Application of the advanced oxidative process on the degradation of the green leaf and purple açaí food dyes with kinetic monitoring and artificial neural network modelling

Abstract

The study evaluated the advanced oxidative processes concerning the degradation of green leaf and purple açaí dyes, as well as the prediction of data through artificial neural networks (ANNs). It was verified that percentage of degradation on the wavelengths (λ) of 215, 248, 523 and 627 nm was 5.95, 49.99, 98.17 and 95.99%, respectively, when UV/H2O2 action and UV-C radiation was applied. A non-linear kinetic model proposed by Chan and Chu presented a good fit to the experimental data, reaching an R2 value between 0.978 and 0.999, for the studied λ. Within the ANN simulations through Statistica 6.0, the multilayer perceptron (MLP) (3-9-4) presented a better fit to the experimental data. However, higher values of R2 were obtained when utilizing the sklearn package with Python language and an MLP (4-5-4) model. Assays with Staphylococcus aureus and Staphylococcus pyogenes bacteria isolates were performed and it was verified that after employing the UV/H2O2 process, there was a decrease in the toxicity of the solution of dyes. In evaluating S. aureus toxicity, normal growth was observed. However, for S. pyogenes bacteria, it was found that when using the UV/H2O2 process, toxicity was evidenced at post-treatment solution concentrations of 100, 70 and 50%.