Abstract
Rapid detection of Aspergillus spp. contamination levels in peanuts was investigated by near infrared (NIR) spectroscopy and electronic nose (E-nose) in this study. Sterilized peanuts artificially inoculated with five Aspergillus strains were stored for 9 days until moldy. NIR spectroscopy and E-nose response characteristics of peanuts were collected at different storage stages (day 0, 3, 6 and 9). Linear discriminant analysis (LDA) was applied to classify samples according to infection level and achieved correct classified rate of 92.11% and 86.84% in prediction by NIR spectroscopy and E-nose, respectively. Moreover, only 4.41% moldy samples were misclassified as acceptable ones by NIR spectroscopy. Quantification of colony counts in peanuts was accomplished using partial least squares regression (PLSR) and good prediction results were achieved by NIR spectroscopy (rp2 = 0.886, RPD = 3.0, LOD = 0.578 log CFU/g) and E-nose (rp2 = 0.785, RPD = 2.1, LOD = 0.808 log CFU/g). The results demonstrate that both NIR spectroscopy and E-nose methods may offer the feasibility for detection of fungal contamination levels in peanuts. Peanut samples naturally contaminated with various fungus should be incorporated in the future to further verify their ability.
Published Version
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