Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose.

Early detection of Aspergillus spp. contamination in rice was investigated by electronic nose (E-nose) in this study. Sterilized rice artificially inoculated with three Aspergillus strains were subjected to GC-MS and E-nose analyses. Principle Component Analysis (PCA), Partial Least Squares Regression (PLSR), Back-propagation neural network (BPNN), Support Vector Machine (SVM) and Learning Vector Quantization (LVQ) were employed for qualitative classification and quantitative regression. GC-MS analysis revealed a significant correlation between the volatile compounds and total amounts/species of fungi. While X-axis barycenters of PC1 scores were significantly correlated with fungal counts, logistic model could be employed to simulate the growth of individual fungus (R2 = 0.978-0.996). Fungal species and counts in rice could be classified and predicted by BPNN (96.4%) and PLSR (R2 = 0.886-0.917), respectively. The results demonstrated that E-nose combined with BPNN might offer the feasibility for early detection of Aspergillus spp. contamination in rice.