This study aimed to classify the stages of oat dough mixing, analyze the mechanisms by which oat dough flocs regulate product quality at each stage, and develop a model to identify these stages, so as to provide a theoretical basis and technical support for enhancing the automation of oat flour product processing.

Oat dough flocs images during oat flour mixing were used as the dataset. Morphological information was first extracted from these images to classify the stages of oat dough mixing. Subsequently, the regulatory mechanisms of dough floc properties on product quality at each stage were elucidated, focusing on gelatinization degree, amylose content, intermolecular forces, textural properties, rheological properties and moisture distribution of dough flocs. Finally, a simple and efficient prediction model for oat dough mixing stages was developed by combining a convolutional neural network (ResNet-50) with a support vector machine (SVM).

Based on changes in the image shadow area combined with cluster analysis, the oat dough mixing process was divided into four distinct stages: water absorption and adhesion, agglomeration into a mass, dynamic equilibrium, and rupture followed by dispersion. Three key trends were observed during the mixing process: first, the gelatinization degree of oat dough flocs gradually increased and stabilized at the dynamic equilibrium stage, while the amylose content decreased gradually and stabilized at the same stage; second, the effects of disulfide bonds, hydrogen bonds, ionic bonds, and hydrophobic interactions progressively enhanced; third, textural analysis showed that from the water absorption and adhesion stage to the dynamic equilibrium stage, the hardness, chewiness, and elasticity of oat dough flocs all increased and reached their maximum values, whereas hardness began to decrease at the rupture and dispersion stage. Additionally, the K value of rheological properties showed an upward trend from the water absorption and adhesion stage to the dynamic equilibrium stage, indicating improved dough strength and stability of the oat dough flocs. Low-field nuclear magnetic resonance (LF-NMR) analysis further revealed that moisture migrated from the free state to the bound state (A_22-1, and A_22-2) and finally reached a stable state at the dynamic equilibrium stage. Notably, the ResNet-50-SVM-based prediction model for oat dough floc image stages achieved a recognition accuracy of 90%.

The oat dough mixing process could be divided into four stages based on the shadow area of dough floc images, with significant variations in dough floc quality across these stages. Specifically, at the dynamic equilibrium stage, particle uniformity and processability of oat dough flocs were optimized, making this stage the ideal processing window for oat noodle production. The established model enabled reliable identification and classification of dough mixing stages, providing the methodological and technical support for the automated processing of oat noodle products.

This study explored the differences and causes of oat lipase activity of different varieties. Providing a theoretical basis for screening varieties with low lipase activity and achieving stable enzyme inactivation effect of oat products.

Six main varieties of three main oat planting regions were selected for the study, and their lipase activity, nutritional indexes, physical traits, and agronomic indexes were measured. To answer the differences in lipase activity of oat varieties, the indicators significantly related to oat lipase were screened by correlation analysis. Through cluster analysis, classified multiple oat samples by lipase activity. Transform data having correlations into composite variables for statistical analysis by principal component analysis. To derive a predictive model for lipase activity, an analytical method combining gray correlation and multiple stepwise regression was used. The indicators correlating with lipase activity were used as independent variables, and the lipase activity was used as dependent variables for quantitative model fitting.

Lipase activity was significantly positively correlated with crude fat content (r=0.32, P<0.05), and the various trends of fat content, unsaturated fatty acid content, lipase activity, and acid value were consistent. Lipase activity was significantly positively correlated with crude protein content (r=0.46, P<0.01), and the higher lipase activity was, the higher percentage of electrophoretic bands located in 31-43 kD were. It was significantly negatively correlated with grain test weight (r=-0.71, P<0.01) and positively associated with growth period (r=0.37, P<0.01). Baiyan 18 and Diyan 1 had low lipase activity and high nutrition varieties according to grey relational analysis, and the relevance value with ideal variety X₀ were 0.951 and 0.883, respectively. Stepwise regression analysis only retained the test weight and protein content as independent variables. The prediction model of lipase activity was established as Y=720.274-2.255×test weight (g·L^-1)+75.761× protein content (%), P<0.01, R²=0.658.

The varieties had significant effects on oat lipase activity. Protein content, fat content, test weight, and growth period were the main influencing factors of oat lipase activity. Grey relational analysis combined with stepwise regression analysis could be used to comprehensively evaluate oat varieties effectively and quickly select varieties with low lipase activity.

This study aimed to explore the effects of oat bran flour and Tartary buckwheat bran flour addition on the structure, quality and digestive characteristics of wheat noodles, so as to confirm their application values in the production of wheat-based food products.

Three types of noodles were prepared with three recombinant flours containing 30% oat bran flour + 70% wheat flour, 30% Tartary buckwheat bran flour + 70% wheat flour, and 15% oat bran flour + 15% Tartary buckwheat bran flour + 70% wheat flour, respectively, with pure wheat noodle as control. The cooking quality, sensory quality and digestive characteristics of different noodles were determined and compared firstly. Then the structural characteristics of protein and starch in noodles were analyzed and compared by Fourier infrared spectrometer, X-ray diffractometer, Size-exclusion high performance liquid chromatography, Scanning electron microscope and Laser confocal scanning microscope, respectively.

The introduction of oat bran flour and Tartary buckwheat bran flour weakened the cooking and sensory quality of noodles, but improved the starch digestion resistance of noodles. The noodle containing 30% oat bran flour showed the lowest glycemic index (GI) of 47.9 among all the noodles. Protein denaturation and reconstruction during noodle cooking facilitated the binding between protein and other molecules higher proportion α-helix and lower proportion β-sheets was found in the protein of cooked oat bran noodles, which led to the transformation of protein aggregates into a loose and extended protein network that enforced the wrapping effect of protein to starch. After cooking, the starch crystal in noodles was destroyed, leading to the reduced orderliness. The addition of oat bran flour and buckwheat bran flour promoted the starch-lipid interactions in noodles, which contributed to the formation of resistant starch. Compared with buckwheat bran four, oat bran flour was more beneficial to maintaining the short-range order of starch and promoting the formation of more V-type crystals in noodle. In addition, the high content of β-glucan in oat bran flour further contributed to the interactions between starch and other molecules.

The addition of oat bran flour and Tartary buckwheat bran flour decreased the cooking and sensory quality of wheat noodle, but reduced the GI value of the noodle. The noodles containing 30% oat bran flour was a low GI food. The addition of oat bran flour and buckwheat bran flour in noodles contributed to the formation of resistant starch deriving from complex starch-protein and starch-lipid interactions during starch gelatinization. Oat bran flour was conductive to maintaining the thermal stability of protein network and improving the digestion resistance of starch in noodle by enhancing the interactions between starch and other molecules.