为揭示云南两个品种基于六堡茶工艺加工后的茶叶的香气成分,探讨较为合适制作六堡茶的茶树品种,采用顶空固相微萃取-气相色谱-质谱联用技术(Headspace Solid-Phase Micro Extraction and Gas Chromatography Mass Spectrometry,HS-SPME-...为揭示云南两个品种基于六堡茶工艺加工后的茶叶的香气成分,探讨较为合适制作六堡茶的茶树品种,采用顶空固相微萃取-气相色谱-质谱联用技术(Headspace Solid-Phase Micro Extraction and Gas Chromatography Mass Spectrometry,HS-SPME-GC/MS),结合相对香气活度值(Relative Odor Activity Value,ROAV)法分析云南两个品种茶叶渥堆发酵的挥发性成分。结果表明,共检测出152种挥发性成分,以醇类、酯类挥发性成分种类为主,以芳樟醇、月桂烯醇、壬醛、二氢猕猴桃内酯、亚麻酸甲酯和棕榈酸甲酯相对含量较高。ROAV贡献值分析结果显示:两个品种加工过程中主要香气贡献挥发性成分有19种,两个样品显著(P<0.05)贡献的香气化合物包括苯甲醇、癸醛、壬醛、α-紫罗酮、1-辛烯-3-醇、雪松醇、芳樟醇、反式-芳樟醇氧化物(呋喃型)。壬醛、癸醛、β-紫罗兰酮等3种挥发性有机化合物对云抗10号(Y6)陈香的形成具有较大贡献,α-雪松醇、长叶蒎烯、β-柏木烯、右旋萜二烯、α-紫罗酮等5种挥发性有机化合物对黑龙潭群体种(H6)陈香透参香的形成具有较大贡献。本研究从香气成分的角度明确了云抗10号、黑龙潭群体种均适合作为六堡茶原料,其中黑龙潭群体种在感官审评、关键香气成分种类和含量都高于云抗10号,因此黑龙潭群体种相较于云抗10号更适合做六堡茶。展开更多
This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization pr...This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.展开更多
文摘为揭示云南两个品种基于六堡茶工艺加工后的茶叶的香气成分,探讨较为合适制作六堡茶的茶树品种,采用顶空固相微萃取-气相色谱-质谱联用技术(Headspace Solid-Phase Micro Extraction and Gas Chromatography Mass Spectrometry,HS-SPME-GC/MS),结合相对香气活度值(Relative Odor Activity Value,ROAV)法分析云南两个品种茶叶渥堆发酵的挥发性成分。结果表明,共检测出152种挥发性成分,以醇类、酯类挥发性成分种类为主,以芳樟醇、月桂烯醇、壬醛、二氢猕猴桃内酯、亚麻酸甲酯和棕榈酸甲酯相对含量较高。ROAV贡献值分析结果显示:两个品种加工过程中主要香气贡献挥发性成分有19种,两个样品显著(P<0.05)贡献的香气化合物包括苯甲醇、癸醛、壬醛、α-紫罗酮、1-辛烯-3-醇、雪松醇、芳樟醇、反式-芳樟醇氧化物(呋喃型)。壬醛、癸醛、β-紫罗兰酮等3种挥发性有机化合物对云抗10号(Y6)陈香的形成具有较大贡献,α-雪松醇、长叶蒎烯、β-柏木烯、右旋萜二烯、α-紫罗酮等5种挥发性有机化合物对黑龙潭群体种(H6)陈香透参香的形成具有较大贡献。本研究从香气成分的角度明确了云抗10号、黑龙潭群体种均适合作为六堡茶原料,其中黑龙潭群体种在感官审评、关键香气成分种类和含量都高于云抗10号,因此黑龙潭群体种相较于云抗10号更适合做六堡茶。
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2022JBGP003in part by the National Natural Science Foundation of China(NSFC)under Grant 62071033in part by ZTE IndustryUniversity-Institute Cooperation Funds under Grant No.IA20230217003。
文摘This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.
