Determination of Total Sugar in Tobacco by Near-Infrared Spectroscopy and Wavelet Transformation-Based Calibration

REFERENCES

• Andersena , C. M. , and R. Bro . 2010 . Variable selection in regression-a tutorial . J. Chemometr. 24 : 728 – 737 .
   Web of Science ®Google Scholar
• Benoudjit , N. , D. Francois , M. Meurens , and M. Verleysen . 2004 . Spectrophotometric variable selection by mutual information . Chemom. Intell. Lab. Syst. 74 : 243 – 251 .
   Web of Science ®Google Scholar
• Cai , J. B. , B. Z. Liu , P. Lin , and Q. D. Su . 2003 . Fast analysis of nicotine related alkaloids in tobacco and cigarette smoke by megabore capillary gas chromatography . J. Chromatogr. A 1017 : 187 – 193 .
   PubMed Web of Science ®Google Scholar
• Dionisio , A. , R. Menezes , and D. A. Mendes . 2004 . Mutual information: A measure of dependency for nonlinear time series . Physica A 344 : 326 – 329 .
   Web of Science ®Google Scholar
• Ehrentreich , F. 2002 . Wavelet transform applications in analytical chemistry . Anal. Bioanal. Chem. 372 : 115 – 121 .
   PubMed Web of Science ®Google Scholar
• Faber , N. M. 2000 . Improved computation of the standard error in the regression coefficient estimates of a multivariate calibration model . Anal. Chem. 72 : 4675 – 4676 .
   PubMed Web of Science ®Google Scholar
• Galváo , R. K. H. , M. C. U. Araújo , G. E. José , M. J. C. Pontes , E. C. Silva , and T. C. B. Saldanha . 2005 . A method for calibration and validation subset partitioning . Talanta. 67 : 736 – 740 .
   PubMed Web of Science ®Google Scholar
• Galváo , R. K. H. , M. C. U. Araújo , M. N. Martins , G. E. José , M. J. C. Pontes , E. C. Silva , and T. C. B. Saldanha . 2006. An application of subagging for the improvement of prediction accuracy of multivariate calibration models. Chemom. Intell. Lab. Syst. 81: 60–67.
   Web of Science ®Google Scholar
• Gao , L. , and S. X. Ren . 2008 . Simultaneous multicomponent analysis of overlapping spectrophotometric signals using a wavelet-based latent variable regression . Spectrochim. Acta A 71 : 959 – 964 .
   Web of Science ®Google Scholar
• Jiang , J. H. , R. J. Berry , H. W. Siesler , and Y. Ozaki , Y. 2002 . Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data . Anal. Chem. 74 : 3555 – 3565 .
   PubMed Web of Science ®Google Scholar
• Jing , M. , W. S. Cai , and X. G. Shao . 2010 . Multiblock partial least squares regression based on wavelet transform for quantitative analysis of near infrared spectra . Chemom. Intell. Lab. Syst. 100 : 22 – 27 .
   Web of Science ®Google Scholar
• Kojadinovic , I. 2004 . Agglomerative hierarchical clustering of continuous variables based on mutual information . Comput. Stat. Data An. 46 : 269 – 294 .
   Web of Science ®Google Scholar
• Laxalde , J. , C. Ruckebusch , O. Devos , N. Caillol , F. Wahl , and L. Duponchel . 2011 . Characterisation of heavy oils using near-infrared spectroscopy: Optimisation of pre-processing methods and variable selection . Anal. Chim. Acta 705 : 227 – 233 .
   PubMed Web of Science ®Google Scholar
• Leardi , R. , and L. Nørgaard . 2004 . Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions . J. Chemometr. 18 : 486 – 497 .
   Web of Science ®Google Scholar
• Li , H. D. , Y. Z. Liang , Q. S. Xu , and D. S. Cao . 2009 . Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration . Anal. Chim. Acta 648 : 77 – 84 .
   PubMed Web of Science ®Google Scholar
• Liang , L. W. , B. Wang , Y. Guo , H. Ni , and Y. L. Ren . 2009 . A support vector machine-based analysis method with wavelet denoized near-infrared spectroscopy . Vib. Spectrosc. 49 : 274 – 277 .
   Web of Science ®Google Scholar
• Liu , Y. , and S. D. Brown . 2004 . Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches . Anal. Bioanal. Chem. 380 : 445 – 452 .
   PubMed Web of Science ®Google Scholar
• Moes , J. J. , M. M. Ruijken , E. Gout , H. W. Frijlink , and M. I. Ugwoke . 2008 . Application of process analytical technology in tablet process development using NIR spectroscopy: Blend uniformity, content uniformity and coating thickness measurements . Int. J. Pharm. 357 : 108 – 118 .
   PubMed Web of Science ®Google Scholar
• Moreira , E. D. T. , M. J. C. Pontes , R. K. H. Galváo , and M. C. U. Araújo . 2009 . Near infrared reflectance spectrometry classification of cigarettes using the successive projections algorithm for variable selection . Talanta 79 : 1260 – 1264 .
   PubMed Web of Science ®Google Scholar
• Oliveri , P. , V. D. Egidio , T. Woodcock , and G. Downey . 2011 . Application of class-modeling techniques to near infrared data for food authentication purposes . Food Chem. 25 : 1450 – 1456 .
   Web of Science ®Google Scholar
• Pinto , L. A. , R. K. H. Galváo , and M. C. U. Araújo . 2010 . Ensemble wavelet modeling for determination of wheat and gasoline properties by near and middle infrared spectroscopy . Anal. Chim. Acta 682 : 37 – 47 .
   PubMed Web of Science ®Google Scholar
• Ren , S. X. , and L. Gao . 2007 . Resolution of overlapping signals in spectrometry using a wavelet packet transform and an Elman recurrent neural network . Anal. Bioanal. Chem. 388 : 215 – 225 .
   PubMed Web of Science ®Google Scholar
• Rossi , F. , A. Lendasse , D. Francois , V. Wertz , and M. Verleysen . 2006 . Mutual information for the selection of relevant variables in spectrometric nonlinear modeling . Chemom. Intell. Lab. Syst. 80 : 215 – 226 .
   Web of Science ®Google Scholar
• Sarraguca , M. C. , S. O. Soares , and J. A. Lopes . 2011 . A near-infrared spectroscopy method to determine aminoglycosides in pharmaceutical formulations . Vib. Spectrosc. 56 : 184 – 192 .
   Web of Science ®Google Scholar
• Shao , X. G. , W. S. Cai , and P. Y. Sun . 1998. Determination of the component number in overlapping multicomponent chromatogram using wavelet transform. Chemom. Intell. Lab. Syst. 43: 147–155.
   Web of Science ®Google Scholar
• Shen , F. , F. Z. Li , D. L. Liu , H. R. Xu , Y. B. Ying , and B. B. Li . 2012 . Ageing status characterization of Chinese rice wines using chemical descriptors combined with multivariate data analysis . Food Contr. 25 : 458 – 463 .
   Web of Science ®Google Scholar
• Shinzawa , H. , J. H. Jiang , P. Ritthiruangdej , and Y. Ozaki . 2006 . Investigations of bagged kernel partial least squares (KPLS) and boosting KPLS with applications to near-infrared (NIR) spectra . J. Chemometr. 20 : 436 – 444 .
   Web of Science ®Google Scholar
• Su , Z. Q. , W. D. Tong , L. M. Shi , X. G. Shao , and W. S. Cai . 2006 . A partial least squares based consensus regression method for the analysis of near-infrared complex spectral data . Anal. Lett. 39 : 2073 – 2083 .
   Web of Science ®Google Scholar
• Tan , C. , and M. L. Li . 2007 . Mutual information-induced interval selection combined with kernel partial least squares for near-infrared spectral calibration . Spectrochim. Acta A 68 : 1266 – 1273 .
   Google Scholar
• Tan , C. , X. Qin , and M. L. Li . 2008 . An ensemble method based on a self-organizing map for near-infrared spectral calibration of complex beverage samples . Anal. Bioanal. Chem. 392 : 515 – 521 .
   PubMed Web of Science ®Google Scholar
• Tan , C. , J. Y. Wang , T. Wu , X. Qin , and M. L. Li . 2010a . An ensemble method based on uninformative variable elimination and mutual information for spectral multivariate calibration . Spectrochim. Acta A 77 : 960 – 964 .
   Web of Science ®Google Scholar
• Tan , C. , J. Y. Wang , T. Wu , X. Qin , and M. L. Li . 2010b . Determination of nicotine in tobacco samples by near-infrared spectroscopy and boosting partial least squares . Vib. Spectrosc. 54 : 35 – 41 .
   Web of Science ®Google Scholar
• Tan , H. W. , and S. D. Brown . 2003 . Multivariate calibration of spectral data using dual-domain regression analysis . Anal. Chim. Acta 490 : 291 – 301 .
   Web of Science ®Google Scholar
• Trygg , J. , and S. Wold . 1998 . PLS regression on wavelet compressed NIR spectra . Chemom. Intell. Lab. Syst. 42 : 209 – 220 .
   Web of Science ®Google Scholar
• Viscarra Rossel , R. A. 2007 . Robust modeling of soil diffuse reflectance spectra by bagging-partial least squares regression . J. Near Infrared Spectrosc. 15 : 39 – 47 .
   Web of Science ®Google Scholar
• Walczak , B. 2000 . Wavelets in chemistry . New York : Elsevier Science .
   Google Scholar
• Wold , S. , M. Sjöström , and L. Eriksson . 2001 . PLS-regression: A basic tool of chemometrics . Chemom. Intell. Lab. Syst. 58 : 109 – 130 .
   Web of Science ®Google Scholar
• Wu , Y. J. , Y. Jin , Y. R. Li , D. Sun , X. S. Liu , and Y. Chen . 2012 . NIR spectroscopy as a process analytical technology (PAT) tool for on-line and real-time monitoring of an extraction process . Vib. Spectrosc. 58 : 109 – 118 .
   Web of Science ®Google Scholar
• Zhang , Y. , Q. Cong , Y. F. Xie , J. X. Yang , and B. Zhao . 2008 . Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine . Spectrochim. Acta A 71 : 1408 – 1413 .
   PubMed Web of Science ®Google Scholar
• Zou , X. B. , J. W. Zhao , J. W. P. Malcolm , H. Mel , and H. P. Mao . 2010 . Variables selection methods in near-infrared spectroscopy . Anal. Chim. Acta 667 : 14 – 32 .
   PubMed Web of Science ®Google Scholar