Comprehensive comparison of multiple quantitative near-infrared spectroscopy models for Aspergillus flavus contamination detection in peanut.

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Comprehensive comparison of multiple quantitative near-infrared spectroscopy models for Aspergillus flavus contamination detection in peanut.

J Sci Food Agric. 2019 May 31;:

Authors: Zhengxuan L, Xiuying T, Zhixiong S, Kefei Y, Lingjuan Z, Yanlei L

Abstract
BACKGROUND: Aspergillus flavus is a major pollutant in moldy peanuts, and it has a great 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. Total molds count (TMC) is an important index to determine the contamination degree and hygiene quality of peanut.
RESULTS: To explore the feasibility of near-infrared spectroscopy (NIRS) technique for rapid detection of the TMC in peanut, quantitative calibration models based on full-band wavelengths and characteristic wavelengths combined with chemometric methods were established. 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 UVE algorithm displayed best result with correlation coefficient in prediction set (RP ) of 0.9577, root mean squared error for the prediction set (RMSEP) of 0.2336 Log CFU/g, residual predictive deviation (RPD) of 3.5041.
CONCLUSIONS: The results showed that NIRS is a rapid practicable analysis method for peanut Aspergillus flavus contamination quantitative detection. NIRS is a promising method for detecting moldy peanut, which provides an insight to guarantee peanut food safety. This article is protected by copyright. All rights reserved.

PMID: 31150109 [PubMed – as supplied by publisher]

Source: Industry