Aspergillus spp. are common dominant fungi in the moldy agricultural products. Different Aspergillus species exhibit high morphological and genetic sequence similarity, making identification challenging. The measurement of mycelial growth in these Aspergillus species often requires a relatively long time. Therefore, there is a need to develop a new rapid method for identifying Aspergillus species and monitoring their growth. The volatile compounds released by five Aspergillus species on culture media were detected by gas chromatography-mass spectrometer (GC–MS), electronic nose and headspace-gas chromatography-ion migration spectrometry (HS-GC-IMS), their characteristic volatile signals were captured, the models for identifying Aspergillus species were established, and prediction models for mycelial growth of five Aspergillus species were developed. The results showed that the LDA classification training set model constructed using the fusion of GC-IMS and electronic nose data achieved a 100% accuracy rate for identifying Aspergillus species in the training set and an 83.3% accuracy rate in the test set, significantly outperforming models based on HS-GC-IMS or electronic nose data alone. When the result combined with morphological analysis, a 100% accuracy rate in identifying Aspergillus species could be achieved. The partial least squares regression (PLSR) prediction models for mycelial growth in Aspergillus species, constructed using both fusion electronic nose data and HS-GC-IMS data, demonstrated high accuracy with prediction accuracies greater than 0.972 for all five species. The prediction model based solely on electronic nose data had lower accuracy, while models using HS-GC-IMS data alone or its combined with electronic nose data achieved good results. This fusion approach of HS-GC-IMS and electronic nose might have the potential to serve as a preliminary screening tool for fungi identification.
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 (R p 2 = 0.909, RMSEP = 0.202) and E-nose (R p 2 = 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 .
危险品化学品经营许可证（带存储）