Synergistic effect of drying methods and ultrasonication on natural deep eutectic solvent extraction of phytochemicals from African spinach (Amaranthus hybridus) stem

The study evaluated the combined effects of drying methods (air drying [AD], hot AD [HAD], microwave drying [MD], and freeze-drying [FD]) and ultrasonication parameters (sonication temperature [STemp]: 40, 50, and 60°C) and heating time (STime: 60 and 120 min) on natural deep eutectic solvent (NADES) extraction of phytochemicals from Amaranthus hybridus stem. Increasing the STemp increased the extraction yield (ECY) of the phytochemicals for all drying methods but increase in the heating time reduced the ECY slightly. MD combined with 60°C ST showed the highest ECY (53%), whereas HAD combined with 40°C ST had the lowest ECY (18%). At 60 min heating time, increasing the ST from 40 to 50°C increased the total phenolic content (TPC) in the extract for most drying methods except MD, and a sonication time of 120 min showed a slightly higher TPC, especially for MD samples. At 60 min sonication, total flavonoid content (TFC, 800 mgQE/g) was highest for AD plus 50°C ST and lowest for AD combined with 60°C (100 mgQE/g), whereas for 120 min sonication, MD and AD with 50°C showed the highest TFC (690 mgQE/g). FD retained better some of the vitamins (thiamine, riboflavin, niacin) but MD retained better vitamin C. The antioxidant capacity was not so much different among the drying methods except for FD, which showed lower values. These results provide a theoretical basis for the synergistic applications of drying and ultrasonication during NADES extraction of phytochemicals from Amaranthus hybridus.

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.