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 project aims to introduce an Automated Vertical Metal Sheet Storage System. The project is aimed at teaching how to make an Automation Vertical Metal Sheet Storage System with the integration of microcontroller devices. The project is divided into two main sections, which are the structure and control systems of the Automation Vertical Metal Sheet Storage System that will be designed and drawn through a computer program and constructed using major aluminum structures upon completion of their actual sizes outlined in the programs. Also, a Microcontroller control system using GX Works 2 program from Mitsubishi PLC has been designed for this purpose where it controls up and down movements as well as sideways movement of the pallet. It also has a weighing capability along with touch screen display for displaying information about the steel plates and controlling the Automation Vertical Metal Sheet Storage System with safety light curtain that protect users safety. These tests have shown that the machine operates normally. There are few mistakes whose rates fall within those expected by humans.
คณะอุตสาหกรรมอาหาร
This study aims to investigate the encapsulation of anthocyanins in water-in-oil-in-water (W/O/W) emulsions and their spray-drying process to enhance anthocyanin stability against external factors such as light, temperature, and pH changes. The W/O/W emulsion was prepared using suitable surfactants and dried using a spray dryer at an inlet temperature of 120–140°C and an outlet temperature not lower than 80°C. The results showed that the composition ratios of water, oil, and surfactants significantly influenced the physical and chemical properties of the emulsion, as well as the encapsulation efficiency of anthocyanins. The spray-dried W/O/W emulsion demonstrated effective anthocyanin retention and improved long-term stability, making it applicable for food and health-related products.
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.