7 NIR Data Exploration and Regression by Chemometrics—A Primer
187
calibrations are becoming more widespread, and they can be problematic in terms
of accuracy and robustness of the calibration models, since they rely on biological
covariance structures, which may not remain constant over time or other (changing)
external factors.
References
1. S.B. Engelsen, Near infrared spectroscopy—a unique window of opportunities. NIR News
27(5), 14 (2016)
2. P.C. Williams, K.H. Norris, Near Infrared Technology in the Agricultural and Food Industries
(American Association of Cereal Chemists, Inc., St. Paul, Mn, 1987)
3. B.G. Osborne, T. Fearn, P.H. Hindle, Practical NIR Spectroscopy with Applications in Food
and Beverage Analysis (Longman Scientific & Technical, Harlow, Essex, UK, 1986)
4. R. DiFoggio, Guidelines for applying chemometrics to spectra: feasibility and error propagation. Appl. Spectrosc. 54(3), 94A (2000)
5. P. Geladi, K. Esbensen, The start and early history of chemometrics. 1. Selected interviews. J.
Chemometrics 4 (5), 337 (1990)
6. S.B. Engelsen, E. Mikkelsen, L. Munck, New approaches to rapid spectroscopic evaluation of
properties in pectic polymers. Progr. Colloid Polym. Sci. 108, 166 (1998)
7. Y. Dong, K.M. Sørensen, S. He, S.B. Engelsen, Gum Arabic authentication and mixture
quantification by near infrared spectroscopy. Food Control 78 (Supplement C), 144 (2017)
8. E. Tønning, L. Nørgaard, S.B. Engelsen, L. Pedersen, K.H. Esbensen, Protein heterogeneity in
wheat lots using single-seed NIT—A Theory of Sampling (TOS) breakdown of all sampling
and analytical errors. Chemometr. Intell. Lab. Syst. 84(1–2), 142 (2006)
9. J. Kjeldahl, A new method for the determination of nitrogen in organic bodies. Anal. Chem.
22, 366 (1883)
10. H.W. Siesler, Y. Ozaki, S. Kawata, H.M. Heise, Near-Infrared Spectroscopy: Principles,
Instruments (Wiley-VCH, Applications, 2008)
11. A. Rinnan, F. van den Berg, S.B. Engelsen, Review of the most common pre-processing
techniques for near-infrared spectra. TRAC-trends Anal Chem 28(10), 1201 (2009)
12. P. Geladi, D. McDougall, H. Martens, Linearization and scatter-correction for near-infrared
reflectance spectra of meat. Appl. Spectrosc. 39(3), 491 (1985)
13. H. Martens, S.A. Jensen, P. Geladi, N-4000 Stavanger, Norway, p 205 (1983)
14. R.J. Barnes, M.S. Dhanoa, S.J. Lister, Standard normal variate transformation and de-trending
of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 43(5), 772 (1989)
15. H. Martens, E. Stark, Extended multiplicative signal correction and spectral interference
subtraction: New preprocessing methods for near infrared spectroscopy. J. Pharm. Biomed.
Anal. 9(8), 625 (1991)
16. H. Martens, J.P. Nielsen, S.B. Engelsen, Light scattering and light absorbance separated by
extended multiplicative signal correction. Application to near-infrared transmission analysis
of powder mixtures. Anal. Chem. 75 (3), 394 (2003)
17. A. Savitzky, M.J.E. Golay, Smoothing and differentiation of data by simplified least squares
procedures. Anal. Chem. 36, 1627 (1964)
18. W.H. Lawton, E.A. Sylvestre, Self modeling curve resolution. Technometrics 13(3), 617 (1971)
19. A. de Juan, J. Jaumot, R. Tauler, Multivariate curve resolution (MCR). Solving the mixture
analysis problem. Anal. Methods 6 (14), 4964 (2014)
20. J. de Leeuw, F.W. Young, Y. Takane, Additive structure in qualitative data: An alternating least
squares method with optimal scaling features. Psychometrika 41(4), 471 (1976)
21. A. de Juan, R. Tauler, Multivariate curve resolution (MCR) from 2000: Progress in concepts
and applications. Crit. Rev. Anal. Chem. 36(3–4), 163 (2006)
187
calibrations are becoming more widespread, and they can be problematic in terms
of accuracy and robustness of the calibration models, since they rely on biological
covariance structures, which may not remain constant over time or other (changing)
external factors.
References
1. S.B. Engelsen, Near infrared spectroscopy—a unique window of opportunities. NIR News
27(5), 14 (2016)
2. P.C. Williams, K.H. Norris, Near Infrared Technology in the Agricultural and Food Industries
(American Association of Cereal Chemists, Inc., St. Paul, Mn, 1987)
3. B.G. Osborne, T. Fearn, P.H. Hindle, Practical NIR Spectroscopy with Applications in Food
and Beverage Analysis (Longman Scientific & Technical, Harlow, Essex, UK, 1986)
4. R. DiFoggio, Guidelines for applying chemometrics to spectra: feasibility and error propagation. Appl. Spectrosc. 54(3), 94A (2000)
5. P. Geladi, K. Esbensen, The start and early history of chemometrics. 1. Selected interviews. J.
Chemometrics 4 (5), 337 (1990)
6. S.B. Engelsen, E. Mikkelsen, L. Munck, New approaches to rapid spectroscopic evaluation of
properties in pectic polymers. Progr. Colloid Polym. Sci. 108, 166 (1998)
7. Y. Dong, K.M. Sørensen, S. He, S.B. Engelsen, Gum Arabic authentication and mixture
quantification by near infrared spectroscopy. Food Control 78 (Supplement C), 144 (2017)
8. E. Tønning, L. Nørgaard, S.B. Engelsen, L. Pedersen, K.H. Esbensen, Protein heterogeneity in
wheat lots using single-seed NIT—A Theory of Sampling (TOS) breakdown of all sampling
and analytical errors. Chemometr. Intell. Lab. Syst. 84(1–2), 142 (2006)
9. J. Kjeldahl, A new method for the determination of nitrogen in organic bodies. Anal. Chem.
22, 366 (1883)
10. H.W. Siesler, Y. Ozaki, S. Kawata, H.M. Heise, Near-Infrared Spectroscopy: Principles,
Instruments (Wiley-VCH, Applications, 2008)
11. A. Rinnan, F. van den Berg, S.B. Engelsen, Review of the most common pre-processing
techniques for near-infrared spectra. TRAC-trends Anal Chem 28(10), 1201 (2009)
12. P. Geladi, D. McDougall, H. Martens, Linearization and scatter-correction for near-infrared
reflectance spectra of meat. Appl. Spectrosc. 39(3), 491 (1985)
13. H. Martens, S.A. Jensen, P. Geladi, N-4000 Stavanger, Norway, p 205 (1983)
14. R.J. Barnes, M.S. Dhanoa, S.J. Lister, Standard normal variate transformation and de-trending
of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 43(5), 772 (1989)
15. H. Martens, E. Stark, Extended multiplicative signal correction and spectral interference
subtraction: New preprocessing methods for near infrared spectroscopy. J. Pharm. Biomed.
Anal. 9(8), 625 (1991)
16. H. Martens, J.P. Nielsen, S.B. Engelsen, Light scattering and light absorbance separated by
extended multiplicative signal correction. Application to near-infrared transmission analysis
of powder mixtures. Anal. Chem. 75 (3), 394 (2003)
17. A. Savitzky, M.J.E. Golay, Smoothing and differentiation of data by simplified least squares
procedures. Anal. Chem. 36, 1627 (1964)
18. W.H. Lawton, E.A. Sylvestre, Self modeling curve resolution. Technometrics 13(3), 617 (1971)
19. A. de Juan, J. Jaumot, R. Tauler, Multivariate curve resolution (MCR). Solving the mixture
analysis problem. Anal. Methods 6 (14), 4964 (2014)
20. J. de Leeuw, F.W. Young, Y. Takane, Additive structure in qualitative data: An alternating least
squares method with optimal scaling features. Psychometrika 41(4), 471 (1976)
21. A. de Juan, R. Tauler, Multivariate curve resolution (MCR) from 2000: Progress in concepts
and applications. Crit. Rev. Anal. Chem. 36(3–4), 163 (2006)