Neural network modeling and principal component analysis of antibacterial activity of chitosan/AgCl-TiO2 colloid treated cotton fabric
Tóm tắt
The present work was undertaken to predict the antibacterial activity of chitosan/AgCl-TiO2 colloid treated cotton fabric with artificial neural network (ANN) using chitosan/AgCl-TiO2 concentration and curing time as predictors. Cotton fabric samples were prepared by treating with different blends of chitosan/AgCl-TiO2 colloid and varying curing time. The antibacterial activity against Staphylococcus aureus (gram positive) and Escherichia coli (gram negative) was measured in terms of % bacterial reduction (% BR). Feedforward neural network models were trained with combination of Levenberg- Marqaurdt algorithm and Bayesian regularization support incorporated in backpropagation. The 10 % cross-validation technique was also carried out to rule out any chance of over-fitting of the trained networks. Furthermore trend analysis was also performed with developed models to understand the effect of input parameters. The promising results realized with excellent coefficient of determination and acceptable mean absolute error during network training and their testing on the novel data patterns. The developed models will benefit the design and development of clean and hygienic textiles. Principal component analysis (PCA) was also performed to visualize and analyze the correlation between the variables and the trend of individual observation on two-dimensional space.
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