Simultaneous Concurrent Assessment of Extra Virgin Olive Oil Adulteration via Fourier Transform Mid-Infrared and UV-Visible Spectroscopy Combined with Partial Least Squares Regression

Abstract

Adulteration of olive oil is a common practice in the industry, where old and commercial oils are mixed with fresh olive oils. Adulteration can negatively affect the quality and authenticity of olive oil, leading to economic fraud and health concerns. Therefore, identifying and quantifying adulteration in olive oil is crucial for ensuring product quality and consumer protection. The objective of this study was to identify and measure the adulteration of fresh olive oils with old oil and commercial oil from the previous harvest year. The study aimed to achieve this goal using spectroscopic techniques in combination with chemometrics. Different spectroscopic techniques, such as FT-MIR and UV-vis spectroscopy, were utilized in this study. Partial least squares (PLS) regression was applied to predict the levels of adulteration in the samples with varying concentrations (0.84 - 52.13 % w/w). Various pre-treatment methods were employed for both FTMIR and UV-Vis spectral data. All the PLS models generated for FT-MIR and UV-Vis spectral data were successful in predicting the levels of adulteration, with high coefficients of determination for both calibration (0.963 - 0.995) and cross validation (0.935 - 0.993) models. The error values for calibration (0.621 % - 2.728 %) and cross validation (0.730 % - 3.314 %) were also low. Based on the results, it was found that the use of second derivative preprocessing for FT-MIR data and SNV preprocessing for UV-Vis data led to the best performance results in quantifying the level of adulteration of olive oil. Spectroscopic techniques in combination with chemometrics can be used to identify and measure the adulteration of olive oil.