ArticleSensors (Basel, Switzerland)2023
Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Advancing Egg Freshness Evaluation with Integrated AI and Spectroscopy.Foods (Basel, Switzerland) · 2026Review
- Transformative artificial intelligence integration in aquatic supply chains: synergizing precision aquaculture with intelligent logistics and data-driven consumption.Food chemistry: X · 2026Review
- Deep Learning-Based Prediction of Fish Freshness and Purchasability Using Multi-Angle Image Data.Foods (Basel, Switzerland) · 2025Article
- Application of Machine Learning in Food Safety Risk Assessment.Foods (Basel, Switzerland) · 2025Review
- Automated seafood freshness detection and preservation analysis using machine learning and paper-based pH sensors.Scientific reports · 2025Article
- Leveraging Feature Fusion of Image Features and Laser Reflectance for Automated Fish Freshness Classification.Sensors (Basel, Switzerland) · 2025Article
- Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods.Foods (Basel, Switzerland) · 2025Review
- Freshness in Salmon by Hand-Held Devices: Methods in Feature Selection and Data Fusion for Spectroscopy.ACS food science & technology · 2024Article
- A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.