메뉴 건너뛰기




Volumn 144, Issue , 2015, Pages 56-62

Using consensus interval partial least square in near infrared spectra analysis

Author keywords

Consensus interval partial least squares; Consensus modeling; Error correlation; Interval partial least squares; Near infrared spectra; Partial least squares

Indexed keywords

ALGORITHM; ARTICLE; CONSENSUS INTERVAL PARTIAL LEAST SQUARE; CONTROLLED STUDY; MEASUREMENT ERROR; NEAR INFRARED SPECTROSCOPY; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; REGRESSION ANALYSIS; ROOT MEAN SQUARE ERROR OF CROSS VALIDATION; ROOT MEAN SQUARE ERROR OF PREDICTION; STATISTICAL ANALYSIS; STATISTICAL CONCEPTS;

EID: 84927631921     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2015.03.008     Document Type: Article
Times cited : (31)

References (38)
  • 1
    • 1942454425 scopus 로고    scopus 로고
    • Review: 204years of near infrared technology: 1800-2003
    • McClure W.F. Review: 204years of near infrared technology: 1800-2003. J. Near Infrared Spectrosc. 2004, 11:487-518.
    • (2004) J. Near Infrared Spectrosc. , vol.11 , pp. 487-518
    • McClure, W.F.1
  • 3
    • 84874559041 scopus 로고    scopus 로고
    • Integration of NIRS and PCA techniques for the process monitoring of a sewage sludge anaerobic digester
    • Reed J.P., Devlin D., Esteves S.R., Dinsdale R., Guwy A.J. Integration of NIRS and PCA techniques for the process monitoring of a sewage sludge anaerobic digester. Bioresour. Technol. 2013, 133:398-404.
    • (2013) Bioresour. Technol. , vol.133 , pp. 398-404
    • Reed, J.P.1    Devlin, D.2    Esteves, S.R.3    Dinsdale, R.4    Guwy, A.J.5
  • 5
    • 84880168185 scopus 로고    scopus 로고
    • Traumatic and degenerative cartilage lesions: arthroscopic differentiation using near-infrared spectroscopy (NIRS)
    • Spahn G., Felmet G., Hofmann G.O. Traumatic and degenerative cartilage lesions: arthroscopic differentiation using near-infrared spectroscopy (NIRS). Arch. Orthop. Trauma Surg. 2013, 133:997-1002.
    • (2013) Arch. Orthop. Trauma Surg. , vol.133 , pp. 997-1002
    • Spahn, G.1    Felmet, G.2    Hofmann, G.O.3
  • 6
    • 59849087955 scopus 로고    scopus 로고
    • Application of a hybrid variable selection method for determination of carbohydrate content in soy milk powder using visible and near infrared spectroscopy
    • Chen X., Lei X. Application of a hybrid variable selection method for determination of carbohydrate content in soy milk powder using visible and near infrared spectroscopy. J. Agric. Food Chem. 2008, 57:334-340.
    • (2008) J. Agric. Food Chem. , vol.57 , pp. 334-340
    • Chen, X.1    Lei, X.2
  • 7
    • 61949254342 scopus 로고    scopus 로고
    • Detecting the quality of glycerol monolaurate: a method for using Fourier transform infrared spectroscopy with wavelet transform and modified uninformative variable elimination
    • Chen X., Wu D., He Y., Liu S. Detecting the quality of glycerol monolaurate: a method for using Fourier transform infrared spectroscopy with wavelet transform and modified uninformative variable elimination. Anal. Chim. Acta 2009, 638:16-22.
    • (2009) Anal. Chim. Acta , vol.638 , pp. 16-22
    • Chen, X.1    Wu, D.2    He, Y.3    Liu, S.4
  • 8
    • 11144325691 scopus 로고
    • Partial least-squares regression: a tutorial
    • Geladi P., Kowalski B.R. Partial least-squares regression: a tutorial. Anal. Chim. Acta 1986, 185:1-17.
    • (1986) Anal. Chim. Acta , vol.185 , pp. 1-17
    • Geladi, P.1    Kowalski, B.R.2
  • 9
    • 84898670728 scopus 로고    scopus 로고
    • A segmented PLS method based on genetic algorithm
    • Huang G., Ruan X., Chen X., Lin D., Liu W. A segmented PLS method based on genetic algorithm. Anal. Methods 2014, 6:2900-2908.
    • (2014) Anal. Methods , vol.6 , pp. 2900-2908
    • Huang, G.1    Ruan, X.2    Chen, X.3    Lin, D.4    Liu, W.5
  • 10
    • 0001681052 scopus 로고
    • The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses
    • Wold S., Ruhe A., Wold H., I.Dunn W.J. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J. Sci. Stat. Comput. 1984, 5:735-743.
    • (1984) SIAM J. Sci. Stat. Comput. , vol.5 , pp. 735-743
    • Wold, S.1    Ruhe, A.2    Wold, H.3    I.Dunn, W.J.4
  • 11
    • 84888303630 scopus 로고    scopus 로고
    • Feature selection for high-dimensional multi-category data using PLS-based local recursive feature elimination
    • You W., Yang Z., Ji G. Feature selection for high-dimensional multi-category data using PLS-based local recursive feature elimination. Expert Syst. Appl. 2014, 41:1463-1475.
    • (2014) Expert Syst. Appl. , vol.41 , pp. 1463-1475
    • You, W.1    Yang, Z.2    Ji, G.3
  • 12
    • 84888306070 scopus 로고    scopus 로고
    • PLS-based recursive feature elimination for high-dimensional small sample
    • You W., Yang Z., Ji G. PLS-based recursive feature elimination for high-dimensional small sample. Knowledge-Based Syst. 2014, 55:15-28.
    • (2014) Knowledge-Based Syst. , vol.55 , pp. 15-28
    • You, W.1    Yang, Z.2    Ji, G.3
  • 13
    • 80054820667 scopus 로고    scopus 로고
    • PLS-based gene selection and identification of tumor-specific genes
    • Ji G., Yang Z., You W. PLS-based gene selection and identification of tumor-specific genes. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 2011, 41:830-841.
    • (2011) IEEE Trans. Syst. Man Cybern. C Appl. Rev. , vol.41 , pp. 830-841
    • Ji, G.1    Yang, Z.2    You, W.3
  • 14
    • 84893126455 scopus 로고    scopus 로고
    • TotalPLS: local dimension reduction for multicategory microarray data
    • You W., Yang Z., Yuan M., Ji G. TotalPLS: local dimension reduction for multicategory microarray data. IEEE Trans. Hum.-Mach. Syst. 2014, 44:1-14.
    • (2014) IEEE Trans. Hum.-Mach. Syst. , vol.44 , pp. 1-14
    • You, W.1    Yang, Z.2    Yuan, M.3    Ji, G.4
  • 15
    • 79952455921 scopus 로고    scopus 로고
    • Using partial least squares and support vector machines for bankruptcy prediction
    • Yang Z., You W., Ji G. Using partial least squares and support vector machines for bankruptcy prediction. Expert Syst. Appl. 2011, 38:8336-8342.
    • (2011) Expert Syst. Appl. , vol.38 , pp. 8336-8342
    • Yang, Z.1    You, W.2    Ji, G.3
  • 16
    • 79953701650 scopus 로고    scopus 로고
    • Fourier transform infrared (FT-IR) spectroscopy and improved principal component regression (PCR) for quantification of solid analytes in microalgae and bacteria
    • Horton R.B., Duranty E., McConico M., Vogt F. Fourier transform infrared (FT-IR) spectroscopy and improved principal component regression (PCR) for quantification of solid analytes in microalgae and bacteria. Appl. Spectrosc. 2011, 65:442-453.
    • (2011) Appl. Spectrosc. , vol.65 , pp. 442-453
    • Horton, R.B.1    Duranty, E.2    McConico, M.3    Vogt, F.4
  • 17
    • 70349396621 scopus 로고    scopus 로고
    • Multivariate concentration determination using principal component regression with residual analysis
    • Keithley R.B., Mark Wightman R., Heien M.L. Multivariate concentration determination using principal component regression with residual analysis. TrAC Trends Anal. Chem. 2009, 28:1127-1136.
    • (2009) TrAC Trends Anal. Chem. , vol.28 , pp. 1127-1136
    • Keithley, R.B.1    Mark Wightman, R.2    Heien, M.L.3
  • 18
    • 85099638398 scopus 로고    scopus 로고
    • Comparison of partial least squares regression (PLSR) and principal components regression (PCR) methods for protein and hardness predictions using the near-infrared (NIR) hyperspectral images of bulk Samples of Canadian wheat
    • Mahesh S., Jayas D., Paliwal J., White N. Comparison of partial least squares regression (PLSR) and principal components regression (PCR) methods for protein and hardness predictions using the near-infrared (NIR) hyperspectral images of bulk Samples of Canadian wheat. Food Bioprocess Technol. 2014, 1-10.
    • (2014) Food Bioprocess Technol. , pp. 1-10
    • Mahesh, S.1    Jayas, D.2    Paliwal, J.3    White, N.4
  • 19
    • 79955656600 scopus 로고    scopus 로고
    • Parallel genetic algorithm co-optimization of spectral pre-processing and wavelength selection for PLS regression
    • Devos O., Duponchel L. Parallel genetic algorithm co-optimization of spectral pre-processing and wavelength selection for PLS regression. Chemom. Intell. Lab. Syst. 2011, 107:50-58.
    • (2011) Chemom. Intell. Lab. Syst. , vol.107 , pp. 50-58
    • Devos, O.1    Duponchel, L.2
  • 20
    • 10444224550 scopus 로고    scopus 로고
    • Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique
    • Chu X.-L., Yuan H.-F., Lu W.-z. Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique. Prog. Chem. 2004, 16:528-542.
    • (2004) Prog. Chem. , vol.16 , pp. 528-542
    • Chu, X.-L.1    Yuan, H.-F.2    Lu, W.-Z.3
  • 21
    • 0028892656 scopus 로고
    • Spectral transformation and wavelength selection in near-infrared spectra classification
    • Wu W., Walczak B., Massart D., Prebble K., Last I. Spectral transformation and wavelength selection in near-infrared spectra classification. Anal. Chim. Acta 1995, 315:243-255.
    • (1995) Anal. Chim. Acta , vol.315 , pp. 243-255
    • Wu, W.1    Walczak, B.2    Massart, D.3    Prebble, K.4    Last, I.5
  • 23
    • 80051681716 scopus 로고    scopus 로고
    • Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver
    • Wu D., Chen X., Zhu X., Guan X., Wu G. Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver. Anal. Methods UK 2011, 3:1790-1796.
    • (2011) Anal. Methods UK , vol.3 , pp. 1790-1796
    • Wu, D.1    Chen, X.2    Zhu, X.3    Guan, X.4    Wu, G.5
  • 24
    • 84923950772 scopus 로고    scopus 로고
    • Identification of heavy metal-contaminated Tegillarca granosa using infrared spectroscopy
    • Chen X., Liu K., Cai J., Zhu D., Chen H. Identification of heavy metal-contaminated Tegillarca granosa using infrared spectroscopy. Anal. Methods 2015, 7:2172-2181.
    • (2015) Anal. Methods , vol.7 , pp. 2172-2181
    • Chen, X.1    Liu, K.2    Cai, J.3    Zhu, D.4    Chen, H.5
  • 26
    • 84887237244 scopus 로고    scopus 로고
    • A simple idea on applying large regression coefficient to improve the genetic algorithm-PLS for variable selection in multivariate calibration
    • Yun Y.-H., Cao D.-S., Tan M.-L., Yan J., Ren D.-B., Xu Q.-S., Yu L., Liang Y.-Z. A simple idea on applying large regression coefficient to improve the genetic algorithm-PLS for variable selection in multivariate calibration. Chemom. Intell. Lab. Syst. 2014, 130:76-83.
    • (2014) Chemom. Intell. Lab. Syst. , vol.130 , pp. 76-83
    • Yun, Y.-H.1    Cao, D.-S.2    Tan, M.-L.3    Yan, J.4    Ren, D.-B.5    Xu, Q.-S.6    Yu, L.7    Liang, Y.-Z.8
  • 27
    • 11344276704 scopus 로고    scopus 로고
    • Application of mid infrared spectroscopy and iPLS for the quantification of contaminants in lubricating oil
    • Borin A., Poppi R.J. Application of mid infrared spectroscopy and iPLS for the quantification of contaminants in lubricating oil. Vib. Spectrosc. 2005, 37:27-32.
    • (2005) Vib. Spectrosc. , vol.37 , pp. 27-32
    • Borin, A.1    Poppi, R.J.2
  • 28
    • 38949127739 scopus 로고    scopus 로고
    • Determination of quercetin in extracts of Ginkgo biloba L. leaves by near-infrared reflectance spectroscopy based on interval partial least-squares (iPLS) model
    • Zhou X., Xiang B., Wang Z., Zhang M. Determination of quercetin in extracts of Ginkgo biloba L. leaves by near-infrared reflectance spectroscopy based on interval partial least-squares (iPLS) model. Anal. Lett. 2007, 40:3383-3391.
    • (2007) Anal. Lett. , vol.40 , pp. 3383-3391
    • Zhou, X.1    Xiang, B.2    Wang, Z.3    Zhang, M.4
  • 29
    • 0033905297 scopus 로고    scopus 로고
    • Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy
    • Norgaard L., Saudland A., Wagner J., Nielsen J.P., Munck L., Engelsen S. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Appl. Spectrosc. 2000, 54:413-419.
    • (2000) Appl. Spectrosc. , vol.54 , pp. 413-419
    • Norgaard, L.1    Saudland, A.2    Wagner, J.3    Nielsen, J.P.4    Munck, L.5    Engelsen, S.6
  • 30
    • 18844432760 scopus 로고    scopus 로고
    • Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions
    • Leardi R., Nørgaard L. Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. J. Chemom. 2004, 18:486-497.
    • (2004) J. Chemom. , vol.18 , pp. 486-497
    • Leardi, R.1    Nørgaard, L.2
  • 31
    • 33947320459 scopus 로고    scopus 로고
    • A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples
    • Li Y., Shao X., Cai W. A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. Talanta 2007, 72:217-222.
    • (2007) Talanta , vol.72 , pp. 217-222
    • Li, Y.1    Shao, X.2    Cai, W.3
  • 32
    • 33244476755 scopus 로고    scopus 로고
    • Ensemble of linear models for predicting drug properties
    • Arodz T., Yuen D.A., Dudek A.Z. Ensemble of linear models for predicting drug properties. J. Chem. Inf. Model. 2006, 46:416-423.
    • (2006) J. Chem. Inf. Model. , vol.46 , pp. 416-423
    • Arodz, T.1    Yuen, D.A.2    Dudek, A.Z.3
  • 33
    • 0142134297 scopus 로고    scopus 로고
    • Multivariate calibration of spectral data using dual-domain regression analysis
    • Tan H., Brown S.D. Multivariate calibration of spectral data using dual-domain regression analysis. Anal. Chim. Acta 2003, 490:291-301.
    • (2003) Anal. Chim. Acta , vol.490 , pp. 291-301
    • Tan, H.1    Brown, S.D.2
  • 34
    • 84922767938 scopus 로고    scopus 로고
    • A consensus successive projections algorithm-multiple linear regression method for analyzing near infrared spectra
    • Liu K., Chen X.J., Li L.M., et al. A consensus successive projections algorithm-multiple linear regression method for analyzing near infrared spectra. Anal. Chim. Acta 2015, 858:16-23.
    • (2015) Anal. Chim. Acta , vol.858 , pp. 16-23
    • Liu, K.1    Chen, X.J.2    Li, L.M.3
  • 35
    • 70350364485 scopus 로고    scopus 로고
    • Stacked partial least squares regression analysis for spectral calibration and prediction
    • Ni W., Brown S.D., Man R. Stacked partial least squares regression analysis for spectral calibration and prediction. J. Chemom. 2009, 23:505-517.
    • (2009) J. Chemom. , vol.23 , pp. 505-517
    • Ni, W.1    Brown, S.D.2    Man, R.3
  • 36
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regressions
    • Breiman L. Stacked regressions. Mach. Learn. 1996, 24:49-64.
    • (1996) Mach. Learn. , vol.24 , pp. 49-64
    • Breiman, L.1
  • 37
    • 15844401385 scopus 로고    scopus 로고
    • Training support vector machines based on stacked generalization for image classification
    • Tsai C.-F. Training support vector machines based on stacked generalization for image classification. Neurocomputing 2005, 64:497-503.
    • (2005) Neurocomputing , vol.64 , pp. 497-503
    • Tsai, C.-F.1
  • 38
    • 84952126648 scopus 로고
    • Validation of regression models: methods and examples
    • Snee R.D. Validation of regression models: methods and examples. Technometrics 1977, 19:415-428.
    • (1977) Technometrics , vol.19 , pp. 415-428
    • Snee, R.D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.