Quantitative Visualization of Fungal Contamination in Peach Fruit Using Hyperspectral Imaging

The non-destructive method for detection of fungal contamination in peach fruit using hyperspectral imaging was evaluated. Growth characteristics of three major spoilage fungi in peach fruit during decay were estimated. Three quantitative prediction models were then constructed to forecast the microbial content from the HSI datasets. The prediction of fungal contamination on the fruit was visualized with different colors. Additionally, principal component analysis (PCA) was applied to reduce the dimensionality of the HSI data and to discriminate the infection degree in peaches. The results showed that partial least squares regression (PLSR) could achieve performance with R-p(2) not less than 0.84in predicting fungal colony counts, while PCA scores successfully identified the infected degrees of samples. This study illustrates that HSI combined with chemometrics can potentially be implemented for the quantitative detection of fungal contamination in peach fruit.