LAUSR

Abstract This study described the rapid detection of milled rice infected with Aspergillus spp. species based on headspace-gas chromatography ion-mobility spectrometry (HS-GC-IMS) and electronic nose (E-nose) combined with chemometrics, namely principal component analysis (PCA), k-nearest neighbor (kNN) and partial least squares regression (PLSR). 3D HS-GC-IMS imaging and their response differences enabled the discrimination among fungal species. kNN was used to differentiate rice samples with cdifferent levels of fungal infection and achieved correct classified rate of 94.44% and 91.67% by HS-GC-IMS and E-nose, respectively. PLSR method was used for quantitative regression of fungal colony counts in rice samples and good prediction performances were achieved by HS-GC-IMS (Rp2 = 0.909, RMSEP = 0.202) and E-nose (Rp2 = 0.864, RMSEP = 0.235). The results indicated that both HS-GC-IMS and E-nose approaches can potentially be implemented for the detection of fungal contamination levels in milled rice, and HS-GC-IMS fingerprinting coupled with chemometrics might be used as an alternative tool for a highly sensitive method. This research might provide scientific information on the rapid, non-destructive, and effective fungal detection system for rice grains.