Study of preprocessing methods for the determination of crystalline phases in binary mixtures of drug substances by X-ray powder diffraction and multivariate calibration

被引:25
作者
Artursson, T
Hagman, A
Björk, S
Trygg, J
Wold, S
Jacobsson, SP [1 ]
机构
[1] AstraZeneca Res & Dev Sodertalje, Analyt Dev, SE-15185 Sodertalje, Sweden
[2] Umea Univ, Dept Organ Chem, Chemometr Res Grp, SE-90187 Umea, Sweden
关键词
XRPD; PLS; pretreatment; wavelet transform; fourier transform; Savitzky-Golay; orthogonal signal correction;
D O I
10.1366/0003702001950805
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
In this paper, various preprocessing methods were tested on data generated by X-ray powder diffraction (XRPD) in order to enhance the partial least-squares (PLS) regression modeling performance, The preprocessing methods examined were 22 different discrete wavelet transforms, Fourier transform, Savitzky-Golay, orthogonal signal correction (OSC), and combinations of wavelet transform and OSC, and Fourier transform and OSC. Root mean square error of prediction (RMSEP) of an independent test set was used to measure the performance of the various preprocessing methods. The best PLS model was obtained with a wavelet transform (Symmlet 8), which at the same time compressed the data set by a factor of 9.5. With the use of wavelet and X-ray powder diffraction, concentrations of less than 10% of one crystal from could be detected in a binary mixture. The linear range was found to be in the range 10-70% of the crystalline form of phenacetin, although semiquantitative work could be carried out down to a level of approximately 2%. Furthermore, the wavelet-pretreated models were able to handle admixtures and deliberately added noise.
引用
收藏
页码:1222 / 1230
页数:9
相关论文
共 10 条
[1]   An introduction to wavelet transforms for chemometricians: A time-frequency approach [J].
Alsberg, BK ;
Woodward, AM ;
Kell, DB .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1997, 37 (02) :215-239
[2]  
[Anonymous], 1989, MULTIVARIATE CALIBRA
[3]   GENERALIZED DIGITAL SMOOTHING FILTERS MADE EASY BY MATRIX CALCULATIONS [J].
BIALKOWSKI, SE .
ANALYTICAL CHEMISTRY, 1989, 61 (11) :1308-1310
[4]  
BYRN SR, 1982, SOLID STATE CHEM DRU, V4, P79
[5]   MULTIVARIATE CALIBRATION OF AN X-RAY DIFFRACTOMETER BY PARTIAL LEAST-SQUARES REGRESSION [J].
KARSTANG, TV ;
EASTGATE, RJ .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1987, 2 (1-3) :209-219
[6]   A THEORY FOR MULTIRESOLUTION SIGNAL DECOMPOSITION - THE WAVELET REPRESENTATION [J].
MALLAT, SG .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1989, 11 (07) :674-693
[7]   Wavelet denoising of Gaussian peaks: A comparative study [J].
Mittermayr, CR ;
Nikolov, SG ;
Hutter, H ;
Grasserbauer, M .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1996, 34 (02) :187-202
[8]   SMOOTHING + DIFFERENTIATION OF DATA BY SIMPLIFIED LEAST SQUARES PROCEDURES [J].
SAVITZKY, A ;
GOLAY, MJE .
ANALYTICAL CHEMISTRY, 1964, 36 (08) :1627-&
[9]   PLS regression on wavelet compressed NIR spectra [J].
Trygg, J ;
Wold, S .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1998, 42 (1-2) :209-220
[10]   Orthogonal signal correction of near-infrared spectra [J].
Wold, S ;
Antti, H ;
Lindgren, F ;
Öhman, J .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1998, 44 (1-2) :175-185