Modeling of sunflower oil treated with lemon balm (Melissa officinalis): Artificial neural networks versus multiple linear regression

This study aimed to develop, evaluate, and compare the performance of artificial neural networks and multiple linear regression models in the estimation of phenolic profile of sunflower oil enriched by lemon balm. Total phenolic material in addition to the quality parameters (induction time and antioxidant activity) of the treated oil was compared to those of the pure sunflower oil. The oxidative stability of the product was increased by almost 7% in terms of induction time, while the phenolic profile was increased by almost 2.5 times. Moreover, the antioxidant activity of sunflower oil was enhanced by similar to 5 times over the pure oil. The values of artificial neural networks and multiple linear regression were calculated as: error rates 0.01% and 8.09%; root-mean-square error values 0.45, and 4.36; R-2 values 0.9958 and 0.6183, respectively.

Automated summary

This study compared the performance of artificial neural networks and multiple linear regression models in estimating the phenolic profile of lemon balm enriched sunflower oil. The results showed that the treated oil had increased oxidative stability and phenolic profile, along with enhanced antioxidant activity. Based on the comparison of error rates, root-mean-square errors, and R-2 values, artificial neural networks demonstrated better predictive ability.