Detection of unexpected frauds: Screening and quantification of maleic acid in cassava starch by Fourier transform near-infrared spectroscopy

卷:227

Fu, Hai-Yan ; Li, He-Dong ; Xu, Lu ; Yin, Qiao-Bo ; Yang, Tian-Ming ; Ni, Chuang ; Cai, Chen-Bo ; Yang, Ji ; She, Yuan-Bin

South Cent Univ Nationalities

关键词: Maleic acid;    Cassava starch;    Fourier transform near-infrared;    spectroscopy (FT-NIR);    Least squares-support vector machine (LS-SVM);    One-class partial least squares (OCPLS);   

DOI  :  10.1016/j.foodchem.2017.01.061

学科分类：食品科学和技术

【 摘 要 】

Fourier transform near-infrared (FT-NIR) spectroscopy and chemometrics were adopted for the rapid analysis of a toxic additive, maleic acid (MA), which has emerged as a new extraneous adulterant in cassava starch (CS). After developing an untargeted screening method for MA detection in CS using one-class partial least squares (OCPLS), multivariate calibration models were subsequently developed using least squares support vector machine (LS-SVM) to quantitatively analyze MA. As a result, the OCPLS model using the second-order derivative (D2) spectra detected 0.6% (w/w) adulterated MA in CS, with a sensitivity of 0.954 and specificity of 0.956. The root mean squared error of prediction (RMSEP) was 0.192 (w/w, %) by using the standard normal variate (SNV) transformation LS-SVM. In conclusion, the potential of FT-NIR spectroscopy and chemometrics was demonstrated for application in rapid screening and quantitative analysis of MA in CS, which also implies that they have other promising applications for untargeted analysis. (C) 2017 Elsevier Ltd. All rights reserved.