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)
-
คณะเทคโนโลยีการเกษตร
The extreme weathers according to PM 2.5 is a global problem with out any borders. This pollutant can directly attack human health. The objective of the study was aimed to develop medicinal plant essential oil emulsions in order to use to decrease PM 2.5 based on chemical characterization of water-soluble anions and cations. A mount of 31 medicinal plant essential oil emulsions were prepared and then initially careened and tested for their efficiency in reducing PM 2.5 under test chamber by spraying method. It was found that spraying for 1 hr with kaffir lime essential oil emulsion at 0.025% concentration could reduce PM 2.5 obtained from engine exhaust pipe effectively when PM 2.5 of 24.7 µg/m3 was detected within 6 hrs, followed by kaffir lime essential oil emulsion at 0.05% and Eucalyptus essential oil emulsion at 0.05% and 0.025% concentration resulting in 27.3, 30.0 and 95.3 µg/m3, respectively. Whereas, water (blank) and control group (water and carboxymethylcellulose, CMC 0.2%) showed high revels of PM 2.5 with 126.4 and 157.3 µg/m3, respectively. This kaffir lime essential oil emulsion at 0.025% concentration showed 3-6 time decline of PM 2.5 upward 2 hrs compared with control group. Field experiment was performed at 3 Bangkok parks, namely, Suantaweewanarom, Suanbankharepirom and Suanthonbureerom. There were many factors affecting the decline of PM 2.5 caused by this essential oil emulsion, particularly, the windy as well as temperature and humidity. PM 2.5 level tended to be decreased after the beginning of spraying. In general, PM 2.5 levels appeared at those 3 parks were decreased rapidly within 1 hr as by average of 21.8 (7.7-27.3) µg/m3, Whereas, decline of only 6.4 (5.0-8.0) µg/m3 was observed in control (water). Incase of calm wind, (10-20 km/hr) this plant essential oil emulsion could even reduce PM 2.5 at 37.0-44.0 µg/m3 and reached to 13.5-16.5 µg/m3 within 3 hrs. As high level of PM 2.5 as 98.0-101.0 µg/m3 , it could reduce PM 2.5 to be an average of 23.0-26.5 µg/m3 within 3 hrs, Whereas, the use of water performed low capacity of PM 2.5 reduction found with only 31.0-40.0 µg/m3. However, windy condition (15-35 km/hr), the efficacy of this essential oil emulsion seem to be lower but tended to work better than using water alone
คณะเทคโนโลยีการเกษตร
During this cooperative education program at the Bang Bo District Agricultural Office, Samut Prakan Province, a study was conducted on the costs and returns of rice cultivation using chemical inputs compared to using biopesticides in combination with chemical inputs among farmers in Bang Phli Noi Subdistrict, Bang Bo District, Samut Prakan Province.The objectives of this study were: To examine the costs and returns of rice cultivation using chemical inputs compared to using biopesticides in combination with chemical inputs among farmers in Bang Phli Noi Subdistrict, Bang Bo District, Samut Prakan Province. To explore the challenges of using biopesticides in rice cultivation among farmers in Bang Phli Noi Subdistrict, Bang Bo District, Samut Prakan Province. The study found that in the 2024/25 growing season, the total production cost for rice cultivation using biopesticides in combination with chemical inputs was 5,099.50 THB per rai, consisting of variable costs of 4,432.50 THB per rai and fixed costs of 667.00 THB per rai. Meanwhile, the total production cost for rice cultivation using only chemical inputs was 5,129.00 THB per rai, consisting of variable costs of 4,390.00 THB per rai and fixed costs of 739.00 THB per rai. The cost difference between the two methods was 114.50 THB per rai. Regarding the returns on rice cultivation in the 2024/25 growing season, the field using biopesticides in combination with chemical inputs yielded 1,000.00 kilograms per rai, with an average selling price of 8,500.00 THB per rai. Farmers earned a total revenue of 8,585.00 THB per rai and a profit of 3,485.50 THB per rai. On the other hand, the field using only chemical inputs yielded 1,000.00 kilograms per rai, with an average selling price of 8,500.00 THB per rai. Farmers earned a total revenue of 8,500.00 THB per rai and a profit of 3,371.00 THB per rai. The total income difference between the two cultivation methods was 114.50 THB per rai. In terms of challenges related to the procurement of biopesticides, it was found that biopesticides are difficult to obtain, with limited or no availability in certain areas. Additionally, relevant agencies do not provide continuous support for biopesticides, making this the most significant issue. Regarding the use of biopesticides, the most critical challenge is that once fresh biopesticides are mixed, they must be used immediately and cannot be stored, as their effectiveness deteriorates over time.
คณะวิทยาศาสตร์
This special problem aims to compare the performance of machine learning methods in time series forecasting using lagged time periods as independent variables. The lagged periods are categorized into three groups: lagged by 10 units, lagged by 15 units, and lagged by 20 units. The study employs four machine learning methods: Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The time series data simulated as independent variables diverse including characteristics: Random Walk data, Trending data, and Non-Linear data, with sample sizes of 100, 300, 500, and 700. The research methodology involves splitting the data into 90% for training and 10% for testing. Simulations and analysis are performed using the R programming language, with 1,000 iterations conducted. The results are evaluated based on the average mean squared error (AMSE) and the average mean absolute percentage error (AMAPE) are calculated to identify the best performing method. The research findings revealed that for Random Walk data, the best performing methods are Random Forest and Support Vector Machine. For Trend data, the best performing methods are Random Forest. For Non-Linear data, the best performing methods are Support Vector Machine. When tested with real-world data, the results show that for the Euro-to-Thai Baht exchange rate, the best methods are Random Forest and Support Vector Machine. For the S&P 500 Index in USD, the best performing methods are Random Forest. For the Bank of America Corp Index in USD, the best performing methods are Support Vector Machine.