Detection of Starch Adulteration in Onion Powder by FT-NIR and FT-IR Spectroscopy

• Published in: Journal of agricultural and food chemistry Vol. 62; no. 38; pp. 9246 - 9251
• Main Authors: Lohumi, Santosh, Lee, Sangdae, Lee, Wang-Hee, Kim, Moon S, Mo, Changyeun, Bae, Hanhong, Cho, Byoung-Kwan
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 24.09.2014; American Chemical Society, Books and Journals Division
• Subjects: absorption; adulterated products; corn starch; Food Contamination - analysis; Fourier transform infrared spectroscopy; least squares; onions; Onions - chemistry; prediction; principal component analysis; reflectance; Spectroscopy, Fourier Transform Infrared - methods; Spectroscopy, Near-Infrared - methods; spices; starch; Starch - analysis; onion powder; starch; Fourier transform NIR and IR spectroscopy; adulteration; principal component analysis; partial least-squares regression
• ISSN: 0021-8561; 1520-5118
• DOI: 10.1021/jf500574m

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.