遗传和数学优化器在蘑菇冷冻干燥中的应用与比较
Application and comparison of genetic and mathematical optimizers for freeze-drying of mushrooms.
作者信息
Tarafdar Ayon, Shahi Navin Chandra
机构信息
1Department of Post Harvest Process and Food Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand 263145 India.
Jodhpur, India.
出版信息
J Food Sci Technol. 2018 Aug;55(8):2945-2954. doi: 10.1007/s13197-018-3212-0. Epub 2018 May 21.
The suitability of genetic algorithm as an optimization tool for freeze drying of mushroom has been explored. The optimized solution set obtained from genetic algorithm was compared to a derivative based goal attainment algorithm () to identify the better optimizer. Regression models for quality parameters of freeze dried button mushrooms were developed and models with ≥ 85% correlation were selected and compiled into an objective function for optimization. Verified optimal solutions revealed that genetic algorithm was more proficient in optimizing physical quality parameters (rehydration and shrinkage ratio) as contrary to which optimized nutritional characteristics (ascorbic acid and protein) better. The ability of genetic algorithm for optimization from the perspective of a consumer was found to be better.
研究了遗传算法作为蘑菇冷冻干燥优化工具的适用性。将遗传算法得到的优化解集与基于导数的目标达成算法进行比较，以确定更好的优化器。建立了冻干香菇品质参数的回归模型，选择相关性≥85%的模型并将其编译成目标函数进行优化。验证后的最优解表明，遗传算法在优化物理品质参数（复水率和收缩率）方面更熟练，而基于导数的目标达成算法在优化营养特性（抗坏血酸和蛋白质）方面表现更好。从消费者角度来看，遗传算法的优化能力更强。
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