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Chemistry LibreTexts

13.1: Chemometric Resources

  • Page ID
    317997
  • Books

    The following small collection of books provide a broad introduction to chemometric methods of analysis. The text by Miller and Miller is a good entry-level textbook suitable for the undergraduate curriculum. The text by Massart, et. al. is a particularly comprehensive resource.

    • Anderson, R. L. Practical Statistics for Analytical Chemists, Van Nostrand Reinhold: New York; 1987.
    • Beebe, K. R.; Pell, R. J.; Seasholtz, M. B. Chemometrics: A Practical Guide, Wiley, 1998.
    • Brereton, Richard G. Data Driven Extraction for Science, 2nd Edition, Wiley, 2018.
    • Graham, R. C. Data Analysis for the Chemical Sciences, VCH Publishers: New York; 1993.
    • Larose, D. T.; Larose, C. D. Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, 2014.
    • Mark, H.; Workman, J. Statistics in Spectroscopy, Academic Press: Boston; 1991.
    • Massart, D. L.; Vandeginste, B. G. M.; Lewi, P. J.; Smeyers-Verbeke, J. Handbook of Chemometrics and Qualimetrics: Part A and Part B, Elsevier, 1997.
    • Miller, J. N.; Miller, J. C. Statistics and Chemometrics for Analytical Chemistry, 7th Edition, Pearson, 2018.
    • Schutt, R.; O'Neil, C. Doing Data Science: Straight Talk From the Frontline, O'Reilly, 2014.
    • Sharaf, M. H.; Illman, D. L.; Kowalski, B. R. Chemometrics, Wiley-Interscience: New York; 1986.

    Although not resources on chemometrics, the following books provide a broad introduction to the statistical methods that underlie chemometrics.

    • Boslaugh, S. Statistics in a Nutshell: A Desktop Quick Reference, O'Reilly, 2013.
    • Larose, D. T.; Larose, C. D. Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, 2014.
    • Schutt, R.; O'Neil, C. Doing Data Science: Straight Talk From the Frontline, O'Reilly, 2014.
    • van Belle, G. Statistical Rules of Thumb, Wiley, 2008.

    The following books provide more specialized coverage of topics relevant to chemometrics.

    • Mason, R. L.; Gunst, R. F.; Hess, J. L. Statistical Design and Analysis of Experiments; Wiley: New York, 1989.
    • Myers, R. H.; Montgomery, D. C. Response Surface Methodology, Wiley, 2002.

    The following books provide guidance on the visualization of data, both in figures and in tables.

    • Bertin, J. Semiology of Graphics, esri press, 1983.
    • Few, S. Now You See It, Analytics Press, 2009.
    • Few, S. Show Me the Numbers, Analytics Press, 2012.
    • Few, S. Information Dashboard Design, Analytics Press, 2013.
    • Robins, N. B. Creating More Effective Graphs, Charthouse, 2013.
    • Tufte, E. R. Envisioning Information, Graphics Press, 1990.
    • Tufte, E. R. Visual Explanations Graphics Press, 1997.
    • Tufte, E. R. The Visual Display of Quantitative Information, Graphics Press, 2001.
    • Tufte, E. R. Beautiful Evidence, Graphics Press, 2006.

    The following textbook provides a broad introduction to analytical chemistry, including sections on chemometric topics.

    • Harvey, D. T. Analytical Chemistry 2.1 (available here and here).

    Articles

    The following paper provides a general theory of types of measurements.

    • Stevens, S. S. "On the Theory of Scales of Measurements," Science, 1946, 103, 677-680.

    The detection of outliers, particularly when working with a small number of samples, is discussed in the following papers.

    • Analytical Methods Committee “Robust Statistics—How Not To Reject Outliers Part 1. Basic Concepts,” Analyst 1989, 114, 1693–1697.
    • Analytical Methods Committee “Robust Statistics—How Not to Reject Outliers Part 2. Inter-laboratory Trials,” Analyst 1989, 114, 1699–1702.
    • Analytical Methods Committee “Rogues and Suspects: How to Tackle Outliers,” AMCTB 39, 2009.
    • Analytical Methods Committee “Robust statistics: a method of coping with outliers,” AMCTB 6, 2001.
    • Analytical Methods Committee “Using the Grubbs and Cochran tests to identify outliers,” Anal. Methods, 2015, 7, 7948–7950.
    • Efstathiou, C. “Stochastic Calculation of Critical Q-Test Values for the Detection of Outliers in Measurements,” J. Chem. Educ. 1992, 69, 773–736.
    • Efstathiou, C. “Estimation of type 1 error probability from experimental Dixon’s Q parameter on testing for outliers within small data sets,” Talanta 2006, 69, 1068–1071.
    • Kelly, P. C. “Outlier Detection in Collaborative Studies,” Anal. Chem. 1990, 73, 58–64.
    • Mitschele, J. “Small Sample Statistics,” J. Chem. Educ. 1991, 68, 470–473.

    The following papers provide additional information on error and uncertainty.

    • Analytical Methods Committee “Optimizing your uncertainty—a case study,” AMCTB 32, 2008.
    • Analytical Methods Committee “Dark Uncertainty,” AMCTB 53, 2012.
    • Analytical Methods Committee “What causes most errors in chemical analysis?” AMCTB 56, 2013.
    • Andraos, J. “On the Propagation of Statistical Errors for a Function of Several Variables,” J. Chem. Educ. 1996, 73, 150–154.
    • Donato, H.; Metz, C. “A Direct Method for the Propagation of Error Using a Personal Computer Spreadsheet Program,” J. Chem. Educ. 1988, 65, 867–868.
    • Gordon, R.; Pickering, M.; Bisson, D. “Uncertainty Analysis by the ‘Worst Case’ Method,” J. Chem. Educ. 1984, 61, 780–781.
    • Guare, C. J. “Error, Precision and Uncertainty,” J. Chem. Educ. 1991, 68, 649–652.
    • Guedens, W. J.; Yperman, J.; Mullens, J.; Van Poucke, L. C.; Pauwels, E. J. “Statistical Analysis of Errors: A Practical Approach for an Undergraduate Chemistry Lab Part 1. The Concept,” J. Chem. Educ. 1993, 70, 776–779
    • Guedens, W. J.; Yperman, J.; Mullens, J.; Van Poucke, L. C.; Pauwels, E. J. “Statistical Analysis of Errors: A Practical Approach for an Undergraduate Chemistry Lab Part 2. Some Worked Examples,” J. Chem. Educ. 1993, 70, 838–841.
    • Heydorn, K. “Detecting Errors in Micro and Trace Analysis by Using Statistics,” Anal. Chim. Acta 1993, 283, 494–499.
    • Hund, E.; Massart, D. L.; Smeyers-Verbeke, J. “Operational definitions of uncertainty,” Trends Anal. Chem. 2001, 20, 394–406.
    • Kragten, J. “Calculating Standard Deviations and Confidence Intervals with a Universally Applicable Spreadsheet Technique,” Analyst 1994, 119, 2161–2165.
    • Taylor, B. N.; Kuyatt, C. E. “Guidelines for Evaluating and Expressing the Uncertainty of NIST Mea- surement Results,” NIST Technical Note 1297, 1994.
    • Van Bramer, S. E. “A Brief Introduction to the Gaussian Distribution, Sample Statistics, and the Student’s t Statistic,” J. Chem. Educ. 2007, 84, 1231.
    • Yates, P. C. “A Simple Method for Illustrating Uncertainty Analysis,” J. Chem. Educ. 2001, 78, 770–771.

    The following articles provide thoughts on the limitations of statistical analysis based on significance testing.

    • Analytical Methods Committee “Significance, importance, and power,” AMCTB 38, 2009.
    • Analytical Methods Committee “An introduction to non-parametric statistics,” AMCTB 57, 2013.
    • Berger, J. O.; Berry, D. A. “Statistical Analysis and the Illusion of Objectivity,” Am. Sci. 1988, 76, 159–165.
    • Kryzwinski, M. “Importance of being uncertain,” Nat. Methods 2013, 10, 809–810.
    • Kryzwinski, M. “Significance, P values, and t-tests,” Nat. Methods 2013, 10, 1041–1042.
    • Kryzwinski, M. “Power and sample size,” Nat. Methods 2013, 10, 1139–1140.
    • Leek, J. T.; Peng, R. D. “What is the question?,” Science 2015, 347, 1314–1315.

    The following papers provide insight into organizing data in spreadsheets and visualizing data.

    • Analytical Methods Committee “Representing data distributions with kernel density estimates,” AMC Technical Brief, March 2006.
    • Broman, K. W.; Woo, K. H. "Data Organiztion in Spreadsheets," The American Statistician, 2018, 72, 2-10.
    • Frigge, M.; Hoaglin, D. C.; Iglewicz, B. “Some Implementations of the Boxplot,” The American Statistician 1989, 43, 50–54.
    • Midway, S. R. "Principles of Effective Data Visualizations," PATTER, 2020, 1(9).
    • Schwabish, J. A. "Ten Guidelines for Better Tables," J. Benefit Cost Anal. 2020, 11, 151-178.

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