There is an increasing demand for alternative pasta formulations with non-conventional flours. Understanding how these non-conventional flours affect the torque and power requirements during dough handling is critical for both product design and process optimization. There is a limited number of studies examining the pasta dough rolling process and the rheological properties, but no study was found about pasta dough made of non-conventional flours.

This study aimed at establishing correlations between the rheological properties of various non-conventional pasta doughs and the mechanical parameters observed during their processing. Ten formulations were prepared using einkorn, whole wheat, spelt, maize, and rice flours in combination with different binding agents, including egg white powder, xanthan gum, and psyllium husk powder. The flour samples were 40% hydrated to knead the dough and the binding agents were added at 1% (w.b.). Different flour–binding agent combinations were used to provide dataset for correlation analysis. Rheological measurements were performed using oscillatory frequency sweep tests and extensional-viscosity analysis to obtain key parameters such as storage modulus consistency coefficient (K_SM), complex-viscosity coefficient (K_CV), flow behavior indices (n_SM, n_CV), and extensional-viscosity (EV).

Dough samples were also processed through a household pasta roller equipped with an ammeter to measure current change that is derived to angular impulse (I_Ang), simulating biaxial deformation during rolling. Correlation analysis revealed strong positive relationships between I_Ang and K_SM (r=0.865), K_CV (r=0.855), and especially EV (r=0.987), indicating that doughs with higher structural resistance required more energy during processing. Conversely, flow behavior indices were negatively correlated (r=-0.749 for n_SM and r=-0.695 for n_CV), suggesting that pseudoplastic doughs were easier to deform.

These findings suggest that real-time torque measurements during pasta dough rolling can serve as a practical predictor (r>0.85) of rheological behaviors (K_SM, K_CV, and EV), supporting rapid screening of novel formulations for alternative pasta development.