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
คณะเทคโนโลยีการเกษตร
This study investigated the effects of seed priming with Chaetomorpha sp. seaweed extract on seed germination and seedling growth of chili pepper. The objective was to examine the influence of seaweed extract concentrations on seed germination and seedling development. Seeds were primed in different concentrations of Chaetomorpha sp. extract, compared with a control treatment. The experiment was conducted using a completely randomized design with four replications. Results showed that seed priming with seaweed extract enhanced seed germination characteristics. Primed seeds exhibited improved germination percentage, germination index, and germination rate compared to the control. Additionally, seedlings from primed seeds showed enhanced root and shoot development. This study demonstrates the potential of Chaetomorpha sp. extract as a promising seed priming agent for improving chili pepper seed quality, which can be applied in the production of high-quality chili pepper seedlings.
คณะวิทยาศาสตร์
In a highly competitive business, understanding customers is crucial for an organization to determine its success. Effective marketing is not just about offering good products, promotions, or services; it also requires strategies to reach and build strong relationships with customer groups. Segmenting customers is one method that helps businesses deeply understand the needs and behaviors of the customers who use their services In this internship, the objective is to understand the behavior of customers purchasing coffee and tea at a large cafe group by analyzing stored customer data. As a result of this process, customer groups purchasing coffee and tea were segmented using Naive Bayes, Random Forest, and Deep Learning techniques to compare the accuracy and suitability of different Machine Learning methods, and the insights gained from this analysis can be for further development in analyzing other data set in the future
คณะวิทยาศาสตร์
Currently, climate change and human activities are causing rapid deterioration of coral reefs worldwide. Monitoring coral health is essential for marine ecosystem conservation. This project focuses on developing an Artificial Intelligence (AI) model to classify coral health into four categories: Healthy, Bleached, Pale, and Dead using Deep Learning techniques. With pre-trained convolutional neural network (CNN) for image classification. To improve accuracy and mitigate overfitting, 5-fold Cross-Validation is employed during training, and the best-performing model is saved. The results of this project can be applied to monitor coral reef conditions and assist marine scientists in analyzing coral health more efficiently and accurately. This contributes to better conservation planning for marine ecosystems in the future.