Abstract
Oil blending is increasingly utilized to improve and model the characteristics of enriched oils. This study aims to investigate the effect of blending refined sunflower oil (rich in essential omega 6 fatty acids) with cold-pressed flaxseed oil (a source of essential omega 3 fatty acids) on the fatty acid composition, quality, color, and sensory characteristics of the resulting oils. Principal component analysis (PCA) showed that the optimal fatty acid composition was achieved in the sample with 20% sunflower oil and 80% flaxseed oil (20S/80F). However, developing a new product is highly complex due to the importance of oil quality and sensory characteristics. Therefore, an Artificial Neural Network (ANN) was applied to optimize the proportions of flaxseed and sunflower oil to create an oil blend with improved nutritional, oxidative, and sensory characteristics compared to the individual oils. The ANN analysis determined the optimal composition of the oil blend to be 51.5% refined sunflower oil and 48.5% cold-pressed flaxseed oil. Sensory characteristics pose a particular challenge in optimization, as flaxseed oil, which increases essential omega 3 fatty acids, has a specific taste that is not widely favored by consumers. Nonetheless, by blending with refined sunflower oil, the resulting optimal blend (51.5% refined sunflower oil and 48.5% cold-pressed flaxseed oil) possesses pleasant sensory characteristics.
Keywords:
artificial neural network; cold-pressed flaxseed oil; oil blend; optimization; refined sunflower oil.