Applications of grazing-angle reflection absorption Fourier transform infrared spectroscopy to the

248 Pages·2007·4.09 MB·English

Applications of grazing-angle reflection
absorption Fourier transform infrared
spectroscopy to the analysis of surface
contamination

A thesis submitted in the partial fulfilment of the requirements for the degree of

Doctor of Philosophy in Chemistry

at the

University of Canterbury
Christchurch
New Zealand

Michelle Hamilton
January 2007

Abstract

Cleaning validation of pharmaceutical manufacturing equipment is required by
legislation. Generally, wet chemical techniques are employed using swabbing and/or
rinse sampling methods. These are generally either selective and time consuming, or less
selective and give results in a shorter period. The infrared reflection absorption
spectroscopy (IRRAS) technique explored here attempts to deliver accurate, selective
surface contamination information in real time to complement current methods and
reduce down-time.

The IRRAS instrument used in this research is a Fourier transform infrared (FTIR)
spectrometer coupled by an IR fibre-optic cable to a grazing-angle sampling head with a
fixed incidence angle of 80°. The introduced flexibility permits collection of in situ
spectra from contaminated surfaces. Calibration models are developed using the
multivariate, linear partial least squares (PLS) statistical method.

The research focuses on sodium dodecyl sulfate (SDS), a model cleaning agent, on metal
(aluminium and stainless steel) and dielectric (glass, EPDM and silicone) surfaces. The
effects of surface finish are investigated for SDS on stainless steel. Calibrations for SDS
and paracetamol in the presence of each other on glass surfaces are examined, as well as a
common industrial cleaner (P3 cosa® PUR80) on polished stainless steel.

For the calibration sets in this thesis, RMSECV values were < 0.41 µg cm-2,
corresponding to conservative surface residues detection limits of better
than ~0.86 µg cm-2. However, RMSECV values depend on the calibration loading range,
and the detection limits were typically ~0.2 µg cm-2 for loading ranges 0–2.5 µg cm-2.
These are below visual detection limits, generally taken to be 1–4 µg cm-2, depending on
the analyte and substrate. This shows that IRRAS is a viable method for the real-time
detection and quantification of surface contamination by surfactants and active
pharmaceutical ingredients on metals and dielectrics.

Acknowledgements
First and foremost I would like to thank my Ph.D. supervisors: Peter Harland and Bryce
Williamson, since without your encouragement, guidance and friendship the completion
of this thesis would not have been possible.

I would also like to thank the present and past members of the Harland-Williamson
group: Ben, Josh and James, for your friendship and stimulating discussions during the
course of my research. I have greatly enjoyed my time at the University of Canterbury
this is in no small part due to the people I have met here; your friendships have been very
much appreciated.

The Department of Chemistry is fortunate to have many talented technical staff, from
whom I have been able to learn during my research. Much of the construction and repairs
completed during this were carried out by the mechanical workshop boys; special thanks
goes to Danny Leonard. The NMR section of my research would also not have been
possible if it was not for the guidance of Martin Lee.

Thanks also go to Mary Thomson and Peter Melling for their support, especially in the
initial stages of the project. Acknowledgement also goes to their company, Remspec
Corporation, Massachusetts, for the provision of a graduate student scholarship and for
the provision of equipment and software, without which this project would not have
started. I would also like to thank the University of Canterbury Evans’ Fund, the New
Zealand Institute of Chemistry (NZIC) and the New Zealand Federation of Graduate
Women (NZEGW) for the provision of funds which have allowed me to travel to a
number of conferences both in New Zealand and overseas.

A final big thank you goes to my family and James, the task of completing this thesis
would not have been possible if it was not for the love, support and patience you have
shown me over the years.

List of publications

1. Grazing-Angle Fiber-Optic Fourier-Transform Infrared Reflection-Absorption
Spectroscopy for the in situ Detection and Quantification of Two Active
Pharmaceutical Ingredients on Glass. Perston, Benjamin B.; Hamilton, Michelle L.;
Harland, Peter W.; Williamson, Bryce E., Thomson, Mary A.; Melling, Peter J.
Analytical Chemistry, 2007, 79(3), 1231-1236.

2. Fiber-Optic Infrared Reflection-Absorption Spectroscopy for Trace Analysis on
Surfaces of Varying Roughness: Sodium Dodecyl Sulfate on Stainless Steel.
Hamilton, Michelle L.; Perston, Benjamin B.; Harland, Peter W.; Williamson, Bryce E.,
Thomson, Mary A.; Melling, Peter J. Applied Spectroscopy, 2006, 60(5), 516-520.

3. Grazing-Angle Fiber-Optic Infrared Reflection-Absorption Spectroscopy for in
Situ Cleaning Validation. Hamilton, Michelle L.; Perston, Benjamin B.; Harland,
Peter W.; Williamson, Bryce E., Thomson, Mary A.; Melling, Peter J. Organic Process
Research & Development, 2005, 9(3), 337-343.

4. Synthesis, structures and rac/meso isomerization behaviour of bisplanar chiral
bis(phosphino-η5-indenyl)iron(II) complexes. Curnow, Owen J.; Fern, Glen M.;
Hamilton, Michelle L.; Jenkins, Elizabeth M. Journal of Organometallic Chemistry,
2004, 689(11), 1897-1910.

5. Mechanistic Studies on a Facile Ring-Flipping Process in Planar Chiral
Ferrocenes under Ambient and High Pressure and Its Relevance to Asymmetric
Catalysis. Curnow, Owen J.; Fern, Glen M.; Hamilton, Michelle L.; Zahl, Achim; van
Eldik, Rudi. Organometallics, 2004, 23(4), 906-912.
LIST OF PUBLICATIONS
6. Absolute electron impact ionization cross-sections for the C to C alcohols.
1 4
Hudson, James E.; Hamilton, Michelle L.; Vallance, Claire; Harland, Peter W. Physical
Chemistry Chemical Physics, 2003, 5(15), 3162-3168.

7. Oxide, Sulfide, Selenide, and Borane Derivatives of Indenylphosphines. Adams,
Julian J.; Berry, David E.; Curnow, Owen J.; Fern, Glen M.; Hamilton, Michelle L.;
Kitto, Heather J.; Pipal, J. Robert. Australian Journal of Chemistry, 2003, 56(11), 1153-
1160.
Contents

LIST OF ABBREVIATIONS 1
INTRODUCTION 3
1. CLEANLINESS VALIDATION 5
1.1 Cleaning validation 5
1.2 What is surface contamination? 6
1.3 When should cleaning validation be performed? 7
1.4 Determination of residual limits 8
1.4.1 The 10 ppm method 10
1.4.2 The pharmaceutical dose method 11
1.4.3 The visible inspection method 12
1.4.4 Residual solvents and cleaning agents 13
1.4.5 Other considerations 13
1.5 How to sample surfaces for residues 14
1.5.1 Swab sampling 14
1.5.2 Rinse sampling 16
1.5.3 Placebo sampling 17
1.5.4 Coupon sampling 17
1.5.5 Other considerations 18
1.6 Surface residue detection methods 18
1.6.1 High-performance liquid chromatography 19
1.6.2 Total organic carbon analysis 20
1.7 Fourier transform infrared spectroscopy and cleaning validation 21
1.8 Where to now? – The future of cleaning validation 23
1.9 References 24

2. INFRARED SPECTROSCOPY 29
2.1 Infrared spectroscopy 29
2.2 Fourier transform infrared spectrometers (FTIR) 30
CONTENTS
2.2.1 The interferogram 32
2.2.2 Spectral resolution 36
2.2.3 Instrument line shape and apodisation 37
2.3 Detectors 38
2.4 Surface analysis using infrared spectroscopy 39
2.4.1 Polarisation of light 41
2.4.2 Reflection Coefficients 42
2.4.3 Metal surfaces 44
2.4.4 Glass surfaces 48
2.4.5 Infrared reflection-absorption spectra 50
2.4.6 Order within the analyte film 53
2.5 Quantitative applications of IRRAS 54
2.6 Conclusions 55
2.7 References 56

3. CHEMOMETRICS 59
3.1 What is chemometrics? 59
3.2 An introduction to multivariate analysis 59
3.3 Data pre-treatments 61
3.4 Classical least squares 64
3.5 Inverse least squares 68
3.6 Factor analysis 71
3.7 Principal component regression 75
3.8 Selection of the number of significant model factors 77
3.9 Partial least squares 83
3.10 Comparison of partial least squares to the other modelling methods 89
3.11 Outlier detection and error analysis for PLS 91
3.12 The methodology adopted in this thesis 101
3.13 References 105