Abstract
Fourier Transform Infrared (FTIR) spectroscopy coupled to chemometrics was developed to detect and quantify the adulteration in white and blue corn dough and white and blue corn tortilla, with corncob. The classification model, soft independent modeling of class analogy (SIMCA), showed 100% correct classification rate for adulterated samples from unadulterated ones. The best quantitative chemometric calibration model was developed with the partial least square (PLS) algorithm showing coefficient of determination (R2) between predicted and actual adulterant concentrations that range from 0.996 to 0.998 for all samples. Standard error of prediction (SEP) for the developed models ranged between 0.395 and 0.590 for all samples. The results showed that mid-infrared spectroscopy in conjunction with multivariate analysis can effectively be used to identify and quantify corncob in white and blue corn dough and in white and blue corn tortilla.
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