This research presented the procedural framework of developing and optimizing an artificial intelligence model for predicting the change of bread texture by different baking enhancers. Emphasis was placed on the impact of various baking enhancers on the Mixolab thermo-mechanical properties of wheat flour and consequent alterations in bread texture. The application of baking enhancers positively contributed to dough formation and stability, producing bread with a soft texture. However, a relatively low Pearson correlation coefficient was observed between a single Mixolab parameter and bread texture (r<0.59). To more accurately predict the texture of bread from the thermo-mechanical features of wheat flour with baking enhancers, five AI models (multiple linear regression, decision tree, stochastic gradient descent, random forest, and multilayer perceptron neural network) were applied, and their prediction performance was compared. The multilayer perceptron neural network model was further utilized to enhance the prediction of bread texture by mitigating overfitting risks. Finally, the hyperparameter tuning (activation function [Leaky ReLU], regularization [0.0001], and dropout [0.1]) led to enhanced model performance (R2 = 0.8109 and RMSE = 0.1096).

Dieldrin, one of the organochlorine pesticides (OCPs), induced the damages in neuroblastoma cells and DNA damages in lymphocytes. The ethanol extracts of A. sessiliflorus leaves were examined for the suppressive effects on the dieldrin-induced cell damages. Moreover, the extract was used to test whether it might inhibit the oxidative DNA damage of lymphocytes using Comet assay. The cell and DNA damage by dieldrin were suppressed in vitro upon treating A. sessiliflorus extract. This result suggests that A. sessiliflorus extract might be useful to reduce dieldrin toxicity.