Fusion vs. Isolation: Evaluating the Performance of Multi-Sensor Integration for Meat Spoilage Prediction
High-throughput and portable sensor technologies are increasingly used in food production/distribution tasks as rapid and non-invasive platforms offering real-time or near real-time monitoring of quality and safety. These are often coupled with analytical techniques, including machine learning, for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/9/1613 |
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| author | Samuel Heffer Maria Anastasiadi George-John Nychas Fady Mohareb |
| author_facet | Samuel Heffer Maria Anastasiadi George-John Nychas Fady Mohareb |
| author_sort | Samuel Heffer |
| collection | DOAJ |
| description | High-throughput and portable sensor technologies are increasingly used in food production/distribution tasks as rapid and non-invasive platforms offering real-time or near real-time monitoring of quality and safety. These are often coupled with analytical techniques, including machine learning, for the estimation of sample quality and safety through monitoring of key physical attributes. However, the developed predictive models often show varying degrees of accuracy, depending on food type, storage conditions, sensor platform, and sample sizes. This work explores various fusion approaches for potential predictive enhancement, through the summation of information gathered from different observational spaces: infrared spectroscopy is supplemented with multispectral imaging for the prediction of chicken and beef spoilage through the estimation of bacterial counts in differing environmental conditions. For most circumstances, at least one of the fusion methodologies outperformed single-sensor models in prediction accuracy. Improvement in aerobic, vacuum, and mixed aerobic/vacuum chicken spoilage scenarios was observed, with performance enhanced by up to 15%. The improved cross-batch performance of these models proves an enhanced model robustness using the presented multi-sensor fusion approach. The batch-based results were corroborated with a repeated nested cross-validation approach, to give an out-of-sample generalised view of model performance across the whole dataset. Overall, this work suggests potential avenues for performance improvements in real-world, minimally invasive food monitoring scenarios. |
| format | Article |
| id | doaj-art-a19f448c7edc4071b74dbc0faadb9ff0 |
| institution | DOAJ |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-a19f448c7edc4071b74dbc0faadb9ff02025-08-20T02:58:47ZengMDPI AGFoods2304-81582025-05-01149161310.3390/foods14091613Fusion vs. Isolation: Evaluating the Performance of Multi-Sensor Integration for Meat Spoilage PredictionSamuel Heffer0Maria Anastasiadi1George-John Nychas2Fady Mohareb3Bioinformatics Group, Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield MK43 0AL, Bedfordshire, UKBioinformatics Group, Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield MK43 0AL, Bedfordshire, UKAgricultural University of Athens, 11855 Athens, GreeceBioinformatics Group, Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield MK43 0AL, Bedfordshire, UKHigh-throughput and portable sensor technologies are increasingly used in food production/distribution tasks as rapid and non-invasive platforms offering real-time or near real-time monitoring of quality and safety. These are often coupled with analytical techniques, including machine learning, for the estimation of sample quality and safety through monitoring of key physical attributes. However, the developed predictive models often show varying degrees of accuracy, depending on food type, storage conditions, sensor platform, and sample sizes. This work explores various fusion approaches for potential predictive enhancement, through the summation of information gathered from different observational spaces: infrared spectroscopy is supplemented with multispectral imaging for the prediction of chicken and beef spoilage through the estimation of bacterial counts in differing environmental conditions. For most circumstances, at least one of the fusion methodologies outperformed single-sensor models in prediction accuracy. Improvement in aerobic, vacuum, and mixed aerobic/vacuum chicken spoilage scenarios was observed, with performance enhanced by up to 15%. The improved cross-batch performance of these models proves an enhanced model robustness using the presented multi-sensor fusion approach. The batch-based results were corroborated with a repeated nested cross-validation approach, to give an out-of-sample generalised view of model performance across the whole dataset. Overall, this work suggests potential avenues for performance improvements in real-world, minimally invasive food monitoring scenarios.https://www.mdpi.com/2304-8158/14/9/1613spoilagemachine learning |
| spellingShingle | Samuel Heffer Maria Anastasiadi George-John Nychas Fady Mohareb Fusion vs. Isolation: Evaluating the Performance of Multi-Sensor Integration for Meat Spoilage Prediction Foods spoilage machine learning |
| title | Fusion vs. Isolation: Evaluating the Performance of Multi-Sensor Integration for Meat Spoilage Prediction |
| title_full | Fusion vs. Isolation: Evaluating the Performance of Multi-Sensor Integration for Meat Spoilage Prediction |
| title_fullStr | Fusion vs. Isolation: Evaluating the Performance of Multi-Sensor Integration for Meat Spoilage Prediction |
| title_full_unstemmed | Fusion vs. Isolation: Evaluating the Performance of Multi-Sensor Integration for Meat Spoilage Prediction |
| title_short | Fusion vs. Isolation: Evaluating the Performance of Multi-Sensor Integration for Meat Spoilage Prediction |
| title_sort | fusion vs isolation evaluating the performance of multi sensor integration for meat spoilage prediction |
| topic | spoilage machine learning |
| url | https://www.mdpi.com/2304-8158/14/9/1613 |
| work_keys_str_mv | AT samuelheffer fusionvsisolationevaluatingtheperformanceofmultisensorintegrationformeatspoilageprediction AT mariaanastasiadi fusionvsisolationevaluatingtheperformanceofmultisensorintegrationformeatspoilageprediction AT georgejohnnychas fusionvsisolationevaluatingtheperformanceofmultisensorintegrationformeatspoilageprediction AT fadymohareb fusionvsisolationevaluatingtheperformanceofmultisensorintegrationformeatspoilageprediction |