Raman imaging analysis of pharmaceutical tablets by two-dimensional (2D) correlation spectroscopy

Chemical properties of active substances and insoluble excipient within tablets such as crystalline structures can be seen as an important index for solubility of ingredients. Spectroscopic imaging can potentially be a solid solution to understanding mechanisms at the molecular level and it may bring useful insight in terms of process analytical technique. In the present study, generalized two-dimensional (2D) correlation spectroscopy is utilized for the Raman image analysis of pharmaceutical tablets to reveal molecular interactions between chemical components. By using a spatial distance as a perturbation variable in 2D correlation scheme, synchronous and asynchronous correlation analysis becomes possible. Two kinds of pharmaceutical tablets, pentoxifylline (PTX) as an active substance and palmitic acid (PA) as an insoluble excipient, are prepared with different grinding times, 0.5 and 45 min. The 2D correlation analysis of Raman images of the tablets clearly reveals both physical and chemical effects of grinding process on the properties of the tablets. Asynchronous correlations indicate that a specific molecular structural change of PTX related to the crystallinity is induced by the grinding process. Namely, the crystallinity of PTX based on CH₂ structure is a key factor to control the solubility of the tablets. Some properties of pharmaceutical tablets, i.e. solubility or distribution of components in turn may become possible by the simple grinding process. Detailed analysis of Raman images becomes possible by the 2D correlation spectroscopy.     

Applications of Raman spectroscopy in pharmaceutical analysis

As Raman spectroscopy enables rapid, non-destructive measurements, the technique appears a most promising tool for on-line process monitoring and analysis in the pharmaceutical industry. This article gives a short introduction to Raman spectroscopy and presents several applications in the pharmaceutical field.     

Multivariate curve-resolution analysis of pesticides in water samples from liquid chromatographic-diode array data

Liquid chromatographic-diode array detection data recorded for aqueous mixtures of 11 pesticides show the combined presence of strongly coeluting peaks, distortions in the time dimension between experimental runs, and the presence of potential interferents not modeled by the calibration phase in certain test samples. Due to the complexity of these phenomena, data were processed by a second-order multivariate algorithm based on multivariate curve resolution and alternating least-squares, which allows one to successfully model both the spectral and retention time behavior for all sample constituents. This led to the accurate quantitation of all analytes in a set of validation samples: aldicarb sulfoxide, oxamyl, aldicarb sulfone, methomyl, 3-hydroxy-carbofuran, aldicarb, propoxur, carbofuran, carbaryl, 1-naphthol and methiocarb. Limits of detection in the range 0.1-2 μg mL⁻¹ were obtained. Additionally, the second-order advantage for several analytes was achieved in samples containing several uncalibrated interferences. The limits of detection for all analytes were decreased by solid phase pre-concentration to values compatible to those officially recommended, i.e., in the order of 5 ng mL⁻¹.     

Chemistry at level of individual aerosol particle using multivariate curve resolution of confocal Raman image

Airborne particles with aerodynamic diameter in the 10-1 microm range have been collected in an industrial/urban zone by impaction and have been investigated by automated confocal Raman microspectrometry. The computer-microcontrolled XY scanning and Z focusing of Raman images provided many pixel Raman spectra which are characteristics of complex mixture at level of individual particle. The large heterogeneity was not resolved by the spatial resolution of the instrument which is limited by the optical diffraction. The severe spectral overlaps generated by heterogeneity were resolved by multivariate curve resolution (MCR) methods. The purity based method (SIMPLISMAX) was used to resolve both luminescence spectra and pure Raman spectra without prior information. The MCR-alternating least square (ALS) was used as a refined method of both spectra and spectral concentrations. The reconstructing Raman images of the respective spectral contribution supply a versatile potential to characterize the chemistry of atmospheric aerosols at the level of the individual particles.     

Chemical imaging of pharmaceutical granules by Raman global illumination and near-infrared mapping platforms

Raman global illumination and near-infrared (NIR) mapping instruments were used to chemically image pharmaceutical granules obtained by the wet granulation process in order to determine whether the API was mixed with the major excipient or granulates on its own. The granules were randomly distributed onto a microscope slide and an average area of about 3.5 mm × 3.5 mm, covering 50-100 granules, was analyzed by both instruments. Light microscopy images of the separated granules were collected before the spectroscopic data acquisition. Both Raman and NIR signals of API and major excipient (mannitol) were easily detected by both techniques which allowed the chemical structure of the granules to be characterised. Most of the granules were found to contain both API and mannitol but pure mannitol and a few pure API granules were also identified. Raman global illumination was found to provide a comprehensive insight into chemical structure of the granules being able to more clearly determine the API in comparison with NIR mapping. Owing to the differences in shapes of the particles and reflection characteristics, visual microscopy and methods based on reflection can be potentially useful for analyzing this particular formulation.     

Multivariate curve resolution applied to in situ X-ray absorption spectroscopy data: An efficient tool for data processing and analysis

Large datasets containing many spectra commonly associated with in situ or operando experiments call for new data treatment strategies as conventional scan by scan data analysis methods have become a time-consuming bottleneck. Several convenient automated data processing procedures like least square fitting of reference spectra exist but are based on assumptions. Here we present the application of multivariate curve resolution (MCR) as a blind-source separation method to efficiently process a large data set of an in situ X-ray absorption spectroscopy experiment where the sample undergoes a periodic concentration perturbation. MCR was applied to data from a reversible reduction–oxidation reaction of a rhenium promoted cobalt Fischer–Tropsch synthesis catalyst. The MCR algorithm was capable of extracting in a highly automated manner the component spectra with a different kinetic evolution together with their respective concentration profiles without the use of reference spectra. The modulative nature of our experiments allows for averaging of a number of identical periods and hence an increase in the signal to noise ratio (S/N) which is efficiently exploited by MCR. The practical and added value of the approach in extracting information from large and complex datasets, typical for in situ and operando studies, is highlighted.     

Detection of malathion in food peels by surface-enhanced Raman imaging spectroscopy and multivariate curve resolution

An analytical methodology was developed for detection of malathion in the peels of tomatoes and Damson plums by surface-enhanced Raman imaging spectroscopy and multivariate curve resolution. To recover the pure spectra and the distribution mapping of the analyzed surfaces, non-negative matrix factorization (NMF), multivariate curve calibration methods with alternating least squares (MCR-ALS) and MCR with weighted alternating least square (MCR-WALS) were utilized. Error covariance matrices were estimated to evaluate the structure of the error over all the data. For the tomato data, NMF-ALS and MCR-ALS presented excellent spectral recovery even in the absence of initial knowledge of the pesticide spectrum. For the Damson plum data, owing to heteroscedastic noise, MCR-WALS produced better results. This methodology enabled detection below to the maximum residue limit permitted for this pesticide. This approach can be implemented for in situ monitoring because it is fast and does not require extensive manipulation of samples, making its use feasible for other fruits and pesticides as well.     

Heterogeneous chemistry between PbSO4 and calcite microparticles using Raman microimaging

Currently, the smelting activities of lead and zinc are the loudest sources of local pollution by emission in the troposphere of dust of micrometer size containing PbSO₄. As the particles evolve in the troposphere, their chemical and physical properties – and hence their characteristics such as toxicity – change by accumulation of atmospheric heterogeneous reactions. Calcite (CaCO₃) represents a large part of the mineral fraction in tropospheric aerosols with aerodynamic diameters less than 10 μm. The calcite particles are expected to react with PbSO₄ particles. In an effort to model the chemical behaviour of PbSO₄ individual particles in the troposphere, we present the in situ Raman imaging results during the course of the reactions in a water droplet of PbSO₄ particles with a calcite microcrystal surface. The computer-microcontrolled XY scanning and Z focusing of confocal Raman imaging combined with multivariate curve resolution (MCR) of Raman images have resolved the severe spectral overlaps of the Raman spectra which are not resolved by the spatial resolution of the instrument (1 μm³). The results pointed out the identification and the mapping of Pb₃(CO₃)₂(OH)₂, PbCO₃ and CaSO₄·2H₂O (gypsum) on the calcite surface.     

Fiber array based hyperspectral Raman imaging for chemical selective analysis of malaria-infected red blood cells

A new setup for Raman spectroscopic wide-field imaging is presented. It combines the advantages of a fiber array based spectral translator with a tailor-made laser illumination system for high-quality Raman chemical imaging of sensitive biological samples.     

Multivariate curve resolution of incomplete fused multiset data from chromatographic and spectrophotometric analyses for drug photostability studies

An advanced and powerful chemometric approach is proposed for the analysis of incomplete multiset data obtained by fusion of hyphenated liquid chromatographic DAD/MS data with UV spectrophotometric data from acid–base titration and kinetic degradation experiments. Column- and row-wise augmented data blocks were combined and simultaneously processed by means of a new version of the multivariate curve resolution-alternating least squares (MCR-ALS) technique, including the simultaneous analysis of incomplete multiset data from different instrumental techniques. The proposed procedure was applied to the detailed study of the kinetic photodegradation process of the amiloride (AML) drug. All chemical species involved in the degradation and equilibrium reactions were resolved and the pH dependent kinetic pathway described.     

Multivariate curve resolution in liquid chromatography—resolving two-way multi-component data using a Varimax extended rotation

The Varimax extended rotations (VER) have been proposed as a new method to mathematically resolve severely overlapped peaks in chromatographic experiments that produce bilinear data. VER employs a four-step procedure to determine the relative concentration and identity of the components that comprise a severely overlapped chromatographic peak. In the first step, the data are pre-processed to ensure that they are in a form suitable for multivariate curve resolution. The second step involves principal component analysis, which reduces the dimensionality of the data matrix while simultaneously retaining the information present in the data. In the third step, a new coordinate system is developed for the data using a Varimax rotation followed by a so-called extended rotation, which assists in identifying the so-called pure regions in the peak. Identifying these regions is crucial to rotating the concentration and spectral matrices towards a solution. The fourth step utilizes alternating least squares (ALS) to improve the estimates of the concentration and spectral profiles of each component. Results from real and simulated data are used to illustrate the efficacy and simplicity of the proposed method.     

A new non-invasive, quantitative Raman technique for the determination of an active ingredient in pharmaceutical liquids by direct measurement through a plastic bottle

The concentration of an active pharmaceutical ingredient (povidone) in a commercial eyewash solution has been measured directly through a plastic (low-density polyethylene: LDPE) container using a wide area illumination (WAI) Raman scheme. The WAI scheme allows excitation using a 6 mm laser spot (focal length: 248 mm) that is designed to cover a wide sample area. As a result, it has the potential to improve the reliability Raman measurements by significantly enhancing representative sample interrogation, thus improving the reproducibility of sampling. It also decreases the sensitivity of sample placement with regard to the excitation focal plane. Simultaneously, isobutyric anhydride was placed in front of the bottles to use for a synchronous external standard configuration. This helps to correct the problematic variation of Raman intensity from the inherent fluctuation in laser power. Using the WAI Raman scheme combined with the synchronous standard method, the povidone concentration was successfully measured with spectral collection that was performed through a plastic barrier. The conventional Raman scheme was difficult to employ for the same purpose because of the degraded spectral reproducibility resulting from the smaller laser illumination area and the sensitivity of such an approach to the position of the sample bottle. The result from this study suggests that the WAI scheme exhibits a strong potential for the non-destructive quantitative analysis of pharmaceuticals measured directly in plastic containers. Preliminary work also shows that similar measurements can also be made in glass bottles. If implemented, this technique could be utilized as a simple and rugged method for quality assurance of final products in a manner consistent with Process analytical technology (PAT) requirements.     

High performance liquid chromatographic analysis of dehydroepiandrosterone and its pharmaceutical tablet formulation

A rapid, sensitive and stability indicating high performance liquid chromatographic method was developed and validated for the analysis of dehydroepiandrosterone (DHEA) in pharmaceutical tablet formulation. The analysis was done on a Supelcosil C(18) column (25 cm x 4.6 mm i.d., 5 microm). The mobile phase consisted of methanol:sodium acetate buffer solution (5 g/L):acetic acid (500 mL/L), 57:42:1, v/v/v, adjusted to pH 5 at a flow rate of 1 mL/min. Detection was carried out at a wavelength of 258 nm. The polynomial regression data for the calibration curve showed good linear relationship in the concentration range of 0.2-1 mg/mL with r = 0.9996. The method was validated for precision, accuracy and recovery. The limit of detection was found to be 50 ng/ microL. The method was applied for the analysis of DHEA in its pharmaceutical tablet formulation. The effects of different buffers and alcohols on the retention of DHEA were studied and the role of acetic acid as an organic phase modifier was also investigated.     

Reliable and fast quantitative analysis of active ingredient in pharmaceutical suspension using Raman spectroscopy

The concentration of acetaminophen in a turbid pharmaceutical suspension has been measured successfully using Raman spectroscopy. The spectrometer was equipped with a large spot probe which enabled the coverage of a representative area during sampling. This wide area illumination (WAI) scheme (coverage area 28.3 mm²) for Raman data collection proved to be more reliable for the compositional determination of these pharmaceutical suspensions, especially when the samples were turbid. The reproducibility of measurement using the WAI scheme was compared to that of using a conventional small-spot scheme which employed a much smaller illumination area (about 100 μm spot size). A layer of isobutyric anhydride was placed in front of the sample vials to correct the variation in the Raman intensity due to the fluctuation of laser power. Corrections were accomplished using the isolated carbonyl band of isobutyric anhydride. The acetaminophen concentrations of prediction samples were accurately estimated using a partial least squares (PLS) calibration model. The prediction accuracy was maintained even with changes in laser power. It was noted that the prediction performance was somewhat degraded for turbid suspensions with high acetaminophen contents. When comparing the results of reproducibility obtained with the WAI scheme and those obtained using the conventional scheme, it was concluded that the quantitative determination of the active pharmaceutical ingredient (API) in turbid suspensions is much improved when employing a larger laser coverage area. This is presumably due to the improvement in representative sampling.     

Multivariate curve resolution applied to the simultaneous analysis of electrochemical and spectroscopic data: study of the Cd(II)/glutathione-fragment system by voltammetry and circular dichroism spectroscopy

Multivariate curve resolution with alternating least squares (MCR-ALS) is applied for the first time to the simultaneous analysis of electrochemical and spectroscopic data. Then, a data analysis is done with augmented matrices constituted by Differential Pulse Polarography and Circular Dichroism data submatrices. The use of proper, and different for each submatrix, constrains in the iterative ALS optimization allows to obtain chemically meaningful results constituted by a common matrix containing the concentration profiles, and two matrices with the pure electrochemical and spectroscopic signals. MCR-ALS is applied to the study of the complexation of Cd by Cys-Gly, a glutathione-fragment of great interest for understanding metal-phytochelatins complexation.     

Multivariate curve resolution: a method of evaluating the kinetics of biotechnological reactions

In biotechnology, strong emphasis is placed on the development of wet chemical analysis and chromatography to separate target components from a complex matrix. In bioprocessing, the development of single compound biosensors is an important activity. The advantages of these techniques are their high sensitivity and specificity. Inline or online monitoring by means of spectroscopy has the potential to be used as an “all-in-one” analysis technique for biotechnological studies, but it lacks specificity. Multivariate curve resolution (MCR) can be used to overcome this limitation. MCR is able to extract the number of components involved in a complex spectral feature, to attribute the resulting spectra to chemical compounds, to quantify the individual spectral contributions, and to use this quantification to develop kinetic models for the process with or without a priori knowledge. After a short introduction to MCR, two applications are presented. In the first example, the spectral features of hemp are monitored and analysed during growth. MCR provides unperturbed spectra on the activity of, for example, lignin and cellulose during plant development. In a second example, the kinetics of a laccase enzyme-catalysed degradation of aromatic hydrocarbons are calculated from UV/VIS spectra.     

多元曲线分辨-交替最小二乘方法在联用数据分辨中的应用

通过对模拟数据和高效毛细管电泳实验数据的分析,讨论了多元曲线分辨-交替最小二乘方法(MCR-ALS)在毛细管电泳-二极管阵列检测(CE-DAD)联用数据分辨中的应用.讨论了几种因素对MCR-ALS单个数据矩阵分辨结果的影响,包括待分析物光谱间的相似程度、浓度曲线的重叠程度以及由渐进因子分析(EFA)所得到的浓度初始值等.MCR-ALS还可用于多个数据矩阵的同时分析,即二阶MCR-ALS.结果表明,与一阶MCR-ALS相比,二阶MCR-ALS方法能够更好地解决各种分辨问题,得到合理和满意的分辨结果.     

Setting local rank constraints by orthogonal projections for image resolution analysis: Application to the determination of a low dose pharmaceutical compound

Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used.     

Identification of pharmaceutical tablets by Raman spectroscopy and chemometrics

Raman spectroscopy has become an attractive tool for the analysis of pharmaceutical solid dosage forms. In the present study it is used to ensure the identity of tablets. The two main applications of this method are release of final products in quality control and detection of counterfeits. Twenty-five product families of tablets have been included in the spectral library and a non-linear classification method, the Support Vector Machines (SVMs), has been employed. Two calibrations have been developed in cascade: the first one identifies the product family while the second one specifies the formulation. A product family comprises different formulations that have the same active pharmaceutical ingredient (API) but in a different amount. Once the tablets have been classified by the SVM model, API peaks detection and correlation are applied in order to have a specific method for the identification and allow in the future to discriminate counterfeits from genuine products. This calibration strategy enables the identification of 25 product families without error and in the absence of prior information about the sample. Raman spectroscopy coupled with chemometrics is therefore a fast and accurate tool for the identification of pharmaceutical tablets.     

Multivariate curve resolution modeling of liquid chromatography-mass spectrometry data in a comparative study of the different endogenous metabolites behavior in two tomato cultivars treated with carbofuran pesticide

A metabonomic study based on the application of multivariate curve resolution and alternating least squares (MCR-ALS) to three-way data sets obtained by liquid chromatography coupled to mass spectrometry detection (LC-MS) was carried out for Rambo and Raf tomato cultivars treated with carbofuran pesticide. Samples were picked up during a 21 days period after treatment and analyzed by LC-MS in scan mode, along with the corresponding blank samples. Then, MCR-ALS was applied to the three-way data sets using column wise augmented matrices, and the evolutionary profiles as a function of the time after treatment were estimated for the metabolites present in both cultivars, as well as their corresponding pure spectra estimations. A comparative study using those estimations showed that some of these metabolites followed different behavior for the different cultivars after treatment. Since all treated and untreated Rambo and Raf samples were picked up according to the same sampling protocol and in a similar state of maturation, any difference in the behavior between profiles can be interpreted as an effect due to the presence of pesticide and to the kind of cultivar. Based on this hypothesis, several PLS-DA approaches were tested to check if it would be possible to classify samples by using the metabolites MCR estimations. Results showed that PLS-DA models for classification of treated or non-treated (blank) samples were the best ones obtained (98.44% of correct classifications for the validation set), which supports the stress effects related to carbofuran treatment. In addition, excellent discrimination among the four groups could be attained (89.06% of correct classifications for the validation set).