An in-depth analysis of Raman and near-infrared chemical images of common pharmaceutical tablets

This study reports on the application of Raman and near-infrared (NIR) imaging techniques for determining the spatial distribution of all (five) components in a common type of pharmaceutical tablet manufactured in two different ways. Multivariate chemical images were produced as principal component (PC) scores, while univariate images were produced by using the most unique spectra selected by the orthogonal projection approach (OPA), a searching algorithm. Multivariate Raman images were obtained for all five components in both tablets, while only two or three components could be imaged with the NIR instrument. Very interesting PC results are reported that in effect cast doubt on the effectiveness of the established criteria for determining signal-related PCs in the Raman data. PCA has been found to be indispensable for imaging the minor components using the Raman data. Significant similarity between the multivariate and univariate chemical images has been noted despite there being considerable spectral overlap within the Raman and, especially, within the NIR mapping data sets. Gray-scale images are carefully thresholded, which allowed for quantitative comparison of the obtained binarized images. A thorough discussion is given on the problems and approximations needed for producing composite images.     

Vibrational spectroscopic and ultrasound analysis for in-process characterization of high-density polyethylene/polypropylene blends during melt extrusion

Spectroscopic techniques such as Raman, mid-infrared (MIR), and near-infrared (NIR) have become indispensable analytical tools for rapid chemical quality control and process monitoring. This paper presents the application of in-line Fourier transform near-infrared (FT-NIR) spectroscopy, Raman spectroscopy, and ultrasound transit time measurements for in-line monitoring of the composition of a series of high-density polyethylene (HDPE)/polypropylene (PP) blends during single-screw extrusion. Melt composition was determined by employing univariate analysis of the ultrasound transit time data and partial least squares (PLS) multivariate analysis of the data from both spectroscopic techniques. Each analytical technique was determined to be highly sensitive to changes in melt composition, allowing accurate prediction of blend content to within +/- 1% w/w (1sigma) during monitoring under fixed extrusion conditions. FT-NIR was determined to be the most sensitive of the three techniques to changes in melt composition. A four-factor PLS model of the NIR blend spectra allowed determination of melt content with a standard prediction error of +/- 0.30% w/w (1sigma). However, the NIR transmission probes employed for analysis were invasive into the melt stream, whereas the single probes adopted for Raman and ultrasound analysis were noninvasive, making these two techniques more versatile. All three measurement techniques were robust to the high temperatures and pressures experienced during melt extrusion, demonstrating each system's suitability for process monitoring and control.     

Use of chemometrics and laser-induced breakdown spectroscopy for quantitative analysis of major and minor elements in aluminum alloys

In the present work, quantitative analysis of major and minor elements in aluminum alloys is investigated using chemometrics and laser-induced plasma spectroscopy with a commercially available laser-induced breakdown (LIBS) spectrometer. Multivariate calibrations use the entire signal matrix for all elements in a single multivariate regression model. This enables accounting for the correlation between variables often referred to as matrix effects in conventional univariate modeling. Modeling the entire signal matrix improves robustness over traditional univariate calibration since it can compensate for matrix effects. Several nonlinear data pretreatment methods have been used to correct for nonlinear behaviors of the analytical signals prior to performing the multivariate calibration. The use of multivariate calibration in combination with cubic implicit nonlinear data pretreatment showed the most accurate results. The accuracy reported with the developed multivariate calibration is better than 5% for the major alloying elements. Based on the results obtained, the use of chemometrics and laser-induced plasma spectroscopy have been successfully applied to the quantitative analysis of major and minor alloying elements in aluminum.     

Quantitative transmission Raman spectroscopy of pharmaceutical tablets and capsules

Quantitative analysis of pharmaceutical formulations using the new approach of transmission Raman spectroscopy has been investigated. For comparison, measurements were also made in conventional backscatter mode. The experimental setup consisted of a Raman probe-based spectrometer with 785 nm excitation for measurements in backscatter mode. In transmission mode the same system was used to detect the Raman scattered light, while an external diode laser of the same type was used as excitation source. Quantitative partial least squares models were developed for both measurement modes. The results for tablets show that the prediction error for an independent test set was lower for the transmission measurements with a relative root mean square error of about 2.2% as compared with 2.9% for the backscatter mode. Furthermore, the models were simpler in the transmission case, for which only a single partial least squares (PLS) component was required to explain the variation. The main reason for the improvement using the transmission mode is a more representative sampling of the tablets compared with the backscatter mode. Capsules containing mixtures of pharmaceutical powders were also assessed by transmission only. The quantitative results for the capsules' contents were good, with a prediction error of 3.6% w/w for an independent test set. The advantage of transmission Raman over backscatter Raman spectroscopy has been demonstrated for quantitative analysis of pharmaceutical formulations, and the prospects for reliable, lean calibrations for pharmaceutical analysis is discussed.     

Investigating dehydration from compacts using terahertz pulsed, Raman, and near-infrared spectroscopy

The purpose of this study was to investigate the dehydration of piroxicam monohydrate (PRXMH) in compacts using terahertz pulsed spectroscopy (TPS), Raman spectroscopy, and reflectance near-infrared (NIR) spectroscopy. Compacts were prepared by using PRXMH and poly(tetrafluoro)ethylene powders and combining them in three different manners before compression to produce compacts in which the PRXMH was dispersed throughout the compact, deposited on one face of the compact, or included as a layer within the compact. TPS was a suitable technique to assess the effect of sample preparation on dehydration, whereas Raman and NIR spectroscopy were limited by their sampling depth and the interference of the polymer matrix. TPS revealed that the dehydration behavior depended largely on the compact preparation method. Non-isothermal dehydration was investigated with all three spectroscopic techniques, combined with principal component analysis (PCA) on samples where the PRXMH was deposited on one face of the compact. In addition, variable temperature X-ray powder diffractometry (VT-XRPD) was used to verify the transformation from PRXMH to anhydrous PRX form I, while thermogravimetric analysis (TGA) was used to monitor the water loss. All three spectroscopic techniques allowed in situ monitoring of the dehydration from the surface layers of the compacts. TPS and Raman spectroscopy detected structural changes of the crystal, while NIR spectroscopy was more sensitive to water loss. PCA of the TPS, Raman spectroscopy, and XRPD data revealed similar dehydration profiles. In contrast, the NIR spectroscopy profile was more similar to the TGA results. The spectroscopic techniques were more suitable than slower techniques such as VT-XRPD for monitoring rapid structural changes that occurred during the dehydration.     

Internal multiple-scattering hole-enhanced Raman spectroscopy: improved backscattering Fourier transform Raman sampling in pharmaceutical tablets utilizing cylindrical-conical holes

The benefits of Raman signal enhancement and improved measurement precision are demonstrated using 180° backscattering Fourier transform Raman (FT-Raman) spectroscopy from drilled cylindrical-conical holes within pharmaceutical tablet cores. Multiple scattering of the incident laser light within the holes results in an increased Raman signal due to the larger Raman sampling volume. This is important for overcoming typical sub-sampling issues encountered when employing FT-Raman backscattering of heterogeneous pharmaceutical tablets. Hole depth and diameter were found to be important experimental parameters and were optimized to yield the greatest signal enhancement. The FT-Raman spectra collected using backscattering from cylindrical-conical holes is compared to typical 180° backscattering from flat surfaces using tablet cores of Excedrin? and Vivarin?. Raman chemical images are used to establish a representative sampling area. We observe a three- to five-fold increase in the Raman intensity and a two-fold improvement in the measurement precision when sampling from cylindrical-conical holes rather than classic backscattering from flat tablet cores. Self-absorption effects on analyte band ratios are negligible in the fingerprint region but are more significant at the higher near-infrared (NIR) absorbances found in the C-H/O-H/-N-H stretching region. The sampling technique will facilitate developing quantitative FT-Raman methods for application to pharmaceutical tablets using the fingerprint spectral region.     

Chemometric analysis of Raman spectroscopic data for process control applications

Two examples are given demonstrating the use of multivariate modeling in Raman process control applications. In one example, principal component analysis (PCA) and principal component regression (PCR) are used to model the curing of a high performance thermoset. The PCA results are found to give more accurate results when compared to univariate methods. In a second example, the octane number of gasoline is accurately modeled using partial least squares (PLS) regression analysis. For both examples, methods of normalization are considered in an effort to overcome the limitations of the single beam nature of Raman spectra.     

Multivariate analysis applied to the study of spatial distributions found in drug-eluting stent coatings by confocal Raman microscopy

Multivariate data analysis was applied to confocal Raman measurements on stents coated with the polymers and drug used in the CYPHER Sirolimus-eluting Coronary Stents. Partial least-squares (PLS) regression was used to establish three independent calibration curves for the coating constituents: sirolimus, poly(n-butyl methacrylate) [PBMA], and poly(ethylene-co-vinyl acetate) [PEVA]. The PLS calibrations were based on average spectra generated from each spatial location profiled. The PLS models were tested on six unknown stent samples to assess accuracy and precision. The wt % difference between PLS predictions and laboratory assay values for sirolimus was less than 1 wt % for the composite of the six unknowns, while the polymer models were estimated to be less than 0.5 wt % difference for the combined samples. The linearity and specificity of the three PLS models were also demonstrated with the three PLS models. In contrast to earlier univariate models, the PLS models achieved mass balance with better accuracy. This analysis was extended to evaluate the spatial distribution of the three constituents. Quantitative bitmap images of drug-eluting stent coatings are presented for the first time to assess the local distribution of components.     

Quantitative analysis of film coating in a fluidized bed process by in-line NIR spectrometry and multivariate batch calibration

A method is described which enables real-time analysis of film coating on pharmaceutical pellets during an industrial manufacturing process. Measurements were conducted on the solid particulate material by near-infrared (NIR) spectrometry utilizing a diffuse reflectance fiber-optic probe positioned inside a fluidized bed process vessel. Time series of NIR spectra from 11 batches generated a three-way data matrix that was unfolded and modeled by partial least squares (PLS) in a multivariate batch calibration. The process conditions were deliberately varied according to an experimental design. This yielded good predictability of the coating thickness with a best model fit, R2 = 0.97, for one PLS-projection, and a root-mean-square error of calibration = 2.2 microm (range tested 0-50 microm). The regression vector was shown to be highly influenced by responses that are both direct (aliphatic C-H stretch overtones) and indirect (aromatic C-H stretch overtones), from film component and core material, respectively. The impact of different data pre-treatment methods on the normalization of the regression vector is reported. Justification of the process calibration approach is emphasized by good correlation between values predicted from NIR data and reference image analysis data on dissected pellets and a theoretical nonlinear coating thickness growth model. General aspects of in-line NIR on solids and multivariate batch calibration are discussed.     

Transmission fourier transform Raman spectroscopy of pharmaceutical tablet cores

Transmission Fourier transform (FT) Raman spectroscopy of pharmaceutical tablet cores is demonstrated using traditional, unmodified commercial instrumentation. The benefits of improved precision over backscattering Raman spectroscopy due to increased sample volume are demonstrated. Self-absorption effects on analyte band ratios and sample probe volume are apparent, however. A survey of near-infrared (NIR) absorption spectra in the FT-Raman spectral range (approximately 0 to 3500 wavenumber shift from 1064 nm, or 1064 to 1700 nm) of molecules with a wide range of NIR-active functional groups shows that although absorption at the laser wavelength (1064 nm) is relatively small, some regions of the Raman spectrum coincide with NIR absorbances of 0.5 per cm or greater. Fortunately, the pharmaceutically important regions of the Raman shift spectrum from 0 to 600 cm(-1) and from 1400 to 1900 cm(-1) exhibit low self-absorption for most organic materials. A statistical analysis of transmission FT-Raman noise in spectra collected from different regions of a pharmaceutical tablet provides insight into both spectral distortion and reduced sampling volume caused by self-absorption.     

Near-infrared spectroscopic monitoring of a series of industrial batch processes using a bilinear grey model

A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.     

Automated wavelength selection for spectroscopic fuel models by symmetrically contracting repeated unmoving window partial least squares

The need for automated quality surveillance of liquid hydrocarbon fuels has driven the development of rapid fuel property modeling from spectroscopic sensor data. The correlation of near-infrared (NIR) and Raman spectroscopic data with jet and diesel fuel properties can be improved by the deliberate selection of continuous wavelength sub-ranges. An automatic wavelength selection strategy would allow for the unsupervised construction of partial least squares (PLS) regression models of increased predictive utility when supervised model construction and maintenance is not feasible. Changeable size moving window partial least squares (CSMWPLS) is one of the most thorough operations suited for this task. Unfortunately, the necessarily large number of PLS model constructions required by an automated version of this procedure limits the evaluation of the predictive ability of the resulting models through full cross-validation results. Presented here is a novel restricted version of the CSMWPLS algorithm in which the initial spectral range selection is accomplished through multiple interval PLS (iPLS) analyses, where analysis windows for the refinement step no longer move, and size changes are limited to a series of symmetric attenuations. It is shown that the proposed algorithm can provide significant PLS model improvements during the course of a fully automated analysis of jet and diesel fuel spectra in less time than an automated CSMWPLS algorithm.     

Wavelength selection for multivariate calibration using tikhonov regularization

Prediction of sample properties using spectroscopic data with multivariate calibration is often enhanced by wavelength selection. This paper reports on a built-in wavelength selection method in which the estimated regression vector contains zero to near-zero coefficients for undesirable wavelengths. The method is based on Tikhonov regularization with the model 1-norm (TR1) and is applied to simulated and near-infrared (NIR) spectral data. Models are also formed from wavelength subsets determined by the standard method of stepwise regression (SWR). Harmonious (bias/variance tradeoff) and parsimonious considerations are compared with and without wavelength selection for principal component regression (PCR), ridge regression (RR), partial least squares (PLS), and multiple linear regression (MLR). Results show that TR1 models generally contain large baseline regions of near-zero coefficients, thereby essentially achieving built-in wavelength selection. For example, wavelengths with spectral interferences and/or poor signal-to-noise ratios obtain near zero regression coefficients. Results often improve with TR1 models, compared to full wavelength PCR, RR, and PLS models. The SWR subset results are similar to those for the TR1 models using the NIR data and worse with the simulated spectral situations. In general, wavelength selection improves prediction accuracy at a sacrifice to a potential increase in variance and the parsimony remains nearly equivalent compared to full wavelength models. New insights gained from the reported studies provide useful guidelines on when to use full wavelengths or use wavelength selection methods. Specifically, when a small number of large wavelength effects (good sensitivity and selectivity) exist, subset selection by SWR (with caution) and TR1 do well. With a small to moderate number of large to moderate sized wavelength effects, TR1 is better. Lastly, when a large number of small effects are present, full wavelengths with the methods of PCR, RR, or PLS are best.     

Analysis of the effect of particle size on polymorphic quantitation by Raman spectroscopy

Raman spectroscopy has been widely used to monitor various aspects of the crystallization process. Although it has long been known that particle size can influence Raman signal, relatively little research has been conducted in this area, in particular for mixtures of organic materials. The aim of this study was to investigate the effect of particle size on quantification of polymorphic mixtures. Several sets of calibration samples containing different particle size fractions were prepared and Raman spectra were collected with different probes. Calibration models were built using both univariate and multivariate analysis. It was found that, for a single component system, Raman intensity decreased with increasing particle size. For mixtures, calibration models generated from the same particle size distribution as the sample yielded relatively good predictions of the actual sample composition. However, if the particle sizes of the calibration and unknown samples were different, prediction errors resulted. For extreme differences in particle sizes, prediction errors of up to 20% were observed. Prediction errors could be minimized by changing the sampling optics employed.     

Optimization of discriminant partial least squares regression models for the detection of animal by-product meals in compound feedingstuffs by near-infrared spectroscopy

This paper evaluates two multivariate strategies for classifying near-infrared (NIR) spectroscopic data for the detection of animal by-product meals (henceforth generically termed AbP) as an ingredient in compound feedingstuffs. Classification models were developed to discriminate between the presence and absence of animal-origin meals in compound feeds using two forms of discriminant partial least squares (PLS) regression: the algorithms PLS1 and PLS2. The training set comprised 433 commercial feeds, of which 148 contained AbP and the other 285 were stated to be AbP-free. Since the initial set contained unequal numbers of each class, the effect of this imbalance was analyzed by applying the same algorithms to a training set containing equal numbers of AbP-free and AbP-containing samples. The best classification model (97.42% of samples correctly classified), obtained with PLS2, that showed less sensitivity to the use of class-unbalanced sets, was externally validated using a set of 18 samples (10 AbP-containing and 8 AbP-free); all samples were correctly classified, except for one AbP-free sample that was classified as containing AbP (false positive). The results suggest that the application of PLS discriminant analysis to NIR spectroscopic data enables detection of AbP, a feed ingredient banned since the bovine spongiform encephalopathy (BSE) crisis; this confirms the value of NIRS qualitative analysis for product authentication purposes.     

Application of quantitative chemometric analysis techniques to direct sampling mass spectrometry

This paper explores the use of direct sampling mass spectrometry coupled with multivariate chemometric analysis techniques for the analysis of sample mixtures containing analytes with similar mass spectra. Water samples containing varying mixtures of toluene, ethyl benzene, and cumene were analyzed by purge-and-trap/direct sampling mass spectrometry. Multivariate calibration models were built using partial least-squares regression (PLS), trilinear partial least-squares regression (tri-PLS), and parallel factor analysis (PARAFAC), with the latter two methods taking advantage of the differences in the temporal profiles of the analytes. The prediction errors for each model were compared to those obtained with simple univariate regression. Multivariate quantitative methods were found to be superior to univariate regression when a unique ion for quantitation could not be found. For prediction samples that contained unmodeled, interfering compounds, PARAFAC outperformed the other analysis methods. The uniqueness of the PARAFAC model allows for estimation of the mass spectra of the interfering compounds, which can be subsequently identified via visual inspection or a library search.     

Chemical image fusion. The synergy of FT-NIR and Raman mapping microscopy to enable a more complete visualization of pharmaceutical formulations

The pharmaceutical industry uses successfully both FT-NIR and Raman microscopy to produce chemical images of solid dosage forms, typically in troubleshooting roles. However, due to the chemical composition of the formulations, it is not always possible to describe the entire chemical formulation by using a single spectroscopic method. As Raman and NIR spectroscopies are complementary in nature, their combined usage offers the opportunity to describe heterogeneous mixtures in more detail. A novel sample referencing approach has been developed that allows data to be acquired from exactly the same area of the sample using both Raman and FT-NIR microscopies. The optimum images for the components are then overlaid, which gives rise to a combined chemical image that visually describes the entire formulation. We have named this approach chemical image fusion (CIF). CIF has been applied to two examples. The first shows how a simple formulation was used to validate the CIF approach. In the second, CIF allowed an entire formulation to be visualized and the cause of tabletting problems determined. CIF provides increased confidence in the results generated by each individual technique and offers a more powerful method for the evaluation of pharmaceutical formulations.     

Near-infrared spectroscopy for cocrystal screening. A comparative study with Raman spectroscopy

Near-infrared (NIR) spectroscopy is a well-established technique for solid-state analysis, providing fast, noninvasive measurements. The use of NIR spectroscopy for polymorph screening and the associated advantages have recently been demonstrated. The objective of this work was to evaluate the analytical potential of NIR spectroscopy for cocrystal screening using Raman spectroscopy as a comparative method. Indomethacin was used as the parent molecule, while saccharin and l-aspartic acid were chosen as guest molecules. Molar ratios of 1:1 for each system were subjected to two types of preparative methods. In the case of saccharin, liquid-assisted cogrinding as well as cocrystallization from solution resulted in a stable 1:1 cocrystalline phase termed IND-SAC cocrystal. For l-aspartic acid, the solution-based method resulted in a polymorphic transition of indomethacin into the metastable alpha form retained in a physical mixture with the guest molecule, while liquid-assisted cogrinding did not induce any changes in the crystal lattice. The good chemical peak selectivity of Raman spectroscopy allowed a straightforward interpretation of sample data by analyzing peak positions and comparing to those of pure references. In addition, Raman spectroscopy provided additional information on the crystal structure of the IND-SAC cocrystal. The broad spectral line shapes of NIR spectra make visual interpretation of the spectra difficult, and consequently, multivariate modeling by principal component analysis (PCA) was applied. Successful use of NIR/PCA was possible only through the inclusion of a set of reference mixtures of parent and guest molecules representing possible solid-state outcomes from the cocrystal screening. The practical hurdle related to the need for reference mixtures seems to restrict the applicability of NIR spectroscopy in cocrystal screening.     

Noninvasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for noninvasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform noninvasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.