Abstract: Peanut products are susceptible to changes in temperature and relative humidity (RH) during storage. Peanuts are easily infected by hazardous fungal species, producing a variety of potent mycotoxins. This study aimed to develop a method for the rapid detection of moldy peanuts. Firstly, clean and fresh peanut kernels were sterilized and inoculated individually with five common hazardous fungal species. Then, the samples were stored at 26 ℃ and 80% RH for 9 days. During this period, spectral information of the peanut samples in the wave number range of 4 000 to 600 cm-1 were collected using attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). The spectral changes of peanut samples infected with different fungal species were analyzed by loading analysis. A quantitative model to predict contamination levels of hazardous fungi in peanut samples was developed by partial least squares regression (PLSR). The results showed that the spectral alterations for the samples were clearly fluctuated during different storage periods. The PLSR model could predict the total number of colonies of single and multiple strains in fungus-infected peanut samples with good?accuracy. Especially, the model provided better prediction of Aspergillus ochraceus 3.6486 infection with a coefficient of determination for the prediction set (Rp2) of 0.915 7, a root mean-square error of cross-validation (RMSECV) of 0.208 0 (lg (CFU/g)) and a residual predictive deviation (RPD) of 2.52. The Rp2, RMSECV and RPD values of the prediction model for total fungal species were 0.780 3, 0.358 0 (lg (CFU/g)) and 1.76, respectively. These findings demonstrated that ATR-FTIR could be used as a reliable analytical method for rapid determination of fungal contamination levels in peanuts during storage.

Key words: attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), peanut kernel, hazardous fungi, rapid detection