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.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.read more
Citations
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Journal ArticleDOI
Using FTIR to predict saccharification from enzymatic hydrolysis of alkali-pretreated biomasses.
TL;DR: This study demonstrates that FTIR spectroscopy combined with PLS regression can be used to rapidly estimate sugar conversions and yields from enzymatic hydrolysis of pretreated plant biomass.
Journal ArticleDOI
Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis.
Agnieszka Smolinska,A-Ch Hauschild,Rianne Fijten,Jan W. Dallinga,Jan Baumbach,F.J. van Schooten +5 more
TL;DR: The current state of the art in data pre-processing and multivariate analysis of breathomics data is described and the community is made aware of the existing data fusion methods, as yet unresearched in breathomics.
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Assessing multi-satellite remote sensing, reanalysis, and land surface models' products in characterizing agricultural drought in East Africa.
Nathan O. Agutu,Nathan O. Agutu,Joseph L. Awange,Joseph L. Awange,Ayalsew Zerihun,Christopher E. Ndehedehe,Michael Kuhn,Yoichi Fukuda +7 more
TL;DR: In this article, the capability of these products to capture agricultural drought impacts was assessed using maize and wheat production data, and the results showed that while all products were similar in drought characterisation in dry areas, the similarity of CHIRPS and GPCC extended over the whole EA.
Journal ArticleDOI
Advances in computational methods to predict the biological activity of compounds.
TL;DR: This review presents an overview of the advances in the computational methods utilized for predicting the biological activity of compounds and a conceptual view of the quantitative structure–activity relationship paradigm and the methodological overview of commonly used machine learning algorithms.
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Mathematical modelling of anaerobic digestion of biomass and waste: Power and limitations
TL;DR: A review of the state-of-the-art in anaerobic digestion modelling can be found in this article, where the most significant simulation and control models are highlighted, and their effectiveness critically discussed.
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.
Journal ArticleDOI
A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation
Bradley Efron,Gail Gong +1 more
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.