Application of ATR-FTIR spectroscopy along with regression modelling for the detection of adulteration of virgin coconut oil with paraffin oil. (January 2020)

• Record Type: Journal Article
• Authors: Amit, ; Jamwal, Rahul; Kumari, Shivani; Dhaulaniya, Amit S.; Balan, Biji; Singh, Dileep Kumar
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 118(2020)
• Publication Date: 2020-01
• Subjects: FTIR spectroscopy -- Adulteration -- Virgin coconut oil (VCO) -- Paraffin oil (PO) -- Chemometrics; ATR Attenuated Total Reflectance -- FTIR Fourier Transform Infrared -- GC Gas Chromatography -- LDA Linear Discriminant Analysis -- PCA Principal Component Analysis -- PCR Principal Component Regression -- PLS-R Partial Least Square Regression -- PO Paraffin Oil -- R2 coefficient of determination -- RPD Residual Predictive Deviation -- RE% Relative Prediction Error -- RMSEC Root Mean Square Error of Calibration -- RMSECV Root Mean Square Error of Cross Validation -- RMSEP Root Mean Square Error of Prediction -- SNV Standard Normal Variate -- VCO Virgin Coconut Oil; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2019.108754
• Languages: English
• ISSNs: 0023-6438

Abstract

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 R 2 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. Highlights: ATR-FTIR spectroscopy along with chemometrics was applied for the detection of PO adulteration in VCO. PCA and LDA successfully differentiated and classified PO adulterants from VCO. Outstanding R 2 (0.999), RMSEC (0.142) and RMSEP (0.162) values obtained for PLS-R. The lowest limit of detection of PO in VCO was predicted as 1% v/v. This whole technique is swift and needs no redundant sample preparation.