PLS-regression:: a basic tool of chemometrics
PLS-regression:: a basic tool of chemometrics
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DOI：
10.1016/s0169-7439(01)00155-1
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发表时间：
2001-10-28
影响因子：
3.9
通讯作者：
Eriksson, L
中科院分区：
文献类型：
--
作者：
Wold, S;Sjöström, M;Eriksson, L
PLS回归（PLSR）是PLS方法中最简单的，在化学和技术中，最常用的形式（两块预测PLS）。PLSR是一种通过线性多变量模型将两个数据矩阵X和Y关联起来的方法，但它超越了传统的回归，因为它还对X和Y的结构进行了建模。PLSR的有用性来自于它能够分析X和Y上具有许多噪声、共线甚至不完整变量的数据。PLSR具有随着相关变量和观测值的增加模型参数的精度提高的优点，本文综述了PLSR作为化学计量学的标准工具在化学和工程中的应用。讨论了基本模型及其假设，并回顾了常用的诊断方法以及对所得参数的解释，使用两个例子作为说明：首先，使用肽的定量结构-活性关系（QSAR）/定量结构-性质关系（QSPR）数据集来概述如何开发、解释和改进PLSR模型。其次，从再生纸制造的数据集进行了分析，以说明时间序列建模的过程数据的PLSR和时滞的X-变量。(C)2001 Elsevier Science B. V.保留所有权利。
PLS-regression (PLSR) is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS). PLSR is a method for relating two data matrices, X and Y, by a linear multivariate model, but goes beyond traditional regression in that it models also the structure of X and Y. PLSR derives its usefulness from its ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. PLSR has the desirable property that the precision of the model parameters improves with the increasing number of relevant variables and observations.This article reviews PLSR as it has developed to become a standard tool in chemometrics and used in chemistry and engineering. The underlying model and its assumptions are discussed, and commonly used diagnostics are reviewed together with the interpretation of resulting parameters.Two examples are used as illustrations: First, a Quantitative Structure-Activity Relationship (QSAR)/Quantitative Structure-Property Relationship (QSPR) data set of peptides is used to outline how to develop, interpret and refine a PLSR model. Second, a data set from the manufacturing of recycled paper is analyzed to illustrate time series modelling of process data by means of PLSR and time-lagged X-variables. (C) 2001 Elsevier Science B.V. All rights reserved.