Zero-waste management is crucial for sustainable food systems, promoting the use of agricultural by-products like rice bran. Rich in bioactive polyphenols with antioxidant and antidiabetic properties, rice bran can enhance the nutritional value of food. Polyphenols can slow starch digestion by forming complexes with starch, making them useful for creating low-glycemic foods. While ultrasonication and freeze-thaw treatments have been beneficial individually, their combined effects on starch-polyphenol complexation remain understudied. This study aimed to evaluate the impact of combining these treatments on the interaction between rice starch and red rice bran polyphenols. The dual treatment increased the complexing index, altered functional properties, and affected granule morphology. Structural analysis indicated non-covalent interactions forming non-V-type complexes. Additionally, starch digestibility was reduced, lowering the estimated glycemic index (eGI) compared to the control. These findings suggest a sustainable and green approach to starch modification, with potential for developing functional food products and advancing zero-waste processing.
The growing emphasis on zero-waste management and sustainable food systems has highlighted rice bran as a valuable yet underutilized by-product rich in bioactive polyphenols with antidiabetic properties. Meanwhile, modifying starch to reduce its glycemic response is crucial for diabetes management. Green processing techniques, such as ultrasonication and freeze-thaw treatment, offer a sustainable way to enhance starch-polyphenol complexation, slowing starch digestion naturally. This study explores the synergistic effects of these methods on rice starch-polyphenol complexes from red rice bran, evaluating their structural, functional, and digestibility properties. The findings demonstrate that dual-treated complexes lower starch digestibility and glycemic index (eGI), making them promise for functional food development. Additionally, this research supports sustainable food processing while contributing to healthier, low-glycemic food alternatives
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
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วิทยาลัยการจัดการนวัตกรรมและอุตสาหกรรม
This research aims to develop a horse treat product that meets the nutritional and health needs of horses by using natural ingredients with beneficial properties, such as oats, wheat flour, corn flour, and organic molasses. These ingredients are rich in fiber, vitamins, and essential minerals for horses, while also enhancing digestive efficiency, reducing the risk of colic, and providing an appropriate energy source. The treat is designed with a shape suitable for a horse’s chewing behavior and is infused with Thai fruit flavors, such as pineapple and ripe mango, to attract horses and make consumption easier. The production process emphasizes cleanliness and safety by selecting organic ingredients and avoiding harmful preservatives. The packaging is designed to maintain product quality for an extended period, prevent moisture, and be convenient for horse owners to use. Additionally, the treat can be used as a reward during horse training, helping to strengthen the bond between the horse and its owner while reducing equine stress. This product serves as both a health-boosting snack and an effective training tool, making it suitable for horses that require highly nutritious supplements. It also provides a new option for horse owners seeking a safe and beneficial product for their horse’s overall well-being.
คณะวิศวกรรมศาสตร์
This cooperative education project aims to enhance the efficiency of Hydrogen Manufacturing Unit 2 (HMU-2) and Pressure Swing Adsorption 3 (PSA-3) by using AVEVA Pro/II process modeling and a Machine Learning model for process simulation. The study found that the AVEVA Pro/II model predicted outcomes with deviations ranging from 0–35%, including a hydrogen flow rate deviation from the PSA unit of 12%, exceeding the company’s acceptable limit of 10%. To address this, a Machine Learning model based on the Random Forest algorithm was developed with hyperparameter tuning. The Machine Learning model demonstrated high accuracy, achieving Mean Squared Errors (MSE) of 8.48 and 0.18 for process and laboratory data, respectively, and R-squared values of 0.98 and 0.88 for the same datasets. It outperformed the AVEVA Pro/II model in predicting all variables and reduced the hydrogen flow rate deviation to 4.75% and 1.35% for production rates of 180 and 220 tons per day, respectively. Optimization using the model provided recommendations for process adjustments, increasing hydrogen production by 7.8 tons per day and generating an additional annual profit of 850,966.23 Baht.