This research aims to investigate the adulteration of Khao Dawk Mali 105 rice based on storage age using Near-Infrared Spectroscopy (NIRS) with Fourier Transform Near-Infrared Spectroscopy (FT-NIR) in the wavenumber range of 12,500 – 4,000 cm-1 (800 – 2,500 nm). Storage duration significantly impacts the quality of cooked rice. This research is divided into two parts: 1) to investigate the feasibility of separating rice according to storage age (1, 2, and 3 years) using the best model created by an Ensemble method combined with Second Derivative, which achieved an accuracy of 96.3%. 2) To investigate adulteration based on storage age by adulterating at 0% (all 2- and 3-year-old rice), 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% (all 1-year-old rice). The best model was created using Gaussian Process Regression (GPR) combined with Smoothing + Multiplicative Scatter Correction (MSC), with coefficients of determination (r²), root mean square error of prediction (RMSEP), bias, and prediction ability (RPD) values of 0.92, 8.6%, 0.9%, and 3.6 respectively. This demonstrates that the adulteration model can be applied to separate rice by storage age (1, 2, and 3 years). Additionally, the color values of rice with different storage ages show differences in L* and b* values.
โรงงานผู้ผลิตข้าวพบปัญหาการปลอมปนของข้าวสารที่มีอายุการเก็บรักษาต่างกัน โดยทั่วไปการคัดแยกการปลอมปนจะใช้วิธีมาตรฐานโดยการหุงข้าว จากนั้นนำข้าวหุงสุกไปวัดเนื้อสัมผัสเพื่อแยกอายุของข้าว ซึ่งใช้เวลาและเป็นการทำลายตัวอย่างและเกิดความล่าช้าในการตรวจสอบคุณภาพข้าวสาร งานวิจัยนี้ใช้เทคนิคเนียร์อินฟราเรดสเปกโทรสโกปี (Near-Infrared Spectroscopy, NIRS) ในการตรวจสอบการปลอมปนของข้าวสารพันธุ์ขาวดอกมะลิ 105 (KDML 105) ที่อายุการเก็บรักษาต่างกันเพื่อแก้ไขปัญหาดังกล่าว
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.
คณะศิลปศาสตร์
"Niyom Thai" represents health-centric footwear adorned with traditional Thai patterns, embodying an innovative approach to sustainable development tailored to the current needs of local communities. These shoes utilize natural materials to mitigate fatigue and integrate safety technologies, including location tracking via a mobile application and heart rate monitoring. This addresses the aspects of convenience and well-being in both daily life and travel
คณะวิศวกรรมศาสตร์
This project focuses on the development of an automatic license plate recognition system that supports both standard and special license plates in Thailand. By utilizing Machine Learning technology, the system enhances the efficiency of license plate reading. It can process data from both images and videos. Users can register and subscribe to the service, allowing them to send data for processing through RESTful API, WebSocket, and registered IP cameras.