Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection

International Journal of Pharmaceutics

Volumn 421, Issue 2, 2011, Pages 269-274

  Kim, Sanghong ^a   Kano, Manabu ^a   Nakagawa, Hiroshi ^b   Hasebe, Shinji ^a  

^a KYOTO UNIVERSITY   (Japan)

^b DAIICHI SANKYO CO   (Japan)

Author keywords

API content estimation; Locally weighted partial least squares; NIR; PAT; Wavelength selection

Indexed keywords

ARTICLE; CALIBRATION; DRUG ANALYSIS; DRUG GRANULE; MATHEMATICAL ANALYSIS; NEAR INFRARED SPECTROSCOPY; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; STATISTICAL ANALYSIS; TABLET; WAVEFORM;

LEAST-SQUARES ANALYSIS; PHARMACEUTICAL PREPARATIONS; SPECTROSCOPY, NEAR-INFRARED;

EID: 81755166220     PISSN: 03785173     EISSN: 18733476     Source Type: Journal    
DOI: 10.1016/j.ijpharm.2011.10.007     Document Type: Article

References (31)

1. Andersen C., and Bro R. Variable selection in regression - a tutorial J. Chemom. 24 2010 728 737
2. Arakawa M., Yamashita Y., and Funatsu K. Genetic algorithm-based wavelength selection method for spectral calibration J. Chemom. 25 2011 10 19
3. Barnes R., Dhanoa M., and Lister S.J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra Appl. Spectrosc. 43 1989 772 777
4. Berntsson O., Danielsson L.G., Lagerholm B., and Folestad S. Quantitative in-line monitoring of powder blending by near infrared reflection spectroscopy Powder Technol. 123 2002 185 193
5. Berthiaux H., Mosorov V., Tomczak L., Gatumel C., and Demeyre J. Principal component analysis for characterising homogeneity in powder mixing using image processing techniques Chem. Eng. Process. 45 2006 397 403
6. Centner V., and Massart D. Optimization in locally weighted regression Anal. Chem. 70 1998 4206 4211
7. Chang S., Baughman E., and McIntosh B. Implementation of locally weighted regression to maintain calibrations on FT-NIR analyzers for industrial processes Appl. Spectrosc. 55 2001 1199 1206
8. Cleveland W., and Devlin S. Locally weighted regression: an approach to regression analysis by local fitting J. Am. Stat. Assoc. 83 1988 596 610
9. Cogdill R., Anderson C., Delgado-Lopez M., Molseed D., Chisholm R., Bolton R., Herkert T., Afnan A., and Drennen J. III Process analytical technology case study part I: feasibility studies for quantitative near-infrared method development AAPS PharmSciTech 6 2005 262 272
10. Eriksson L., Johansson E., Kettaneh-Wold N., and Wold S. Multi- and Megavariate Data Analysis. Principles and Applications 2001 Umetrics Academy
11. FDA 2004. Pharmaceutical cGMPs for the 21st century - a risk-based approach final report.
12. Fujiwara K., Kano M., and Hasebe S. Development of correlation-based clustering method and its application to software sensing Chemom. Intell. Lab. Syst. 101 2010 130 138
13. Fujiwara K., Kano M., Hasebe S., and Takinami A. Soft-sensor development using correlation-based just-in-time modeling AIChE J. 55 2009 1754 1765
14. ICH 2005. ICH harmonised tripartite guideline - pharmaceutical development Q8 (R2).
15. ICH 2005. ICH harmonised tripartite guideline - quality risk management Q9.
16. ICH 2008. ICH harmonised tripartite guideline - pharmaceutical quality system Q10.
17. Jiang J.H., James R., Siesler B., and Ozaki Y. 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 2002 3555 3565
18. Jouen-Rimbauda D., and Massart D.L. Genetic algorithms as a tool for wavelength selection in multivariate calibration Anal. Chem. 67 1995 4295 4301
19. Kano M., and Ogawa M. The state of the art in chemical process control in Japan: good practice and questionnaire survey J. Process Control 20 2010 969 982
20. Li W., and Worosila G. Quantitation of active pharmaceutical ingredients and excipients in powder blends using designed multivariate calibration models by near-infrared spectroscopy Int. J. Pharm. 295 2005 213 219
21. Moes J.J., Ruijken M.M., Gout E., Frijlink H.W., and Ugwoke M.I. Application of process analytical technology in tablet process development using NIR spectroscopy: blend uniformity, content uniformity and coating thickness measurements Int. J. Pharm. 357 2008 108 118
22. Nørgaard L., Saudland A., Wagner J., Nielsen J., Munck L., and Engelsen S. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy Appl. Spectrosc. 54 2000 413 419
23. Pérez-Marin D., Garrido-Varo A., and Guerrero J. Non-linear regression methods in NIRS quantitative analysis Talanta 72 2007 28 42
24. Reich G. Near-infrared spectroscopy and imaging: basic principles and pharmaceutical applications Adv. Drug Deliv. Rev. 57 2005 1109 1143
25. Roggo Y., Chalus P., Maurer L., Lema-Martinez C., Edmond A., and Jent N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies J. Pharm. Biomed. Anal. 44 2007 683 700
26. Savitzky A., and Golay M. Smoothing and differentiation of data by simplified least squares procedures Anal. Chem. 36 1964 1627 1639
27. Shigemori H., Kano M., and Hasebe S. Optimum quality design system for steel products through locally weighted regression model J. Process Control 21 2011 293 301
28. Sulub Y., Wabuyele B., Gargiulo P., Pazdan J., Cheney J., Berry J., Gupta A., Shah R., Wu H., and Khan M. Real-time on-line blend uniformity monitoring using near-infrared reflectance spectrometry: a noninvasive off-line calibration approach J. Pharm. Biomed. Anal. 49 2009 48 54
29. Virtanen S., Antikainen O., and Yliruusi J. Uniformity of poorly miscible powders determined by near infrared spectroscopy Int. J. Pharm. 345 2007 108 115
30. Wang Z., Isaksson T., and Kowalski B. New approach for distance measurement in locally weighted regression Anal. Chem. 66 1994 249 260
31. Wu H., Tawakkul M., White M., and Khan M. Quality-by-design (QbD): an integrated multivariate approach for the component quantification in powder blends Int. J. Pharm. 372 2009 39 48