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

BACKGROUND 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. RESULTS 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. CONCLUSIONS 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.