为研究加工方式对玉筋鱼干风味的影响,实验按加工方式分为加盐煮制冷风干燥(boiling in salt solution followed by cold air drying,SCC)组和冷风干燥(cold air drying,CD)组。采用电子鼻技术、气相离子迁移谱(gas chromatography-ion mobility spectroscopy,GC-IMS)技术、氨基酸自动分析技术、高效液相色谱技术测定玉筋鱼干中的风味成分。结果表明,不同加工方式制作的玉筋鱼干在气味、滋味方面存在显著差异。电子鼻、GC-IMS技术均能区分不同工艺制作的玉筋鱼干气味,采用GC-IMS技术共分析出68 种挥发性成分,庚醛、戊醛、3-甲基丁醛对玉筋鱼干独特风味的形成有重要影响,其中3-甲基丁醛源自CD工艺,其区别于SCC工艺气味的关键物质。玉筋鱼干中的主要鲜味氨基酸是Glu,主要呈味核苷酸是肌苷酸;CD组玉筋鱼干中的鲜味氨基酸和甜味氨基酸含量占总游离氨基酸的比重高于SCC组,同时CD组滋味活性值、味精当量值均高于SCC组,所以仅采用CD工艺制作的玉筋鱼干滋味优于加盐煮制后CD工艺制作的玉筋鱼干。 相似文献
Complex diagrammatic guide signs (DGSs) are widely used. To study the influence of DGSs with different complexities on drivers’ cognition, four types 相似文献
Traditional multi-objective evolutionary algorithms treat each objective equally and search randomly in all solution spaces without using preference information. This might reduce the search efficiency and quality of solutions preferred by decision makers, especially when solving problems with complicated properties or many objectives. Three reference point based algorithms which adopt preference information in optimization progress, e.g., R-NSGA-II, r-NSGA-II and g-NSGA-II, have been shown to be effective in finding more preferred solutions in theoretical test problems. However, more efforts are needed to test their effectiveness in real-world problems. This study conducts a comparison of the above three algorithms with a standard algorithm NSGA-II on a reservoir operation problem to demonstrate their performance in improving the search efficiency and quality of preferred solutions. Under the same calculation times of the objective functions, Pareto optimal solutions of the four algorithms are used in the empirical comparison in terms of the approximation to the preferred solutions. Three performance indicators are then adopted for further comparison. Results show that R-NSGA-II and r-NSGA-II can improve the search efficiency and quality of preferred solutions. The convergence and diversity of their solutions in the concerned region are better than NSGA-II, and the closeness degree to the reference point can be increased by 42.8%, and moreover the number of preferred solutions can be increased by more than 3 times when part of objectives are preferred. By contrast, g-NSGA-II shows worse performance. This study exhibits the performance of three reference point based algorithms and provides insights in algorithm selection for multi-objective reservoir optimization problems.