Background: Aspergillus flavus is a major pollutant in moldy peanuts, and it has a large influence on the taste of food. The secondary metabolites of Aspergillus flavus, including aflatoxin B1 (AFB1) and aflatoxin B2 (AFB2), are highly toxic and can expose humans to high risk. The total mold count (TMC) is an important index to determine the contamination degree and hygiene quality of peanut.
Results: Quantitative calibration models were established based on full-band wavelengths and characteristic wavelengths, combined with chemometric methods, to explore the feasibility of the use of near-infrared spectroscopy (NIRS) for rapid detection of the TMC in peanuts. The successive projection algorithm (SPA) and elimination of uninformative variables (UVE) algorithms were used to extract the characteristic wavelengths. In comparison, the model built by original spectrum, selected with the UVE algorithm, gave the best result, with a correlation coefficient in a prediction set (RP ) of 0.9577, a root mean square error for the prediction set (RMSEP) of 0.2336 Log CFU/g, and a residual predictive deviation (RPD) of 3.5041.
Conclusions: The results showed that NIRS is a rapid, practicable method for the quantitative detection of peanut Aspergillus flavus contamination. It is a promising method for detecting moldy peanuts and increasing peanut safety. © 2019 Society of Chemical Industry.
Keywords: SPA-MLR; UVE-PLS; characteristic wavelengths; near-infrared spectroscopy; peanut; total mold count.
© 2019 Society of Chemical Industry.