Chemometrics in analytical chemistry—part II: modeling, validation, and applications

The contribution of chemometrics to important stages throughout the entire analytical process such as experimental design, sampling, and explorative data analysis, including data pretreatment and fusion, was described in the first part of the tutorial “Chemometrics in analytical chemistry.” This is the second part of a tutorial article on chemometrics which is devoted to the supervised modeling of multivariate chemical data, i.e., to the building of calibration and discrimination models, their quantitative validation, and their successful applications in different scientific fields. This tutorial provides an overview of the popularity of chemometrics in analytical chemistry.

[1]  Romà Tauler,et al.  Chemometrics in analytical chemistry—part I: history, experimental design and data analysis tools , 2017, Analytical and Bioanalytical Chemistry.

[2]  Romà Tauler,et al.  Relevant aspects of unmixing/resolution analysis for the interpretation of biological vibrational hyperspectral images , 2017 .

[3]  R. A. van den Berg,et al.  Centering, scaling, and transformations: improving the biological information content of metabolomics data , 2006, BMC Genomics.

[4]  L. Buydens,et al.  Comparing support vector machines to PLS for spectral regression applications , 2004 .

[5]  J. Callis,et al.  Prediction of gasoline octane numbers from near-infrared spectral features in the range 660-1215 nm , 1989 .

[6]  Jürgen Popp,et al.  Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets. , 2018, Analytical chemistry.

[7]  Silvia Lanteri,et al.  Chemometrics in Food Chemistry , 1987, Chemometrics and Species Identification.

[8]  Jonathan V. Sweedler,et al.  Mass Spectrometry Imaging , 2010, Methods in Molecular Biology.

[9]  R. Brereton.  One‐class classifiers , 2011 .

[10]  Hadi Parastar,et al.  Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists. , 2018, Angewandte Chemie.

[11]  Beata Walczak,et al.  Concept of (dis)similarity in data analysis , 2012 .

[12]  Michelle Becker.  Mass Spectrometry Imaging Principles And Protocols , 2016 .

[13]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[14]  S. Wold,et al.  SIMCA: A Method for Analyzing Chemical Data in Terms of Similarity and Analogy , 1977 .

[15]  A. Pomerantsev,et al.  Concept and role of extreme objects in PCA/SIMCA , 2014 .

[16]  Sandra Maurer,et al.  Fundamentals Of Analytical Chemistry , 2016 .

[17]  Charles K. Bayne,et al.  Multivariate Analysis of Quality: An Introduction , 2002, Technometrics.

[18]  Edward C. Holmes,et al.  Primer Master: a new program for the design and analysis of PCR primers , 1996, Comput. Appl. Biosci..

[19]  R. Brereton,et al.  Partial least squares discriminant analysis: taking the magic away , 2014 .

[20]  C. D. Beaumont,et al.  Regression Diagnostics — Identifying Influential Data and Sources of Collinearity , 1981 .

[21]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[22]  Peter Filzmoser,et al.  Robust continuum regression , 2005 .

[23]  David E. Booth,et al.  Multi-Way Analysis: Applications in the Chemical Sciences , 2005, Technometrics.

[24]  Philippe Besse,et al.  Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems , 2011, BMC Bioinformatics.

[25]  Romà Tauler,et al.  Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: Overview and workflow , 2016 .

[26]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[27]  Harald Martens,et al.  Reducing over-optimism in variable selection by cross-model validation , 2006 .

[28]  Romà Tauler,et al.  Vibrational spectroscopic image analysis of biological material using multivariate curve resolution–alternating least squares (MCR-ALS) , 2015, Nature Protocols.

[29]  Age K. Smilde,et al.  ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data , 2005, Bioinform..

[30]  Erik Johansson,et al.  Multivariate design and modeling in QSAR , 1996 .

[31]  Laura M. Cole,et al.  Imaging Mass Spectrometry , 2017, Methods in Molecular Biology.

[32]  Romà Tauler,et al.  Compression strategies for the chemometric analysis of mass spectrometry imaging data , 2016 .

[33]  M. Mørup,et al.  Non-linear calibration models for near infrared spectroscopy. , 2014, Analytica chimica acta.

[34]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

[35]  Eric R. Ziegel,et al.  Chemometrics: Statistics and Computer Application in Analytical Chemistry , 2001, Technometrics.

[36]  W. W. Muir,et al.  Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1980 .

[37]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[38]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[39]  Oxana Ye. Rodionova,et al.  Discriminant analysis is an inappropriate method of authentication , 2016 .

[40]  Sherif Sakr,et al.  Handbook of Big Data Technologies , 2017 .

[41]  Steven Miller.  Portable device for live tissue imaging. , 2006, Analytical chemistry.

[42]  J. Suykens,et al.  A tutorial on support vector machine-based methods for classification problems in chemometrics. , 2010, Analytica chimica acta.

[43]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[44]  Paul Geladi,et al.  Techniques and applications of hyperspectral image analysis , 2007 .

[45]  B. Kowalski,et al.  Theory of analytical chemistry , 1994 .

[46]  Richard G. Brereton,et al.  Chemometrics: Data Analysis for the Laboratory and Chemical Plant , 2003 .

[47]  F. Marini,et al.  Validation of chemometric models - a tutorial. , 2015, Analytica chimica acta.

[48]  M. Setou.  [Imaging mass spectrometry]. , 2010, Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan.