Heffer Samuel, Anastasiadi Maria, Nychas George-John, Mohareb Fady
Bioinformatics Group, Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield MK43 0AL, Bedfordshire, UK.
Agricultural University of Athens, 11855 Athens, Greece.
Foods. 2025 May 2;14(9):1613. doi: 10.3390/foods14091613.
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.
高通量和便携式传感器技术越来越多地用于食品生产/配送任务，作为提供质量和安全实时或近实时监测的快速、非侵入性平台。这些技术通常与包括机器学习在内的分析技术相结合，通过监测关键物理属性来估计样品的质量和安全。然而，所开发的预测模型的准确性往往因食品类型、储存条件、传感器平台和样本大小而异。这项工作探索了各种融合方法以实现潜在的预测增强，通过汇总从不同观测空间收集的信息：红外光谱辅以多光谱成像，通过估计不同环境条件下的细菌数量来预测鸡肉和牛肉的腐败情况。在大多数情况下，至少有一种融合方法在预测准确性方面优于单传感器模型。在有氧、真空和有氧/真空混合的鸡肉腐败情况下观察到了性能提升，性能提高了多达15%。这些模型改进的跨批次性能证明了使用所提出的多传感器融合方法增强了模型的稳健性。基于批次的结果通过重复嵌套交叉验证方法得到了证实，以给出整个数据集上模型性能的样本外广义视图。总体而言，这项工作为现实世界中微创食品监测场景的性能改进提出了潜在途径。
