Cleaning validation, in which cleaned surfaces are analysed for residual material, is an important process in pharmaceutical manufacturing and research facilities. Current procedures usually consist of either swab or rinse-water sampling followed by analysis of the samples. The analysis step is typically either rapid but unselective (conductivity, pH, total organic carbon, etc.), or selective but time-consuming (HPLC). This thesis describes the development of an in situ surface-spectroscopic analysis that removes the need for swab sampling and is both rapid and selective. This method has the potential to complement existing analyses to increase the efficiency of cleaning-validation protocols. The spectrometric system consists of a Fourier-transform infrared (FTIR) spectrometer coupled to a fibre-optic grazing-angle reflectance probe, and allows the measurement of infrared reflection-absorbance spectra (IRRAS) from flat surfaces in ~10 s. Multivariate chemometric methods, such as partial least squares (PLS) regression, are used to exploit the high information content of infrared spectra to obtain selective analyses without physical separation of the analyte or analytes from whatever interfering species may be present. Multivariate chemometric models require considerably more effort for calibration and validation than do traditional univariate techniques. This thesis details suitable methods for preparing calibration standards by aerosol deposition, optimising and validating the model by cross- and test-set validation, and estimating the uncertainty by resampling and formula-based approaches. Successful calibration models were demonstrated for residues of acetaminophen, a model active pharmaceutical ingredient (API), on glass surfaces. The root-mean-square error of prediction (RMSEP) was ~0.07 µg cm⁻². Simultaneous calibration for acetaminophen and aspirin, another API, gave a similar RMSEP of 0.06 µg cm⁻² for both compounds, demonstrating the selectivity of the method. These values correspond to detection limits of ~0.2 µg cm⁻², well below the accepted visual detection limit of ~1-4 µg cm⁻². The sensitivity of the method with a stainless steel substrate was found to depend strongly on the surface finish, with highly polished surfaces giving more intense IRRAS. RMSEP values of 0.04- 0.05 µg cm⁻² were obtained for acetaminophen on stainless steel with three different finishes. For this system, severe nonlinearity was encountered for loadings 1.0 µg cm⁻². From the results presented in this thesis, it is clear that IRRAS has potential utility in cleaning validation as a complement to traditional techniques.

Cleaning validation, in which cleaned surfaces are analysed for residual material, is an important process in pharmaceutical manufacturing and research facilities. Current procedures usually consist of either swab or rinse-water sampling followed by analysis of the samples. The analysis step is typically either rapid but unselective (conductivity, pH, total organic carbon, etc.), or selective but time-consuming (HPLC). This thesis describes the development of an in situ surface-spectroscopic analysis that removes the need for swab sampling and is both rapid and selective. This method has the potential to complement existing analyses to increase the efficiency of cleaning-validation protocols. The spectrometric system consists of a Fourier-transform infrared (FTIR) spectrometer coupled to a fibre-optic grazing-angle reflectance probe, and allows the measurement of infrared reflection-absorbance spectra (IRRAS) from flat surfaces in ~10 s. Multivariate chemometric methods, such as partial least squares (PLS) regression, are used to exploit the high information content of infrared spectra to obtain selective analyses without physical separation of the analyte or analytes from whatever interfering species may be present. Multivariate chemometric models require considerably more effort for calibration and validation than do traditional univariate techniques. This thesis details suitable methods for preparing calibration standards by aerosol deposition, optimising and validating the model by cross- and test-set validation, and estimating the uncertainty by resampling and formula-based approaches. Successful calibration models were demonstrated for residues of acetaminophen, a model active pharmaceutical ingredient (API), on glass surfaces. The root-mean-square error of prediction (RMSEP) was ~0.07 µg cm⁻². Simultaneous calibration for acetaminophen and aspirin, another API, gave a similar RMSEP of 0.06 µg cm⁻² for both compounds, demonstrating the selectivity of the method. These values correspond to detection limits of ~0.2 µg cm⁻², well below the accepted visual detection limit of ~1-4 µg cm⁻². The sensitivity of the method with a stainless steel substrate was found to depend strongly on the surface finish, with highly polished surfaces giving more intense IRRAS. RMSEP values of 0.04- 0.05 µg cm⁻² were obtained for acetaminophen on stainless steel with three different finishes. For this system, severe nonlinearity was encountered for loadings 1.0 µg cm⁻². From the results presented in this thesis, it is clear that IRRAS has potential utility in cleaning validation as a complement to traditional techniques.