Validation of chemometric models - A tutorial

Analytica Chimica Acta

Volumn 893, Issue , 2015, Pages 14-24

  Westad, Frank ^a   Marini, Federico ^b  

^a CAMO SOFTWARE AS   (Norway)

^b SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Chemometrics; Cross validation; Resampling; Test set; Validation

Indexed keywords

ANALYTICAL GEOCHEMISTRY; CHEMICAL ANALYSIS;

CHEMOMETRICS; CROSS VALIDATION; RESAMPLING; TEST SETS; VALIDATION;

STATISTICAL TESTS;

ANALYSIS OF VARIANCE; BOOTSTRAPPING; CALIBRATION; CHEMOMETRICS; CONTROLLED STUDY; CROSS MODEL VALIDATION; GENETIC MARKER; JACKKNIFE TEST; MEASUREMENT REPEATABILITY; MONTE CARLO METHOD; PREDICTION; PRIORITY JOURNAL; PROCESS MODEL; QUANTITATIVE TRAIT LOCUS; REPRODUCIBILITY; REVIEW; STRUCTURE ACTIVITY RELATION; TOTAL ORGANIC CARBON; VALIDATION STUDY;

EID: 84941934780     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2015.06.056     Document Type: Review

References (51)

1. Harshmann R. How can I know if it's real?" A catalogue of diagnostics for use with three-mode factor analysis and multidimensional scaling. Research Methods for Multimode Data Analysis 1984, 566-591. Praeger, New York. H.G. Law, C.W. Snyder, J. Hattie, R.P. McDonald (Eds.).
2. Andersen C.M. New Aspects of Chemometrics Applied to Spectroscopy 2003, 54-55. The Royal Veterinary and Agricultural University, Ph.D. Thesis.
3. Andersen C.M., Bro R., Brockhoff P.B. Quantifying and handling errors in instrumental measurements using the measurement error theory. J. Chemom. 2003, 17:621-629.
4. Liu R., Chen W., Xu K. The influence of experimental design on the model precision in the noninvasive glucose sensing by near-infrared spectroscopy. Proc. SPIE 2008, 6826. (Optics in Health Care and Biomedical Optics III), 682626/1-682626/10.
5. Hocking R.R. The Analysis of Linear Models 1985, Brooks/Cole, Monterey, CA.
6. Pendleton O.J., Von Tress M., Bremer R. Interpretation of the four types of analysis of variance tables in SAS. Comm. Stat. Theor. Meth. 1986, 15:2785-2808.
7. Searle S.R., Casella G., McCulloch C.E. Variance Components 1992, John Wiley and Sons, Hoboken, NJ.
8. Miller J., Miller J.C. Statistics and Chemometrics for Analytical Chemistry 2010, Pearson Education Limited, Harlow, UK. sixth ed.
9. Brereton R.G. Chemometrics: Data Analysis for the Laboratory and Chemical Plant 2003, John Wiley and Sons, New York, NY.
10. Esbensen K., Geladi P. Principles of proper validation: use and abuse of re-sampling for validation. J. Chemom. 2010, 24:168-187.
11. Kos G., Lohninger H., Krska R. Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy. Mycotoxin Res. 2003, 19:149-153.
12. Beleites C., Salzer R. Assessing and improving the stability of chemometric models in small sample size situations. Anal. Bioanal. Chem. 2008, 390:1261-1271.
13. Wood J.E., Allaway D., Boult E., Scott I.M. Operationally realistic validation for prediction of cocoa sensory qualities by high-throughput mass spectrometry. Anal. Chem. 2010, 82:6048-6055.
14. Hawkins D.M., Kraker J. Deterministic fallacies and model validation. J. Chemom. 2010, 24:188-193.
15. 2!. J. Mol. Graph. Model 2002, 20:269-276.
16. Baumann K., Albert H., Von Korff M. A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part I. Search algorithm, theory and simulations,. J. Chemom. 2002, 16:339-350.
17. O'Connell M.-L., Ryder A.G., Leger M.N., Howley T. Qualitative analysis using Raman spectroscopy and chemometrics: a comprehensive model system for narcotics analysis,. Appl. Spectrosc. 2010, 64:1109-1121.
18. Xu Q.S., Liang Y.Z. Monte Carlo cross validation. Chemom. Intell. Lab. 2001, 56:1-11.
19. Xu Q.S., Liang Y.Z., Du Y.P. Monte Carlo cross-validation for selecting model. Prediction error in multivariate calibration. J. Chemom. 2004, 18:112-120.
20. Urban Hjort J.S. Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap 1994, 40-56. Chapman and Hall, London, UK.
21. Nørgaard L., Bro R. PLS regression in the food industry. A study of N-PLS regression and variable selection for improving prediction errors and interpretation. Les Methods PLS. Symposium International PLS 99, CISIA - CERESTA, France 1999, 187-202. M. Tenenhaus, A. Morineau (Eds.).
22. Anderssen E., Dyrstad K., Westad F., Martens H. Reducing over-optimism in variable selection by cross-model validation. Chemom. Intell. Lab. Syst. 2006, 84:69-74.
23. Westad F., Afseth N.K., Bro R. Finding relevant spectral regions between spectroscopic techniques by use of cross model validation and partial least squares regression. Anal. Chim. Acta 2007, 595:323-327.
24. Stone M. Cross-validatory choice and assessment of statistical prediction, J Roy. Stat. Soc. B 1974, 36:111-147.
25. Efron B. The Jackknife, the Bootstrap, and Other Resampling Plans. CBMS-NSF Regional Conference Series in Applied Mathematics 1982, Society for Industrial and Applied Mathematics, Philadelphia, PA.
26. Efron B. Bootstrap methods: another look at the jackknife. Ann. Stat. 1979, 1:1-26.
27. Xu L., Xu Q.S., Yang M., Zhang H.Z., Cai C.B., Jiang J.H., Wu H.L., Yu R.Q. On estimating model complexity and prediction errors in multivariate calibration: generalized resampling by random sample weighting (RSW). J. Chemom. 2011, 25:51-58.
28. Kennard R.W., Stone L.A. Computer aided design of experiments. Technometrics 1969, 11:137-148.
29. Snee R.D. Validation of regression models: methods and examples. Technometrics 1977, 19:415-428.
30. Wu W., Walczak B., Massart D.L., Heuerding S., Erni F., Last I.R., Prebble K.A. Artificial neural networks in classification of NIR spectral data : design of the training set. Chemom. Intell. Lab. Syst. 1996, 33:35-46.
31. Rajer-Kanduč K., Zupan J., Majcen N. Separation of data on the training and test set for modeling: a case study for modeling of five colours properties of a white pigment. Chemom. Intell. Lab. Syst. 2003, 65:221-229.
32. Marini F., Magrì A.L., Bucci R., Magrì A.D. Use of different artificial neural networks to resolve binary blends of monocultivar Italian olive oils. Anal. Chim. Acta 2007, 599:232-240.
33. De Juan A., Tauler R. Chemometrics applied to unravel multicomponent processes and mixtures. Revisiting latest trends in multivariate resolution. Anal. Chim. Acta 2003, 500:195-210.
34. Westad F., Kermit M. Independent component analysis. Comprehensive Chemometrics 2009, vol. 2:pp.227-248. Elsevier, Oxford, UK.
35. Windig W., Guilment J. Interactive self-modeling mixture analysis. Anal. Chem. 1991, 65:1425-1432.
36. Rajko R. Comments on "near-infrared hyperspectral unmixing based on a minimum volume criterion for fast and accurate chemometric characterization of counterfeit tablets". Anal. Chem. 2010, 82:8750-8752.
37. Bro R. PARAFAC. Tutorial and applications. Chemom. Intell. Lab. Syst. 1997, 38:149-171.
38. Munck L., Norgaard L., Engelsen S.B., Bro R., Andersson C.A. Chemometrics in food science - a demonstration of the feasibility of a highly exploratory, inductive evaluation strategy of fundamental scientific significance. Chemom. Intell. Lab. 1998, 44:31-60.
39. Bellato S., Del Frate V., Redaelli R., Sgrulletta D., Bucci R., Magrì A.D., Marini F. Use of near infrared reflectance and transmittance coupled to robust calibration for the evaluation of nutritional value in naked oats. J. Agric. Food Chem. 2011, 59:4349-4360.
40. G.E. Ritchie, Pharmaceutical Analysis/New Technology, Purdue Pharma L.P., 444 Saw Mill River Road, Ardsley, NY 10502. Data set available at: (Last accessed 02.01.2013). http://www.idrc-chambersburg.org/ss20022012.html.
41. Westad F., Martens H. Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression. J. Near Infrared Spectrosc. 2000, 8:117-124.
42. Bjørnstad A., Westad F., Martens H. Analysis of genetic marker-phenotype relationships by jack-knifed partial least squares regression (PLSR). Hereditas 2004, 141:149-165.
43. Ni Y., Chen S., Kokot S. Spectrophotometric determination of metal ions in electroplating solutions in the presence of EDTA with the aid of multivariate calibration and artificial neural networks. Anal. Chim. Acta 2002, 463:305-316.
44. Greensill C.V., Walsh K.B. Calibration transfer between miniature photodiode array-based spectrometers in the near infrared assessment of mandarin soluble solids content. J. Near Infrared Spectrosc. 2002, 10:27-35.
45. Kazeminy A., Hashemi S., Williams R.L., Ritchie G.E., Rubinovitz R., Sen S. A comparison of near infrared method development approaches using a drug product on different spectrophotometers and chemometric software algorithms. J. Near Infrared Spectrosc. 2009, 17:233-245.
46. Preisner O., Lopes J.A., Menezes J.C. Uncertainty assessment in FT-IR spectroscopy based bacteria classification models. Chemom. Intell. Lab. 2008, 94:33-42.
47. Murtaugh P. Performance of several variable-selection methods applied to real ecological data. Ecol. Lett. 2009, 12:1061-1068.
48. Reunanen J. Overfitting in making comparisons between variable selection methods. J. Mach. Learn. Res. 2003, 3:1371-1382.
49. Martens H., Martens M. Multivariate Analysis of Quality. An Introduction 2001, John Wiley and Sons, New York, NY.
50. Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemom. 2000, 14:643-655.
51. Box G.P.E. Teaching engineers experimental design with a paper helicopter. Qual. Eng. 1992, 4:453-459.