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J Sci Food Agric

Title:		Untargeted rapid differentiation and targeted growth tracking of fungal contamination in rice grains based on headspace-gas chromatography-ion mobility spectrometry
Author(s):		Gu S; Wang Z; Wang J;
Address:		"Department of Biosystems Engineering, Zhejiang University, Hangzhou, P. R. China. School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, P. R. China"
Journal Title:		J Sci Food Agric
Year:		2022
Volume:		20211221
Issue:		9
Page Number:		3673 - 3682
DOI:		10.1002/jsfa.11714
ISSN/ISBN:		1097-0010 (Electronic) 0022-5142 (Linking)
Abstract:		"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 (R(2) = 0.902-0.980). PLSR could effectively be used to predict fungal colony counts in rice samples (R(2) = 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. (c) 2021 Society of Chemical Industry"
Keywords:		Gas Chromatography-Mass Spectrometry/methods Humans Ion Mobility Spectrometry/methods Least-Squares Analysis *Oryza/microbiology *Volatile Organic Compounds/chemistry Hs-gc-ims Svm VOC profiling fungal growth milled rice;
Notes:		"MedlineGu, Shuang Wang, Zhenhe Wang, Jun eng 2017YFD0400102/the National Key Research and Development Program of China/ England 2021/12/11 J Sci Food Agric. 2022 Jul; 102(9):3673-3682. doi: 10.1002/jsfa.11714. Epub 2021 Dec 21"