ABSTRACT: 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 Rp
2 of 0.98 and a standard error of prediction (SEP) of 1.18% for the FT-NIR data and an Rp
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: starch, onion powder, adulteration, Fourier transform NIR and IR spectroscopy, partial least-squares regression,
principal component analysis