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Journal ArticleDOI

PLS-regression: a basic tool of chemometrics

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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Citations
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PLS-SEM: Indeed a Silver Bullet

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.
Journal ArticleDOI

Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review

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.
Journal ArticleDOI

MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis.

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.
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Performance of some variable selection methods when multicollinearity is present

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.
Journal ArticleDOI

Data-driven Soft Sensors in the process industry

TL;DR: Characteristics of the process industry data which are critical for the development of data-driven Soft Sensors are discussed.
References
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Multivariate design of process experiments (M-DOPE)

TL;DR: Some variations of the partial least squares (PLS) algorithm called ‘selective PLS’ are introduced to separate the variables into a small number of orthogonal groups that form the basis for experimental designs and processoptimization.
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