Monitoring oxidative stability of oleogels and oils by vibrational spectroscopic techniques

Evaluating the oxidative stability of oleogels and oils during storage is essential, considering the effects that humidity, temperature and light can have on their composition, structure, and sensory properties. However, most methods for evaluating this quality parameter are laborious and use environmentally harmful chemicals. Thus, our proposal was to explore the use of non-destructive, eco-friendly, portable, fast, and effective near-infrared (NIR) and Raman spectrometers to predict the oxidative stability of oleogels and their respective oils over storage for 8 months, evaluating peroxide index and free fatty acid as quality parameters. Three types of oleogels containing 95 % (w/w) oil (sunflower, soy, olive) and 5 % (w/w) beeswax were produced. The NIR spectra evaluated over storage were pre-processed and principal component analysis was performed to observe how spectral information was affected. Classification and regression models were then performed by partial least squares discriminant analysis. PLSDA models to classify storage period yielded 83 % and 71 % accuracy using handheld and portable Raman and NIR spectra, respectively. Performance of partial least squares regression model for peroxide index and free fatty acid content achieved residual predictive deviations and coefficients of determination higher than 2.5 and 0.8, respectively, for the external validation set. Since only beeswax was used as the structuring agent, other oleogelators need to be explored to assess the robustness of the techniques. However, NIR and Raman spectroscopy are promising techniques for classifying and predicting compositional change of oleogels during storage, with greater agility and less risk to the environment than conventional techniques.