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A new multivariate approach for the optimisation of the simultaneous distillation-extraction technique for free fatty acids using a face centred cube experimental design: application to Parmigiano-Reggiano cheese

机译:利用面心立方实验设计优化游离脂肪酸的同时蒸馏-萃取技术的新多元方法:在帕马森奶酪中的应用

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A face centred cube (FCC) experimental design was used to investigate effects of extraction time, solvent temperature and sample temperature on the simultaneous distillation-extraction (SDE) extraction yields of 13 fatty acids from Parmigiano-Reggiano cheese, and to find the best extraction conditions for these compounds. Each factor was tested at three levels; 20 experiments were carried out, each producing 13 responses. In order to create a mathematical model representing the relationships between factors and responses and to determine the best conditions for the SDE procedure, GC peak areas were processed by using the multiple regression analysis. The backward stepwise selection of the regression coefficients led to the identification of seven groups of compounds having similar behaviour with respect to the three variables investigated. The same set of models was found by performing principal component analysis (PCA) on the t-values of the regression coefficients of the unrefined polynomials and cluster analysis on the scores of the 13 acids obtained by PCA. The best experimental conditions were evaluated for each compound. The use of the FCC experimental design allowed to evaluate not only the main effects of the variables analysed, but also curvature effects and interaction effects among them. (C) 1999 Elsevier Science B.V. All rights reserved. [References: 23]
机译:使用面心立方(FCC)实验设计来研究提取时间,溶剂温度和样品温度对帕马森奶酪中13种脂肪酸的同时蒸馏提取(SDE)提取产率的影响,并寻找最佳提取方法这些化合物的条件。每个因素都在三个级别上进行了测试;进行了20个实验,每个实验产生13个响应。为了创建表示因子与响应之间关系的数学模型并确定SDE程序的最佳条件,使用多元回归分析对GC峰面积进行了处理。回归系数的向后逐步选择导致确定了针对所研究的三个变量具有相似行为的七组化合物。通过对未精制多项式的回归系数的t值进行主成分分析(PCA),并对通过PCA获得的13种酸的得分进行聚类分析,可以找到同一组模型。评估了每种化合物的最佳实验条件。 FCC实验设计的使用不仅可以评估所分析变量的主要影响,还可以评估它们之间的曲率影响和相互作用。 (C)1999 Elsevier Science B.V.保留所有权利。 [参考:23]

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