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Journal ArticleDOI

PLS-regression: a basic tool of chemometrics

28 Oct 2001-Chemometrics and Intelligent Laboratory Systems (Elsevier)-Vol. 58, Iss: 2, pp 109-130
TL;DR: PLS-regression (PLSR) as mentioned in this paper is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS) is a method for relating two data matrices, X and Y, by a linear multivariate model.
About: This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 2001-10-28. It has received 7861 citations till now. The article focuses on the topics: Partial least squares regression.
Citations
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Journal ArticleDOI
TL;DR: The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
Abstract: Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.

11,624 citations

Journal ArticleDOI
TL;DR: An overview of NIR spectroscopy for measuring quality attributes of horticultural produce is given in this article, where the problem of calibration transfer from one spectrophotometer to another is introduced as well as techniques for calibration transfer.

1,780 citations

Journal ArticleDOI
TL;DR: For a reversed-phase LC-MS/MS analysis of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compounds, highlighting the chemotaxonomic relationships between theAlgal strains.
Abstract: Data-independent acquisition (DIA) in liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) provides comprehensive untargeted acquisition of molecular data. We provide an open-source software pipeline, which we call MS-DIAL, for DIA-based identification and quantification of small molecules by mass spectral deconvolution. For a reversed-phase LC-MS/MS analysis of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compounds, highlighting the chemotaxonomic relationships between the algal strains.

1,609 citations

Journal ArticleDOI
TL;DR: The nature of the VIP method is explored and it is compared with other methods through computer simulation experiments considering four factors–the proportion of the number of relevant predictor, the magnitude of correlations between predictors, the structure of regression coefficients, andThe magnitude of signal to noise.

1,595 citations

Journal ArticleDOI
TL;DR: Characteristics of the process industry data which are critical for the development of data-driven Soft Sensors are discussed.

1,399 citations


Cites methods from "PLS-regression: a basic tool of che..."

  • ...…to data-driven Soft Sensors are the Principle Component Analysis (Jolliffe, 2002) in a combination with a regression model, Partial Least Squares (Wold et al., 2001), Artificial Neural Networks (Bishop, 1995; Principe et al., 2000; Hastie et al., 2001), Neuro-Fuzzy Systems (Jang et al., 1997;…...

    [...]

  • ...One way is by transforming the input variables into a new reduced space with less co-linearity as it is done in the case of the PCA (Jolliffe, 2002) and PLS (Wold et al., 2001; Abdi, 2003)....

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References
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Book
01 Jan 2000
TL;DR: This book should be valuable for medicinal, agricultural and theoretical chemists, biochemists and biologists, as well as for other scientists interested in drug design.
Abstract: Progress in medicinal chemistry and in drug design depends on our ability to understand the interactions of drugs with their biological targets. Classical QSAR studies describe biological activity in terms of physicochemical properties of substituents in certain positions of the drug molecules. The purpose of this book is twofold: On the one hand, both the novice and the experienced user will be introduced to the theory and application of 3D QSAR analyses, and on the other, a comprehensive overview of the scope and limitations of these methods is given. The detailed discussion of the present state of the art should enable scientists to further develop and improve these powerful new tools. The greater part of the book is dedicated to the theoretical background of 3D QSAR and to a discussion of CoMFA applications. In addition, various other 3D QSAR approaches and some CoMFA-related methods are described in detail. Thus, the book should be valuable for medicinal, agricultural and theoretical chemists, biochemists and biologists, as well as for other scientists interested in drug design. Its content, starting at a very elementary level and proceeding to the latest methodological results, the strengths and limitations of 3D QSAR approaches, makes the book also appropriate as a text for teaching and for graduate student courses.

427 citations

Journal ArticleDOI
TL;DR: In this paper, a method for statistical analysis of two independent samples with respect to difference in location is investigated, using the partial least squares projections to latent structures (PLS) with cross-validation.
Abstract: A method for statistical analysis of two independent samples with respect to difference in location is investigated. The method uses the partial least squares projections to latent structures (PLS) with cross-validation. The relation to classical methods is discussed and a Monte Carlo study is performed to describe how the distribution of the test-statistic employed depends on the number of objects, the number of variables, the percentage variance explained by the first PLS-component and the percentage missing values. Polynomial approximations for the dependency of the 50 per cent and the 5 per cent levels of the test-statistic on these factors are given. The polynomial for the 50 per cent level is complicated, involving several first-, second- and third-degree terms, whereas the polynomial for the 5 per cent level is dependent only on the number of objects and the size of the first component. A separate Monte Carlo experiment indicates that a moderate difference in sample size does not affect the distribution of the test-statistic. The multi-sample location problem is also studied and the effect of increasing the number of samples on the test-statistic is shown in simulations.

415 citations

Journal ArticleDOI
TL;DR: Kettaneh-Wold et al. as discussed by the authors used partial least squares (PLS) for the analysis of mixture data, when both mixture and process variables are involved.

409 citations

Journal ArticleDOI
TL;DR: In this article, the problem of estimating scores from an existing PCA or PLS model when new observation vectors are incomplete is treated, and several methods for estimating score from data with missing measurements are presented, and analyzed.

349 citations

Journal ArticleDOI
TL;DR: A fast and memory‐saving PLS regression algorithm for matrices with large numbers of objects is presented and a condensed matrix algebra version of the kernel algorithm is given together with the MATLAB code.
Abstract: A fast and memory-saving PLS regression algorithm for matrices with large numbers of objects is presented. It is called the kernel algorithm for PLS. Long (meaning having many objects, N) matrices X (N × K) and Y (N × M) are condensed into a small (K × K) square ‘kernel’ matrix XTYYTX of size equal to the number of X-variables. Using this kernel matrix XTYYTX together with the small covariance matrices XTX (K × K), XTY (K × M) and YTY (M × M), it is possible to estimate all necessary parameters for a complete PLS regression solution with some statistical diagnostics. The new developments are presented in equation form. A comparison of consumed floating point operations is given for the kernel and the classical PLS algorithm. As appendices, a condensed matrix algebra version of the kernel algorithm is given together with the MATLAB code.

345 citations