7 NIR Data Exploration and Regression by Chemometrics—A Primer
189
46. T. Mehmood, K.H. Liland, L. Snipen, S. Saebo, A review of variable selection methods in
partial least squares regression. Chemometr. Intell. Lab. Syst. 118, 62 (2012)
47. B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans. Soc. Ind. Appl. Math.
Philadelphia, Pennsylvania (1982)
48. H. Martens, M. Martens, Modified Jack-knife estimation of parameter uncertainty in bilinear
modelling by Partial Least Squares Regression (PLSR). Food Qual. Prefer. 11(1–2), 5 (2000)
49. S. Wold, E. Johansson, E. Cocchi, ESCOM, Leiden, Holland (1993) p. 523
50. I.G. Chong, C.H. Jun, Performance of some variable selection methods when multicollinearity
is present. Chemometr. Intell. Lab. Syst. 78(1–2), 103 (2005)
51. Å. Rinnan, M. Andersson, C. Ridder, S.B. Engelsen, Recursive weighted partial least squares
(rPLS): An efficient variable selection method using PLS. J. Chemom. 28(5), 439 (2014)
52. L. Nørgaard, A. Saudland, J. Wagner, J.P. Nielsen, L. Munck, S.B. Engelsen, Interval partial
least squares regression (iPLS): A comparative chemometric study with an example from the
near infrared spectroscopy. Appl. Spectrosc. 54(3), 413 (2000)
53. R.A. Fisher, The correlation between relatives on the supposition of Mendelian inheritance.
Philos Trans R Soc Edinburgh 52, 399 (1918)
54. A.K. Smilde, J.J. Jansen, H.C.J. Hoefsloot, R. Lamers, J. van der Greef, M.E. Timmerman,
ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed
metabolomics data. Bioinformatics 21(13), 3043 (2005)
55. J.A. Westerhuis, E.J.J. van Velzen, H.C.J. Hoefsloot, A.K. Smilde, Multivariate paired data
analysis: Multilevel PLSDA versus OPLSDA. Metabolomics 6(1), 119 (2010)
56. G. Zwanenburg, H.C.J. Hoefsloot, J.A. Westerhuis, J.J. Jansen, A.K. Smilde, ANOVAprincipal component analysis and ANOVA-simultaneous component analysis: A comparison.
J. Chemom. 25(10), 561 (2011)
57. A.L. Pomerantsev, O.Y. Rodionova, Process analytical technology: A critical view of the
chemometricians. J. Chemom. 26(6), 299 (2012)
58. E. Skibsted, S.B. Engelsen, in Encyclopedia of Spectroscopy and Spectrometry (Second Edition)
(Academic Press, Oxford, 2010)
59. P.C. Williams, Application of near-infrared reflectance spectroscopy to analysis of cereal-grains
and oilseeds. Cereal Chem. 52(4), 561 (1975)
60. G. Huang, G.B. Huang, S.J. Song, K.Y. You, Trends in extreme learning machines: A review.
Neural Netw. 61, 32 (2015)
61. S. Mahesh, A. Manickavasagan, D.S. Jayas, J. Paliwal, N.D.G. White, Feasibility of nearinfrared hyperspectral imaging to differentiate Canadian wheat classes. Biosyst. Eng. 101(1),
50 (2008)
62. A.P. Teixeira, R. Oliveira, P.M. Alves, M.J.T. Carrondo, Advances in on-line monitoring and
control of mammalian cell cultures: Supporting the PAT initiative. Biotechnol. Adv. 27(6), 726
(2009)
63. E. Borras, J. Ferre, R. Boque, M. Mestres, L. Acena, O. Busto, Data fusion methodologies for
food and beverage authentication and quality assessment—A review. Anal. Chim. Acta 891, 1
(2015)
64. I. Noda, Generalized 2-dimensional correlation method applicable to infrared, Raman and other
types of spectroscopy. Appl. Spectrosc. 47(9), 1329 (1993)
65. E. Alm, R. Bro, S.B. Engelsen, B. Karlberg, R.J.O. Torgrip, Vibrational overtone combination
spectroscopy (VOCSY)—A new way of using IR and NIR data. Anal. Bioanal. Chem. 388(1),
179 (2007)
66. C.E. Eskildsen, M.A. Rasmussen, S.B. Engelsen, L.B. Larsen, N.A. Poulsen, T. Skov, Quantification of individual fatty acids in bovine milk by infrared spectroscopy and chemometrics:
Understanding predictions of highly collinear reference variables. J. Dairy Sci. 97(12), 7940
(2014)
189
46. T. Mehmood, K.H. Liland, L. Snipen, S. Saebo, A review of variable selection methods in
partial least squares regression. Chemometr. Intell. Lab. Syst. 118, 62 (2012)
47. B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans. Soc. Ind. Appl. Math.
Philadelphia, Pennsylvania (1982)
48. H. Martens, M. Martens, Modified Jack-knife estimation of parameter uncertainty in bilinear
modelling by Partial Least Squares Regression (PLSR). Food Qual. Prefer. 11(1–2), 5 (2000)
49. S. Wold, E. Johansson, E. Cocchi, ESCOM, Leiden, Holland (1993) p. 523
50. I.G. Chong, C.H. Jun, Performance of some variable selection methods when multicollinearity
is present. Chemometr. Intell. Lab. Syst. 78(1–2), 103 (2005)
51. Å. Rinnan, M. Andersson, C. Ridder, S.B. Engelsen, Recursive weighted partial least squares
(rPLS): An efficient variable selection method using PLS. J. Chemom. 28(5), 439 (2014)
52. L. Nørgaard, A. Saudland, J. Wagner, J.P. Nielsen, L. Munck, S.B. Engelsen, Interval partial
least squares regression (iPLS): A comparative chemometric study with an example from the
near infrared spectroscopy. Appl. Spectrosc. 54(3), 413 (2000)
53. R.A. Fisher, The correlation between relatives on the supposition of Mendelian inheritance.
Philos Trans R Soc Edinburgh 52, 399 (1918)
54. A.K. Smilde, J.J. Jansen, H.C.J. Hoefsloot, R. Lamers, J. van der Greef, M.E. Timmerman,
ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed
metabolomics data. Bioinformatics 21(13), 3043 (2005)
55. J.A. Westerhuis, E.J.J. van Velzen, H.C.J. Hoefsloot, A.K. Smilde, Multivariate paired data
analysis: Multilevel PLSDA versus OPLSDA. Metabolomics 6(1), 119 (2010)
56. G. Zwanenburg, H.C.J. Hoefsloot, J.A. Westerhuis, J.J. Jansen, A.K. Smilde, ANOVAprincipal component analysis and ANOVA-simultaneous component analysis: A comparison.
J. Chemom. 25(10), 561 (2011)
57. A.L. Pomerantsev, O.Y. Rodionova, Process analytical technology: A critical view of the
chemometricians. J. Chemom. 26(6), 299 (2012)
58. E. Skibsted, S.B. Engelsen, in Encyclopedia of Spectroscopy and Spectrometry (Second Edition)
(Academic Press, Oxford, 2010)
59. P.C. Williams, Application of near-infrared reflectance spectroscopy to analysis of cereal-grains
and oilseeds. Cereal Chem. 52(4), 561 (1975)
60. G. Huang, G.B. Huang, S.J. Song, K.Y. You, Trends in extreme learning machines: A review.
Neural Netw. 61, 32 (2015)
61. S. Mahesh, A. Manickavasagan, D.S. Jayas, J. Paliwal, N.D.G. White, Feasibility of nearinfrared hyperspectral imaging to differentiate Canadian wheat classes. Biosyst. Eng. 101(1),
50 (2008)
62. A.P. Teixeira, R. Oliveira, P.M. Alves, M.J.T. Carrondo, Advances in on-line monitoring and
control of mammalian cell cultures: Supporting the PAT initiative. Biotechnol. Adv. 27(6), 726
(2009)
63. E. Borras, J. Ferre, R. Boque, M. Mestres, L. Acena, O. Busto, Data fusion methodologies for
food and beverage authentication and quality assessment—A review. Anal. Chim. Acta 891, 1
(2015)
64. I. Noda, Generalized 2-dimensional correlation method applicable to infrared, Raman and other
types of spectroscopy. Appl. Spectrosc. 47(9), 1329 (1993)
65. E. Alm, R. Bro, S.B. Engelsen, B. Karlberg, R.J.O. Torgrip, Vibrational overtone combination
spectroscopy (VOCSY)—A new way of using IR and NIR data. Anal. Bioanal. Chem. 388(1),
179 (2007)
66. C.E. Eskildsen, M.A. Rasmussen, S.B. Engelsen, L.B. Larsen, N.A. Poulsen, T. Skov, Quantification of individual fatty acids in bovine milk by infrared spectroscopy and chemometrics:
Understanding predictions of highly collinear reference variables. J. Dairy Sci. 97(12), 7940
(2014)