Scatter plotting in multivariate data analysis

Journal of Chemometrics

Volumn 17, Issue 8-9, 2003, Pages 503-511

  Geladi, Paul ^a   Manley, Marena ^b   Lestander, Torbjörn ^a  

^a SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

^b STELLENBOSCH UNIVERSITY   (South Africa)

Author keywords

Biplots; Interpretation of scatter plots; Line plots; Multivariate data analysis; Near infrared spectroscopy; PARAFAC; Partial least squares regression; Plotting; Principal component analysis; Visualization

Indexed keywords

DATA HANDLING; DATA VISUALIZATION; INFORMATION ANALYSIS; INFRARED DEVICES; LEAST SQUARES APPROXIMATIONS; MULTIVARIANT ANALYSIS; NEAR INFRARED SPECTROSCOPY; VISUALIZATION;

BIPLOTS; CHEMOMETRICES; INTERPRETATION OF SCATTER PLOT; LINE PLOT; MULTIVARIATE DATA ANALYSIS; PARAFAC; PARTIAL LEAST SQUARE REGRESSION; PLOTTING; PRINCIPAL-COMPONENT ANALYSIS; SCATTER PLOTS;

PRINCIPAL COMPONENT ANALYSIS;

ANALYTIC METHOD; CHEMOMETRICS; CONFERENCE PAPER; DATA ANALYSIS; INFRARED SPECTROSCOPY; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MEASUREMENT; MULTIVARIATE ANALYSIS; PARAMETER; PRINCIPAL COMPONENT ANALYSIS; REFERENCE VALUE; REGRESSION ANALYSIS; SCORING SYSTEM; TECHNIQUE;

EID: 0242354128     PISSN: 08869383     EISSN: None     Source Type: Journal    
DOI: 10.1002/cem.814     Document Type: Conference Paper

References (32)

1. Huff D. How to Lie with Statistics. Victor Gollancz: London, 1954.
2. Monmonier MS. How to Lie with Maps. University of Chicago Press: Chicago, IL, 1991.
3. Cleveland WS. The Elements of Graphing Data. Wadsworth Advanced Books and Software: Monterey, CA, 1985.
4. Cleveland WS, McGill ME (eds). Dynamic Graphics for Statistics. Wadsworth and Brooks/Cole: Belmont, CA, 1988.
5. Tufte ER. The Visual Display of Quantitative Data. Graphics Press: Cheshire, CT, 1983.
6. Wegman EJ. Hyperdimensional data analysis using parallel coordinates. J. Am. Statist. Assoc. 1990; 411: 664-675.
7. Meyers RH, Montgomery DC. Response Surface Methodology. Process and Product Optimization Using Designed Experiments. Wiley-Interscience: New York, 1995.
8. Geladi P, Grahn H. Multivariate image analysis. In Encyclopedia of Analytical Chemistry, Meyers R (ed.). Wiley: Chichester, 2000; 13540-13562.
9. Jackson JE. A User's Guide to Principal Components. Wiley: New York, 1991.
10. Van Zyl L, Manley M, Osborne B. Using different sample holders in determining protein and moisture content in whole wheat flour by menas of Fourier transform near infrared (FT-NIR) spectroscopy. S. Afr. J. Plant Soil 2001; 18: 50-55.
11. Beebe KR, Pell RJ, Seasholtz M-B. Chemometrics. A Practical Guide. Wiley: New York, 1998.
12. Smilde AK. Three-way analyses. Problems and prospects. Chemometrics Intell. Lab. Syst. 1992; 15: 143-157.
13. Bro R. PARAFAC: tutorial and applications. Chemometrics Intell. Lab. Syst. 1997; 38: 149-171.
14. Law HG, Snyder CW, Hattie J, McDonald RK (eds). Research Methods for Multimode Data Analysis. Praeger: New York, 1984.
15. Coppi R, Bolasco S (eds). Multiway Data Analysis. North Holland: Amsterdam, 1989.
16. Andersson C, Bro R (eds). Special issue. Multiway analysis. J. Chemometrics 2000; 14: 103-331.
17. Geladi P, Bergner H, Ringqvist L. From experiments to images to particle size histograms to multiway analysis. An example of peat dewatering. J. Chemometrics 2000; 14: 197-211.
18. Geladi P, Åberg P. Three-way modeling of a batch organic synthesis process monitored by near infrared spectroscopy. J. Near Infrared Spectrosc. 2001; 9: 1-9.
19. Geladi P, Forsström J. Monitoring of a batch organic synthesis by infrared spectroscopy: modeling and interpretation of three-way data. J. Chemometrics 2002; 16: 329-338.
20. Kiers HAL. Some procedures for displaying results from three-way methods. J. Chemometrics 2000; 14: 151-170.
21. Lestander TA, Odén P-C, Geladi P. 2- and 3-way analysis of NIR scans from seed crossings. In Near Infrared Spectroscopy. Proceedings of the 10th International Conference, Davies A, Cho R (eds). NIR Publications: Chichester, 2002; 385-388.
22. Martens H, Naes T. Multivariate Calibration. Wiley: Chichester, 1989.
23. Brown PJ. Measurement, Regression and Calibration. Clarendon: Oxford, 1993.
24. Geladi P. Some recent trends in the calibration literature. Chemometrics Intell. Lab. Syst. 2002; 60: 211-224.
25. Eriksson L, Johansson E, Kettaneh-Wold N, Wold S. Multi- and Megavariate Data Analysis. Principles and Applications. Umetrics: Umeå, 2001.
26. Ergon R. PLS score-loading correspondence and bi-orthogonal factorization. J. Chemometrics 2002; 16: 368-373.
27. Gabriel KR. The biplot-graphic display of matrices with applications to principal component analysis. Biometrika 1971; 58: 453-467.
28. Kroonenberg PM. Three-mode Principal Component Analysis. DSWO Press: Leiden, 1983.
29. Brereton RG (ed.). Multivariate Pattern Recognition in Chemometrics, Illustrated by Case Studies. Elsevier: Amsterdam, 1992.
30. Martens H, Martens M. Multivariate Analysis of Quality. An Introduction. Wiley: Chichester, 2001.
31. Gemperline PJ, Miller KH, West TL, Weinstein JE, Hamilton JC, Bray JT. Principal component analysis, trace elements, and blue crab shell disease. Anal. Chem. 1992; 64: 523A-532A.
32. Lestander TA, Odén P-C. Separation of viable and nonviable filled Scots pine seeds by differentiating between drying rates using single seed near infrared transmittance spectroscopy. Seed Sci. Technol. 2002; 30: 383-392.