carp muscle hardness, followed by the NB model (93.75% accuracy, 91.83% specificity, and 94% sensitivity), whereas the ANN model had the lowest prediction performance (85.42% accuracy, 81.05% specificity, and 85% sensitivity). These machine learning methods provided objective, cheap, fast, and reliable classification for in vivo crisp grass carp and also prove useful for muscle quality evaluation of other freshwater fish. |
| first_indexed | 2024-03-10T15:02:48Z |
| format | Article |
| id | doaj.art-14afce82a7044aff954bdb6aa99b1d28 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T15:02:48Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-14afce82a7044aff954bdb6aa99b1d282023-11-20T20:02:48ZengMDPI AGFoods2304-81582020-11-01911161510.3390/foods9111615Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood IndexesBing Fu0Gen Kaneko1Jun Xie2Zhifei Li3Jingjing Tian4Wangbao Gong5Kai Zhang6Yun Xia7Ermeng Yu8Guangjun Wang9Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaSchool of Arts & Sciences, University of Houston-Victoria, Victoria, TX 77901, USAPearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaPearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaPearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaPearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaPearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaPearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaPearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaPearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, ChinaCrisp grass carp products from China are becoming more prevalent in the worldwide fish market because muscle hardness is the primary desirable characteristic for consumer satisfaction of fish fillet products. Unfortunately, current instrumental methods to evaluate muscle hardness are expensive, time-consuming, and wasteful. This study sought to develop classification models for differentiating the muscle hardness of crisp grass carp on the basis of blood analysis. Out of the total 264 grass carp samples, 12 outliers from crisp grass carp group were removed based on muscle hardness (<9 N), and the remaining 252 samples were used for the analysis of seven blood indexes including hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), glucose 6-phosphate dehydrogenase (G6PD), malondialdehyde (MDA), glutathione (GSH/GSSH), red blood cells (RBC), platelet count (PLT), and lymphocytes (LY). Furthermore, six machine learning models were applied to predict the muscle hardness of grass carp based on the training (152) and testing (100) datasets obtained from the blood analysis: random forest (RF), naïve Bayes (NB), gradient boosting decision tree (GBDT), support vector machine (SVM), partial least squares regression (PLSR), and artificial neural network (ANN). The RF model exhibited the best prediction performance with a classification accuracy of 100%, specificity of 93.08%, and sensitivity of 100% for discriminating crisp grass carp muscle hardness, followed by the NB model (93.75% accuracy, 91.83% specificity, and 94% sensitivity), whereas the ANN model had the lowest prediction performance (85.42% accuracy, 81.05% specificity, and 85% sensitivity). These machine learning methods provided objective, cheap, fast, and reliable classification for in vivo crisp grass carp and also prove useful for muscle quality evaluation of other freshwater fish.https://www.mdpi.com/2304-8158/9/11/1615meat qualitymuscle hardnessclassification modelrandom forest |
| spellingShingle | Bing Fu Gen Kaneko Jun Xie Zhifei Li Jingjing Tian Wangbao Gong Kai Zhang Yun Xia Ermeng Yu Guangjun Wang Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood Indexes Foods meat quality muscle hardness classification model random forest |
| title | Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood Indexes |
| title_full | Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood Indexes |
| title_fullStr | Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood Indexes |
| title_full_unstemmed | Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood Indexes |
| title_short | Value-Added Carp Products: Multi-Class Evaluation of Crisp Grass Carp by Machine Learning-Based Analysis of Blood Indexes |
| title_sort | value added carp products multi class evaluation of crisp grass carp by machine learning based analysis of blood indexes |
| topic | meat quality muscle hardness classification model random forest |
| url | https://www.mdpi.com/2304-8158/9/11/1615 |
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