Abstract
Near-infrared reflectance (NIR) spectroscopy combined with chemometric techniques was developed for classification and quantification of cheaper starches (corn and wheat starch) in ultrafine granular powder of Shanyao (UGPSY). By performing orthogonal partial least squares discrimination analysis (OPLS-DA), NIR could efficiently distinguish among authentic UGPSY and UGPSY adulterated with cornstarch and wheat starch. In addition, the starch content in adulterated UGPSY was determined by NIR coupled with an appropriate multivariate calibration method. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were performed comparatively to calibrate the regression model. Experimental results showed that the performance of the siPLS model is the best compared to PLS and iPLS. These results show that the combination of NIR spectroscopy and chemometric methods offers a simple, fast and reliable method for the classification and quantification of the ultrafine granular powder of the herb.
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