Introduction to Multivariate Statistical Analysis in Chemometrics

By K. Varmuza, Peter Filzmoser. 2009

1,130 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5f1d43e93713fa161d4af6f3273843c74a23cef9

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In Analytical Chemistry

Citations in the corpus

Sparse PCA: Optimal rates and adaptive estimation (2012, background): “For example, in spectroscopy, the variation of the infrared and ultraviolet spectra is driven by the concentration levels of a small number of chemical components in the system [53].”
GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013, methodology): “Crucial to the robustness and efficiency of this estimator is h, and we specify a default value of h = 0.75n, following the recommendation of Varmuza and Filzmoser (2009).”
Environmental metabolomics: an emerging approach to study organism responses to environmental stressors (2013, methodology): “Cross-validation methods such as the leave-one-out cross validation (LOOCV) are used for this purpose (Westerhuis et al. 2008; Varmuza and Filzmoser 2009; Whitfield Åslund et al. 2011b).”
Linear regression with compositional explanatory variables (2012, neutral): “[22], T0 = γ̂0 √ S2{(Z′Z)−1}0,0 ; Ti = γ̂i √ S2{(Z′Z)−1}i,i , i = 1, .”
A novel variable reduction method adapted from space-filling designs (2014, methodology): “The classification Error Rate (ER) was calculated as the complement of Non Error Rate (1-NER), where NER was calculated as the average of class sensitivities [35].”
Sparse Principal Component Analysis and Iterative Thresholding (2011, background): “For instance, in chemometrics, x i can be a vector of the logarithm of the absorbance or reﬂectance spectra measured with noise, where the columns of A are characteristic spectral responses of diﬀerent chemical components, and u i ’s the concentration levels of these components [33].”
Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy (2020, methodology): “…were scaled or not scaled for PLS by dividing centered wavenumber-specific absorbances by their standard deviations (Mevik and Wehrens, 2007; Varmuza and Filzmoser, 2009), whereas for SVM both pre-treated spectra and predictor were always scaled to zero mean and unit variance prior to…”
Principal Component Analysis (2018, methodology): “These techniques are normally used in studies related to data-driven methods, in which empirical methods are employed [78].”
Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats (2020, methodology): “PLSR is known for handling collinear data with a relatively large number of variables which are also collinear (Varmuza and Filzmoser 2016).”
Metabolic fingerprinting as an indicator of biodiversity: towards understanding inter-specific relationships among Homoscleromorpha sponges (2011, methodology): “The first step was the measure of similarity between samples using Euclidean distance, after which the agglomerative clustering was applied using the complete linkage algorithm (Varmuza and Filzmoser 2009).”