Non-targeted fingerprinting approach for rapid quantification of mustard oil adulteration with linseed oil: An economically motivated adulteration

Mustard oil (MO) is a popular edible oil and an essential component of the Indian diet due to its characteristic flavor and pungency. Recently, Economically motivated adulteration (EMA), a type of oil fraud, becomes an evolving threat to consumers. In the present work, MO adulteration with Linseed oil (LSO) was analyzed using attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy. Based on spectral information of adulterated samples, exploratory method such as Principal component analysis (PCA) and classification method like Linear discriminant analysis (LDA) were employed that correctly classified adulterated samples, providing an accurate classification of 100 %. Furthermore, quantitative methods like Principal component regression (PCR) and partial least square regression (PLSR) were used to compare raw, first, and second derivatives of three selected optimized spectral regions to obtain the best model. The PLSR model for the first derivative of optimized spectral region II (1800 to 600 cm(-1)) showed excellent precision and accuracy ability in predicting adulterated samples with high R-2 (0.999), RPD (63.42) and low standard error values (RMSECV; 0.216 v/v, RMSEP; 0.167 v/v and RE%; 3.45). Therefore, our results showed that the optimized statistical model has the potential to rapidly detect LSO adulteration in MO as low as 0.5 % v/v.

Automated summary

The study analyzed the adulteration of mustard oil with linseed oil using ATR-FTIR spectroscopy, and found that PCA and LDA could accurately classify adulterated samples, while PCR and PLSR were able to predict adulterated samples, providing an effective method for detecting adulteration.