Authentication and identification of adulterants in virgin coconut oil using ATR/FTIR in tandem with DD-SIMCA one class modeling

This study evaluates the use of Fourier transform infrared spectroscopy with attenuated total reflectance (ATR/FTIR) in tandem with data driven soft independent modeling of class analogy (DD-SIMCA) to check authenticity and monitor virgin coconut oil adulteration. By using infrared spectra of pure samples and samples adulterated with canola, corn, sunflower and soybean, one class models were developed to evaluate the authenticity and adulteration of virgin coconut oil. The proposed methodology was able to confirm the authenticity and to detect the adulteration with all tested oils in a concentration range of 10–40%. Also, it was possible to identify the four adulterants oils studied with 88–100% of sensitivity and 96–100% of specificity. The results indicated that ATR/FTIR spectroscopy in conjunction with a one-class strategy based on DD-SIMCA is a clean and fast methodology that can be easily implemented for virgin coconut oil purity control. [Display omitted] •Combination of Infrared spectroscopy and Data Driven Soft independent modeling of class analogy.•Monitoring of authenticity in virgin coconut oils.•Identification of canola, corn, sunflower and soybean oils as adulterants into virgin coconut oils.•Utilization of unknown adulterated samples in prediction set since DD-SIMCA was used as one class method.