This study identified the optimal enzymatic treatment for improving the foaming characteristics of oat globulin, and alkaline protease was found to be the most effective enzyme. The impact of alkaline protease on the foaming properties and structural changes in oat globulin was explored. The results show that the foaming capacity of oat globulin hydrolysates is negatively correlated with surface hydrophobicity and positively correlated with the degree of hydrolysis. The results of circular dichroism (CD) and size-exclusion chromatography (SEC) indicate that hydrolysis generated smaller, disordered peptides. Under equilibrium conditions at a 2% concentration, a reduction of 1.62 mN/m in surface tension and an increase of 3.82 μm in foam film thickness were observed. These peptides reduce surface tension between air and water, forming larger, thicker, and more stable foams. Compared to untreated oat globulin, the foaming capacity of hydrolyzed ones increased by 87.17%. Under comparable conditions, these findings demonstrate that limited hydrolyzed oat globulin exhibits potential as an effective plant-based foaming agent up to a degree of hydrolysis of 15.06%.

Matsutake mushrooms, known for their high value, present challenges due to their seasonal availability, difficulties in harvesting, and short shelf life, making it crucial to extend their post-harvest preservation period. In this study, we developed three quality predictive models of Matsutake mushrooms using three different methods. The quality changes of Matsutake mushrooms were experimentally analyzed under two cases (case A: Temperature control and sealing measures; case B: Alteration of gas composition) with various parameters including the hardness, color, odor, pH, soluble solids content (SSC), and moisture content (MC) collected as indicators of quality changes throughout the storage period. Prediction models for Matsutake mushroom quality were developed using three different methods based on the collected data: multiple linear regression (MLR), support vector regression (SVR), and an artificial neural network (ANN). The comparative results reveal that the ANN outperforms MLR and SVR as the optimal model for predicting Matsutake mushroom quality indicators. To further enhance the ANN model’s performance, optimization techniques such as the Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient backpropagation algorithm techniques were employed. The optimized ANN model achieved impressive results, with an R-Square value of 0.988 and an MSE of 0.099 under case A, and an R-Square of 0.981 and an MSE of 0.164 under case B. These findings provide valuable insights for the development of new preservation methods, contributing to the assurance of a high-quality supply of Matsutake mushrooms in the market.

Tibetan butter and Indian ghee are both fat products derived from cow’s milk or other dairy products that are rich in nutrients. Although both Tibetan butter and Indian ghee are primarily produced by filtering, heating, separating, cooling, and molding, there are differences in their production processes. Tibetan butter is produced in a process similar to that of butter, while Indian ghee is clarified butter obtained by further extraction based on the obtained butter. Both types of ghee are susceptible to adulteration; Indian ghee is primarily adulterated with vegetable oils, animal fats, and other fats or non-fats, while Tibetan butter is typically adulterated with animal body fat and non-fats, including mashed potatoes. There are numerous research reports on the detection techniques for adulteration in Indian ghee, while there are very few reports on the detection technology for adulteration of Tibetan butter. Studies have shown that techniques such as gas chromatography (GC), Fourier-transform infrared spectroscopy (FTIR), and electronic nose (E-nose), either individually or in combination, are efficient in distinguishing adulterated Indian ghee. These findings could serve as a reference for the detection of adulteration in Tibetan butter in the future.

Drying is an important preservation method of casein. Traditional natural draining and drying processes have low efficiency, long processing time, and poor product quality, which urgently need to be improved. This study investigated the effects of pre-dehydration intensities (30 N 30 min (PreD1) and 50 N 30 min (PreD2)) and drying methods (including pulsed vacuum drying (PVD), infrared drying (IRD), and hot air drying (HAD)) on the drying kinetics, drying modeling, and quality of yak milk casein. These findings reveal that PreD2 and PVD both had a positive impact on shortening the drying time. Compared to other combined treatments, PreD2-PVD had the shortest drying time of 6 h. The Midilli–Kucuk mathematical model effectively predicted the drying of casein. The yak milk casein powder treated with PreD2-PVD possessed a higher content of gross compositions, superior color, lower levels of fat oxidation and 5-hydroxymethylfurfural (5-HMF), and higher emulsifying activity index (EAI) and emulsion stability index (ESI) values. Overall, combining pre-dehydration with PVD proved effective in improving the drying rate and maintaining a good quality of yak milk casein, showing promising potential for industrial applications.

Drying significantly influences the quality of micellar casein (MC) powder. This study investigated the effects of three pre-freezing temperatures (−20 °C, −80 °C, and liquid nitrogen) prior to freeze drying on the structure and rennet-induced gelation properties of MC powder. The results showed that as the pretreatment temperature decreased, the degree of disruption to the secondary and tertiary protein structures was reduced, while the particle size gradually increased. In terms of rennet-induced gel properties, the untreated raw MC consistently outperformed all MC powder samples. Among the MC powders, the sample pre-frozen at −80 °C and then freeze-dried (FD-80) exhibited the highest gel strength and a relatively shorter rennet coagulation time. The observed microstructures of the rennet-induced gel were consistent with the rheological results, showing that samples with smaller particle sizes formed more regular and compact gel networks. In conclusion, the MC powder prepared via pre-freezing at −80 °C and then freeze-drying better preserved protein structure and demonstrated superior rennet-induced gel properties, which were closely related to particle size. This study provides theoretical insights for the application of MC powder in products such as cheese, processed cheese, and protein-fortified foods.