Rapid detection of fumonisin B-1 and B-2 in ground corn samples using smartphone-controlled portable near-infrared spectrometry and chemometrics
文献类型： 外文期刊
作者： Shen, Guanghui 1 ; Kang, Xiaocun 1 ; Su, Jianshuo 1 ; Qiu, Jianbo 1 ; Liu, Xin 1 ; Xu, Jianhong 1 ; Shi, Jianrong 1 ; Mohamed, Sherif Ramzy 3 ;
作者机构： 1.Jiangsu Acad Agr Sci, Key Lab Agroprod Safety Risk Evaluat Nanjing,Mini, Minist Sci & Technol,Inst Food Safety & Nutr,Coll, Jiangsu Key Lab Food Qual & Safety,State Key Lab, Nanjing, Peoples R China
2.Jiangsu Univ, Sch Food & Biol Engn, Zhenjiang, Jiangsu, Peoples R China
3.Natl Res Ctr, Food Toxicol & Contaminants Dept, Giza, Egypt
关键词： Corn; Portable near-infrared spectrometer; Fumonisin; Rapid detection
期刊名称：FOOD CHEMISTRY （ 影响因子：9.231； 五年影响因子：8.795 ）
ISSN： 0308-8146
年卷期： 2022 年 384 卷
页码：
收录情况： SCI
摘要： A portable near-infrared (NIR) spectrometer coupled with chemometrics for the detection of fumonisin B-1 and B-2 (FBs) in ground corn samples was proposed in the present work. A total of 173 corn samples were collected, and their FB contents were determined by HPLC-MS/MS. Partial least squares (PLS), support vector machine (SVM) and local PLS based on global PLS score (LPLS-S) algorithms were employed to construct quantitative models. The performance of the SVM and LPLS-S was better than that of PLS, and the LPLS-S presented the lowest RMSEP (12.08 mg/kg) and the highest RPD (3.44). Partial least squares-discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA) were used to classify corn samples according to the maximum residue limit (MRL) of FBs, and the discriminant accuracy of both the PLS-DA and SVM-DA algorithms was above 86.0%. Thus, the present study provided a rapid method for monitoring FB contamination in corn samples.
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