Skip to main content
NCBI home page
AAPS PharmSciTech logoLink to AAPS PharmSciTech
. 2007 Nov 9;8(4):109–119. doi: 10.1208/pt0804096

Determination of figures of merit for near-infrared and raman spectrometry by net analyte signal analysis for a 4-component solid dosage system

Steven M Short 1, Robert P Cogdill 1, Carl A Anderson 1,✉
PMCID: PMC2750682  PMID: 18181556

Abstract

Process analytical technology has elevated the role of sensors in pharmaceutical manufacturing. Often the ideal technology must be selected from many suitable candidates based on limited data. Net analyte signal (NAS) theory provides an effective platform for method characterization based on multivariate figures of merit (FOM). The objective of this work was to demonstrate that these tools can be used to characterize the performance of 2 dissimilar analyzers based on different underlying spectroscopic principles for the analysis of pharmaceutical compacts. A fully balanced, 4-constituent mixture design composed of anhydrous theophylline, lactose monohydrate, microcrystalline cellulose, and starch was generated; it consisted of 29 design points. Six 13-mm tablets were produced from each mixture at 5 compaction levels and were analyzed by near-infrared and Raman spectroscopy. Partial least squares regression and NAS analyses were performed for each component, which allowed for the computation of FOM. Based on the calibration error statistics, both instruments were capable of accurately modeling all constituents. The results of this work indicate that these statistical tools are a suitable platform for comparing dissimilar analyzers and illustrate the complexity of technology selection.

Keywords: Near-infrared, Raman, partial least squares, analyte signal, calibration, tablet

Full Text

The Full Text of this article is available as a PDF (490.8 KB).

Footnotes

Themed Issue: Process Analytical Technology

Guest Editor — Ajaz Hussain

References

  • 1.Bugay DE, Brittain HG. Raman spectroscopy. In: Brittain H, editor. Spectroscopy of Pharmaceutical Solids. vol. 160. New York, NY: Taylor & Francis; 2006. pp. 271–312. [Google Scholar]
  • 2.Cogdill RP, Drennen JK. Near-infrared spectroscopy. In: Brittain H, editor. Spectroscopy of Pharmaceutical Solids. vol. 160. New York, NY: Taylor & Francis; 2006. pp. 313–412. [Google Scholar]
  • 3.Afseth NK, Segtnan VH, Marquardt BJ, Wold JP. Raman and near-infrared spectroscopy for quantification of fat composition in a complex food model system. Appl Spectrosc. 2005;59:1324–1332. doi: 10.1366/000370205774783304. [DOI] [PubMed] [Google Scholar]
  • 4.Furukawa T, Masahiro W, Siesler HW, Ozaki Y. Discrimination of various poly(propylene) copolymers and prediction of their ethylene content by near-infrared and Raman spectroscopy in combination with chemometric methods. J Appl Polym Sci. 2003;87:616–625. doi: 10.1002/app.11351. [DOI] [Google Scholar]
  • 5.Nordon A, Meunier C, McGill CA, Littlejohn D. Comparison of calibration methods for the monitoring of a fluorobenzene batch reaction using low-field 19F NMR, 1H NMR, NIR, and Raman spectrometries. Appl Spectrosc. 2002;56:515–520. doi: 10.1366/0003702021954971. [DOI] [Google Scholar]
  • 6.Nordon A, Mills A, Burn RT, Cusick FM, Littlejohn D. Comparison of non-invasive NIR and Raman spectrometries for determination of alcohol content of spirits. Anal Chim Acta. 2005;548:148–158. doi: 10.1016/j.aca.2005.05.067. [DOI] [Google Scholar]
  • 7.Qiao Y, van Kempen TATG. Comparison of Raman, mid, and near infrared spectroscopy for predicting the amino acid content in animal meals. J Anim Sci. 2004;82:2596–2600. doi: 10.2527/2004.8292596x. [DOI] [PubMed] [Google Scholar]
  • 8.Chung H, M-S Ku. Comparison of near-infrared, infrared, and Raman spectroscopy for the analysis of heavily petroleum products. Appl Spectrosc. 2000;54:239–245. doi: 10.1366/0003702001949168. [DOI] [Google Scholar]
  • 9.Ku M-S, Chung H. Comparison of near-infrared and Raman spectroscopy for the determination of chemical and physical properties of naphtha. Appl Spectrosc. 1999;53:557–564. doi: 10.1366/0003702991946910. [DOI] [Google Scholar]
  • 10.Lorber A, Faber K, Kowalski BR. Net analyte signal calculation in multivariate calibration. Anal Chem. 1997;69:1620–1626. doi: 10.1021/ac960862b. [DOI] [Google Scholar]
  • 11.Lorber A. Error propagation and figures of merit for quantification by solving matrix equations. Anal Chem. 1986;58:1167–1172. doi: 10.1021/ac00297a042. [DOI] [Google Scholar]
  • 12.Brown CD. Discordance between net analyte signal theory and practical multivariate calibration. Anal Chem. 2004;76:4364–4373. doi: 10.1021/ac049953w. [DOI] [PubMed] [Google Scholar]
  • 13.Morgan DR. Spectral absorption pattern detection and estimation, I: analytical techniques. Appl Spectrosc. 1977;31:404–415. doi: 10.1366/000370277774463418. [DOI] [Google Scholar]
  • 14.Olivieri AC, Faber NM, Ferre J, Boque R, Kalivas JH, Mark H. Uncertainty estimation and figures of merit for multivariate calibration. Pure Appl Chem. 2006;78:633–661. doi: 10.1351/pac200678030633. [DOI] [Google Scholar]
  • 15.Haaland DM. Classical versus inverse least squares methods in quantitative spectral analyses. Spectroscopy. 1987;2:56–57. [Google Scholar]
  • 16.Boelens HF, Kok WT, de Noord OE, Smilde AK. Performance optimization of spectroscopic process analyzers. Anal Chem. 2004;76:2656–2663. doi: 10.1021/ac0353987. [DOI] [PubMed] [Google Scholar]
  • 17.Xu L, Schechter I. A calibration method free of optimum factor number selection for automated multivariate analysis. Experimental and theoretical study. Anal Chem. 1997;69:3722–3730. doi: 10.1021/ac970402y. [DOI] [Google Scholar]
  • 18.Ferre J, Brown SD, Rius FX. Improved calculation of the net analyte signal in inverse multivariate calibration. J Chemom. 2001;15:537–553. doi: 10.1002/cem.647. [DOI] [Google Scholar]
  • 19.Bro R, Andersen CM. Theory of net analyte signal vectors in inverse regression. J Chemom. 2003;17:646–652. doi: 10.1002/cem.832. [DOI] [Google Scholar]
  • 20.Martens H, Naes T. Multivariate Calibration. New York, NY: John Wiley and Sons; 1989. [Google Scholar]
  • 21.Xu L, Schechter I. Wavelength selection for simultaneous spectroscopic analysis. Experimental and theoretical study. Anal Chem. 1996;68:2392–2400. doi: 10.1021/ac951142s. [DOI] [Google Scholar]
  • 22.Goicoechea HC, Olivieri AC. Chemometric assisted simultaneous spectrophotometric determination of four-component nasal solutions with a reduced number of calibration samples. Anal Chem Acta. 2002;453:289–300. doi: 10.1016/S0003-2670(01)01232-6. [DOI] [Google Scholar]
  • 23.Braga JWB, Poppi RJ. Figures of merit for the determination of the polymorphic purity of carbamazepine by infrared spectroscopy and multivariate calibration. J Pharm Sci. 2004;93:2124–2134. doi: 10.1002/jps.20109. [DOI] [PubMed] [Google Scholar]
  • 24.Geladi P, Kowalski BR. Partial least-squares regression: a tutorial. Anal Chem Acta. 1986;185:1–17. doi: 10.1016/0003-2670(86)80028-9. [DOI] [Google Scholar]
  • 25.De Jong S. SIMPLS: an alternative approach to partial least squares regression. Chemom Intell Lab Syst. 1993;18:251–263. doi: 10.1016/0169-7439(93)85002-X. [DOI] [Google Scholar]
  • 26.ICH ICH harmonised tripartite guideline: validation of analytical procedures: text and methodology. Fed Regist. 1997;62:27463–27467. [Google Scholar]
  • 27.Haaland DM, Thomas EV. Partial least-squares methods for spectral analyses, 1: relation to other quantitative calibration methods and the extraction of qualitative information. Anal Chem. 1988;60:1193–1202. doi: 10.1021/ac00162a020. [DOI] [Google Scholar]
  • 28.Long GL, Winefordner JD. Limit of detection: a closer look at the IUPAC definition. Anal Chem. 1983;55:712A–724A. doi: 10.1021/ac00258a001. [DOI] [Google Scholar]
  • 29.Savitzky A, Golay MJE. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 1964;36:1627–1639. doi: 10.1021/ac60214a047. [DOI] [Google Scholar]
  • 30.Marbach R. On Wiener filtering and the physics behind statistical modeling. J Biomed Opt. 2002;7:130–147. doi: 10.1117/1.1427051. [DOI] [PubMed] [Google Scholar]

Articles from AAPS PharmSciTech are provided here courtesy of American Association of Pharmaceutical Scientists

RESOURCES