Two bio-oleogels were investigated. These materials were produced with a combination of canola and soybean oil with 4, 6, 8, and 10% of beeswax (by weight). Sensible heat storage capacity, melting parameters, and enthalpies were investigated by the differential scanning calorimetry (DSC) test. An ordinary DSC dynamic test was performed. Cycles of heating and cooling were performed, as well as tests with different heating rates. According to the results, the materials present a melting temperature between −16 to −12 °C and a total latent heat between 22.9 and 367.6 J/g. BC10 (canola oil with 10% beeswax) was the sample with the best performance, with a latent heat of 367.6 J/g and a melting temperature of −13.6 °C, demonstrating its possible use as a phase change material for cold storage.

This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R² > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17–3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.