Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural Network
To achieve rapid detection of astaxanthin content in Antarctic krill meal, a determination method for astaxanthin content in krill meal was established using computer vision and convolutional neural networks. A total of 70 Antarctic krill meal samples were analyzed using high-performance liquid chro...
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The editorial department of Science and Technology of Food Industry
2025-02-01
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| Series: | Shipin gongye ke-ji |
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| Online Access: | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024030200 |
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| author | Quantong ZHANG Yao ZHENG Liu YANG Shuaishuai ZHANG Quanyou GUO |
| author_facet | Quantong ZHANG Yao ZHENG Liu YANG Shuaishuai ZHANG Quanyou GUO |
| author_sort | Quantong ZHANG |
| collection | DOAJ |
| description | To achieve rapid detection of astaxanthin content in Antarctic krill meal, a determination method for astaxanthin content in krill meal was established using computer vision and convolutional neural networks. A total of 70 Antarctic krill meal samples were analyzed using high-performance liquid chromatography to determine their astaxanthin contents as label, and corresponding images of the samples were acquired using a computer vision system to form the dataset and the dataset was augmented. The model was built using the TensorFlow learning framework. The 5-fold cross-validation was used to tune and evaluate the model and select the optimal parameter model. The optimal parameter model was evaluated by randomly dividing the dataset, and 30 images from the dataset were randomly selected for model validation. The results showed that the optimal hyperparameters model with a root mean square error (RMSE) of 3.59 was preserved through a five-fold cross-validation. For model evaluation, the model was repeated three times. The mean values of the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and RMSE for the test set were 0.9626, 1.49, 4.22, and 2.05, respectively. For model validation, the relative errors ranged from 0.10% to 6.46%, indicating small deviations between the predictions and the observations. The astaxanthin content prediction model demonstrated high accuracy, enabling quick and nondestructive detection of astaxanthin content in krill meal samples. |
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| institution | Kabale University |
| issn | 1002-0306 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | The editorial department of Science and Technology of Food Industry |
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| series | Shipin gongye ke-ji |
| spelling | doaj-art-5a6fe4693b454a34a8a3cac2416d627c2025-01-21T07:24:08ZzhoThe editorial department of Science and Technology of Food IndustryShipin gongye ke-ji1002-03062025-02-01463111810.13386/j.issn1002-0306.20240302002024030200-3Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural NetworkQuantong ZHANG0Yao ZHENG1Liu YANG2Shuaishuai ZHANG3Quanyou GUO4College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaCollege of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaTo achieve rapid detection of astaxanthin content in Antarctic krill meal, a determination method for astaxanthin content in krill meal was established using computer vision and convolutional neural networks. A total of 70 Antarctic krill meal samples were analyzed using high-performance liquid chromatography to determine their astaxanthin contents as label, and corresponding images of the samples were acquired using a computer vision system to form the dataset and the dataset was augmented. The model was built using the TensorFlow learning framework. The 5-fold cross-validation was used to tune and evaluate the model and select the optimal parameter model. The optimal parameter model was evaluated by randomly dividing the dataset, and 30 images from the dataset were randomly selected for model validation. The results showed that the optimal hyperparameters model with a root mean square error (RMSE) of 3.59 was preserved through a five-fold cross-validation. For model evaluation, the model was repeated three times. The mean values of the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and RMSE for the test set were 0.9626, 1.49, 4.22, and 2.05, respectively. For model validation, the relative errors ranged from 0.10% to 6.46%, indicating small deviations between the predictions and the observations. The astaxanthin content prediction model demonstrated high accuracy, enabling quick and nondestructive detection of astaxanthin content in krill meal samples.http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024030200computer visionconvolutional neural networkantarctic krillastaxanthinrapid detection |
| spellingShingle | Quantong ZHANG Yao ZHENG Liu YANG Shuaishuai ZHANG Quanyou GUO Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural Network Shipin gongye ke-ji computer vision convolutional neural network antarctic krill astaxanthin rapid detection |
| title | Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural Network |
| title_full | Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural Network |
| title_fullStr | Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural Network |
| title_full_unstemmed | Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural Network |
| title_short | Rapid Detection of Astaxanthin in Antarctic Krill Meal by Computer Vision Combined with Convolutional Neural Network |
| title_sort | rapid detection of astaxanthin in antarctic krill meal by computer vision combined with convolutional neural network |
| topic | computer vision convolutional neural network antarctic krill astaxanthin rapid detection |
| url | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024030200 |
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