## 3 Results and Discussion
### Machine Learning  
In this study, we conducted an analysis to predict the effectiveness of identifying compounds with varying concentrations. The comparative analysis of regression models helps illustrate how the compounds in question affect their identification efficiency by determining the size of liposomes. Nowadays, concentration-dependent identification of substances is a key aspect in the pharmaceutical industry [43], especially when they can be a potential threat to the environment [44,45]. Analytical methods and artificial intelligence are becoming effective solutions for identifying compounds and drugs [19,46]. [PERSON] et al. [21] conducted the first liposome size prediction activities (output variables), obtaining an optimal determination rate [21]. [PERSON] et al. conducted a similar study on liposome prediction using machine learning methods. They also demonstrated optimal models characterized by a high coefficient of determination for the test set at 0.83, considering, in addition to liposome size, polydispersity index, zeta potential and encapsulation [47]. In this research, the concentration prediction of compounds was focused on considering only liposome size (input variables), which was analyzed so effectively by [PERSON] et al. [21].  
In the results, optimal neural models were obtained for the regression problem posed. Mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R\({}^{2}\)) were determined for each model to determine performance, insight into accuracy and how the size of liposomes fit due to the acid concentration of each compound. MSE and MAE allow the determination of the efficiency of predicting compound concentration due to liposome size. In the case of the coefficient of determination, a model fit with the highest possible score is sought [48]. Table 1 shows five adequate models that had the lowest MSE and MAE values. In Table 1, the best model was MLP 1:1-100-50-50-6:1, which predicted the efficiency of identifying the compound resveratrol (RS). The selected MLP 1:1-100-50-50-6:1 model achieved the lowest MSE value of 0.0148 for the test set. This means that this model showed high performance in identifying acid concentration to factual data informing the size of liposomes. As with the MSE, the MAE value seeks to obtain the lowest egg to determine the optimal neural network model. In Table 1, the lowest MAE value was achieved by the MLP 1:1-100-50-20-6:1 network associated with oleanolic acid (OA). The regression models (optimal R\({}^{2}\) min. 0.7) resulted in MLP 1:1-100-50-50-6:1, MLP 1:1-100-50-20-6:1, MLP 1:1-100-50-50-6:1 and MLP 1:1-200-100-50-50-6:1 networks. This  
Figure 8: DPH fluorescence intensity changes during AAPH-induced phosphatidylcholine membranes peroxidation in the presence of oleanolic acid and oleanolic acid mixed with apigenin, rutin, resveratrol and ferulic acid.  
means that four models fit the data well, namely the models with resveratrol (RS), oleanolic acid (OA), ferulic acid (FA) and rutin (RT), compared to the model identifying apigenin (AP) (R\({}^{2}\) = 0.5563). The highest rate of determination was achieved by the network identifying resveratrol (RS), for which an R\({}^{2}\) of 0.8247 was achieved in the test set.