Catherine Rébufa

Assessing petroleum oils biodegradation by chemometric analysis of spectroscopic data.

This study was conducted to classify petroleum oils in terms of their biodegradation stage by using spectroscopic analysis associated to chemometric treatments. Principal Component Analysis (PCA) has been applied on infrared and UV fluorescence spectra of Brazilian and Pyrenean oils. For Brazil samples, the method allowed to distinguish the biodegraded oils from the non-affected ones. Pyrenean sampling including oils at different levels of biodegradation has been chosen to follow their alteration rate. PCA loadings have shown spectral regions which have differentiated oils after biodegradation whereas Simple-to-use Interactive Self-Modelling Mixture Analysis (SIMPLISMA) has permitted to obtain a repartition in terms of components families (saturated, aromatic and polar ones) characterizing chemical composition of oils at different biodegradation degrees. Results are in good agreement with conclusions of usual hydrocarbon biomarker analysis.

Prediction of Source Rock Origin by Chemometric Analysis of Fourier Transform Infrared—Attenuated Total Reflectance Spectra of Oil Petroleum: Evaluation of Aliphatic and Aromatic Fractions by Self-Modeling Mixture Analysis

This study describes a new methodology for the interpretation of Fourier transform infrared (FT-IR) attenuated total reflectance (ATR) spectra of Algerian, Brazilian, and Venezuelan crude oils. It is based on a comparative study between a chemometric treatment and the classical one, which refers to indices calculation. In fact, the combined use of FT-IR indices and principal component analysis (PCA) has led to the classification of the studied samples in terms of geographic distribution. Quantitative analysis has been successfully realized by the supervised method partial least squares (PLS), which has permitted the prediction of the locations of oils. We have also applied another mathematical processing method, simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), to evaluate the aromatic and aliphatic composition of the oils by extracting pure spectra representative of the different fractions.

Highlighting metabolic indicators of olive oil during storage by the AComDim method

Lipid oxidation during olive oil storage induces changes in the metabolite content of the oil, which can be measured using so-called quality indices. High values indicate poor quality oils that should be labeled accordingly or removed from the market. Based on quality indices measured over two years for two olive oils, the AComDim method was used to highlight the influence of five factors (olive oil type, oxygen, light, temperature and storage time) on oxidative stability during storage. To identify the significant factors, two full factorial experimental designs were built, each containing four of the five factors examined. The results showed that all five factors, as well as some two-factor interactions, were significant. Phenols and hydroperoxides were identified as being the most sensitive to these factors, and potential markers for the ageing of olive oil.

NIR spectroscopy for the quality control of Moringa oleifera (Lam.) leaf powders: Prediction of minerals, protein and moisture contents

A rapid methodology was developed to simultaneously predict water content and activity values (aw) of Moringa oleifera leaf powders (MOLP) using near infrared (NIR) signatures and experimental sorption isotherms. NIR spectra of MOLP samples (n = 181) were recorded. A Partial Least Square Regression model (PLS2) was obtained with low standard errors of prediction (SEP of 1.8% and 0.07 for water content and aw respectively). Experimental sorption isotherms obtained at 20, 30 and 40 °C showed similar profiles. This result is particularly important to use MOLP in food industry. In fact, a temperature variation of the drying process will not affect their available water content (self-life). Nutrient contents based on protein and selected minerals (Ca, Fe, K) were also predicted from PLS1 models. Protein contents were well predicted (SEP of 2.3%). This methodology allowed for an improvement in MOLP safety, quality control and traceability.