Discrimination and growth tracking of fungi contamination in peaches using electronic nose

Abstract

A non-destructive method for detection of fungal contamination in peaches using an electronic nose (E-nose) is presented. Peaches were inoculated with three common spoilage fungi, Botrytis cinerea, Monilinia fructicola and Rhizopus stolonifer and then stored for various periods. E-nose was then used to analyze volatile compounds generated in the fungi-inoculated peaches, which was then compared with the growth data (colony counts) of the fungi. The results showed that changes in volatile compounds in fungi-inoculated peaches were correlated with total amounts and species of fungi. Terpenes and aromatic compounds were the main contributors to E-nose responses. While principle component analysis (PC1) scores were highly correlated with fungal colony counts, Partial Least Squares Regression (PLSR) could effectively be used to predict fungal colony counts in peach samples. The results also showed that the E-nose had high discrimination accuracy, demonstrating the potential use of E-nose to discriminate among fungal contamination in peaches.