Defatted wheat germ as source of polyphenols—Optimization of microwave-assisted extraction by RSM and ANN approach

Dough Rheological Properties, Microstructure and Bread Quality of Wheat-Germinated Bean Composite Flour

“…A possibility for improving the bread quality is to use germinated legume grains in bread making [ 3 , 4 , 5 , 6 , 7 ]. This is due to the advantages of the germination process [ 8 , 9 ], which increases the bioavailability of nutrients due to the fact that some compounds are broken down in small components that become easier to digest and to be absorbed by the human body [ 10 , 11 , 12 , 13 ]. Additionally, germination increases the amount of desirable compounds, such as phenolic ones [ 14 ], and some minerals, such as calcium, magnesium, zinc and iron [ 15 , 16 , 17 , 18 ], decrease the amount of antinutritive factors such as phytic acid that combine with minerals and result in phytates [ 19 , 20 , 21 , 22 ].…”

Organic cherry laurel (Prunus laurocerasus) vinegar enriched with bioactive compounds with ultrasound technology using artificial neural network (ANN) and response surface methodology (RSM): Antidiabetic, antihypertensive, cytotoxic activities, volatile profile and optical microstructure

“…Recently, the artificial neural network (ANN) has attracted attention as a nonlinear computational model tool used in modeling and optimization of food processes. ANN is designed as a prediction tool that can simulate various systems and has higher efficiency, accuracy, and flexibility in experimental data fitting, nonlinear correlation estimation compared to RSM (Dadgar et al, 2015;Said et al, 2020;Teslić et al, 2019;Yang et al, 2019). There is no study comparing the ANN and RSM models of ultrasound treatment of vinegar and optimization of its bioactive components.…”

Reliable prediction of n-heptane isomerization over Pt/(CrOx/ZrO2)-HMS via comparative assessment of regularization networks and surface response methodologies

“…Data-driven models including response surface methodologies (RSMs) and articial neural networks (ANNs) have drawn much attention in the catalytic systems. 27 These methods have been identied to perform remarkably well in elucidating the underlying mechanism of the input parameters without prior information on the nature of the problem. 28 In 2016, Elfghi has examined the evaluation capabilities of RSM and ANN to determine the research octane number (RON) of reformate yielded from the naphtha catalytic unit.…”