Most rice is consumed as cooked, milled rice, but a small portion is also ground into flour or separated into a starch fraction and used by the food industry as a gluten-free ingredient. This study aims to find out if different types of rice flour and starch, such as white and colored rice, could be used in industry. This study employs green modification techniques to slow down the digestion process by combining polyphenols with starch. Our initial study found that the raw colored rice has a lower glycemic index than other types of rice, such as brown or white rice. Another study that looked at how the quality of colored rice flour was changed by different methods also discovered that out of the six green methods (annealing, heat moisture treatment, ultrasound, pregelatinization, wet-microwave, and dry-microwave). It found that ultrasound improved the polyphenol bioaccessibility in the rice flour and reduced the digestion rate. The pregelatinization process led to the flour having high solubility and an estimated glycemic index. Different techniques affected the flour/starch quality in different ways. Therefore, for further industrial application, it could also be easier to select the method for food product based on their required techno-functional quality of flour/starch. In addition to the modification techniques, this study showed that the high bioaccessible polyphenol content and high polyphenol content in rice greatly slowed down the rate of digestion. This study also open for further exploring the possibility of using high polyphenol agricultural waste to modify starch and flour in a sustainable manner.
Rice (Oryza sativa L.) is a crucial staple crop that supplies nutritional sustenance for half of the global population (Shao et al., 2018). Moreover, rice constitutes a significant commercial crop in Thailand, with its grains serving as a staple food, and a diverse array of rice types is present throughout the nation (Suebpongsang et al., 2020). Multiple rice types have been developed in Thailand, including colored/pigmented rice and non-pigmented rice, commonly referred to as brown and white rice, which can be utilized in many industries (Yamuangmorn & Prom, 2021). Furthermore, rice is regarded as a naturally gluten-free ingredient. Rice flour or starch can be utilized to create a variety of products, including cookies, bread, noodles, and crackers. Due to the elevated carbohydrate content in rice, most rice products are regarded as having a high glycemic index (GI). The link between polyphenols and digestive behavior has become a topic of attention, particularly for rice and its products. Researchers predominantly conducted comparisons of the features of pigmented rice and non-pigmented rice (with/without eGI) across various areas or types (Tangsrianugul et al., 2019; Verma & Srivastav, 2020; Waewkum & Singthong, 2021). Nevertheless, there remains a deficiency in information regarding the association between endogenous antioxidant qualities and other starch characteristics, which might be further considered for predicting the nutritional value of rice and for enhancing the foundational knowledge necessary for the development of rice products. The versatility of rice flour in industrial applications is primarily influenced by its physicochemical properties and usefulness. Unprocessed rice has limited utility and applicability (Iqbal et al., 2023). Consequently, novel techniques are necessary to enhance the quality of rice flours for further processing. Therefore, this research may offer essential insights for further investigation into select rice varieties and modification techniques for specific industrial applications by utilizing the starch-polyphenols complex concept.
คณะวิศวกรรมศาสตร์
Currently, lithium batteries are widely used in electronic devices and electric vehicles, making the estimation of their State of Health (SOH) crucial. Accurate SOH estimation helps extend battery lifespan, reduce maintenance costs, and prevent safety issues such as overheating or explosions. This project aims to study and analyze mathematical models of batteries and develop SOH estimation techniques using Neural Networks to enhance accuracy and evaluation speed. The experiment involved collecting charge and discharge data from three lithium battery cells under controlled temperature conditions while maintaining a constant current. The current, voltage, and time data were recorded and analyzed to determine the battery capacity for each cycle. These data were then used to train a Neural Network model. The results demonstrated an effective method for predicting battery health status. The outcomes of this project can contribute to the development of a Battery Management System (BMS) that improves battery efficiency and longevity. Additionally, it provides a foundation for applying artificial intelligence techniques in the energy sector effectively.
คณะแพทยศาสตร์
This study explores the application of deep convolutional neural networks (CNNs) for accurate pill identification, addressing the limitations of traditional human-based methods. Using a dataset of 1,250 images across 10 household remedy drugs, various CNN architectures, including YOLO models, were tested under different conditions. Results showed that natural lighting was optimal for imprinted pills, while a lightbox improved detection for plain pills. The YOLOv5-tiny model demonstrated the best detection accuracy, and efficientNet_b0 achieved the highest classification performance. While the model showed strong results, its generalization is limited by sample size and drug variability. Nonetheless, this approach holds promise for enhancing medication safety and reducing errors in outpatient care.
คณะวิศวกรรมศาสตร์
The Diabetes Meal Management Application is a digital health tool designed to empower Type 2 diabetic patients in managing their diet and blood sugar levels more effectively. With features like personalized meal recommendations, nutrient tracking, and seamless integration with wearable blood glucose monitors via Blood sugar measuring device (CGM), the application enables users to monitor glucose fluctuations in real time and adjust dietary choices accordingly. Built with the Flutter framework and supported by a backend of Express.js and MongoDB, the application prioritizes a user-friendly interface, ensuring easy navigation and encouraging consistent engagement with meal planning and health tracking. Preliminary user trials show that the application contributes to more stable blood sugar levels and improved adherence to dietary recommendations, helping users reduce health risks associated with diabetes complications. By offering a proactive approach to diabetes management, the application reduces the need for frequent clinical interventions, thus potentially lowering medical costs over time. This project highlights the promising role of digital health solutions in supporting personalized diabetes care, emphasizing the potential for scalable, user-centered interventions that foster long-term health improvements for diabetic patients.