Application of Fourier Transform Near-Infrared (FT-NIR) spectroscopy for detection of adulteration in palm sugar

Abstract

The aim of this research was to detect adulteration in palm sugar by coconut sugar using FT-NIR spectroscopy with two chemometric methods namely partial least squares regression (PLSR) and principal component analysis (PCA). The absorbance spectra were taken using the FT-NIRFlex-500 Solid. Several spectral pre-processing methods used were the 1st Savitzky-Golay Derivative, Normalization, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Baseline. Coconut sugar as adulterant with various concentration ranging from 0 to 100% were added to the palm sugar. A total of 77 spectra of pure and adulterated palm sugar samples were divided into two groups in which 51 samples used for developing calibration model and 26 samples used for developing prediction model. The spectral obtained were pre-processed and analyzed using The Unscrambler X version 10.4. The best transformation of PLSR was MSC with coefficient of determination (Rc2) of 0.93 and the root mean square error (RMSE) of 0.07 for calibration. By using prediction data sets, the model resulted in coefficient of determination of prediction (Rp2) of 0.91 and a root mean square error of prediction (RMSEP) of 0.09. Based on this result, FT-NIR spectroscopy combined with chemometrics is a promising method in food authentication.