Self-modeling curve resolution techniques applied to comparative analysis of volatile components of Iranian saffron from different regions

Volatile components of saffron from different regions of Iran were extracted by ultrasonic-assisted solvent extraction (USE) and were analyzed by gas chromatography-mass spectrometry (GC-MS). Self-modeling curve resolution (SMCR) was proposed for resolving the co-eluted GC-MS peak clusters into pure chromatograms and mass spectra. Multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least square (MCR-ALS) were successfully used for this purpose. The accuracy of the qualitative and quantitative results was improved considerably using SMCR techniques. Comparison of the results of saffron from different regions of Iran showed that their volatile components are different from chemical components and relative percentages points of view. Safranal is the main component of all samples. In addition, 4-hydroxy-2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde (HTCC), 2(5H)-furanone, 2,4,4-trimethyl-3-carboxaldehyde-5-hydroxy-2,5-cyclohexadien-1-one and 2(3H)-furanone, dihydro-4-hydroxy were common in all samples with high percentages. The results proved that combining of SMCR techniques with USE-GC-MS produces a powerful tool for the analysis of the complex samples.     

Self-modeling curve resolution method applied for the evaluation of dissolution testing data: A case study of meloxicam-mannitol binary systems

This paper introduces some chemometric methods, i.e., self-modeling curve resolution (SMCR), multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis (PARAFAC and PARAFAC2), which are used to evaluate in vitro dissolution testing data detected by a UV-vis spectrophotometer on meloxicam-mannitol binary systems. These systems were chosen because of their relative simplicity to apply as part of the validation process illustrating the effectiveness of the developed and applied chemometric method. The paper illustrates the failure of PARAFAC methods used before for pharmaceutical data evaluations as well, and we suggest application of the feasible band form given by SMCR as a more general procedure.Steps to improve the dissolution behavior of drugs have become among the most interesting aspects of pharmaceutical technology, and our results show that a larger particle size of meloxicam is advantageous for dissolution. Instead of the use of only one characteristic wavelength, appropriate chemometric methods can furnish more information from dissolution testing data, i.e., the individual dissolution rate profiles and the individual spectra for all the components can be obtained without resorting to any separation techniques such as HPLC.     

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.     

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.     

Chemometric resolution of ATR-IR spectra data for polycondensation reaction of bis(hydroxyethylterephthalate) with a combination of self-modeling curve resolution (SMCR) and local rank analysis

Self-modeling curve resolution (SMCR) methods, simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and alternating least squares (ALS) were used to calculate pure concentration profiles and pure spectra for the two-way spectral data collected during the on-line polycondensation reaction of bis(hydroxyethylterephthalate) with an ATR-FT-IR spectrometer. In order to improve the resolution results, SIMPLISMA was combined with local rank analysis method, fixed size moving window evolving factor analysis (FSMWEFA) to search for selective regions of various components and then look for the purest wavenumber variables in the selective regions. Such combination allows more accurate determination of the number of chemical components in the reaction system and the calculations of more accurate concentration profiles and spectra.     

NIR analysis of pharmaceutical samples without reference data: improving the calibration

Using an appropriate set of samples to construct the calibration set is crucial with a view to ensuring accurate multivariate calibration of NIR spectroscopic data. In this work, we developed and optimized a new methodology for incorporating physical variability in pharmaceutical production based on the NIR spectrum for the process. Such a spectrum contains the spectral changes caused by each treatment applied to the component mixture during the production process. The proposed methodology involves adding a set of process spectra (viz. difference spectra between those for production tablets and a laboratory mixture of identical nominal composition) to the set of laboratory samples, which span the wanted concentration range, in order to construct a calibration set incorporating all physical changes undergone by the samples in each step of the production process. The best calibration model among those tested was selected by establishing the influence of spectral pretreatments used to obtain the process spectrum and construct the calibration models, and also by determining the multiplying factor m to be applied to the process spectra in order to ensure incorporation of all variability sources into the calibration model. The specific samples to be included in the calibration set were selected by principal component analysis (PCA). To this end, the new methodology for constructing calibration sets for determining the Active Principle Ingredients (API) and excipients was applied to Irbesartan tablets and validated by application to the API and excipients of paracetamol tablets. The proposed methodology provides simple, robust calibration models for determining the different components of a pharmaceutical formulation.     

Quantitative analysis of the hydration of lithium salts in water using multivariate curve resolution of near-infrared spectra

The hydration process of lithium iodide, lithium bromide, lithium chloride and lithium nitrate in water was analyzed quantitatively by applying multivariate curve resolution alternating least squares (MCR-ALS) to their near infrared spectra recorded between 850 nm and 1100 nm. The experiments were carried out using solutions with a salt mass fraction between 0% and 72% for lithium bromide, between 0% and 67% for lithium nitrate and between 0% and 62% for lithium chloride and lithium iodide at 323.15 K, 333.15 K, 343.15 K and 353.15 K, respectively. Three factors were determined for lithium bromide and lithium iodide and two factors for the lithium chloride and lithium nitrate by singular value decomposition (SVD) of their spectral data matrices. These factors are associated with various chemical environments in which there are aqueous clusters containing the ions of the salts and non-coordinated water molecules. Spectra and concentration profiles of non-coordinated water and cluster aqueous were retrieved by MCR-ALS. The amount of water involved in the process of hydration of the various salts was quantified. The results show that the water absorption capacity increases in the following order LiI < LiBr < LiNO₃ < LiCl. The salt concentration at which there is no free water in the medium was calculated at each one of the temperatures considered. The values ranged between 62.6 and 65.1% for LiBr, 45.5–48.3% for LiCl, 60.4–61.2% for LiI and 60.3–63.7% for LiNO₃. These values are an initial approach to determining the concentration as from which crystal formation is favored.     

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.     

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.     

Content uniformity determination of pharmaceutical tablets using five near-infrared reflectance spectrometers: a process analytical technology (PAT) approach using robust multivariate calibration transfer algorithms

Near-infrared calibration models were developed for the determination of content uniformity of pharmaceutical tablets containing 29.4% drug load for two dosage strengths (X and Y). Both dosage strengths have a circular geometry and the only difference is the size and weight. Strength X samples weigh approximately 425 mg with a diameter of 12 mm while strength Y samples, weigh approximately 1700 mg with a diameter of 20 mm. Data used in this study were acquired from five NIR instruments manufactured by two different vendors. One of these spectrometers is a dispersive-based NIR system while the other four were Fourier transform (FT) based. The transferability of the optimized partial least-squares (PLS) calibration models developed on the primary instrument (A) located in a research facility was evaluated using spectral data acquired from secondary instruments B, C, D and E. Instruments B and E were located in the same research facility as spectrometer A while instruments C and D were located in a production facility 35 miles away. The same set of tablet samples were used to acquire spectral data from all instruments. This scenario mimics the conventional pharmaceutical technology transfer from research and development to production. Direct cross-instrument prediction without standardization was performed between the primary and each secondary instrument to evaluate the robustness of the primary instrument calibration model. For the strength Y samples, this approach was successful for data acquired on instruments B, C, and D producing root mean square error of prediction (RMSEP) of 1.05, 1.05, and 1.22%, respectively. However for instrument E data, this approach was not successful producing an RMSEP value of 3.40%. A similar deterioration was observed for the strength X samples, with RMSEP values of 2.78, 5.54, 3.40, and 5.78% corresponding to spectral data acquired on instruments B, C, D, and E, respectively. To minimize the effect of instrument variability, calibration transfer techniques such as piecewise direct standardization (PDS) and wavelet hybrid direct standardization (WHDS) were used. The PDS approach, the RMSEP values for strength X samples were lowered to 1.22, 1.12, 1.19, and 1.08% for instruments B, C, D, and E, respectively. Similar improvements were obtained using the WHDS approach with RMSEP values of 1.36, 1.42, 1.36, and 0.98% corresponding to instruments B, C, D, and E, respectively.     

Tensile deformation of isotactic polypropylene (iPP) and iPP-nanocomposite studied by rheo-optical near-infrared (NIR) spectroscopy

Tensile deformations of isotactic polypropylene (iPP) and its nanocomposite were examined by a rheo-optical characterization technique based on near-infrared (NIR) spectroscopy to derive the submolecular-level understanding of the deformation mechanism during a tensile test. Sets of NIR spectra of the iPP samples were collected by using an acousto-optic tunable filter (AOTF) NIR spectrometer coupled with a tensile testing machine as an excitation device. Mechanical deformation of the samples was readily captured as strain-dependent NIR spectra. However, the main feature of the NIR spectra was overwhelmed by the contribution from the baseline change due to the substantial decrease in the sample thickness and subsequent change in the NIR light scattering. The variation of the spectral feature suggests that the deformation of the iPP involves the elongation of the rubbery amorphous chains prior to the displacement of the crystalline lamellae, providing elastic and subsequent plastic deformations during the tensile testing. In addition, it is revealed that the nanoclay layers dispersed within the iPP matrix restrict the elongation of the amorphous chains. Such interaction makes iPP hard and brittle, so that it yields no obvious ductile fracture during the tensile deformation.     

Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900–1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R²) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.     

Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

Spectroscopic imaging techniques provide spatial and spectral information about a sample simultaneously and are finding ever-increasing application in the pharmaceutical industry. Effective extraction of chemical information from imaging data sets is a crucial step during the application of imaging techniques. Multivariate imaging data analysis methods have been reported but few applications of these methods for pharmaceutical samples have been demonstrated. In this study, a bilayer model tablet consisting of avicel, lactose, sodium benzoate, magnesium stearate and red dye was prepared using custom press tooling, and Raman mapping data were collected from a 400 μm × 400 μm area of the tablet surface. Several representative multivariate methods were selected and used in the analysis of the data. Multivariate data analysis methods investigated include principal component analysis (PCA), cluster analysis, direct classical least squares (DCLS) and multivariate curve resolution (MCR). The relative merits and drawbacks of each technique for this application were evaluated. In addition, some practical issues associated with the use of these methods were addressed including data preprocessing, determination of the optimal number of clusters in cluster analysis and the optimization of window size in second derivative calculation.     

In situ assessment of atorvastatin impurity using MALDI mass spectrometry imaging (MALDI-MSI)

The analysis of impurities and degradation products in pharmaceutical preparations are usually performed by chromatographic techniques such as high-performance liquid chromatography (HPLC). This approach demands extensive analysis time, mostly due to extraction and separation phases. These steps must be carried out in samples in order to adapt them to the requirements of the analytical method of choice. In the present contribution, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was employed to quantify an important degradation product in atorvastatin calcium 80 mg tablets: the atorvastatin lactone. Through the standard of the impurity, it was possible to perform quantitative analysis directly on the drug tablet, using a quick and novel approach, suitable for quality control processes in the pharmaceutical industry.     

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.     

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

Near-infrared chemical imaging used for in-line analysis of inside adhesive layers in textile laminates

This paper demonstrates for the first time that near-infrared (NIR) chemical imaging can be used for in-line analysis of textile lamination processes. In particular, it was applied for the quantitative determination of the applied coating weight and for monitoring of the spatial distribution of hot melt adhesive layers using chemometric approaches for spectra evaluation. Layers with coating weights between about 25 and 130 g m⁻² were used for the lamination of polyester fabrics and nonwovens as well as for polyurethane foam. It was shown that quantitative data with adequate precision can be actually obtained for layers applied to materials with significantly heterogeneous surface structure such as foam or for hidden layers inside fabric laminates. Even the coating weight and the homogeneity of adhesive layers in composites consisting of black textiles only could be quantitatively analyzed. The prediction errors (RMSEP) determined in an external validation of each calibration model were found to range from about 2 g m⁻² to 6 g m⁻² depending on the specific system under investigation. All calibration models were applied for chemical imaging in order to prove their performance for monitoring the thickness and the homogeneity of adhesive layers in the various textile systems. Moreover, they were used for the detection of irregularities and coating defects. Investigations were carried out with a large hyperspectral camera mounted above a conveyor. Therefore, this method allows large-area monitoring of the properties of laminar materials. Consequently, it is potentially suited for process and quality control during the lamination of fabrics, foams and other materials in field-scale.     

Rheo-optical near-infrared (NIR) spectroscopy study of low-density polyethylene (LDPE) in conjunction with projection two-dimensional (2D) correlation analysis

A rheo-optical characterization technique based on near-infrared (NIR) spectroscopy is developed specifically to probe the submolecular-level deformation caused during a mechanical test. An illustrative example of the mechanical deformation of low-density polyethylene (LDPE) is provided to show how it can be utilized. A set of NIR spectra of the polymer sample were collected by using an acousto-optic tunable filter (AOTF) NIR spectrometer coupled with a tensile testing machine as an excitation device. While the substantial level of variation of spectral intensity was readily captured during the mechanical deformation of the LDPE, main feature of the NIR spectra was overwhelmed by the contribution from the baseline change. Projection 2D correlation analysis was then applied to selectively extract the signal contribution from the baseline fluctuation. The 2D correlation spectra revealed the predominant extension of amorphous tie chains followed by the rotation of crystalline lamellae, which induce elastic and plastic deformation of the LDPE, respectively.     

Application of band-target entropy minimization (BTEM) and residual spectral analysis to in situ reflection-absorption infrared spectroscopy (RAIRS) data from surface chemistry studies

The band-target entropy minimization (BTEM) curve resolution technique has been used to analyze in situ reflection-absorption infrared spectroscopy (RAIRS) data of CO chemisorption on Ni(1 1 1) single crystal surfaces. The bilinearity assumption for pRAIRS data, that is, negative logarithm to the base 10 of raw reflectance RAIRS data, was found to be sufficiently valid for the test data. A total of 11 real pure component pRAIRS spectra were elucidated via BTEM in tandem with an iterative residual spectral data analysis. Furthermore, 2 abstract pure component right singular vectors were found to account for all the pRAIRS non-linearities, baseline drifts and other spectral noise. In total, 100.2% of the pRAIRS signals were accounted for by these 13 spectral components. The 11 real pure component pRAIRS spectra and their corresponding relative concentration kinetic sequences correlate with 6 well-known adsorbed CO domain structures. Moreover, amongst the BTEM resolved spectra were five new bands that were not previously observed using conventional visual identification methods adopted by surface chemists. These new bands engendered new understanding to the mechanism of CO chemisorption on Ni(1 1 1). The combination of BTEM with residual spectral analysis was thus demonstrated to be efficacious for curve resolution of in situ RAIRS data obtained from surface chemistry studies.