Real-time mildew detection and gradation in simulated containerized soybeans: Insights from GC-IMS analysis of mVOCs and VOCs

In the context of bulk grain container transportation, the complex logistics can lead to grain mildew and subsequent economic losses. Therefore, there is a pressing need to explore swift and real-time mildew detection technology. Our investigation, simulating actual transportation conditions, revealed that Aspergillus, Penicillium, and Rhizopus were the primary molds responsible for soybean mildew during container transportation. Utilizing gas chromatography-ion migration spectroscopy (GC-IMS), we analyzed the correlation between the mVOCs (microbial volatile organic compounds) produced by dominant mold and the VOCs emitted during soybean mildew. Principal Component Analysis (PCA) and clustering results demonstrated the distinctive identification of VOCs in soybeans with varying degrees of mildew. The mildew degree significantly influenced the content variation of VOCs. As the mildew degree increased, the concentrations of nonanal, octanal, etc. progressively decreased, contrasting with the rising levels of phenylacetaldehyde, 3-methyl-2-butenal, etc. Therefore, the combination of GC-IMS with chemometrics proves to be a viable method for identifying the mildew degree of soybeans. Therefore, this study underscores the importance of implementing effective mildew detection techniques in the challenging context of bulk grain container transportation.