Variable selection in PCA in sensory descriptive and consumer data
Variable selection in PCA in sensory descriptive and consumer data
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DOI：
10.1016/s0950-3293(03)00015-6
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发表时间：
2003-07-01
影响因子：
5.3
通讯作者：
Martens, H
中科院分区：
文献类型：
--
作者：
Westad, F;Hersleth, M;Martens, H
本文提出了一种识别多变量模型中显著变量的一般方法。将该方法应用于感官数据、描述性数据和消费者数据的主成分分析。该方法基于交叉验证/切割法的不确定度估计，强调了模型验证的重要性。学生根据负荷量及其估计的标准不确定度进行的t检验被用来计算每个分量对每个变量的重要性。两个数据集被用来演示这如何通过指示曲线图中每个变量的重要程度来帮助数据分析员解释加载曲线图。相关负荷对可视化变量之间的相关结构的有用性也被证明。(C)2003爱思唯尔科学有限公司。保留所有权利。
This paper presents a general method for identifying significant variables in multivariate models. The methodology is applied on principal component analysis (PCA) of sensory descriptive and consumer data. The method is based on uncertainty estimates from cross-validation/jack-knifing, where the importance of model validation is emphasised. Student's t-tests based on the loadings and their estimated standard uncertainties are used to calculate significance on each variable for each component. Two data sets are used to demonstrate how this aids the data-analyst in interpreting loading plots by indicating degree of significance for each variable in the plot. The usefulness of correlation loadings to visualise correlation structures between variables is also demonstrated. (C) 2003 Elsevier Science Ltd. All rights reserved.