A Comparative Study between Response Surface Methodology and Genetic Algorithm in Optimization and Extraction of Leaf Protein Concentrate from Diplazium esculentum of Assam

J. Saha, S. Chakraborty, S. C. Deka
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引用次数: 4

Abstract

Fern is a seedless vascular plant that reproduces via spores and has various usefulness. This study was carried out to optimize the conditions of leaf protein concentrate extraction using ultrasound from defatted fern type Diplazium esculentum. The extraction of defatted fern protein was conducted using ultrasound. Rotatable central composite design (RCCD) of response surface methodology was used for identification of the best condition and extraction yield optimization. An attempt with genetic algorithm optimization was also carried out and revealed that optimized results were of highest desirability as compared to response surface methodology. The final optimum results, by using genetic algorithm was observed to be 21.12 min of sonication time, 56.88 °C temperature, 7.59 pH and 66.2 ml of solvent for an optimum protein yield of 33.79% where desirability value was 1.00. UHPLC analysis of the sample revealed the presence of all the essential amino acids, except tryptophan.
响应面法与遗传算法优化提取阿萨姆邦白杨叶蛋白的比较研究
蕨类植物是一种无籽维管植物,通过孢子繁殖,具有多种用途。本研究以脱脂蕨类植物双芒为原料,对超声提取叶片浓缩蛋白的工艺条件进行了优化。采用超声法提取蕨类植物脱脂蛋白。采用响应面法旋转中心复合设计(RCCD)确定最佳提取条件,优化提取得率。并尝试了遗传算法优化,结果表明,与响应面方法相比,优化结果是最理想的。通过遗传算法得到的最佳结果为:超声时间21.12 min,温度56.88 °C, pH值7.59,溶剂用量66.2 ml,最佳蛋白得率为33.79%,理想值为1.00。UHPLC分析显示,除色氨酸外,样品中所有必需氨基酸均存在。
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