Swift and precise detection of argemone oil adulteration in virgin coconut oil by implementing ATR-FTIR spectroscopy integrated with multivariate chemometrics and regression modelling

Abstract

Virgin coconut oil (VCO), being one of the most priced and healthy edible oil, is at great risk of mixing with cheaper oils like argemone oil (AO). Fourier transform infrared (FTIR) spectroscopy with an Attenuated total reflection (ATR) accessory was used along with multivariate chemometrics and regression modelling for the classification and evaluation of numerous concentrations of AO (0.5–30% v/v) in VCO. LDA showed a 100% correct classification for both the initial and cross-validation groups for all the subsets of AO blends. Principal components regression (PCR) and Partial least squares regression (PLS-R) calibration models were developed and compared for normal, 1st, and 2nd derivatives of the combined informational spectral domain (3010–2800 cm−1 & 1800–700 cm−1) and separate informational regions of the spectra 3010–2800 cm−1 and 1800–700 cm−1 to get the best-fitting models. PCR model for the 1st derivative and PLS-R model for the 2nd derivative spectra of the region of 1800–700 cm−1 gave exquisite outcomes of prediction with immense accuracy and precision with the highest R2 of 0.999, and the root mean square error of prediction (RMSEP) of 0.241% v/v for PCR and R2 of 0.999 and RMSEP of 0.229% v/v for PLS-R respectively. The lowest detectable limit of AO in VCO was estimated as 0.5% v/v.