Journal of Agricultural and Food Chemistry · 2014 · 134 citations · 18 references
Natural SciencesChemistryFood ScienceFood AnalysisSpectroscopic AnalysisSpectroscopyAbsorption SpectroscopyCarbohydrate SpectroscopyInfrared SpectroscopyNear-infrared SpectroscopyOptical SpectroscopySpectroscopic Food AnalysisMultivariate CalibrationNear-infrared SensingPartial Least Squares RegressionReflectanceStarch Adulteration DetectionStarch Content AnalysisStarch Physicochemical PropertiesStarch Quality Analysis
The study applied FT‑NIR and FT‑IR spectroscopy to 180 pure and adulterated onion‑powder samples, preprocessed the reflectance spectra, and built a partial‑least‑squares regression model to predict starch content. The PLSR model achieved R² = 0.98 (SEP = 1.18 %) for FT‑NIR and R² = 0.90 (SEP = 3.12 %) for FT‑IR, showing FT‑NIR was more predictive, and the approach can rapidly detect starch adulteration in other spices.
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.
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