Comprehensive Characterization of Oils and Fats of Six Species from the Colombian Amazon Region with Industrial Potential

Hyperspectral identification of oil adulteration using machine learning techniques

Food adulteration is a global concern, drawing attention from safety authorities due to its potential health risks. Detecting and categorizing oil adulteration is crucial for consumer safety and food industry integrity. This research explores hyperspectral imaging (HSI) analysis to identify substandard oil adulteration at different stages. Using the non-destructive HSI Specim Fx 10 system, a method for precise and easy imaging-based fraud detection and classification was proposed. The 670 oil samples, including pure (Almond, Mustard, Coconut, Olive) and adulterated (Sunflower, Castor, Liquid Paraffin), were analyzed. The Savitzky-Golay filter preprocessed the images to remove noise and smooth spectral signatures. The oils were identified using various machine learning approaches, including Support Vector Machines, Logistic Regression, Linear Discriminant Analysis, Random Forests, Decision Trees, K-Nearest Neighbors, and Naïve Bayes with Linear Discriminant Analysis excelling in identification. Performance parameters, including precision, recall, F1-score, and overall accuracy, were calculated. The proposed method achieved a validation accuracy of 100%, outperforming numerous state-of-the-art approaches. This study introduces a robust pipeline for effective oil adulteration detection, offering a significant advancement in food safety and quality control.

Novel Organogels from Mauritia flexuosa L.f and Caryodendron orinocense Karst.: A Topical Alternative

Organogels have importance for topical applications because they can be used to deliver drugs in a controlled and prolonged fashion. These are materials consisting of a three-dimensional network of organic molecules dispersed in a solvent. Recent studies have demonstrated that the solvent could be replaced by oils from non-conventional biologic sources. There is a diversity of not-explored species in the Amazon that are promising sources of vegetable oils with a promising composition. This study developed an organogel with buriti (Mauritia flexuosa L.f) and cacay (Caryodendron orinocense Karst.) oils, using cetostearyl alcohol as an organogelator due to its compatibility, stability, security, affordability, and it is readily available. The oils were characterized, and the organogels were synthesized by studying their crystal evolution and oil-binding capacity. The microstructure was evaluated with polarized light microscopy, fractal dimension, FTIR spectroscopy, XRD, and thermal and rheological analyses. It was found that the critical gelation concentration was higher for cacay oil as it possessed a higher amount of polyunsaturated triacylglycerols. The crystals of the buriti organogel had a smaller lamellar shape, a greater surface area, and physical and thermal stability; although, it presented a slower crystal evolution due to the low number of minor compounds and a greater number of saturated triacylglycerols. The polar fraction of the organogelators as well as triacylglycerol and minor polar compounds are important in forming crystallization nuclei. The study showed that Amazonian oils in crystallization processes form microstructures with differentiating physicochemical properties.