Coffee is a critical agricultural commodity to be used to produce a premium beverage to serve people worldwide. Coffee microbiome turned to be an essential tool to improve the bean quality through the natural fermentation. Therefore, understanding the microbial diversities could create the final product's better quality. This study investigated the natural microbial consortium during the wet process fermentation of coffee onsite in Thailand to characterize the microorganisms involved in correlation toward the biochemical characteristics and metabolic attributes. Roasting is another important step in developing the complex flavor/ aroma that make coffee to be enjoyable. During the roasting process, the beans undergo many complex and alternatively change in the physicochemical properties from the gained substances in the fermentation process. The changing in the formation of the substances responsible for the sensory qualities, physicochemical/ aroma attributes as well as the health benefits of the final product. Using the starter culture could also develop the distinguished characteristics of coffee (Research collaboration with Van Hart company)
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คณะวิศวกรรมศาสตร์
This research project focuses on the design and development of a Manual Control Robot using Load Cell technology to enhance precision and reduce the time required for robot control. The use of automation robots in industries still presents challenges due to the complexity of programming and control. Therefore, developing a manual control system that responds to force input in all directions can significantly improve the efficiency of robots, making them more suitable for tasks requiring precise and intricate control. The study integrates Load Cell sensors, an HX711 amplifier circuit, and an Arduino UNO R3 to develop a control module that translates user-applied forces into commands for an RV-7FRL-D industrial robotic arm. Additionally, MATLAB is utilized for processing Load Cell data to analyze and optimize the robot’s movement accuracy. The results demonstrate that the developed system effectively reduces robot setup time while simplifying and improving control flexibility. This project represents a crucial step in enhancing the capabilities of industrial robots, allowing for seamless human-robot interaction through a manual control system that directly responds to user-applied forces.
คณะอุตสาหกรรมอาหาร
In the development of high protein jasmine rice products, hydrocolloids, HPMC at 0, 0.25, 0.5 and 1% w/v and MD at 10% w/v were used. This hydrocolloid contained 30% w/v dissolved protein and was coated with raw jasmine rice. It was found that different amounts of HPMC affected the adhesion of proteins in rice. Then, the hydrocolloid with the best adhesion, 0.25% w/v, was used to find the optimum amount for coating rice at ratios of 1:3 and 1:5, which affected protein content, texture, color, water retention and sensory acceptability.
วิทยาลัยอุตสาหกรรมการบินนานาชาติ
The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.