Rapid and non-destructive approach for the detection of fried mustard oil adulteration in pure mustard oil via ATR-FTIR spectroscopy-chemometrics

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometrics was effectively applied for the rapid detection and accurate quantification of fried mustard oil (FMO) adulteration in pure mustard oil (PMO). PMO was adulterated with FMO in the range of 0.5-50% v/v. Principal component analysis (PCA) elucidated the studied adulteration using two components with an explained variance of 97%. The linear discriminant analysis (LDA) was adopted to classify the adulterated PMO samples with FMO. LDA model showed 100% accuracy initially, as well as when cross-validated. To enhance the overall quality of models, characteristic spectral regions were optimized, and principal component regression (PCR) and partial least square regression (PLS-R) models were constructed with high accuracy and precision. PLS-R model for the 2nd derivative of the optimized spectral region 1260-1080 cm(-1) showed best results for prediction sample sets in terms of high R-2 and residual predictive deviation (RPD) value of 0.999 and 31.91 with low root mean square error (RMSE) and relative prediction error (RE %) of 0.53% v/v and 3.37% respectively. Thus, the suggested method can detect up to 0.5% v/v of adulterated FMO in PMO in a short time interval. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The ATR-FTIR spectroscopy combined with chemometrics was used to detect and quantify adulterated FMO in PMO, achieving a high accuracy. PCA and LDA were employed for data analysis and classification, while PLS-R models were developed to predict the presence of FMO in PMO with high accuracy.