Spectral Pre-processing Techniques Research Articles

Raman spectroscopy for the process analysis of the manufacturing of a suspension metered dose inhaler

The purpose of this research was to demonstrate the utility of Raman spectroscopy for process analysis of a suspension metered dose inhaler manufacturing process. Chemometric models were constructed for the quantification of ethanol and active pharmaceutical ingredient such that both could be monitored in real-time during the compounding and filling operations via tank measurements and recirculation line flow-cell measurements. Different spectral preprocessing techniques were used to delineate the effects of mixing speed and temperature changes from actual concentration effects. Raman spectroscopy offers advantages in time savings and quality of information over the standard methods of analysis for respiratory formulations, such as a drug content assay via HPLC and ethanol testing via GC. The successful implementation of this work will allow formulation scientists to quantitatively assess both the formulation (e.g., the concentration of active pharmaceutical ingredient (API) and ethanol), as well as the manufacturing process (e.g., determination of mixing endpoints) in real-time.

Use of spectral pre-processing methods to compensate for the presence of packaging film in visible–near infrared hyperspectral images of food products

The presence of polymeric packaging film in images of food products may modify spectra obtained in hyperspectral imaging (HSI) experiments, leading to undesirable image artefacts which may impede image classification. Some pre-processing of the image is typically required to reduce the presence of such artefacts. The objective of this research was to investigate the use of spectral pre-processing techniques to compensate for the presence of packaging film in hyperspectral images obtained in the visible–near infrared wavelength range (445–945 nm), with application in food quality assessment. A selection of commonly used pre-processing methods, used individually and in combination, were applied to hyperspectral images of flat homogeneous samples, imaged in the presence and absence of different packaging films (polyvinyl chloride and polyethylene terephthalate). Effects of the selected pre-treatments on variation due to the film’s presence were examined in principal components score space. The results show that the combination of first derivative Savitzky–Golay followed by standard normal variate transformation was useful in reducing variations in spectral response caused by the presence of packaging film. Compared to other methods examined, this combination has the benefits of being computationally fast and not requiring a priori knowledge about the sample or film used.

Mid-infrared spectroscopy as a tool for rapid determination of internal quality parameters in tomato

The objective of this study was to investigate the possibility of predicting the quality parameters of tomato by mid-infrared spectroscopy. For 2 years, tomato samples, representing a large variability in the chemical composition, were scanned using the attenuated total reflectance accessory of a Fourier transform spectrometer in the wavenumber region between 4000 and 400 cm −1 . Calibration models were developed using partial least squares (PLS) regression method and were tested with internal validation sample set in the first year. Different spectral preprocessing techniques were investigated and different spectral regions were selected to optimise the calibration models. In addition, the models obtained in 2007 were used to predict the soluble solids, dry matter and total acidity in tomato harvested in 2008. The regression models of the spectra showed reasonable ability to estimate the dry matter, soluble solids, total acidity, citric acid and individual sugars contents in tomatoes grown in 2007, with high coefficients of determination (from 0.98 for soluble solids to 0.92 for fructose) and low percentage errors of prediction (from 3% to 7% for soluble solids and citric acid, respectively). By contrast, the predictive capability of model for malic acid was unsatisfactory with error of prediction of 26%. When the best calibration models from 2007 were used to predict the selected parameters in tomato picked in 2008, very high correlations were obtained, confirmed by low errors of prediction, from 4% to 7% for dry matter and total acidity, respectively. Mid-infrared spectroscopy is an attractive alternative for standard methods for determination of internal quality parameters in tomato.

Investigation of Chemometric Instrumental Transfer Methods for High-Resolution NMR

The implementation of direct standardization (DS), piecewise direct standardization (PDS), and double-window piecewise direct standardization (DWPDS) instrumental transfer techniques for high-resolution (1)H NMR spectral data was explored. The ability to transfer a multivariate calibration model developed for a "master or target" NMR instrument configuration to seven different ("secondary") NMR instrument configurations was measured. Partial least-squares (PLS) calibration of glucose, glycine, and citrate metabolite relative concentrations in model mixtures following mapping of the secondary instrumental configurations using DS, PDS, or DWPDS instrumental transfer allowed the performance of the different transfer methods to be assessed. Results from these studies suggest that DS and PDS transfer techniques produce similar improvements in the error of prediction compared to each other and provide a significant improvement over standard spectral preprocessing techniques including reference deconvolution and spectral binning. The DS instrumental transfer method produced the largest percent improvement in the predictions of concentrations for these model mixtures but, in general, required that additional transfer calibration standards be used. Limitations of the different instrumental transfer methods with respect to sample subset selection are also discussed.

Spectral Pre-Treatments of Hyperspectral near Infrared Images: Analysis of Diffuse Reflectance Scattering

Scattering effects are often encountered when measuring diffuse reflectance near infrared (NIR) spectra of solid and semi-solid materials. How does this phenomenon effect hyperspectral imaging of powders? A series of hyperspectral NIR images of particle size fractions of commercial grade salt and sugar were acquired. Spectral pre-processing techniques, including Kubelka–Munk, standard normal variate and absorbance transforms, unit length or unit area normalisation, first and second derivative transforms, and several variants of multiplicative scatter corrections (MSC) were applied to the images and examined for their effectiveness at reducing or eliminating scatter effects. Principal component analysis (PCA) scoreplots produced expected results: derivative transforms reduced variance, but did not eliminate the particle size dependencies; piecewise MSC transforms reduced the data to two clusters, one for salt and one for sugar. Partial least squares (PLS) regression was applied to examine the impact of the pre-processing transforms on prediction of particle size. RMSEP values between 10 and 50 μm were determined for particle fractions ranging between 140 and 315 μm for all transforms except the piecewise MSC; in spite of the reduction in additive and multiplicative effects, enough correlated variance remained after application of the pre-processing transforms to allow prediction of particle size ranges from PLS models. Additional scatter effect information was obtained by examining particle size distribution histograms and spatial particle size mappings facilitated by the hyperspectral images.

Process analytical technology case study part I: Feasibility studies for quantitative near-infrared method development

This article is the first of a series of articles detailing the development of near-infrared (NIR) methods for solid-dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to qualify the capabilities of instrumentation and sample handling systems, evaluate the potential effect of one source of a process signature on calibration development, and compare the utility of reflection and transmission data collection methods. A database of 572 production-scale sample spectra was used to evaluate the interbatch spectral variability of samples produced under routine manufacturing conditions. A second database of 540 spectra from samples produced under various compression conditions was analyzed to determine the feasibility of pooling spectral data acquired from samples produced at diverse scales. Instrument qualification tests were performed, and appropriate limits for instrument performance were established. To evaluate the repeatability of the sample positioning system, multiple measurements of a single tablet were collected. With the application of appropriate spectral preprocessing techniques, sample repositioning error was found to be insignificant with respect to NIR analyses of product quality attributes. Sample shielding was demonstrated to be unnecessary for transmission analyses. A process signature was identified in the reflection data. Additional tests demonstrated that the process signature was largely orthogonal to spectral variation because of hardness. Principal component analysis of the compression sample set data demonstrated the potential for quantitative model development. For the data sets studied, reflection analysis was demonstrated to be more robust than transmission analysis.

Analysis of neoplastic changes in mouse lung using Fourier-transform infrared microspectroscopy

Methods based on infrared microspectroscopy were explored as a means to distinguish normal from neoplastic lung tissue. Mice were exposed to urethane, a known environmental carcinogen. After 3–8 months, lungs were removed, snap-frozen, sectioned and analyzed by standard histological methods and by infrared microspectroscopy. Neoplasms were readily observed in mice treated with urethane. Ultra-structurally, the neoplasms were composed entirely of type II alveolar cells displaying intracellular lamellar bodies. A fusion of two spectral pre-processing techniques, optimal region selection and linear discriminant analysis (LDA), was used to search for infrared spectral signatures distinguishing normal from neoplastic tissue. These techniques showed clear and reproducible differences between the complex spectra of these tissue types, suggesting that infrared microspectroscopy in conjunction with spectral processing technology may be useful to reveal subtle spectral differences occurring following induction of neoplastic changes and to interpret their biochemical origins.

Strategy for constructing robust multivariate calibration models

In multivariate calibrations usually a minimal residual error in the model's predictions is aimed at, while generally less attention is paid to the robustness of the model with respect to changes in instrumentation, laboratory conditions, or sample composition. In this paper, we propose a strategy for selecting a multivariate calibration model which possesses a small prediction error and, simultaneously, is less sensitive to the above-mentioned variations. The strategy is applied to calibration models used to predict the density of poly(ethylene terephthalate) (PET) yarns from the Raman spectra. The strategy implies that spectra of calibration samples are measured under circumstances under which the application will be implemented, and spectra of a smaller set under different conditions (variations in ambient temperature, laser power, and laser frequency) according to an experimental design. The prediction results of the calibration model are used in a ruggedness test in order to test the sensitivity. In this study various calibration models using different spectral preprocessing techniques are tested. These ruggedness results together with the prediction error are used to select a good model. Moreover, it is possible in this way to provide the boundaries for the experimental conditions, where the model is valid.