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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คณะสถาปัตยกรรม ศิลปะและการออกแบบ
This work got the idea of bringing the car culture of Thai teenagers to present in a new way through our perspective. Create characters and bring various elements within the culture to combine with what we like. Whether it's stickers, posters and band shirts with acrylic paint techniques.
คณะอุตสาหกรรมอาหาร
The Ginbanirose project aims to develop herbal extracts for alleviating menstrual pain using key ingredients: roselle, banana inflorescence, and ginger. These ingredients contain bioactive compounds with anti-inflammatory, antioxidant, and pain-relieving properties. The extracts are enhanced through liposome encapsulation technology, which improves absorption and stability. The production process involves herbal extraction, freeze-drying, and liposome formulation using lecithin and stabilizers. Experimental results demonstrate high phenolic content and antioxidant activity via the DPPH method. Ginbanirose addresses women’s quality of life concerns while offering significant business opportunities in the rapidly growing herbal market, particularly in the Asia-Pacific region.
คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.