Principal Component Regression in Noninvasive Pineapple Soluble Solids Content Assessment Based On Shortwave Near Infrared Spectrum
The Principal component regression (PCR) is a
combination of principal component analysis (PCA) and multiple linear regression (MLR). The objective of this paper is to revise the
use of PCR in shortwave near infrared (SWNIR) (750-1000nm) spectral analysis. The idea of PCR was explained mathematically and
implemented in the non-destructive assessment of the soluble solid
content (SSC) of pineapple based on SWNIR spectral data. PCR achieved satisfactory results in this application with root mean
squared error of calibration (RMSEC) of 0.7611 Brix°, coefficient of determination (R2) of 0.5865 and root mean squared error of crossvalidation
(RMSECV) of 0.8323 Brix° with principal components
(PCs) of 14.
[1] H. Abdi and L. J. Williams, "Principal component analysis," Wiley
Interdisciplinary Reviews: Computational Statistics, vol. 2, pp. 433-459,2010.
[2] S. Serneels and T. Verdonck, "Principal component regression for data containing outliers and missing elements," Computational Statistics &
Data Analysis, vol. 53, pp. 3855-3863, 2009.
[3] T. Næs and B.-H. Mevik, "Understanding the collinearity problem in
regression and discriminant analysis," Journal of Chemometrics, vol. 15,
pp. 413-426, 2001.
[4] M. A. Kramer, "Nonlinear principal component analysis using
autoassociative neural networks," AIChE Journal, vol. 37, pp. 233-243,
1991.
[5] W. W. Hsieh, "Nonlinear principal component analysis by neural
networks," Tellus A, vol. 53, pp. 599-615, 2001.
[6] A. H. Monahan, "Nonlinear Principal Component Analysis by Neural
Networks: Theory and Application to the Lorenz System," Journal of
Climate, vol. 13, pp. 821-835, 2000.
[7] M. Scholz, et al., "Non-linear PCA: a missing data approach,"
Bioinformatics, vol. 21, pp. 3887-3895, 2005.
[8] U. Kruger, et al., "Developments and Applications of Nonlinear
Principal Component Analysis - a Review," ed, 2007, pp. 1-43.
[9] W. Wu, et al., "Kernel-PCA algorithms for wide data Part II: Fast crossvalidation
and application in classification of NIR data," Chemometrics
and Intelligent Laboratory Systems, vol. 37, pp. 271-280, 1997.
[10] C. Ding and X. He, "K-means clustering via principal component
analysis," presented at the Proceedings of the twenty-first international
conference on Machine learning, Banff, Alberta, Canada, 2004.
[11] B. J. Kim and I. K. Kim, "Incremental Nonlinear PCA for
Classification," in Knowledge Discovery in Databases: PKDD 2004.
vol. 3202, J.-F. Boulicaut, et al., Eds., ed: Springer Berlin / Heidelberg,
2004, pp. 291-300.
[12] S. Ledauphin, et al., "Simplification and signification of principal
components," Chemometrics and Intelligent Laboratory Systems, vol.
74, pp. 277-281, 2004.
[13] A. S. Barros and D. N. Rutledge, "Segmented principal component
transform-principal component analysis," Chemometrics and Intelligent
Laboratory Systems, vol. 78, pp. 125-137, 2005.
[14] J. Camacho, et al., "Data understanding with PCA: Structural and
Variance Information plots," Chemometrics and Intelligent Laboratory
Systems, vol. 100, pp. 48-56, 2010.
[15] T. Sato, "Application of principal-component analysis on near-infrared
spectroscopic data of vegetable oils for their classification," Journal of
the American Oil Chemists' Society, vol. 71, pp. 293-298, 1994.
[16] R. De Maesschalck, et al., "The Development of Calibration Models For
Spectroscopic Data Using Principal Component Regression," Internet
Journal of Chemistry, vol. 2, 1999.
[17] T. Næs, et al., A User-Friendly Guide to Multivariate Calibration and
Classification: NIR Publications, 2002.
[18] B. Park, et al., Principal component regression of near-infrared
reflectance spectra for beef tenderness prediction vol. 44. St. Joseph,
MI, ETATS-UNIS: American Society of Agricultural Engineers, 2001.
[19] C. W. Chang, et al., Near-infrared reflectance spectroscopy-principal
components regression analyses of soil properties vol. 65. Madison, WI,
ETATS-UNIS: Soil Science Society of America, 2001.
[20] P. J. Gemperline, "Principal Component Analysis," in Practical Guide
To Chemometrics, Second Edition, ed: CRC Press, 2006, pp. 69-104.
[21] E. Anderson, et al., LAPACK Users' Guide, Third Edition ed.: Society
for Industrial and Applied Mathematics, 1999.
[1] H. Abdi and L. J. Williams, "Principal component analysis," Wiley
Interdisciplinary Reviews: Computational Statistics, vol. 2, pp. 433-459,2010.
[2] S. Serneels and T. Verdonck, "Principal component regression for data containing outliers and missing elements," Computational Statistics &
Data Analysis, vol. 53, pp. 3855-3863, 2009.
[3] T. Næs and B.-H. Mevik, "Understanding the collinearity problem in
regression and discriminant analysis," Journal of Chemometrics, vol. 15,
pp. 413-426, 2001.
[4] M. A. Kramer, "Nonlinear principal component analysis using
autoassociative neural networks," AIChE Journal, vol. 37, pp. 233-243,
1991.
[5] W. W. Hsieh, "Nonlinear principal component analysis by neural
networks," Tellus A, vol. 53, pp. 599-615, 2001.
[6] A. H. Monahan, "Nonlinear Principal Component Analysis by Neural
Networks: Theory and Application to the Lorenz System," Journal of
Climate, vol. 13, pp. 821-835, 2000.
[7] M. Scholz, et al., "Non-linear PCA: a missing data approach,"
Bioinformatics, vol. 21, pp. 3887-3895, 2005.
[8] U. Kruger, et al., "Developments and Applications of Nonlinear
Principal Component Analysis - a Review," ed, 2007, pp. 1-43.
[9] W. Wu, et al., "Kernel-PCA algorithms for wide data Part II: Fast crossvalidation
and application in classification of NIR data," Chemometrics
and Intelligent Laboratory Systems, vol. 37, pp. 271-280, 1997.
[10] C. Ding and X. He, "K-means clustering via principal component
analysis," presented at the Proceedings of the twenty-first international
conference on Machine learning, Banff, Alberta, Canada, 2004.
[11] B. J. Kim and I. K. Kim, "Incremental Nonlinear PCA for
Classification," in Knowledge Discovery in Databases: PKDD 2004.
vol. 3202, J.-F. Boulicaut, et al., Eds., ed: Springer Berlin / Heidelberg,
2004, pp. 291-300.
[12] S. Ledauphin, et al., "Simplification and signification of principal
components," Chemometrics and Intelligent Laboratory Systems, vol.
74, pp. 277-281, 2004.
[13] A. S. Barros and D. N. Rutledge, "Segmented principal component
transform-principal component analysis," Chemometrics and Intelligent
Laboratory Systems, vol. 78, pp. 125-137, 2005.
[14] J. Camacho, et al., "Data understanding with PCA: Structural and
Variance Information plots," Chemometrics and Intelligent Laboratory
Systems, vol. 100, pp. 48-56, 2010.
[15] T. Sato, "Application of principal-component analysis on near-infrared
spectroscopic data of vegetable oils for their classification," Journal of
the American Oil Chemists' Society, vol. 71, pp. 293-298, 1994.
[16] R. De Maesschalck, et al., "The Development of Calibration Models For
Spectroscopic Data Using Principal Component Regression," Internet
Journal of Chemistry, vol. 2, 1999.
[17] T. Næs, et al., A User-Friendly Guide to Multivariate Calibration and
Classification: NIR Publications, 2002.
[18] B. Park, et al., Principal component regression of near-infrared
reflectance spectra for beef tenderness prediction vol. 44. St. Joseph,
MI, ETATS-UNIS: American Society of Agricultural Engineers, 2001.
[19] C. W. Chang, et al., Near-infrared reflectance spectroscopy-principal
components regression analyses of soil properties vol. 65. Madison, WI,
ETATS-UNIS: Soil Science Society of America, 2001.
[20] P. J. Gemperline, "Principal Component Analysis," in Practical Guide
To Chemometrics, Second Edition, ed: CRC Press, 2006, pp. 69-104.
[21] E. Anderson, et al., LAPACK Users' Guide, Third Edition ed.: Society
for Industrial and Applied Mathematics, 1999.
@article{"International Journal of Electrical, Electronic and Communication Sciences:62693", author = "K. S. Chia and H. Abdul Rahim and R. Abdul Rahim", title = "Principal Component Regression in Noninvasive Pineapple Soluble Solids Content Assessment Based On Shortwave Near Infrared Spectrum", abstract = "The Principal component regression (PCR) is a
combination of principal component analysis (PCA) and multiple linear regression (MLR). The objective of this paper is to revise the
use of PCR in shortwave near infrared (SWNIR) (750-1000nm) spectral analysis. The idea of PCR was explained mathematically and
implemented in the non-destructive assessment of the soluble solid
content (SSC) of pineapple based on SWNIR spectral data. PCR achieved satisfactory results in this application with root mean
squared error of calibration (RMSEC) of 0.7611 Brix°, coefficient of determination (R2) of 0.5865 and root mean squared error of crossvalidation
(RMSECV) of 0.8323 Brix° with principal components
(PCs) of 14.", keywords = "Pineapple, Shortwave near infrared, Principal component regression, Non-invasive measurement; Soluble solids content", volume = "7", number = "5", pages = "555-4", }
