Enhancing basil essential oil microencapsulation using pectin/casein biopolymers: Optimization through D-optimal design, controlled release modeling, and characterization.

A D-optimal design was employed to optimize the microencapsulation (MEC) of basil essential oil (BEO) within a biopolymer matrix using the complex coacervation technique. BEO microcapsules (BEO-MCs) obtained under the optimal conditions exhibited high yield and efficiency with 80.45 ± 0.01 % and 93.10 ± 0.18 %, respectively. The successful MEC of BEO with an average particle size of 4.81 ± 2.86 µm was confirmed by ATR-FTIR, X-RD, and SEM analyses. Furthermore, the thermal stability of BEO-MCs was assessed using TGA-DSC analysis, which provided valuable insights into the MC's thermal stability. Furthermore, the proposed model, with a high R2 value (0.99) and low RMSE (1.56 %), was the most suitable one among the tested models for the controlled release kinetics of the optimal BEO-MCs under simulated gastrointestinal conditions. The successful optimization of BEO MEC using biopolymers through the D-optimal design could be a promising avenue for food and pharmaceutical industries, providing new strategies for the development of effective products.

Dragonfly algorithm-support vector machine approach for prediction the optical properties of blood.

Knowledge of the optical properties of blood plays important role in medical diagnostics and therapeutic applications in laser medicine. In this paper, we present a very rapid and accurate artificial intelligent approach using Dragonfly Algorithm/Support Vector Machine models to estimate the optical properties of blood, specifically the absorption coefficient, and the scattering coefficient using key parameters such as wavelength (nm), hematocrit percentage (%), and saturation of oxygen (%), in building very highly accurate Dragonfly Algorithm-Support Vector Regression models (DA-SVR). 1000 training and testing sets were selected in the wavelength range of 250-1200 nm and the hematocrit of 0-100%. The performance of the proposed method is characterized by high accuracy indicated in the correlation coefficients (R) of 0.9994 and 0.9957 for absorption and scattering coefficients, respectively. In addition, the root mean squared error values (RMSE) of 0.972 and 2.9193, as well as low mean absolute error values (MAE) of 0.2173 and 0.2423, this result showed a strong match with the experimental data. The models can be used to accurately predict the absorption and scattering coefficients of blood, and provide a reliable reference for future studies on the optical properties of human blood.