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.

L'application de la spectroscopie Raman en criminalistique pour l'analyse du colorant des fibres textiles en acrylique, coton et laine

RESUME La méthode de la spectroscopie Raman est une technique d'analyse chimique basée sur l'exploitation du phénomène de diffusion de la lumière (light scattering). Ce phénomène fut observé pour la première fois en 1928 par Raman et Krishnan. Ces observations permirent à Raman d'obtenir le Prix Nobel en physique en 1930. L'application de la spectroscopie Raman a été entreprise pour l'analyse du colorant de fibres textiles en acrylique, en coton et en laine de couleurs bleue, rouge et noire. Nous avons ainsi pu confirmer que la technique est adaptée pour l'analyse in situ de traces de taille microscopique. De plus, elle peut être qualifiée de rapide, non destructive et ne nécessite aucune préparation particulière des échantillons. Cependant, le phénomène de la fluorescence s'est révélé être l'inconvénient le plus important. Lors de l'analyse des fibres, différentes conditions analytiques ont été testées et il est apparu qu'elles dépendaient surtout du laser choisi. Son potentiel pour la détection et l'identification des colorants imprégnés dans les fibres a été confirmé dans cette étude. Une banque de données spectrale comprenant soixante colorants de référence a été réalisée dans le but d'identifier le colorant principal imprégné dans les fibres collectées. De plus, l'analyse de différents blocs de couleur, caractérisés par des échantillons d'origine inconnue demandés à diverses personnes, a permis de diviser ces derniers en plusieurs groupes et d'évaluer la rareté des configurations des spectres Raman obtenus. La capacité de la technique Raman à différencier ces échantillons a été évaluée et comparée à celle des méthodes conventionnelles pour l'analyse des fibres textiles, à savoir la micro spectrophotométrie UV-Vis (MSP) et la chromatographie sur couche mince (CCM). La technique Raman s'est révélée être moins discriminatoire que la MSP pour tous les blocs de couleurs considérés. C'est pourquoi dans le cadre d'une séquence analytique nous recommandons l'utilisation du Raman après celle de la méthode d'analyse de la couleur, à partir d'un nombre de sources lasers le plus élevé possible. Finalement, la possibilité de disposer d'instruments équipés avec plusieurs longueurs d'onde d'excitation, outre leur pouvoir de réduire la fluorescence, permet l'exploitation d'un plus grand nombre d'échantillons. ABSTRACT Raman spectroscopy allows for the measurement of the inelastic scattering of light due to the vibrational modes of a molecule when irradiated by an intense monochromatic source such as a laser. Such a phenomenon was observed for the first time by Raman and Krishnan in 1928. For this observation, Raman was awarded with the Nobel Prize in Physics in 1930. The application of Raman spectroscopy has been undertaken for the dye analysis of textile fibers. Blue, black and red acrylics, cottons and wools were examined. The Raman technique presents advantages such as non-destructive nature, fast analysis time, and the possibility of performing microscopic in situ analyses. However, the problem of fluorescence was often encountered. Several aspects were investigated according to the best analytical conditions for every type/color fiber combination. The potential of the technique for the detection and identification of dyes was confirmed. A spectral database of 60 reference dyes was built to detect the main dyes used for the coloration of fiber samples. Particular attention was placed on the discriminating power of the technique. Based on the results from the Raman analysis for the different blocs of color submitted to analyses, it was possible to obtain different classes of fibers according to the general shape of spectra. The ability of Raman spectroscopy to differentiate samples was compared to the one of the conventional techniques used for the analysis of textile fibers, like UV-Vis Microspectrophotometry (UV-Vis MSP) and thin layer chromatography (TLC). The Raman technique resulted to be less discriminative than MSP for every bloc of color considered in this study. Thus, it is recommended to use Raman spectroscopy after MSP and light microscopy to be considered for an analytical sequence. It was shown that using several laser wavelengths allowed for the reduction of fluorescence and for the exploitation of a higher number of samples.

Use of NIR spectroscopy and multivariate process spectra calibration methodology for pharmaceutical solid samples analysis

Accomplish high quality of final products in pharmaceutical industry is a challenge that requires the control and supervision of all the manufacturing steps. This request created the necessity of developing fast and accurate analytical methods. Near infrared spectroscopy together with chemometrics, fulfill this growing demand. The high speed providing relevant information and the versatility of its application to different types of samples lead these combined techniques as one of the most appropriated. This study is focused on the development of a calibration model able to determine amounts of API from industrial granulates using NIR, chemometrics and process spectra methodology.

X-ray photoelectron spectroscopy analysis of ion¿beam¿induced oxidation of GaAs and AlGaAs

The oxidation of GaAs and AlGaAs targets subjected to O2+ bombardment has been analyzed, using in situ x¿ray photoelectron spectroscopy, as a function of time until steady state is reached. The oxides formed by the O2+ bombardment have been characterized in terms of composition and binding energy. A strong energy and angular dependence for the oxidation of As relative to Ga is found. Low energies as well as near normal angles of incidence favor the oxidation of As. The difference between Ga and As can be explained in terms of the formation enthalpy for the oxide and the excess supply of oxygen. In an AlGaAs target the Al is very quickly completely oxidized irrespective of the experimental conditions. The steady state composition of the altered layers show in all cases a preferential removal of As.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Photoelectron spectroscopy for surface analysis: X-ray and UV excitation

This article summarizes the basic principles of photoelectron spectroscopy for surface analysis, with examples of applications in material science that illustrate the capabilities of the related techniques.

Phosphorus analysis in soil under herbaceous perennial leguminous cover by nuclear magnetic spectroscopy

The availability and the reserves of organic phosphorus are controlled by its mineralization rate and are also influenced by changes in soil management. The objective of this study was to evaluate the influence of soil covering with different leguminous plant on soil organic P by 31P-NMR spectroscopy. Alkaline soil extracts were obtained from two depths (0-5 and 5-10 cm) of an Ultisol cultivated with herbaceous perennial leguminous plants (Arachis pintoi, Pueraria phaseoloides, Macroptilium atropurpureum). In an adjacent area, samples of the same soil cover with a secondary tropical forest and grass (Panicum maximum) were also collected. The leguminous management was divided into with removal and without removal of shoot parts after cut on soil surface. Phosphate monoesters are the dominant P species in all soil samples and P diesters accumulated on the superficial layer of secondary forest soil. The P amount of this fraction is higher for the legume covered soil when compared with the grass covered soil. The permanence of leguminous plants on the topsoil after the cut promoted an increase in P diester/P monoester ratios. These findings can be accounted for an enhancement of P availability to plants in soils cultivated with leguminous plants.

Selective resonance suppression 1H-[13C] NMR spectroscopy with asymmetric adiabatic RF pulses.

Despite obvious improvements in spectral resolution at high magnetic field, the detection of 13C labeling by 1H-[13C] NMR spectroscopy remains hampered by spectral overlap, such as in the spectral region of 1H resonances bound to C3 of glutamate (Glu) and glutamine (Gln), and C6 of N-acetylaspartate (NAA). The aim of this study was to develop, implement, and apply a novel 1H-[13C] NMR spectroscopic editing scheme, dubbed "selective Resonance suppression by Adiabatic Carbon Editing and Decoupling single-voxel STimulated Echo Acquisition Mode" (RACED-STEAM). The sequence is based on the application of two asymmetric narrow-transition-band adiabatic RF inversion pulses at the resonance frequency of the 13C coupled to the protons that need to be suppressed during the mixing time (TM) period, alternating the inversion band downfield and upfield from the 13C resonance on odd and even scans, respectively, thus suppressing the detection of 1H resonances bound to 13C within the transition band of the inversion pulse. The results demonstrate the efficient suppression of 1H resonances bound to C3 of Glu and Gln, and C4 of Glu, which allows the 1H resonances bound to C6 of NAA and C4 of Gln to be revealed. The measured time course of the resolved labeling into NAA C6 with the new scheme was consistent with the slow turnover of NAA.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Raman spectroscopy and microspectroscopy of reactive dyes on cotton fibres: analysis and detection limits

A collaborative study on Raman spectroscopy and microspectrophotometry (MSP) was carried out by members of the ENFSI (European Network of Forensic Science Institutes) European Fibres Group (EFG) on different dyed cotton fabrics. The detection limits of the two methods were tested on two cotton sets with a dye concentration ranging from 0.5 to 0.005% (w/w). This survey shows that it is possible to detect the presence of dye in fibres with concentrations below that detectable by the traditional methods of light microscopy and microspectrophotometry (MSP). The MSP detection limit for the dyes used in this study was found to be a concentration of 0.5% (w/w). At this concentration, the fibres appear colourless with light microscopy. Raman spectroscopy clearly shows a higher potential to detect concentrations of dyes as low as 0.05% for the yellow dye RY145 and 0.005% for the blue dye RB221. This detection limit was found to depend both on the chemical composition of the dye itself and on the analytical conditions, particularly the laser wavelength. Furthermore, analysis of binary mixtures of dyes showed that while the minor dye was detected at 1.5% (w/w) (30% of the total dye concentration) using microspectrophotometry, it was detected at a level as low as 0.05% (w/w) (10% of the total dye concentration) using Raman spectroscopy. This work also highlights the importance of a flexible Raman instrument equipped with several lasers at different wavelengths for the analysis of dyed fibres. The operator and the set up of the analytical conditions are also of prime importance in order to obtain high quality spectra. Changing the laser wavelength is important to detect different dyes in a mixture.

Force Spectroscopy with Dual-Trap Optical Tweezers: Molecular Stiffness Measurements and Coupled Fluctuations Analysis

ABSTRACT Dual-trap optical tweezers are often used in high-resolution measurements in single-molecule biophysics. Such measurements can be hindered by the presence of extraneous noise sources, the most prominent of which is the coupling of fluctuations along different spatial directions, which may affect any optical tweezers setup. In this article, we analyze, both from the theoretical and the experimental points of view, the most common source for these couplings in dual-trap optical-tweezers setups: the misalignment of traps and tether. We give criteria to distinguish different kinds of misalignment, to estimate their quantitative relevance and to include them in the data analysis. The experimental data is obtained in a, to our knowledge, novel dual-trap optical-tweezers setup that directly measures forces. In the case in which misalignment is negligible, we provide a method to measure the stiffness of traps and tether based on variance analysis. This method can be seen as a calibration technique valid beyond the linear trap region. Our analysis is then employed to measure the persistence length of dsDNA tethers of three different lengths spanning two orders of magnitude. The effective persistence length of such tethers is shown to decrease with the contour length, in accordance with previous studies.

Statistical Analysis of Spectral Images

Tämä diplomityö liittyy Spektrikuvien tutkimiseen tilastollisen kuvamallin näkökulmasta. Diplomityön ensimmäisessä osassa tarkastellaan tilastollisten parametrien jakaumien vaikutusta väreihin ja korostumiin erilaisissa valaistusolosuhteissa. Havaittiin, että tilastollisten parametrien väliset suhteet eivät riipu valaistusolosuhteista, mutta riippuvat kuvan häiriöttömyydestä. Ilmeni myös, että korkea huipukkuus saattaa aiheutua värikylläisyydestä. Lisäksi työssä kehitettiin tilastolliseen spektrimalliin perustuvaa tekstuurinyhdistämisalgoritmia. Sillä saavutettiin hyviä tuloksia, kun tilastollisten parametrien väliset riippuvuussuhteet olivat voimassa. Työn toisessa osassa erilaisia spektrikuvia tutkittiin käyttäen itsenäistä komponenttien analyysia (ICA). Seuraavia itsenäiseen komponenttien analyysiin tarkoitettuja algoritmia tarkasteltiin: JADE, kiinteän pisteen ICA ja momenttikeskeinen ICA. Tutkimuksissa painotettiin erottelun laatua. Paras erottelu saavutettiin JADE- algoritmilla, joskin erot muiden algoritmien välillä eivät olleet merkittäviä. Algoritmi jakoi kuvan kahteen itsenäiseen, joko korostuneeseen ja korostumattomaan tai kromaattiseen ja akromaattiseen, komponenttiin. Lopuksi pohditaan huipukkuuden suhdetta kuvan ominaisuuksiin, kuten korostuneisuuteen ja värikylläisyyteen. Työn viimeisessä osassa ehdotetaan mahdollisia jatkotutkimuskohteita.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Computational Techniques for Spectral Image Analysis

Multispectral images are becoming more common in the field of remote sensing, computer vision, and industrial applications. Due to the high accuracy of the multispectral information, it can be used as an important quality factor in the inspection of industrial products. Recently, the development on multispectral imaging systems and the computational analysis on the multispectral images have been the focus of a growing interest. In this thesis, three areas of multispectral image analysis are considered. First, a method for analyzing multispectral textured images was developed. The method is based on a spectral cooccurrence matrix, which contains information of the joint distribution of spectral classes in a spectral domain. Next, a procedure for estimating the illumination spectrum of the color images was developed. Proposed method can be used, for example, in color constancy, color correction, and in the content based search from color image databases. Finally, color filters for the optical pattern recognition were designed, and a prototype of a spectral vision system was constructed. The spectral vision system can be used to acquire a low dimensional component image set for the two dimensional spectral image reconstruction. The data obtained by the spectral vision system is small and therefore convenient for storing and transmitting a spectral image.

Characterization of the antibiotic doripenem using physicochemical methods: chromatography, spectrophotometry, spectroscopy and thermal analysis

Doripenem was characterized through physicochemical and spectroscopic techniques, as well as thermal analysis. TLC (Rf = 0.62) and HPLC (rt = 7.4 min) were found to be adequate to identify the drug. UV and infrared spectra showed similar profile between doripenem bulk and standard. The ¹H and 13C NMR analysis revealed chemical shifts that allowed identifying the drug. Thermal analysis demonstrated three steps with mass loss, at 128, 178 and 276 ºC. The work was successfully applied to qualitative analysis of doripenem, showing the reported methods can be used for physicochemical characterization of doripenem