Combination of Modified Optical Path Length Estimation and Correction and Moving Window Partial Least Squares to Waveband Selection for the Fourier Transform Near-Infrared Determination of Pectin in Shaddock Peel

被引:14
作者
Chen, Huazhou [1 ,2 ]
Tang, Guoqiang [1 ]
Song, Qiqing [1 ]
Ai, Wu [1 ]
机构
[1] Guilin Univ Technol, Coll Sci, Guilin 541004, Guangxi, Peoples R China
[2] Guilin Univ Technol, Guangxi Key Lab Spatial Informat & Geomat, Guilin 541004, Guangxi, Peoples R China
关键词
Combination optimization; FT-NIR; MWPLS; OPLECm; Shaddock peel pectin; QUANTITATIVE SPECTROSCOPIC ANALYSIS; MULTIVARIATE CALIBRATION; REGRESSION;
D O I
10.1080/00032719.2013.784912
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The combination of modified optical path length estimation and correction (OPLECm) and moving window partial least squares (MWPLS) was applied to the waveband selection for the Fourier transform near-infrared (FT-NIR) determination of pectin in shaddock peel samples. For MWPLS modeling, the changeable parameter of waveband width was set as the 21 piece-window size selected by the piece-wise multiplicative scatter correction (PMSC) method. Further, OPLECm was implemented for multiplicative scattering correction, and the MWPLS models were optimized by comparing the predictive results of three different cases using OPLECm pretreatment. The optimum case was PLS modeling based on the waveband selected by a moving window and successively pretreated by OPLECm (MW+OPLECm+PLS). The selected waveband was 8573-7784cm(-1) with a width of 410. The optimal number of PLS components was 11, and the corresponding RMSEP, the R-P, and RRMSEP were 0.4176wt%, 0.9185, and 8.43%, respectively. This model was better than other models for MW+OPLECm+PLS. The optimally selected waveband also showed an appreciative result in the validation part. This indicates that MWPLS modeling combined with OPLECm provides an appreciable improvement in the FT-NIR quantitative analysis of pectin in shaddock peel. In addition, a method was proposed for the classification of calibration samples and the prediction samples based on the principles of vector normalization, and all the calibration models and the prediction results were obtained on this classification.
引用
收藏
页码:2060 / 2074
页数:15
相关论文
共 25 条
[1]  
Burns D.M., 2001, HDB NEAR INFRARED AN
[2]   Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods [J].
Chen, Huazhou ;
Pan, Tao ;
Chen, Jiemei ;
Lu, Qipeng .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2011, 107 (01) :139-146
[3]   Extracting chemical information from spectral data with multiplicative light scattering effects by optical path-length estimation and correction [J].
Chen, Zeng-Ping ;
Morris, Julian ;
Martin, Elaine .
ANALYTICAL CHEMISTRY, 2006, 78 (22) :7674-7681
[4]   Near-infrared reflectance spectroscopy and multivariate calibration techniques applied to modelling the crude protein, fibre and fat content in rapeseed meal [J].
Daszykowski, M. ;
Wrobel, M. S. ;
Czarnik-Matusewicz, H. ;
Walczak, B. .
ANALYST, 2008, 133 (11) :1523-1531
[5]  
Feng B. M., 2001, J SHENYANG PHARM U, V18, P228
[6]   LINEARIZATION AND SCATTER-CORRECTION FOR NEAR-INFRARED REFLECTANCE SPECTRA OF MEAT [J].
GELADI, P ;
MACDOUGALL, D ;
MARTENS, H .
APPLIED SPECTROSCOPY, 1985, 39 (03) :491-500
[7]   AN EXAMPLE OF 2-BLOCK PREDICTIVE PARTIAL LEAST-SQUARES REGRESSION WITH SIMULATED DATA [J].
GELADI, P ;
KOWALSKI, BR .
ANALYTICA CHIMICA ACTA, 1986, 185 :19-32
[8]   Measurement of pesticide residues in food based on diffuse reflectance IR spectroscopy [J].
Hiroaki, I ;
Toyonori, N ;
Eiji, T .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2002, 51 (05) :886-890
[9]   Evaluation of spectral pretreatments, partial least squares, least squares support vector machines and locally weighted regression for quantitative spectroscopic analysis of soils [J].
Igne, Benoit ;
Reeves, James B., III ;
McCarty, Gregory ;
Hively, W. Dean ;
Lund, Eric ;
Hurburgh, Charles R., Jr. .
JOURNAL OF NEAR INFRARED SPECTROSCOPY, 2010, 18 (03) :167-176
[10]   Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and hear-infrared spectroscopic data [J].
Jiang, JH ;
Berry, RJ ;
Siesler, HW ;
Ozaki, Y .
ANALYTICAL CHEMISTRY, 2002, 74 (14) :3555-3565