EXPRESS: A rapid and nondestructive approach for identification of foodborne bacteria using hyperspectral imaging and multimodal technology.
TL;DR
An optimized machine learning framework integrated with HSI that incorporates multimodal learning and a multi-head attention mechanism, enabling deeper extraction and fusion of spectral profiles and intensity images features indicates that the optimized HSI approach holds great potential as an effective tool for rapid detection of microbial contamination in food products.
Abstract
Foodborne illnesses pose a serious threat to food safety and cause substantial economic losses. Hyperspectral imaging (HSI) has emerged as a powerful tool for rapid and non-destructive identification of foodborne pathogens. However, the high chemical similarity among different pathogen categories presents a challenge for accurate discrimination. To address this issue, we developed an optimized machine learning framework integrated with HSI that incorporates multimodal learning and a multi-head attention mechanism, enabling deeper extraction and fusion of spectral profiles and intensity images features. In visualization analyses, the deeply fused features demonstrated excellent inter-species separability, and the proposed multimodal multi-head attention fusion (MMAF) strategy achieved a high identification accuracy of 96.29%, representing an improvement of 4.17% over the single-modal approach. These results indicate that the optimized HSI approach, driven by advanced machine learning, holds great potential as an effective tool for rapid detection of microbial contamination in food products.