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

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Abstract

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.

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References

  1. Brereton RG, Jansen J, Lopes J, Marini F, Pomerantsev A, Rodionova O, et al. Chemometrics in analytical chemistry—part I: history, experimental design and data analysis tools. Anal Bioanal Chem. 2017;409:5891–9.

  2. Kalivas JH, Calibration Methodologies in Comprehensive Chemometrics, Brown S, Tauler R, Walczak B (Eds.). Amsterdam:Elsevier; 2009, Vol.3, chapter 3.01.

  3. Belsley DA, Kuh E, Welsch RE. Identifying influential data and sources of collinearity. New York: John Wiley & Sons; 1980.

    Book  Google Scholar 

  4. Brereton RG. One Class Classifiers. J Chemometr. 2011;25:225–46.

    Article  CAS  Google Scholar 

  5. Wold S, Sjostrom M. SIMCA: a method for analyzing chemical data in terms of similarity and analogy, in Kowalski, BR (Ed) Chemometrics Theory and Application, American Chemical Society Symposium Series 52, Wash., D.C.:American Chemical Society; 1977, 243–282.

  6. Pomerantsev A, OYe R. Concept and role of extreme objects in PCA/SIMCA. J Chemometr. 2014;28:429–38.

    Article  CAS  Google Scholar 

  7. Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugenics. 1936;1936:179M.

    Article  Google Scholar 

  8. Barker M, Rayens W. Partial least squares for discrimination. J Chemom. 2003;17:166–73.

    Article  CAS  Google Scholar 

  9. Brereton RG, Lloyd GR. Partial least squares discriminant analysis: taking the magic away. J Chemom. 2014;28:221–35.

    Google Scholar 

  10. Rodionova YO, Titova AV, Pomerantsev AL. Discriminant analysis is an inappropriate method of authentication TRAC trends. Anal Chem. 2016;78(4):17–22.

    CAS  Google Scholar 

  11. Anderssen E, Dyrstad K, Westad F, Martens H. Reducing over-optimism in variable selection by cross-model validation Chemomet. Intell Lab Syst. 2006;84:69–74.

    Article  CAS  Google Scholar 

  12. Centner V, Massart DL, de Noord OE, de Jong S, Vandeginste B. Sterna C. Anal Chem. 1996;68:3851–8.

    Article  CAS  PubMed  Google Scholar 

  13. Serneels S, Filzmoser P, Croux C, Van Espen PJ. Chemometr Intell Lab Syst. 2005;76:197–204.

    Article  CAS  Google Scholar 

  14. Zerzucha P, Walczak B. Concept of (dis)similarity in data analysis TRAC trends. Anal Chem. 2012;38:116–28.

    CAS  Google Scholar 

  15. Harshman R. How can I know if it's real? A catalogue of diagnostics for use with three-mode factor analysis and multidimensional scaling. In: Law HG, Snyder Jr CW, Hattie J, Mc Donald RP, editors. Research Methods for Multimode Data Analysis. New York: Praeger; 1984. p. 566–91.

    Google Scholar 

  16. Westad F, Marini F. Validation of chemometric models—a tutorial. Anal Chim Acta. 2015;893:14–24.

    Article  CAS  PubMed  Google Scholar 

  17. Booksh KS, Kowalski BR. Theory of analytical chemistry. Anal Chem. 1994;66(15):782A–91A.

    Article  CAS  Google Scholar 

  18. Forina M, Lanteri S, Armarino C. Chemometrics in food chemistry, in Chemometrics and species identification. Berlin: Springer; 1987. p. 91–143.

    Book  Google Scholar 

  19. Kelly JJ, Barlow CH, Jinguji TM, Callis JB. Ana Chem 1989;61(4);313–320.

  20. Wise BM, Gallagher NB. J Process Contr 1996;6(6);329–348.

  21. Sharaf MA, Illman DL, Kowalski BR. Chemometrics, chemical analysis, vol. 82. New York: John Wiley and Sons; 1986.

    Google Scholar 

  22. Hopke PK. Receptor Modling in Environmental Chemistry, New York: John Wiley Sons; 1981; Hopke PK. Modeling for air quality management, Amsterdam:Elsevier; 1991.

  23. Eriksson L, Johansson E. Multivariate design and modeling in QSAR. Chemometr Intell Lab. 1996;34:1–19.

    Article  CAS  Google Scholar 

  24. Eriksson L, Byrne T, Johansson E, Trygg J, Wikström C. Multi- and megavariate data analysis basic principles and applications, Umeå. 3rd ed. Sweden: Umetrics academy; 2013.

    Google Scholar 

  25. Parastar H, Tauler R. Big (bio)chemical data mining using Chemometric methods: a need for chemists. Angew Chem Int. 2018; https://doi.org/10.1002/anie.201801134.

  26. Cao K, Lê Boitard S, Besse P. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. Bioinformatics. 2011;12:253.

    PubMed  Google Scholar 

  27. Smilde AK, Jansen JJ, Hoefsloot HCJ, Lamers RJAN, van der Greef J, Timmerman ME. ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics. 2005;21:3043–8.

    Article  CAS  PubMed  Google Scholar 

  28. van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics. 2006;7:142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gorrochategui E, Jaumot J, Lacorte S, Tauler R. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow. TrAC - Trends in Anal Chem. 2016;82:425–42.

    Article  CAS  Google Scholar 

  30. Grahn HF, Geladi P, editors. Techniques and applications of hyperspectral image analysis. Chichester, UK: John Wiley & Sons Ltd; 2005.

    Google Scholar 

  31. Geladi P, Grahn H. Multivariate image analysis in chemistry and related areas: chemometric image analysis. Chichester UK: Wiley; 1996.

    Google Scholar 

  32. Olmos V, Benítez L, Marro M, Loza-Alvarez P, Piña B, Tauler R, et al. Relevant aspects of unmixing/resolution analysis for the interpretation of biological vibrational hyperspectral images. TrAC-Trends in Anal Chem. 2017;94:130–40.

  33. Felten J, Hall H, Jaumot J, Tauler R, de Juan A, Gorzsás A. Vibrational spectroscopic image analysis of biological material using multivariate curve resolution–alternating least squares (MCR-ALS). Nat Protoc. 2015;10:217–40.

    Article  CAS  PubMed  Google Scholar 

  34. Piqueras S, Bedia C, Beleites C, Krafft C, Popp J, Maeder M, et al. Handling different spatial resolution in image fusion by multivariate curve resolution-alternating least squares for incomplete image multisets. Anal Chem. 2018;90(11):6757–65.

  35. Setou M. (Ed.) Imaging mass spectrometry. Protocols for Mass Microscopy, Berlin:Springer; 2010.

  36. Rubakhin SS, Sweedler JV (Eds), mass spectrometry imaging. Principles and protocols. New York: Humana Press; 2010.

  37. Bedia C, Tauler R, Jaumot J. Compression strategies for the chemometric analysis of mass spectrometry imaging data. J Chemom. 2016;30:575–88.

    Article  CAS  Google Scholar 

  38. Skoog DA, West DM, Holler FJ, Crouch SR. Fundamentals of analytical chemistry. Ninth ed. Belmont, CA: Brooks/Cole; 2014.

  39. Christian GD, Dasgupta PN, Schug KA. Analytical chemistry. seventh ed. New York: Wiley; 2013.

  40. Zomaya AY, Sakr S. Handbook of big data technologies. Berlin: Springer; 2017.

    Book  Google Scholar 

  41. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Know Disc. 1998;2:121–67.

    Article  Google Scholar 

  42. Kohonen T. Self-Organizing maps. Third ed. Berlin: Springer; 2001.

  43. Schmidhuber J. Deep learning in neural networks: an overview http://arxiv.org/abs/1404.7828, 2014.

  44. Lutsa J, Ojedaa F, Van de Plasa R, De Moora B, Van Huffel S, Suykens JAK. A tutorial on support vector machine-based methods for classification problems in chemometrics. Anal Chim Acta. 2010;665:129–45.

    Article  CAS  Google Scholar 

  45. Nia W, Nørgaard L, Mørup M. Non-linear calibration models for near infrared spectroscopy. Anal Chim Acta. 2014;813:1–14.

    Article  CAS  Google Scholar 

  46. Thissen U, Pepers M, Ustun B, Melssen WJ, Buydens LMC. Comparing support vector machines to PLS for spectral regression applications. Chemometr Intell Lab Syst. 2004;73:169–79.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. School of Chemistry, University of Bristol, Cantocks Close, Bristol, BS8 1TS, UK

    Richard G. Brereton

  2. Institute for Molecules and Materials, Radboud University Nijmegen, Postvak 61, P.O. Box 9010, 6500 GL, Nijmegen, The Netherlands

    Jeroen Jansen

  3. Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003, Lisbon, Portugal

    João Lopes

  4. Department of Chemistry, University of Rome La Sapienza, Piazzale Aldo Moro 5, 00185, Rome, Italy

    Federico Marini

  5. Institute of Chemical Physics RAS, 4, Kosygin Str, 119991, Moscow, Russia

    Alexey Pomerantsev & Oxana Rodionova

  6. Irstea, UMR ITAP, 361 Rue Jean-François Breton, 34000, Montpellier, France

    Jean Michel Roger

  7. Institute of Chemistry, University of Silesia, 40-006, Katowice, Poland

    Beata Walczak

  8. IDAEA-CSIC, Jordi Girona 18-26, 08034, Barcelona, Spain

    Romà Tauler

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  1. Richard G. Brereton
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  2. Jeroen Jansen
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  3. João Lopes
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  4. Federico Marini
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  5. Alexey Pomerantsev
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  6. Oxana Rodionova
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  7. Jean Michel Roger
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  8. Beata Walczak
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  9. Romà Tauler
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Correspondence to Romà Tauler.

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Brereton, R.G., Jansen, J., Lopes, J. et al. Chemometrics in analytical chemistry—part II: modeling, validation, and applications. Anal Bioanal Chem 410, 6691–6704 (2018). https://doi.org/10.1007/s00216-018-1283-4

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  • DOI: https://doi.org/10.1007/s00216-018-1283-4

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