ArticleFoods (Basel, Switzerland)2020
Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage.
Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Leakage-Free Benchmarking of Electronic Noses for Beef Freshness: A Signal-Richness Criterion for Model Selection.Foods (Basel, Switzerland) · 2026Article
- Multimodal AI for Real-Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation.Food science & nutrition · 2026Review
- Enhancing Food Safety in the Cold Chain Through Internet of Things and Artificial Intelligence.Journal of food science · 2026Review
- Machine Learning-Based Shelf Life Estimator for Dates Using a Multichannel Gas Sensor: Enhancing Food Security.Sensors (Basel, Switzerland) · 2025Article
- Band-Engineered α-FeNanomaterials (Basel, Switzerland) · 2025Article
- The role of microbiota in fish spoilage: biochemical mechanisms and innovative preservation strategies.Antonie van Leeuwenhoek · 2025Review
- A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics.Biosensors · 2024Review
- The Application of Artificial Intelligence and Big Data in the Food Industry.Foods (Basel, Switzerland) · 2023Review
- Effect of Biobased Cling Films on Cheese Quality: Color and Aroma Analysis for Sustainable Food Packaging.Foods (Basel, Switzerland) · 2023Article
- Application of Artificial Intelligence in Food Industry-a Guideline.Food engineering reviews · 2022Review
- Prediction in the Dynamics and Spoilage ofFoods (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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