Application of ATR-FTIR spectroscopy along with regression modelling for the detection of adulteration of virgin coconut oil with paraffin oil

Abstract

Fourier transform infrared (FTIR) spectroscopy equipped with Attenuated total reflection (ATR) was employed along with chemometrics to develop the methodology for classification and quantification of virgin coconut oil (VCO) from its adulterant paraffin oil (PO) in proportions of 1–18% v/v PO. Out of a total of 128 samples, based on spectral data of 96 samples, Principal Component Analysis (PCA) was used on the selected informative region (3000-2800 cm−1 and 1800-700 cm−1) and Linear Discriminant Analysis (LDA) was applied on selected 13 wavenumbers obtained from the loading plot. LDA accurately classified 100% of the initial groups as well as when cross-validated. For quantification, Principal Component Regression (PCR) and Partial Least Square Regression (PLS-R) calibration models were developed and compared for normal, first and second derivatives of the combined optimized region and separate optimized regions 3000-2800 cm−1 and 1800-700 cm−1 to get a robust calibration model. PLS-R model for 2nd derivative of optimized spectra (1800-700 cm−1) showed best results for prediction with high precision and accuracy (RPD: 38.17, RE%: 0.05), the high R2 value of 0.999 and low root mean square error (RMSE) of 0.16% v/v. The lowest limit of detection of PO in VCO was predicted as 1% v/v.