A decision fusion method based on hyperspectral imaging and electronic nose techniques for moisture content prediction in frozen-thawed pork

This paper studies the feasibility of combining hyperspectral imaging (HSI) and electronic nose (E-nose) techniques to improve the prediction performance of moisture content (MC) in frozen-thawed pork. In this experiment, the data of 240 pork samples under different freeze-thaw cycles (0–5 times) were collected using HSI and E-nose technology. Afterward, the spectra and image (color and texture) information were extracted from the HSI sensor, while the odor information was extracted from the E-nose sensor. In this setting, an improved decision fusion method was proposed to fuse this information, thus detecting the MC in pork. Based on single information and its fusion at different fusion levels (pixel, feature, traditional decision fusion, and improved decision fusion), partial least squares regression (PLSR) prediction models were established and compared. The results show that the model of improved decision fusion based on HSI and E-nose fusion technology exhibits the best prediction capability with the determination coefficient of prediction set (R²p) of 0.9533 and root mean square error of prediction set (RMSEP) of 0.3869. This study demonstrates that the proposed method is an effective data fusion method, and the combination of HSI and E-nose technology improves the prediction performance of MC in frozen-thawed pork.