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Analysis of volatile compounds and α-dicarbonyl compounds in coffee soaked with organic acids
Hyunbeen Park,Seungjun Lee,Jooyeon Park,Haeun Lee,Kwang-Geun Lee 한국식품영양과학회 2021 한국식품영양과학회 학술대회발표집 Vol.2021 No.10
In this study, volatile and α-dicarbonyl compounds (α-DCs) such as glyoxal, methylglyoxal (MGO) and diacetyl (DA) were analyzed in Arabica coffee. The Arabica coffee was soaked in 6 organic acid mixture (tartaric acid, malic acid, succinic acid, ascorbic acid, citric acid, citric acid, citric acid monohydrate) for different conditions (1, 3, 6, 12 hours). Volatile compounds were analyzed by solid phase microextraction (SPME) using gas chromatography-mass spectrometer detector (GC-MSD). α-DCs were analyzed by gas chromatography-nitrogen phosphorous detector (GC-NPD). The highest concentration of volatile compounds was detected in the sample prepared by succinic acid, 128.9% higher than the sample without soaking. The level of total α-DCs was 12.90 to 100.32 μg/mL. GO, MGO and DA ranged from 0.81 to 7.74 μg/mL, l 8.53 to 89.08 μg/mL and 0.43 to 6.74 μg/mL, respectively. Arabica coffee prepared with several organic acids has higher amounts of volatile compounds and lower α-DCs concentration than coffee without soaking (p<0.05). The results obtained in this study would be useful for increasing the flavor of Arabica coffee and reducing α-DCs.
Improved Robustness of Reinforcement Learning Based on Uncertainty and Disturbance Estimator
Jinsuk Choi,Hyunbeen Park,Jongchan Baek,Soohee Han 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
This paper proposes a method to improve the robustness of RLs based on model-free uncertainty and disturbance estimator (RL-based UDE). In the real environment, instead of using optimal trajectory and control techniques to perform complex tasks, it learns through RL and supplements robustness by using uncertainty and disturbance estimator (UDE). From UDE, the robotics system can be improved the stability by appropriately canceling the uncertainty and disturbance without efforts to obtain model information; hence the UDE can compensate for the performance degradation of RL when system is non-stationary. In addition, the performance can be improved by reducing the sensor noise from low-pass filter of UDE. It is shown through an experiment that the proposed RL-based UDE provides robustness.