This research aims to optimize the production process of gamma-aminobutyric acid (GABA) in fermented pineapple juice using probiotics and acetic acid bacteria (AAB), which are microorganisms with the potential to enhance GABA levels. This process has been developed to improve the nutritional value of fermented pineapple juice and to increase the economic value of Thai pineapples, which have long suffered from low market prices. This study focuses on determining the optimal conditions for GABA production by examining factors such as sugar content, pH levels, fermentation duration, and L-glutamate concentration, as well as the co-cultivation of probiotics and acetic acid bacteria. The experiments are conducted using controlled fermentation techniques, and the bioactive components of the fermented juice are analyzed with advanced instruments such as HPLC and GC-MS. The findings of this research are expected to contribute to the development of formulations and production processes for a high-GABA pineapple-based functional beverage. This product could offer health benefits such as stress reduction, cognitive function enhancement, and relaxation while also strengthening the potential of Thailand’s fermented food and beverage industry.
การพัฒนากระบวนการผลิตกรดแกมมา-อะมิโนบิวทิริก (γ -aminobutyric acid) ในเครื่องดื่มจากน้ำสับปะรดด้วยโพรไบโอติกซึ่งเน้นการใช้แบคทีเรียกรดอะซิติกและโพรไบโอติกเพื่อเพิ่มประสิทธิภาพการผลิต GABA โดยที่มาของการวิจัยนี้เกิดจากความต้องการแก้ไขปัญหาราคาสับปะรดของไทยซึ่งมีราคาต่ำมาเป็นเวลานาน การผลิตเครื่องดื่มที่มี GABA สูง มีประโยชน์ต่อสุขภาพ เช่น ช่วยในการผ่อนคลาย ลดความวิตกกังวล และส่งเสริมการทำงานของสมอง
คณะวิศวกรรมศาสตร์
Artificial intelligence for agriculture and environment is a collection of significant models for enviromental friendly Thailand development. The models create with machine learning and deep learning by Near infrared spectroscopy research center for agricultural and food products, including: Determining the nutrient needs (N P K) of durian trees by measuring durian leaves using a non-destructive technique using artificial intelligence, Identification of combustion properties of biomass from fast-growing trees and agricultural residues using non-destructive techniques combined with artificial intelligence, and Evaluation of global warming due to biomass combustion using non-destructive techniques using artificial intelligence. The basic technology used is Near infrared Fourier transform spectroscopy technology which measurement and output display can be done quickly without chemical, no requirement for special expert, and measurement price per sample is very low. But the instrument cannot be produced in Thailand.
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.
คณะวิทยาศาสตร์
This research aims to select the location of the beverage distribution center of Thai Spirit Industry Co., Ltd. with the lowest total cost of transportation. using a mathematical model by considering the Muang districts of all 76 provinces, excluding Chachoengsao Province, where the factory is located. In the present study, four scenarios were divided: 1) when only one distribution center was required; 2) when more than one distribution center was established; 3) when it was divided into 4 regions. There can only be one distribution center in one region, and 4) when it is divided into four regions, where more than one distribution center can be established in one region. When processed with the program IBM ILOG CPLEX Optimization Studio, the results are summarized as follows: Scenario 1, when only one distribution center is assigned. The total transportation cost is 786,107.75 baht/month. Scenario 2, when more than one distribution center can be established. The total transportation cost is 252,338.98 baht/month. Scenario 3, when divided into 4 regions by requiring only one distribution center in one region. The total transportation cost is 401,499.61 baht/month. Scenario 4, when divided into 4 regions by requiring that there is more than one distribution center in each region. The total transportation cost is 258,666.22 baht/month.