Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage
Salmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/9/11/1579 |
| _version_ | 1797549259982110720 |
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| author | Huanhuan Feng Mengjie Zhang Pengfei Liu Yiliu Liu Xiaoshuan Zhang |
| author_facet | Huanhuan Feng Mengjie Zhang Pengfei Liu Yiliu Liu Xiaoshuan Zhang |
| author_sort | Huanhuan Feng |
| collection | DOAJ |
| description | Salmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic nose spoilage detection for quality parameters changes and freshness under cold storage conditions. The salmon samples were analyzed and divided into three groups in an incubator set at 0 °C, 4 °C, and 6 °C. The quality parameters, i.e., texture, color, sensory, and pH changes, were measured and evaluated at different temperatures after 0, 3, 6, 9, 12, and 14 days of cold storage. The principal component analysis (PCA) algorithm can be used to cluster electronic nose information. Furthermore, a Convolutional Neural Networks and Support Vector Machine (CNN-SVM) based algorithm is used to cluster the freshness level of salmon samples stored in a specific storage condition. In the tested samples, the results show that the training dataset of freshness is about 95.6%, and the accuracy rate of the test dataset is 93.8%. For the training dataset of corruption, the accuracy rate is about 91.4%, and the accuracy rate of the test dataset is 90.5%. The overall accuracy rate is more than 90%. This work could help to reduce quality loss during salmon cold storage. |
| first_indexed | 2024-03-10T15:12:08Z |
| format | Article |
| id | doaj.art-92ca97504d354fa096911b88f5774c28 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T15:12:08Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-92ca97504d354fa096911b88f5774c282023-11-20T19:16:34ZengMDPI AGFoods2304-81582020-10-01911157910.3390/foods9111579Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold StorageHuanhuan Feng0Mengjie Zhang1Pengfei Liu2Yiliu Liu3Xiaoshuan Zhang4College of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaDepartment of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, 7491 Trondheim, NorwayCollege of Engineering, China Agricultural University, Beijing 100083, ChinaSalmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic nose spoilage detection for quality parameters changes and freshness under cold storage conditions. The salmon samples were analyzed and divided into three groups in an incubator set at 0 °C, 4 °C, and 6 °C. The quality parameters, i.e., texture, color, sensory, and pH changes, were measured and evaluated at different temperatures after 0, 3, 6, 9, 12, and 14 days of cold storage. The principal component analysis (PCA) algorithm can be used to cluster electronic nose information. Furthermore, a Convolutional Neural Networks and Support Vector Machine (CNN-SVM) based algorithm is used to cluster the freshness level of salmon samples stored in a specific storage condition. In the tested samples, the results show that the training dataset of freshness is about 95.6%, and the accuracy rate of the test dataset is 93.8%. For the training dataset of corruption, the accuracy rate is about 91.4%, and the accuracy rate of the test dataset is 90.5%. The overall accuracy rate is more than 90%. This work could help to reduce quality loss during salmon cold storage.https://www.mdpi.com/2304-8158/9/11/1579IoT-enabled monitoring system (IoTMS)electronic nosesalmonConvolutional Neural Networks and Support Vector Machine (CNN-SVM)freshness |
| spellingShingle | Huanhuan Feng Mengjie Zhang Pengfei Liu Yiliu Liu Xiaoshuan Zhang Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage Foods IoT-enabled monitoring system (IoTMS) electronic nose salmon Convolutional Neural Networks and Support Vector Machine (CNN-SVM) freshness |
| title | Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage |
| title_full | Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage |
| title_fullStr | Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage |
| title_full_unstemmed | Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage |
| title_short | Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage |
| title_sort | evaluation of iot enabled monitoring and electronic nose spoilage detection for salmon freshness during cold storage |
| topic | IoT-enabled monitoring system (IoTMS) electronic nose salmon Convolutional Neural Networks and Support Vector Machine (CNN-SVM) freshness |
| url | https://www.mdpi.com/2304-8158/9/11/1579 |
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