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Journal ArticleDOI

PLS-regression: a basic tool of chemometrics

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TLDR
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.
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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.

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General Framework for Latent Variable Model Inversion for the Design and Manufacturing of New Products

TL;DR: In this paper, the authors present a framework that aims to give a holistic view of the optimization formulations that can arise from the need to invert an LVRM, and an example of these formulations can be found in Figure 1.
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Fluorescence spectroscopy as a potential metabonomic tool for early detection of colorectal cancer

TL;DR: The present findings show how fluorescence Spectroscopy and chemometrics can help in cancer diagnostics, and with PARAFAC fluorescence spectroscopy can be a potential metabonomic tool.
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Comprehensive Monitoring of Nonlinear Processes Based on Concurrent Kernel Projection to Latent Structures

TL;DR: A new approach called concurrent kernel PLS (CKPLS) is presented to detect faults comprehensively for nonlinear processes to solve quality-relevant process monitoring.
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Simultaneous voltammetric determination of levodopa, carbidopa and benserazide in pharmaceuticals using multivariate calibration.

TL;DR: The methodology proposed based on DPV data processed with the PLS-1 algorithm was able to quantify simultaneously levodopa, carbidopa and benserazide in its pharmaceuticals formulations using a ternary calibration model for these drugs in presence of excipients.
References
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Book

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.
Book

A User's Guide to Principal Components

TL;DR: In this paper, the authors present a directory of Symbols and Definitions for PCA, as well as some classic examples of PCA applications, such as: linear models, regression PCA of predictor variables, and analysis of variance PCA for Response Variables.
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A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation

TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.
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