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

Food Chem. 2019 Sep 15:292:325-335. doi: 10.1016/j.foodchem.2019.04.054. Epub 2019 Apr 16.

Abstract

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.

Keywords: BPNN; Electronic nose; Fungal growth; Rice kernels.

MeSH terms

  • Aspergillus / growth & development*
  • Aspergillus / metabolism
  • Electronic Nose*
  • Gas Chromatography-Mass Spectrometry
  • Least-Squares Analysis
  • Neural Networks, Computer
  • Oryza / chemistry
  • Oryza / metabolism
  • Oryza / microbiology*
  • Principal Component Analysis
  • Support Vector Machine
  • Volatile Organic Compounds / analysis

Substances

  • Volatile Organic Compounds