Abstract
This is the third of a series of articles detailing the development of near-infrared spectroscopy methods for solid dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to develop a system for continuous calibration monitoring and formulate an appropriate strategy for calibration transfer. Indcators of high-flux noise (noise factor level) and wave-length uncertainty were developed. These measurements, in combination with Hotelling’s T2 and Q residual, are used to continuously monitor instrument performance and model relevance. Four calibration transfer techniques were compared. Three established techniques, finite impulse response filtering, generalized least squares weighting, and piecewise direct standardization were evaluated. A fourth technique, baseline subtraction, was the most effective for calibration transfer. Using as few as 15 transfer samples, predictive capability of the analytical method was maintained across multiple instruments and major instrument maintenance.
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References
Cogdill RP, Anderson CA, Delgado-Lopez M. Process Analytical Technology Case Study, Part I: Feasibility Studies for Quantitative NIR Method Development.AAPS Pharm Sci Tech. 2005;6:E262-E272.
Cogdill RP, Anderson CA, Delgado-Lopez M. Process Analytical Technology Case Study, Part II: Development and Validation of Quantitative for Tablet API Content and Hardness.AAPS Pharm Sci Tech. 2005;6:E273-E283.
Food and Drug Administration.PAT—A Framework for Innovative Manufacturing and Quality Assurance, Draft Guidance, Rockvill, MD: 2003.
Box GEP, Jenkins GM, Reinsel G.Time Series Analysis. Englewood Cliffs, NJ: Prentice Hall; 1994.
Jackson JE, Mudholkar GS. Control procedures for residuals associated with principal components analysis.Technometrics. 1979;21:341–349.
Williams P, Norris K.Near-Infrared Technology in the Agricultural and Food Industries. St. Paul, MN: American Association of Cereal Chemists; 2001.
Greensill CV, Wolfs PJ, Speigelman CH, Walsh KB. Calibration transfer between PDA-based spectrometers in the NIR assessment of melon soluble solids content.J Appl Spectrosc. 2001;55:647–653.
Fearn T. Standardisation and calibration transfer for near iInfrared instruments: a review.J Near Infrared Spectrosc. 2001;9:229–244.
Zeaiter M, Roger JM, Bellon-Maurel V, Rutledge DN. Robustness of models developed by multivariate calibration. Part I: the assessment of robustness.Trends Analyt Chem. 2004;23:157–170.
Fearn I. On orthogonal signal correction.Chemom Intell Lab Syst. 2000;50:47–52.
Sjöblom J, Svensson O, Josefson M, Kullberg H, Wold S. An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra.Chemom Intell Lab Syst. 1998;44:229–244.
Wold S, Antti H, Lindgren F, Öhman J. Orthogonal signal correction of near-infrared spectra.Chemom Intell Lab Syst. 1998;44:175–185.
Andersson CA. Direct orthogonalization.Chemom Intell Lab Syst. 1999;47:51–63.
Haaland DM, Melgaard DK. New prediction-augmented classical least squares (PACLS) methods: Application to unmodeled interferents.J Appl Spectrosc. 2000;54:1303–1312.
Wise BM, Martens H, Hoy M. Calibration transfer by generalized least squares. Eigenvector Research Incorporated Report. Available at: http://www.eigenvector.com/Docs/. Accessed February 4, 2005.
Bouveresse E, Massart D, Dardenne P. Calibration transfer across near-infrared spectrometric instruments using Shenk’s algorithm: effects of different standardisation samples.Anal Chim Acta. 1994;297:405–416.
Dardenne P. Standardisation of near-infrared instruments, influence of the calibration methods and the size of the cloning set. In: Davies AMC, Cho RK, eds.Near Infrared Spectroscopy: Proceedings of the 10th International Conference. Chichester, West Sussex, UK: NIR Publications; 2002:23–28.
Shenk J. Standardizing NIR instruments. In: Biston R, Bartiaux-Thill N, eds.Third International Conference on Near-Infrared Spectroscopy. Gembloux, Belgium: Agricultural Research Centre Publishing; 1991:649–654.
Welle R, Greten W, Bernhard R, et al. Near-infrared spectroscopy on chopper to measure maize forage quality parameters online.Crop Sci. 2003;43:1407–1413.
Wang Y, Veltkamp D, Kowalski BR. Multivariate instrument standardization.Anal Chem. 1991;63:2750–2756.
Wang Y, Kowalski BR. Temperature-compensating calibration transfer for near-infrared filter instruments.Anal Chem. 1993;65:1301–1303.
Wang Z, Dean T, Kowalski BR. Additive Background Correction in Multivariate Instrument Standardization.Anal Chem. 1995; 67:2379–2385.
Gallagher NB. Development and benchmarking of multivariate statistical process control tools for a semiconductor etch process: improving robustness through model updating.IFAC ADCHEM ’97 1997. Available at: www.eigenvector.com/About/NBGev.html. Accessed February 4, 2005.
Wise BM, Ricker NL. Identification of finite impulse response models with principal components regression: frequency-response properties.Process Contr Qual. 1992;4:77–86.
Funk DB. New methods for wavelength standardisation for near-infrared spectrophotometers, Part 1: review of current standardisation methodology.J Near Infrared Spectrosc. 1996;4:101–106.
Manning CJ, Griffiths PR. Noise sources in step-scan FT-IR spectrometry.J Appl Spectrosc. 1997;51:1092–1101.
Martens H, Næs T.Multivariate Calibration. New York, NY: John Wiley and Sons; 1989.
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Published: October 6, 2005
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Cogdill, R.P., Anderson, C.A. & Drennen, J.K. Process analytical technology case study, part III: Calibration monitoring and transfer. AAPS PharmSciTech 6, 39 (2005). https://doi.org/10.1208/pt060239
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DOI: https://doi.org/10.1208/pt060239