主成分分析步骤对主成分回归影响的光谱模拟研究

Spectral simulation study on the influence of the principal component analysis step on principal component regression.

作者信息

Hasegawa Takeshi

机构信息

Applied Molecular Chemistry, College of Industrial Technology, Nihon University, 1-2-1 Izumi-cho, Narashino, Chiba 275-8575, Japan.

出版信息

Appl Spectrosc. 2006 Jan;60(1):95-8. doi: 10.1366/000370206775382749.

DOI:10.1366/000370206775382749

PMID:16454919

Abstract

Principal component regression (PCR) is unique in that the principal component analysis (PCA) step is explicitly involved in the central part of the method. In the present paper, the PCA part is examined in order to study the influence of noise in spectra on PCR by spectral simulation. It has been suggested, as a result, that PCR calibration would have a large inaccuracy when the estimated number of basis factors analyzed by the eigenvalue method is less than that by cross-validation, which was studied by use of synthesized spectra. This instability is because the minute noise is largely enhanced by the PCA calculation via the normalization of loadings. At the same time, the noise enhancement by PCA has also been characterized to influence the estimation of basis factors.

摘要

主成分回归（PCR）的独特之处在于，主成分分析（PCA）步骤明确地参与到该方法的核心部分。在本文中，通过光谱模拟来研究PCA部分，以便探讨光谱中的噪声对PCR的影响。结果表明，当用特征值法估计的基因素数量少于交叉验证法估计的数量时（这是通过合成光谱进行研究的），PCR校准会有很大的不准确性。这种不稳定性是因为微小的噪声通过主成分分析计算中载荷的归一化而被大大增强。同时，主成分分析对噪声的增强作用也被证明会影响基因素的估计。