This study aimed to develop a formula and production process for snacks made from germinated brown rice flour and banana flour using the extrusion process. The results indicated that both germinated brown rice flour and banana flour could be effectively used as the main raw materials for snack production via extrusion. The proportion of flour in the formula and production conditions, such as moisture content of the raw materials, barrel temperature, and screw speed, significantly influenced the nutritional value, bioactive compound levels, and antioxidant activity of the final products.
ขนมขบเคี้ยวส่วนใหญ่ทำจากแป้งและไขมัน ทำให้มีแคลอรี่สูง ในขณะที่ปริมาณโปรตีน แร่ธาตุ และสารออกฤทธิ์ทางชีวภาพค่อนข้างต่ำ การบริโภคขนมขบเคี้ยวอย่างต่อเนื่องและมากเกินไปอาจส่งผลกระทบต่อสุขภาพ ดังนั้น การพัฒนาผลิตภัณฑ์ขนมขบเคี้ยวให้มีคุณค่าทางโภชนาการสูงขึ้นจึงเป็นเรื่องสำคัญ โดยสามารถทำได้ผ่านการปรับสูตรส่วนผสมโดยใช้วัตถุดิบที่มีคุณค่าทางโภชนาการสูง และการพัฒนากระบวนการผลิตที่ไม่ต้องทอด เช่น การใช้กระบวนการเอกซ์ทรูชัน การศึกษาในครั้งนี้จึงมุ่งพัฒนาผลิตภัณฑ์ขนมขบเคี้ยวจากแป้งข้าวกล้องงอกและแป้งกล้วยน้ำว้า ซึ่งมีคุณค่าทางโภชนาการและสารออกฤทธิ์ทางชีวภาพโดดเด่น พร้อมทั้งศึกษาสภาวะที่เหมาะสมของกระบวนการเอกซ์ทรูชันเพื่อให้ผลิตภัณฑ์มีคุณภาพและเป็นประโยชน์ต่อสุขภาพ ตรงตามความต้องการของผู้บริโภคในปัจจุบัน
คณะวิศวกรรมศาสตร์
Nowadays, rail transportation has a significant impact on people's lives and economic growth. Consequently, the number of rail systems being built around our country has dramatically increased. This process causes various types of pollution, such as noise and rail-way vibration, which can badly affect the life of citizens who live nearby. The most popular way to solve this problem recently is to decrease the noise from the sound source or to adjust the vibration by attaching a Track Damper to the railway. This technique is being used in many countries especially in Europe and Australia because it is cheap and has high efficiency. The key piece called Track Dampers are made by AUT company’s Thailand for a period of time. The company produces Track Dampers for the owner of the technology so as to sell more than 300,000 pieces of it overseas. Furthermore, the demand of Track Dampers grows as the railway systems expand. Unfortunately, the imported synthetic materials, which are used to create Track Dampers, are made from environmentally unfriendly sources. As a result, this research aims to develop the product to be environmentally-safe by replacing some imported materials with Thai’s local content; which are natural rubber and rubber crumbs. Furthermore, the product will be added value by mounting with embedded sensors for real-time monitoring of track vibration, noise, and rail temperature. All embedded devices developed will sense, collect, and automatically send to cloud by wireless technology platform. The AI and IOT platform will also be developed for safety, security, and maintenance proposed of railway track system. However, in conducting research, there will be close collaboration with AUT company through design, production, and testing. The outcome of this research is to upgrade AUT company from tier 2 manufacturer (TRL 8-9) to tier 1 manufacturer (TRL 7-8) which will be served the Thailand competitiveness enhancing strategic goal.
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.
คณะวิทยาศาสตร์
This project aims to investigate and develop an energy storage system utilizing solar energy sources through the integration of solar cell technology and Graphene Quantum Dot Battery, representing a novel approach to enhancing energy storage efficiency and prolonging the lifespan of renewable energy systems. The selection of graphene and quantum dots as materials for battery development is attributed to their exceptional properties, including high electrical conductivity, charge storage capacity, efficient energy transfer, and enhanced stability.