An effective method fusing electronic nose and fluorescence hyperspectral imaging for the detection of pork freshness

Multi-sensor data fusion aims to integrate information about an object from various sources to make more effective decisions. In this study, the feasibility of combining fluorescence hyperspectral imaging (F-HSI) with electronic nose (E-nose) for non-destructive detection of pork freshness was explored. Fusion methods become crucial considering the correlation and potential redundancy in multi-sensor data. Early fusion and late fusion are popular methods, but early fusion may suffer from the curse of dimensionality due to the large amount of data, while late fusion may overlook the interactions between data sources. This work proposed a novel fusion network called the Hybrid Fusion Attention Network (HFA-Net) to tackle these challenges. The model incorporated early fusion with an attention mechanism into a late fusion architecture, running them in parallel. This design aims to capture relationships between different data types and expand the feature space. Focusing on important features by adding the Squeeze and Excite (SE) module can compress features and improve performance. The accuracy of predicting pork TVB-N content using this method was assessed and compared with single-sensor data, as well as early and late fusion methods of multi-sensor data. Our findings indicate that the multi-modal data fusion model outperformed the single-sensor data model, with the HFA-Net fusion model achieving the best prediction results (R2 = 0.9373, RMSE = 0.4897 mg/100 g). In conclusion, this paper proposed a novel end-to-end data fusion approach that integrates F-HSI and E-nose for pork freshness detection.