Adulteration of onion powder with cornstarch was identified by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra of 180 pure and adulterated samples (1-35 wt % starch) were collected and preprocessed to generate calibration and prediction sets. A multivariate calibration model of partial least-squares regression (PLSR) was executed on the pretreated spectra to predict the presence of starch. The PLSR model predicted adulteration with an R(p)2 of 0.98 and a standard error of prediction (SEP) of 1.18% for the FT-NIR data and an R(p)2 of 0.90 and SEP of 3.12% for the FT-IR data. Thus, the FT-NIR data were of greater predictive value than the FT-IR data. Principal component analysis on the preprocessed data identified the onion powder in terms of added starch. The first three principal component loadings and β coefficients of the PLSR model revealed starch-related absorption. These methods can be applied to rapidly detect adulteration in other spices.
Keywords: Fourier transform NIR and IR spectroscopy; adulteration; onion powder; partial least-squares regression; principal component analysis; starch.