Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5133301 | Food Chemistry | 2017 | 7 Pages |
â¢Untargeted detection of maleic acid (MA) in cassava starch (CS) was developed.â¢One-class partial least squares detected 0.6% (w/w) or more MA in CS.â¢Taking second-order derivatives improved the specificity for untargeted detection.â¢Accurate calibration of MA was achieved by least-squares support vector machines.
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%Â (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 0.192Â (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.