Untargeted rapid differentiation and targeted growth tracking of fungal contamination in rice grains based on headspace-gas chromatography-ion mobility spectrometry.

Abstract

Milled rice are prone to be contaminated with spoilage or toxigenic fungi during storage, which may pose a real threat to human health. Most traditional methods require long periods of time for enumeration and quantification. However, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) technology could characterize the complex volatile organic compounds (VOCs) released from samples in a non-destructive and environmentally friendly manner. Thus, this study described an innovative HS-GC-IMS strategy for analyzing VOC profiles to detect fungal contamination in milled rice. A total of 24 typical target compounds were identified. Analysis of variance-partial least squares regression (APLSR) showed significant correlations between the target compounds and colony counts of fungi. While the changes of selected volatile components (acetic acid, 3-hydroxy-2-butanone and oct-en-3-ol) in fungi-inoculated rice had sufficiently high positive correlations with the colony counts, the logistic model could effectively be used to monitor the growth of individual fungus (R2 =0.902-0.980). PLSR could effectively be used to predict fungal colony counts in rice samples (R2 =0.831-0.953), and the different fungi-inoculated rice samples at 24 h could be successfully distinguished by support vector machine (SVM) (94.6%). The ability of HS-GC-IMS to monitor fungal infection would help to prevent contaminated rice grains from entering the food chain. This result indicated that HS-GC-IMS three-dimensional fingerprints may be appropriate for the early detection of fungal infection in rice grains. © 2021 Society of Chemical Industry.