Study on the relationship between morphological characterizations of dough crumb particles at different mixing stages and noodles quality

To overcome the existing artificial experience evaluation approaches of wheat dough crumb (DC), image processing technique combined migration learning technique has been developed as the precise quantitative on-line evaluation methodology, the core indicator with stacked shaded area (SSA) for DC image evaluation can be used for DC quality evaluation. This study used image characteristics of DC to explore the correlation between noodle qualities at different mixing stages and uniformity of particles. The results showed that the changing regularities of noodle qualities were generally consistent at different mixing stages and finally reached stability. The mean value of SSA of DC showed a highly significant positive correlation with cooking loss (r = 0.93) and a highly significant negative correlation with elasticity and hardness (r = −0.90, r = −0.71) of noodles, indicating that DC can be used to predict noodle qualities. This study is instructive for achieving automatic control of mixing process during noodles production.