Abstract
Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
We demonstrated that the Artificial neural networks (ANN) method had a higher regression number and a lower MSE than the data analysis (DA) model
This study confirmed that the ANN was the better approach than the DA for predicting liposome size (LZ)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ANN works as a “Universal algebraic function” that contemplates noise from experimental data [17], which can help predict the liposome size, finding patterns and relationships between the two data inputs. Comparative studies of both techniques are required to determine the one with the best performance [18]. Periodic Disturbance Mixer (PDM) is a micromixer designed for liposome proIn this work, compared This has amodel polynomial equation that estimates layer feed-forward using the FRR andnetwork, TFR [14].known as Fitnet [25], in a PDM when temperature, geometry, solvents, andwork, lipidswe arecompared constants,the and FRR andmodel. We demonstrated that the ANN method had a higher regression number and a lower MSE than the DA model
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