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Highlights

A multi-layered perceptron type artificial neural network (ANN) was developed to predict the farinograph properties of wheat dough.

Abstract. Farinograph analysis serves as the standard for determining the baking quality of wheat flour. It measures the water absorption (WA), dough development time (DDT), and stability (DS) of wheat dough. These characteristics depend on wheat flour properties such as protein content, moisture, ash, and falling number. Additionally, farinograph analysis is costly, time-consuming, and requires skilled personnel. Therefore, an artificial neural network (ANN) model was developed to predict the farinograph properties of wheat dough as a function of flour properties. The models were developed using data from 192 wheat samples. Multi-layer perceptron-type feed-forward ANN models with increasing complexity were developed for each response variable, i.e., ANN-WA, ANN-DDT, and ANN-DS, and model success was evaluated via mean squared error (MSE) and correlation coefficient (r). The optimal models had two to four hidden layers, each with five to sixty neurons, and exhibited the lowest MSE and highest r values. In terms of predictive performance, the models ANN-WA and ANN-DDT (r = 0.79) demonstrated superior performance when compared with ANN-DS (r = 0.63). A feature importance analysis was conducted to provide insight on variable contributions, underscoring the significance of flour protein content in the model‘s prediction. The study explored the applicability of data-driven ANN models in predicting the rheological characteristics of dough. The developed models could serve as a decision support tool and aid millers in adjusting mill settings and bakers in modifying dough mixing based on dough rheology.