N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil.

The use of chemometric tools is progressing to scientific areas where analytical chemistry is present, such as food science. In analytical food evaluation, oils represent an important field, allowing the exploration of the antioxidant effects of herbs and seeds. However, traditional methodologies have some drawbacks which must be overcome, such as being time-consuming, requiring sample preparation, the use of solvents/reagents, and the generation of toxic waste. The objective of this study is to evaluate the protective effect provided by plant-based substances (directly, or as extracts), including pumpkin seeds, poppy seeds, dehydrated goji berry, and Provençal herbs, against the oxidation of antioxidant-free soybean oil. Synthetic antioxidants tert-butylhydroquinone and butylated hydroxytoluene were also considered. The evaluation was made through thermal degradation of soybean oil at different temperatures, and near-infrared spectroscopy was employed in an n-way mode, coupled with Parallel Factor Analysis (PARAFAC) to extract nontrivial information. The results for PARAFAC indicated that factor 1 shows oxidation product information, while factor 2 presents results regarding the antioxidant effect. The plant-based extract was more effective in improving the frying stability of soybean oil. It was also possible to observe that while the oxidation product concentration increased, the antioxidant concentration decreased as the temperature increased. The proposed method is shown to be a simple and fast way to obtain information on the protective effects of antioxidant additives in edible oils, and has an encouraging potential for use in other applications.

Thermal rice oil degradation evaluated by UV-Vis-NIR and PARAFAC.

Thermal rice oil degradation was evaluated by UV-Vis and NIR in three-way arrays assembled with samples, different temperatures, and the absorbance at different wavelengths by applying PARAFAC chemometric method. The loadings matrix of the mode corresponding to the samples (scores) contains the information related to the samples. The loadings on the temperature mode resemble kinetics profiles. These profiles change with the nature of the component responsible for the factor and evolve with the heating temperature. The loadings on the spectral mode reveal the antioxidants γ-oryzanol and tocopherol, and oxidation products are the components responsible for the two factors. The results achieved showed that the antioxidants concentration decreases starting at 70 °C while oxidation products start to increase more pronounced after 90 °C. The proposed method is shown to be a simple and fast way to obtain information about the oxidative stability of rice oils.

Multi-block data analysis using ComDim for the evaluation of complex samples: Characterization of edible oils.

The ComDim chemometrics method for multi-block analysis was employed to evaluate thirty-two vegetable oil samples analyzed by near infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopy, and by Gas Chromatography with flame ionization detection (GC-FID) for their fatty acids composition. This unsupervised pattern recognition method was able to extract information from the tables of results that could be presented in informative graphs showing the relationship between the samples through the scores, the predominance of information in particular tables through the saliences and the contribution of the variables in each table which were responsible for the similarities observed in the samples, through the loadings plots. It was possible to infer similarities and differences among the samples studied according to the specific absorption in the UV-Vis and NIR region, as well as their fatty acids composition. The proposed methodology demonstrates the applicability of ComDim for the characterization of samples when different variables (different techniques) describe the same samples. In this particular study, the ComDim chemometrics method was able to discriminate samples by their characteristics and compositions.