College of Engineering, China Agricultural University, Beijing, PR China.
J Sci Food Agric. 2019 Oct;99(13):5671-5679. doi: 10.1002/jsfa.9828. Epub 2019 Jul 10.
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.
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 (R ) 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.
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.
黄曲霉是霉变花生中的主要污染物，对食品口感影响较大。黄曲霉的次级代谢产物包括黄曲霉毒素 B1（AFB1）和黄曲霉毒素 B2（AFB2），具有高毒性，会使人类面临高风险。总霉菌数（TMC）是判断花生污染程度和卫生质量的重要指标。
通过全波段波长和特征波长，结合化学计量学方法，建立了定量校准模型，探讨了近红外光谱（NIRS）快速检测花生中 TMC 的可行性。采用连续投影算法（SPA）和消除不相关变量（UVE）算法提取特征波长。相比之下，原始光谱与 UVE 算法结合选择特征波长建立的模型效果最佳，其在预测集中的相关系数（R）为 0.9577，预测集的均方根误差（RMSEP）为 0.2336 Log CFU/g，剩余预测偏差（RPD）为 3.5041。
结果表明，NIRS 是一种快速、实用的定量检测花生黄曲霉污染的方法。它是一种很有前途的检测霉变花生、提高花生安全性的方法。 © 2019 化学工业协会。
