Food Freshness Prediction Platform Utilizing Deep Learning-Based Multimodal Sensor Fusion of Volatile Organic Compounds and Moisture Distribution

Abstract

Various sensing methods have been developed for food spoilage research, but in practical applications, the accuracy of these methods is frequently constrained by the limitation of single-source data and challenges in cross-validating multimodal data. To address these issues, a new method combining multidimensional sensing technology with deep learning-based dynamic fusion has been developed, which can precisely monitor the spoilage process of beef. This study designs a gas sensor based on surface-enhanced Raman scattering (SERS) to directly analyze volatile organic compounds (VOCs) adsorbed on MIL-101(Cr) with amine-specific adsorption for data collection while also evaluating the moisture distribution of beef through low-field nuclear magnetic resonance (LF-NMR), providing multidimensional recognition and readings. By introducing the self-attention mechanism and SENet scaling features into the multimodal deep learning model, the system is able to adaptively fuse and focus on the important features of the sensors. After training, the system can predict the storage time of beef under controlled storage conditions, with an R² value greater than 0.98. Furthermore, it can provide accurate freshness assessments for beef samples under unknown storage conditions. Relative to single-modality methods, accuracy improves from 90 to over 97%. Overall, the newly developed dynamic fusion deep learning multimodal model effectively integrates multimodal information, enabling the fast and reliable monitoring of beef freshness.