کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5133301 | 1492065 | 2017 | 7 صفحه PDF | دانلود رایگان |
- Untargeted detection of maleic acid (MA) in cassava starch (CS) was developed.
- One-class partial least squares detected 0.6%Â (w/w) or more MA in CS.
- Taking second-order derivatives improved the specificity for untargeted detection.
- 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.
Journal: Food Chemistry - Volume 227, 15 July 2017, Pages 322-328