Quantitative analysis of diphenhydramine hydrochloride in pharmaceutical wafers using near infrared and Raman spectroscopy

Wafers with varying concentrations of diphenhydramine hydrochloride (DPH-HCl) as active pharmaceutical ingredient (API) were prepared and their near infrared (NIR) and Raman spectra recorded. The purpose of this study was to compare the suitability of these two vibrational spectroscopic techniques for the quantification of DPH-HCl in pharmaceutical wafers. Partial least squares (PLS1) calibration models with different data pretreatments were tested. Both NIR and Raman spectroscopy proved to be suitable to predict DPH-HCl contents at lower concentration ranges. At higher concentrations, interference by crystallization processes was observed. For investigating the general applicability of the quantification methods, two commercially available products were examined.     

Direct, non-destructive quantitative measurement of an active pharmaceutical ingredient in an intact capsule formulation using Raman spectroscopy

The active pharmaceutical ingredient (ambroxol) in an intact capsule formulation has been non-destructively quantified using Raman spectroscopy. To improve the problem of insufficient representive sampling inherent in Raman measurements, we have employed a wide area illumination (WAI) scheme that enables much improved sample coverage through a circular excitation laser spot with a 6 mm diameter. One of the anticipated sources of variation for this measurement was variation in the capsule shells. However, the WAI scheme significantly decreased the spectral variation among empty capsules compared to a measurement with a traditional small-spot excitation. Therefore, measurement variations emanating from the capsule shell did not significantly influence the accuracy of the determination of ambroxol concentrations. The resulting standard error of prediction (SEP) using the WAI scheme was comparable to that from previous Raman measurements which used a conventional small-spot excitation and employed a sampling scheme that involved rotation of an ambroxol pellet. It is further noteworthy that the SEP was also similar to that obtained from the use of transmission NIR spectroscopy, which was achieved by collection of spectra of the powdered capsule contents removed from the shell. The proposed Raman measurement using the WAI scheme in this case was sufficient to achieve the quantitative measurement of the active pharmaceutical ingredient (API) content of capsules non-destructively.     

Screening adulteration of polypropylene bottles with postconsumer recycled plastics for oral drug package by near-infrared spectroscopy

Adulteration of pharmaceutical packaging containers with postconsumer recycled plastic materials was considerably difficult to identify due to the similar chemical compositions of virgin and recycled plastics. In the present study, near-infrared (NIR) spectroscopy coupled with conformity test was proposed to screen the adulteration of pharmaceutical packaging containers. Two kinds of representative screening models were investigated on polypropylene (PP) bottles for oral drug package. The reliability of the screening models was validated through studying the identification reliability, specificity, and robustness of the methods. The minimum spiking level of two modeled adulterants at the proportion of 20% could be detected, and the unqualified sample from a domestic manufacturer was rejected by this developed method. This strategy represents a rapid and promising analytical method for screening the adulteration of pharmaceutical plastic packaging containers with postconsumer recycled plastics.     

Monitoring of the Sol-Gel Synthesis of Organic-inorganic Hybrids by FTIR Transmission,FTIR/ATR,NIR and Raman Spectroscopy

Summary: The sol-gel synthesis of organic-inorganic hybrids based on triethoxysilane- terminated poly(ethylene oxide) and tetraethylorthosilicate was monitored in-situ using three spectroscopic methods (FTIR/ATR, Raman, NIR). These spectroscopic methods allow in-situ monitoring of the evolution of hybrid materials starting from the modification of the polymer and the early steps of hydrolysis up to the network formation. By application of ²⁹Si solid-state NMR spectroscopy the assignment and quantification of the Raman bands to different end groups and different cross-linking states was made. The sol-gel reaction was also followed by in-line NIR spectroscopy. A multivariate data analysis was accomplished to obtain a conversion-time curve. Furthermore, we investigated spin-coated films on wafers using FTIR transmission spectroscopy.     

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.     

FT-Raman,FT-infrared and NIR spectroscopic characterization of oxygen-delignified kraft pulp treated with hydrogen peroxide under acidic and alkaline conditions

The effects of bleaching treatment of oxygen-delignified softwood kraft pulp with hydrogen peroxide under acidic and alkaline conditions were studied using standard technological techniques and spectroscopic analytical methods: near-infrared (NIR), Fourier-transform infrared (FTIR) and Fourier-transform (FT) Raman spectroscopies. Among the three tested spectroscopic techniques, NIR analysis appeared to be the most appropriate in terms of possible technological applications. The use of NIR spectroscopy combined with multivariate data analysis allowed to create models for pulp bleaching monitoring based on CIE L*a*b* measurements. Near-infrared and FTIR spectroscopic studies allowed differentiating between the effects of the acidic and alkaline peroxide bleaching stages, but failed in relation to the delignification process. The most representative bands in the FTIR and FT-Raman spectra in terms of delignification and chromophore removal exhibited no correlation with standard technological measurement results.     

Confocal fluorescence and Raman microscopy in industrial research

 Modern chemical and pharmaceutical industrial research benefits from improved spectroscopic tools. New developments in confocal fluorescence and Raman microscopy lead to an increase in sensitivity, selectivity and speed of microscopic imaging and fluctuation analysis resulting in a better understanding of structure–property relationships essential for targeted development. In this paper we report on the application of fluorescence and Raman microscopy for characterizing the morphology of polymeric multiphase solid-state samples and on new developments in the corresponding correlation spectroscopies for the characterization of the dynamics of complex colloidal systems in the liquid state. In the case of fluorescence new technological opportunities are gained by two-photon excitation. Received: 5 February 1998 Accepted: 16 February 1998     

Micro-Raman spectroscopic identification of bacterial cells of the genus Staphylococcus and dependence on their cultivation conditions

Microbial contamination is not only a medical problem, but also plays a large role in pharmaceutical clean room production and food processing technology. Therefore many techniques were developed to achieve differentiation and identification of microorganisms. Among these methods vibrational spectroscopic techniques (IR, Raman and SERS) are useful tools because of their rapidity and sensitivity. Recently we have shown that micro-Raman spectroscopy in combination with a support vector machine is an extremely capable approach for a fast and reliable, non-destructive online identification of single bacteria belonging to different genera. In order to simulate different environmental conditions we analyzed in this contribution different Staphylococcus strains with varying cultivation conditions in order to evaluate our method with a reliable dataset. First, micro-Raman spectra of the bulk material and single bacterial cells that were grown under the same conditions were recorded and used separately for a distinct chemotaxonomic classification of the strains. Furthermore Raman spectra were recorded from single bacterial cells that were cultured under various conditions to study the influence of cultivation on the discrimination ability. This dataset was analyzed both with a hierarchical cluster analysis (HCA) and a support vector machine (SVM).     

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.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

Comparison of near-infrared and Raman spectroscopy for the determination of the density of polyethylene pellets

In this study, we compare near-infrared (NIR) and Raman spectroscopy for the determination of the density of linear low density polyethylene (PE) (in a pellet form). As generally known, Raman spectral features are more selective than those of NIR for most chemical samples. NIR spectroscopy has been more extensively used for the quantitative analysis of polymers, but Raman spectroscopy is the better choice as long as the problem of reproducibility of Raman measurements (especially for solid samples), mostly resulting from insufficient sample representation due to probing only localized chemical information and the sensitivity of sample placement with regard to the focal plane, can be overcome. To improve sample representation and reproducibility of Raman measurements, we have employed the wide area illumination (WAI) Raman scheme, capable of illuminating a laser onto a large sample area (28.3 mm²) for Raman spectral collection (a 6-mm laser spot with a focal length of 248 mm). Diffuse reflectance NIR spectra of PE pellets were collected using a sample moving system which allowed for the scanning of large areas. The prediction error was 0.0008 g cm⁻³ for Raman spectroscopy and 0.0011 g cm⁻³ for NIR spectroscopy. The harmonization of inherently selective Raman features and a reproducible spectral collection with correct sample representations using the WAI scheme led to an accurate determination of the density of the PE pellets.     

Comparison of near infrared and microwave resonance sensors for at-line moisture determination in powders and tablets

In this paper we demonstrate the feasibility of replacing KF for water content testing in bulk powders and tablets with at-line near infrared (NIR) or microwave resonance (MR) methods. Accurate NIR and MR prediction models were developed with a minimalistic approach to calibration. The NIR method can accurately predict water content in bulk powders in the range of 0.5-5% w/w. Results from this method were compared to a MR method. We demonstrated excellent agreement of both NIR and MR methods for powders vs. the reference KF method. These methods are applicable to in-process control or quality control environments. One of the aims of this study was to determine if a calibration developed for a particular product could be used to predict the water content of another product (with related composition) but containing a different active pharmaceutical ingredient (API). We demonstrated that, contrary to the NIR method, a general MR method can be used to predict water content in two different types of blends. Finally, we demonstrated that a MR method can be developed for at-line moisture determination in tablets.     

Chemometrics in pharmaceutical analysis: an introduction, review, and future perspectives

Chemometrics is the application of statistical and mathematical methods to analytical data to permit maximum collection and extraction of useful information. The utility of chemometric techniques as tools enabling multidimensional calibration of selected spectroscopic, electrochemical, and chromatographic methods is demonstrated. Application of this approach mainly for interpretation of UV-Vis and near-IR (NIR) spectra, as well as for data obtained by other instrumental methods, makes identification and quantitative analysis of active substances in complex mixtures possible, especially in the analysis of pharmaceutical preparations present in the market. Such analytical work is carried out by the use of advanced chemical instruments and data processing, which has led to a need for advanced methods to design experiments, calibrate instruments, and analyze the resulting data. The purpose of this review is to describe various chemometric methods in combination with UV-Vis spectrophotometry, NIR spectroscopy, fluorescence spectroscopy, electroanalysis, chromatographic separation, and flow-injection analysis for the analysis of drugs in pharmaceutical preparations. Theoretical and practical aspects are described with pharmaceutical examples of chemometric applications. This review will concentrate on gaining an understanding of how chemometrics can be useful in the modern analytical laboratory. A selection of the most challenging problems faced in pharmaceutical analysis is presented, the potential for chemometrics is considered, and some consequent implications for utilization are discussed. The reader can refer to the citations wherever appropriate.     

Near-infrared spectroscopy applications in pharmaceutical analysis

Near-infrared (NIR) spectroscopy is a fast and non-destructive analytical technique that offers many advantages for a broad range of industrial applications. In this work, we reviewed recent developments in the pharmaceutical domain where it can be applied from raw material identification to final product release. The characteristics of NIR allow the technique to be implemented as a process analytical technology (PAT). Moreover, recent instrumental developments open the perspectives of numerous applications in the NIR imaging area. After “Introduction”, according to their subject, the applications are discussed in the parts “Identification”, “Water content”, “Assay” and “Other applications”.     

Building the quality into pellet manufacturing environment--feasibility study and validation of an in-line quantitative near infrared (NIR) method

The present study focuses on the implementation of an in-line quantitative near infrared (NIR) spectroscopic method for determining the active content of pharmaceutical pellets. The first aim was to non-invasively interface a dispersive NIR spectrometer with four realistic particle streams existing in the pellets manufacturing environment. Regardless of the particle stream characteristics investigated, NIR together with Principal Component Analysis (PCA) was able to classify the samples according to their active content. Further, one of these particle stream interfaces was non-invasively investigated with a FT-NIR spectrometer. A predictive model based on Partial Least Squares (PLS) regression was able to determine the active content of pharmaceutical pellets. The NIR method was finally validated with an external validation set for an API concentration range from 80 to 120% of the targeted active content. The prediction error of 0.9% (root mean standard error of prediction, RMSEP) was low, indicating the accuracy of the NIR method. The accuracy profile on the validation results, an innovative approach based on tolerance intervals, demonstrated the actual and future performance of the in-line NIR method. Accordingly, the present approach paves the way for real-time release-based quality system.     

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.     

Identification and Spectroscopic Characterization of Nonheme Iron(III) Hypochlorite Intermediates

Fe^III–hypohalite complexes have been implicated in a wide range of important enzyme‐catalyzed halogenation reactions including the biosynthesis of natural products and antibiotics and post‐translational modification of proteins. The absence of spectroscopic data on such species precludes their identification. Herein, we report the generation and spectroscopic characterization of nonheme Fe^III–hypohalite intermediates of possible relevance to iron halogenases. We show that Fe^III‐OCl polypyridylamine complexes can be sufficiently stable at room temperature to be characterized by UV/Vis absorption, resonance Raman and EPR spectroscopies, and cryo‐ESIMS. DFT methods rationalize the pathways to the formation of the Fe^III‐OCl, and ultimately Fe^IV?O, species and provide indirect evidence for a short‐lived Fe^II‐OCl intermediate. The species observed and the pathways involved offer insight into and, importantly, a spectroscopic database for the investigation of iron halogenases.     

Testing the performance of pure spectrum resolution from Raman hyperspectral images of differently manufactured pharmaceutical tablets

Chemical imaging is a rapidly emerging analytical method in pharmaceutical technology. Due to the numerous chemometric solutions available, characterization of pharmaceutical samples with unknown components present has also become possible. This study compares the performance of current state-of-the-art curve resolution methods (multivariate curve resolution-alternating least squares, positive matrix factorization, simplex identification via split augmented Lagrangian and self-modelling mixture analysis) in the estimation of pure component spectra from Raman maps of differently manufactured pharmaceutical tablets. The batches of different technologies differ in the homogeneity level of the active ingredient, thus, the curve resolution methods are tested under different conditions. An empirical approach is shown to determine the number of components present in a sample. The chemometric algorithms are compared regarding the number of detected components, the quality of the resolved spectra and the accuracy of scores (spectral concentrations) compared to those calculated with classical least squares, using the true pure component (reference) spectra. It is demonstrated that using appropriate multivariate methods, Raman chemical imaging can be a useful tool in the non-invasive characterization of unknown (e.g. illegal or counterfeit) pharmaceutical products.     

Monitoring of pigmented and wooden surfaces in accelerated ageing processes by FT-Raman spectroscopy and multivariate control charts

Two of the most suitable analytical techniques used in the field of cultural heritage are NIR (near-infrared) and Raman spectroscopy. FT-Raman spectroscopy coupled to multivariate control charts is applied here for the development of a new method for monitoring the conservation state of pigmented and wooden surfaces. These materials were exposed to different accelerated ageing processes in order to evaluate the effect of the applied treatments on the goods surfaces. In this work, a new approach based on the principles of statistical process control (SPC) to the monitoring of cultural heritage, has been developed: the conservation state of samples simulating works-of-art has been treated like an industrial process, monitored with multivariate control charts, owing to the complexity of the spectroscopic data collected.The Raman spectra were analysed by principal component analysis (PCA) and the relevant principal components (PCs) were used for constructing multivariate Shewhart and cumulative sum (CUSUM) control charts. These tools were successfully applied for the identification of the presence of relevant modifications occurring on the surfaces. CUSUM charts however proved to be more effective in the identification of the exact beginning of the applied treatment. In the case of wooden boards, where a sufficient number of PCs were available, simultaneous scores monitoring and residuals tracking (SMART) charts were also investigated. The exposure to a basic attack and to high temperatures produced deep changes on the wooden samples, clearly identified by the multivariate Shewhart, CUSUM and SMART charts. A change on the pigment surface was detected after exposure to an acidic solution and to the UV light, while no effect was identified on the painted surface after the exposure to natural atmospheric events.     

Non-Invasive Identification of Nutrient Components in Grain

Digital farming is a modern agricultural concept that aims to maximize the crop yield while simultaneously minimizing the environmental impact of farming. Successful implementation of digital farming requires development of sensors to detect and identify diseases and abiotic stresses in plants, as well as to probe the nutrient content of seeds and identify plant varieties. Experimental evidence of the suitability of Raman spectroscopy (RS) for confirmatory diagnostics of plant diseases was previously provided by our team and other research groups. In this study, we investigate the potential use of RS as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of nutrient components in the grains from 15 different rice genotypes. We demonstrate that spectroscopic analysis of intact rice seeds provides the accurate rice variety identification in ~86% of samples. These results suggest that RS can be used for fully automated, fast and accurate identification of seeds nutrient components.