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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คณะวิทยาศาสตร์
Air pollution, particularly PM2.5, is a major environmental and public health concern in Bangkok. Instead of predicting PM2.5 levels, this project aims to identify the most significant factors influencing PM2.5 concentration. By analyzing historical air quality, weather, and other environmental data, we will determine which variables—such as temperature, humidity, wind speed, or other pollutants—have the greatest impact on PM2.5 fluctuations.
คณะบริหารธุรกิจ
This project aims to develop seafood dipping sauce and Jaew sauce in solid cube form to address the limitations of liquid sauces, which can be difficult to carry and prone to spillage, as well as powdered sauces, which may lose their texture and authentic flavor. The research and development process focuses on utilizing distinct ingredients and innovative production techniques to enhance the quality and functionality of the product. The primary objective of this project is to introduce an innovative solution that improves the convenience of consumption and transportation while preserving the original taste and quality of traditional dipping sauces. The expected outcome is a novel dipping sauce product in solid cube form that is easy to carry, minimizes the risk of spillage, and holds potential for commercial development in the food industry.
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future