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The commercial sesame oil was selected as the research subject and its volatile flavor components was extracted through solvent-assisted flavor evaporation (SAFE). Then, the aroma substances of the sesame oil were semi-quantitatively analyzed based on triple quadrupole gas chromatography-mass spectrometry. Finally, the relative odor activity values (ROAV) were combined to analyze the terms of volatile flavor contributions. The results showed that a total of 107 volatile flavor components were identified in sesame oil, of which 35 substances were seem as contributed flavor (ROAV≥0. 01), including 4 sulfides, 5 phenols, 10 pyrazines, 4 furans, 3 aldehydes, 4 thiazoles, 2 pyrroles, 2 thiophenes, and 1 indole. They provided "cabbage" "fishy" "sulfur" "smoky" "popcorn" "oil" and "roasted sesame". In particular, compounds 14 flavor components with ROAV≥0. 10 were determined as key flavor components, such as acetyl pyrazine, 2-furanmethanol, 2,3-dimethyl-pyrazine, 2,3,5-trimethylpyrazine, dimethyl disulfide, dimethyl trisulphide, methyl furfuryl disulfide, methyl thiolacetate, 4-vinyl-2- methoxyphenol, 2-methoxyphenol, hexanal, 2-ethyl-6-methyl-pyrazine, 2-methyl-3-furanothiol and 3-ethyl-2,5- dimethyl-pyrazine. Using this method to determine the key volatile flavor components of sesame oil, not only could expand the flavor database of sesame oil, but also could provide an important material basis for evaluating and regulating the flavor quality of sesame oil. [ABSTRACT FROM AUTHOR]

To fully utilize the voltage regulation capacity and interaction characteristics of the Transmission and Distribution (T&D) system, a novel Modified Partially Observed Markov Decision Process (MPOMDP)‐based Reinforcement Learning (RL) scheme for Autonomous Voltage Control is proposed, which incorporates Demand Response (DR) and cooperation with the Transmission Network. The proposed scheme consists of two vital components: an MPOMDP block and a Modified Asynchronous Advantage Actor‐Critic‐based RL block. The MPOMDP block innovatively exploits the confidence interval of the observed state to make a better perception of the precise system state by introducing two new probability vectors. Then the MPOMDP block is fed into the underlying architecture of the RL block for asynchronously capturing features and optimal decision‐making, where the solving framework additionally brings in a public data buffer to realize boundary information sharing. Case studies are conducted on a modified T&D system considering N‐1 contingencies, with a training dataset from a district in Suzhou, China. Simulation results demonstrate that the proposed scheme can achieve significant voltage optimization while ensuring fast convergence speed. [ABSTRACT FROM AUTHOR]