Implementation of enhanced correlation maps in near infrared chemical images: Application in pharmaceutical research

Recent developments in Hyperspectral Imaging equipment have made possible the use of this analytical technique for fast scanning of sample surfaces. This technique has turned out to be especially useful in Pharmacy, where information about the distribution of the components in the surface of a tablet can be obtained. One particular application of Hyperspectral Chemical Imaging is the search for singularities inside pharmaceutical tablets, e.g. coating defects. Nevertheless, one problem has to be faced: how to analyze a sample without any previous knowledge about it, or having only the minimum information about the tablet.In this work a new methodology, based on correlation coefficients, is introduced to obtain valuable information about one Hyperspectral Image (detection of defects, punctual contaminants, etc.) without any previous knowledge. The methodology combines Principal Component Analysis (PCA), correlation coefficient between one specific pixel included in the image and the rest of the image; and a new enhanced contrast function to obtain more selective chemical and spatial information about the image. To illustrate the applicability of the proposed methodology, real tablets of ibuprofen have been studied.The proposed methodology is presented as a control technique to detect batch variability, defects in final tablets and punctual contaminants, being a potential supplementary tool for quality controls. In addition, the usefulness of the proposed methodology is not exclusive to NIR-CI devices, but to any hyperspectral and multivariate image system.     

Multivariate curve resolution of pH gradient flow injection mixture analysis with correction of the Schlieren effect

Multivariate curve resolution using alternating least squares (MCR-ALS) was used to quantify ascorbic (AA) and acetylsalicylic (ASA) acids in four pharmaceutical samples using a flow injection analysis (FIA) system with pH gradient and a diode array (DAD) spectrometer as a detector. Four different pharmaceutical drugs were analyzed, giving a data array of dimensions 51 x 291 x 61, corresponding respectively to number of samples, FIA times and spectral wavelengths. MCR-ALS was applied to these large data sets using different constraints to have optimal resolution and optimal quantitative estimations of the two analytes (AA and ASA). Since both analytes give an acid-basic pair of species contributing to the UV recorded signal, at least four components sholuld be proposed to model AA and ASA in synthetic mixture samples. Moreover, one additional component was needed to resolve accurately the Schlieren effect and another additional component was also needed to model the presence of possible interferences (like caffeine) in the commercial drugs tablets, giving therefore a total number of 6 independent components needed. The best quantification relative errors were around 2% compared to the reference values obtained by HPLC and by the oxidation-reduction titrimetric method, for ASA and AA respectively. In this work, the application of MCR-ALS allowed for the first time the full resolution of the FIA diffusion profile due to the Schlieren effect as an independent signal contribution, suggesting that the proposed MCR-ALS method allows for its accurate correction in FIA-DAD systems.     

Application of multivariate curve resolution alternating least squares (MCR-ALS) to the quantitative analysis of pharmaceutical and agricultural samples

Application of multivariate curve resolution alternating least squares (MCR-ALS), for the resolution and quantification of different analytes in different type of pharmaceutical and agricultural samples is shown. In particular, MCR-ALS is applied first to the UV spectrophotometric quantitative analysis of mixtures of commercial steroid drugs, and second to the near-infrared (NIR) spectrophotometric quantitative analysis of humidity and protein contents in forage cereal samples. Quantitative results obtained by MCR-ALS are compared to those obtained using the well established partial least squares regression (PLSR) multivariate calibration method.     

Rank analysis and simultaneous determination of acetylsalicylic acid,paracetamol, and caffeine in pharmaceuticals using second-order data and multivariate curve resolution-alternating least squares (MCR-ALS)

Mixtures of acetylsalicylic acid (ASA), paracetamol (PAR), and caffeine (CAF) have been successfully analyzed by constrained multivariate curve resolution-alternating least squares (MCR-ALS). The MCR-ALS methodology adequately exploits the second-order advantage which enables quantitation of analyte in the presence of unknown and uncalibrated interferences. The procedure simultaneously takes into account the spectroscopic and pH-dependent properties of the compounds, which leads to a higher selectivity. Specially, for CAF determination fully protonated or deprotonated forms of CAF are not dominant in the pH range of data acquisition but spectral changes with pH were recorded and used for accurate determination of CAF. Furthermore, quantitative determination of an analyte in a complex mixture is performed using a synthetic solution as standard containing only the analyte of interest. Even in the presence of the rank deficiencies, in most cases accurate quantitation with relative errors in prediction lower than 5 % was obtained. The procedure was successfully applied to the analysis of real samples (pharmaceuticals) using synthetic external standards. Percent relative errors of 4.03, 3.26, and 5.85 were obtained for ASA, PAR, and CAF, respectively, in A.C.A tablets and 4.49 and 2.75 for PAR and CAF, respectively in Novafen capsules.     

Study of the similarity between distribution maps of concentration in near-infrared spectroscopy chemical imaging obtained by different multivariate calibration approaches

Nowadays, near-infrared spectroscopy chemical imaging (NIR-CI) has been widely used in pharmaceutical analysis since it provides important surface information about the samples. In this work the information of NIR-CI at the pixel level was compared through calculation of the similarity between distribution maps of concentration obtained by different multivariate calibration approaches. The comparison was performed by using four different multivariate methods (MCR, MLR, CLS and PLS) in analysis of carbamazepine pharmaceutical formulations. For global determination, all models developed showed RMSEP below 1.9% (w/w) for active principal ingredient (API) and better than 4.6% (w/w) for excipients. Also, the distribution maps obtained by PLS, CLS and MCR showed great similarity for all compounds of the formulation as well with concentrations in the tablets. However, comparing the distribution maps obtained by MLR with those from the other chemometric tools, a lower similarity was observed. Thus, this fitted model does not ensure, by itself, that the images obtained are reliable or accurate. The paper also compares the distribution maps of concentrations obtained from all constituents present in the pharmaceutical formulation with their respective micrographs.     

Application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to remote sensing hyperspectral imaging

The application of the MCR-ALS method is demonstrated on two simulated remote sensing spectroscopic images and on one experimental reference remote sensing spectroscopic image obtained by the Airborn Visible/Infrared Imaging Spectrometer (AVIRIS). By application of MCR-ALS, the spectra signatures of the pure constituents present in the image and their concentration distribution at a pixel level are estimated. Results obtained by MCR-ALS are compared to those obtained by other methods frequently used in the remote sensing spectroscopic imaging field like VCA and MVSA. In the case of the analysis of the experimental data set, the resolved pure spectra signatures were compared to reference spectra from USGS library for their identification. In all cases, results were also evaluated for the presence of rotational ambiguities using the MCR-BANDS method. The obtained results confirmed that the MCR-ALS method can be successfully used for remote sensing hyperspectral image resolution purposes. However, the amount of rotation ambiguity still present in the solutions obtained by this and other resolution methods (like VCA or MVSA) can still be large and it should be evaluated with care, trying to reduce its effects by selecting the more appropriate constraints. Only in this way it is possible to increase the reliability of the solutions provided by these methods and decrease the uncertainties associated to their use.     

Study of ε-caprolactone polymerization by NIR spectroscopy

Near-infrared (NIR) spectroscopy is proposed for the in-line quantitative and kinetic study of the polymerization of ε-caprolactone and eventually to facilitate real-time control of the manufacturing process. Spectra were acquired with a fibre-optic probe operating in transflectance mode immersed in the reactor. The NIR data acquired were processed using a multivariate curve resolution alternating least squares (MCR-ALS) algorithm. The proposed method allows calculation of the concentration and spectral profiles of the species involved in the reaction. The key point of this method is the lack of reference concentrations needed to perform the MCR-ALS method. The use of an extended spectral matrix using both process and pure analyte spectra solves the rank deficiency. The concentration profiles obtained were used to calculate a kinetic fitting of the reaction, but the method was improved by applying kinetic constraints (hard modelling). The rate constants of batches at different temperatures and the energy of activation for this reaction were calculated. Whenever possible, the hard modelling combined with the MCR-ALS method improves the fit of the experimental data: the results show good correlation between the NIR and reference data and allow the collection of high-quality kinetic information on the reaction (rate constants and energy of activation).     

Self-modeling curve resolution (SMCR) analysis of near-infrared (NIR) imaging data of pharmaceutical tablets

The idea of quality by design (QbD) has been proposed in pharmaceutical field. QbD is a systematic approach to control the product performance based on the scientific understanding of the product quality and its manufacturing process. In the present study, near-infrared (NIR) imaging is utilized as a tool to achieve this concept. A practical use of a chemometrics technique called self-modeling curve resolution (SMCR) is demonstrated with NIR imaging analysis of pharmaceutical tablets containing two ingredients, a soluble active ingredient, pentoxifylline (PTX), and an insoluble excipient, palmitic acid. Concentration profiles obtained by SMCR reveal that the homogenous distribution of chemical ingredients strongly depends on the grinding time and that its process plays a central role in quantitative control, say sustained-release of PTX. In addition, pure component spectra by SMCR indicate a sequential change of specific NIR peak intensities following the increase of the grinding time. The spectra change shows a molecular structure change related to its crystallinity during grinding process. Accordingly, this study clearly demonstrates that NIR imaging combined with SMCR can be a powerful tool to reveal chemical or physical mechanism induced by the manufacturing process of pharmaceutical products and that it may be a solid solution for QbD of pharmaceutical products.     

Investigation into classification/sourcing of suspect counterfeit Heptodin™ tablets by near infrared chemical imaging

Near infrared chemical imaging (NIR-CI) analysis was performed on 55 counterfeit Heptodin™ tablets obtained from a market survey and an additional 11 authentic Heptodin™ tablets for comparison. The aim of the study was to investigate whether NIR-CI can be used to detect the counterfeit tablets and to classify/source them so as to understand the possible number of origins to aid investigators and authorities to shut down counterfeiting operations. NIR-CI combined with multivariate analysis is particularly suited to compare chemical and physical properties of samples, since it is a quick and non-destructive method of analysis. Counterfeit tablets were easily distinguished from the authentic ones. Principal component analysis (PCA) and k-means clustering were performed on the data set. The results from both analyses grouped the counterfeit tablets in 13 main groups. The main groups found with both methods were quite consistent. Out of the 55 tablets only 18% contained the correct active pharmaceutical ingredient (API), i.e., the anti-viral drug lamivudine. The remaining 82% of counterfeit tablets contained talc and starch as main excipients. The API containing tablets classified into three main groups, based mainly on the amount of lamivudine present in the tablet. The group which had close to the correct amount of lamivudine sub-classified into three groups. From the analysis carried out, it is likely that the counterfeit tablets originate from as many as 15 different sources.     

NIR imaging spectroscopy for quantification of constituents in polymers thin films loaded with paracetamol

Thin films loaded with the drug paracetamol were produced from polymer blends formed by hydroxypropylmethylcellulose (HPMC), polyvinylpyrrolidone (PVP) and polyethyleneglycol (PEG), at various mass ratios of polymers and drug defined by a d-optimal experimental design. NIR hyperspectral images were obtained from each thin film formulation and the pixel-to-pixel quantification of the constituents were carried out by partial least square (PLS) and multivariate curve resolution–alternating least square (MCR-ALS) with three different calibration/validation strategies. These strategies differ in the way to construct the calibration and validation matrices and they had to be carried out to suppress the bias on the quantification of the constituents in the polymer blend. The errors of prediction in the models from MCR-ALS were influenced by the calibration/validation strategy employed, but they were similar to the ones from PLS model. Concentration distribution maps were built after pixel-to-pixel predictions and their characteristics were analyzed.     

Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm⁻².     

Simultaneous voltammetric determination of acetylsalicylic acid,caffeine and paracetamol in pharmaceutical formulations using screen-printed carbon electrode

Electrochemical oxidation of Paracetamol (PAR), Acetylsalicylic acid (ASA) and Caffeine (CAF) was investigated employing square wave stripping voltammetry (SWSV) using screen–printed carbon electrodes (SPCE). Determinations were performed in 0.1 mol L⁻¹ BR buffer (pH 2.0) without accumulation step. SWV were obtained by scanning the potential from 0.00 to 1.40 V employing a scan increment of 4 mV, pulse amplitude 25 mV and frequency of 25 Hz. PAR, ASA and CAF presents oxidation signals at 0.45, 1.03 and 1.32 V. The detection limits were 1.2, 1.7 and 1.7 mg L⁻¹, respectively. The method was applied in the PAR-ASA-CAF determination on pharmaceutical formulations.     

Validation and transferability study of a method based on near-infrared hyperspectral imaging for the detection and quantification of ergot bodies in cereals

In recent years, near-infrared (NIR) hyperspectral imaging has proved its suitability for quality and safety control in the cereal sector by allowing spectroscopic images to be collected at single-kernel level, which is of great interest to cereal control laboratories. Contaminants in cereals include, inter alia, impurities such as straw, grains from other crops, and insects, as well as undesirable substances such as ergot (sclerotium of Claviceps purpurea). For the cereal sector, the presence of ergot creates a high toxicity risk for animals and humans because of its alkaloid content. A study was undertaken, in which a complete procedure for detecting ergot bodies in cereals was developed, based on their NIR spectral characteristics. These were used to build relevant decision rules based on chemometric tools and on the morphological information obtained from the NIR images. The study sought to transfer this procedure from a pilot online NIR hyperspectral imaging system at laboratory level to a NIR hyperspectral imaging system at industrial level and to validate the latter. All the analyses performed showed that the results obtained using both NIR hyperspectral imaging cameras were quite stable and repeatable. In addition, a correlation higher than 0.94 was obtained between the predicted values obtained by NIR hyperspectral imaging and those supplied by the stereo-microscopic method which is the reference method. The validation of the transferred protocol on blind samples showed that the method could identify and quantify ergot contamination, demonstrating the transferability of the method. These results were obtained on samples with an ergot concentration of 0.02 % which is less than the EC limit for cereals (intervention grains) destined for humans fixed at 0.05 %.

Development of a rapid process monitoring method for dry-coated tableting process by using near-infrared spectroscopy

A nondestructive transmittance near-infrared (NIR) method for detecting off-centered cores in dry-coated (DC) tablets was developed as a monitoring system in the DC tableting process. Caffeine anhydrate was used as a core active pharmaceutical ingredient (API), and DC tablets were made by the direct compression method. NIR spectra were obtained from these intact DC tablets using the transmittance method. The reference assay was performed with HPLC. Calibration models were generated by partial least squares (PLS) regression and principal component regression (PCR) utilizing external validations. Hierarchical cluster analysis (HCA) of the results confirmed that NIR spectroscopy correctly detected off-centered cores in DC tablets. We formulated and used the Centering Index (CI) to evaluate the precision of core alignment and generated an NIR calibration model that could correctly predict this index. The principal component (PC) 1 loading vector of the final calibration model indicated that it could specifically detect the misalignment of tablet cores. The model also had good linearity and accuracy. The CIs of unknown sample tablets predicted by the final calibration model and those calculated through the HPLC analysis were closely parallel with each other. These results demonstrate the validity of the final calibration model and the utility of the transmittance NIR spectroscopic method developed in this study as a monitoring system in DC tableting process.     

Application of a newly developed portable NIR imaging device to monitor the dissolution process of tablets

We have recently developed a novel portable NIR imaging device (D-NIRs), which has a high speed and high wavelength resolution. This NIR imaging approach has been developed by utilizing D-NIRs for studying the dissolution of a model tablet containing 20 % ascorbic acid (AsA) as an active pharmaceutical ingredient and 80 % hydroxypropyl methylcellulose, where the tablet is sealed by a special cell. Diffuse reflectance NIR spectra in the 1,000 to 1,600 nm region were measured during the dissolution of the tablet. A unique band at around 1,361 nm of AsA was identified by the second derivative spectra of tablet and used for AsA distribution NIR imaging. Two-dimensional change of AsA concentration of the tablet due to water penetration is clearly shown by using the band-based image at 1,361 nm in NIR spectra obtained with high speed. Moreover, it is significantly enhanced by using the intensity ratio of two bands at 1,361 and 1,354 nm corresponding to AsA and water absorption, respectively, showing the dissolution process. The imaging results suggest that the amount of AsA in the imaged area decreases with increasing water penetration. The proposed NIR imaging approach using the intensity of a specific band or the ratio of two bands combined with the developed portable NIR imaging instrument, is a potentially useful practical way to evaluate the tablet at every moment during dissolution and to monitor the concentration distribution of each drug component in the tablet.

Sequential injection analysis for the simultaneous determination of clavulanic acid and amoxicillin in pharmaceuticals using second-order calibration.

In this paper, we report on a method for quantifying clavulanic acid and amoxicillin simultaneously in pharmaceuticals using sequential injection analysis (SIA) with a diode-array spectrophotometric detector and multivariate curve resolution with alternating least squares (MCR-ALS). We optimized the experimental parameters so that the analytical sequence could distinguish the concentrations and spectrum profiles of the species of interest with optimum resolution quality. After establishing the optimum conditions, we quantified clavulanic acid and amoxicillin in four pharmaceuticals. In most cases our results were slightly higher than those in the prospectus of the pharmaceutical. The relative standard deviations were below 5% for amoxicillin and below 7% for clavulanic acid. These results are acceptable because, to prevent degradation due to bacteriostatic activity, the concentration of the main reactant is usually higher.     

Content uniformity of pharmaceutical solid dosage forms by near infrared hyperspectral imaging: A feasibility study

Near-infrared imaging systems simultaneously record spectral and spatial information. Each measurement generates a data cube containing several thousand spectra. Chemometric methods are therefore required to extract qualitative and quantitative information. The aim of this study was to determine the feasibility of quantifying active pharmaceutical ingredient (API) and excipient content in pharmaceutical formulations using hyperspectral imaging.Two kinds of tablets with a range of API content were analysed: a binary mixture of API and cellulose, and a pharmaceutical formulation with seven different compounds. Two pixel sizes, 10 μm/pixel and 40 μm/pixel, were compared, together with two types of spectral pretreatment: standard normal variate (SNV) normalization and Savitzky-Golay smoothing. Two methods of extracting concentrations were compared: the partial least squares 2 (PLS2) algorithm, which predicts the content of several compounds simultaneously, and the multivariate classical least squares (CLS) algorithm based on pure compound reference spectra without calibration.Best content predictions were achieved using 40 μm/pixel resolution and the PLS2 method with SNV normalized spectra. However, the CLS method extracted distribution maps with higher contrast and was less sensitive to noisy spectra and outliers; its API predictions were also highly correlated to real content, indicating the feasibility of predicting API content using hyperspectral imaging without calibration.     

Near-infrared determination of active substance content in intact low-dosage tablets

Near-infrared (NIR) spectroscopy can be applied to determine the active substance content of tablets. Its great advantage lies in the minimal sample preparation required, which helps to reduce the potential for error. The aim of this study is to show the feasibility of this method on low-dosage tablets. The influence of various spectral pretreatments [standard normal variate (SNV), multiplicative scatter correction (MSC), second derivative (D2), orthogonal signal correction (OSC), separately and combined] and regression methods on prediction error are compared. Partial least square (PLS) regression provided better prediction than principal component regression (PCR). SNV was applied to the first data set and SNV and a second derivative to the second set to maximise model accuracy for quantifying the active substance of intact pharmaceutical products using diffuse reflectance NIR. The models yielded standard errors of prediction (SEP) of 0.1768 and 0.0682 mg for the two products. The experiments were conducted with two low-dosage pharmaceutical forms and results of NIR predictions were comparable to currently approved methods. Diffuse reflectance NIR has the potential to become a reliable and robust quality control method for determining active tablet content.     

Resolution and segmentation of hyperspectral biomedical images by multivariate curve resolution-alternating least squares

MCR-ALS is a resolution method that has been applied in many different fields, such as process analysis, environmental data and, recently, hyperspectral image analysis. In this context, the algorithm provides the distribution maps and the pure spectra of the image constituents from the sole information in the raw image measurement. Based on the distribution maps and spectra obtained, additional information can be easily derived, such as identification of constituents when libraries are available or quantitation within the image, expressed as constituent signal contribution. This work summarizes first the protocol followed for the resolution on two examples of kidney calculi, taken as representations of images with major and minor compounds, respectively. Image segmentation allows separating regions of images according to their pixel similarity and is also relevant in the biomedical field to differentiate healthy from non-healthy regions in tissues or to identify sample regions with distinct properties. Information on pixel similarity is enclosed not only in pixel spectra, but also in other smaller pixel representations, such as PCA scores. In this paper, we propose the use of MCR scores (concentration profiles) for segmentation purposes. K-means results obtained from different pixel representations of the data set are compared. The main advantages of the use of MCR scores are the interpretability of the class centroids and the compound-wise selection and preprocessing of the input information in the segmentation scheme.