13.1: Chemometric Resources

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Jun 6, 2021
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- 13: Resources
- 13.2: R Resources

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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.

Websites

NIST Engineering Statistics HandbookST (https://www.itl.nist.gov/div898/handbook/)
Rice Virtual Lab in Statistics (https://onlinestatbook.com/rvls.html)
Statistics for Analytical Chemistry (https://science.widener.edu/svb/stats/stats.html)

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