Information fusion of hyperspectral imaging and electronic nose for evaluation of fungal contamination in strawberries during decay

The non-destructive method developed based on hyperspectral imaging (HSI) and electronic nose (E-nose) to rapidly detect microbial content and quality attributes of strawberries during decay, was evaluated. Principal component analysis (PCA) was applied to reduce the dimensionality of the data and to extract featured information from the HSI and E-nose data. Quantitative prediction models were developed to forecast the microbial contents and the quality attributes of strawberries. The results showed that the changes in exterior appearances (i.e., color) and interior compositions (i.e., total soluble solids and titratable acidity) of fungi-infected strawberries during storage were highly correlated with the microbial content. Ten essential PCs (with over 99% cumulative contribution) extracted from HSI and E-nose datasets were needed for directly improve the prediction models. The model constructed based on the raw information fusion of HSI and E-nose data did not improve the prediction accuracy. By contrast, the model constructed based on featured information fusion with essential PCs had better prediction performance than that constructed based on single dataset (HSI or E-nose). The best prediction model was able to predict colony-forming units with a 0.925 R-P(2) and RMSEP of 0.38 log(10) (CFU g(-1)). This study illustrates that the combination of the two sensing techniques can potentially be implemented for the detection of safety and quality of strawberries.