Knoop microhardness and FT-Raman evaluation of composite resins: influence of opacity and photoactivation source

The aim of this study was to evaluate the degree of conversion by Knoop microhardness (KHN) and FT-Raman spectroscopy (FTIR) of one nanofilled (Filtek Supreme-3M-ESPE [FS]) and one microhybrid composite (Charisma-Heraeus-Kulzer [CH]), each with different opacities, namely enamel, dentin, and translucent, which were photo-activated by a quartz-tungsten-halogen lamp (QTH) and a light-emitting diode (LED). Resin was bulk inserted into a disc-shaped mold that was 2.0 mm thick and 4 mm in diameter, obtaining 10 samples per group. KHN and FTIR values were analyzed by two-way ANOVA and Tukey's tests (α = 0.05). Nanofilled resin activated by a LED presented higher microhardness values than samples activated by a QTH for dentin opacity (p < 0.05). The microhybrid resin showed no differences in KHN or FTIR values with different activation sources or opacity. The nanofilled dentin and enamel resins showed lower FTIR values than the translucent resin. The KHN values of the translucent resins were not influenced by the light source.

Spectral Region Optimization for Raman-Based Optical Biopsy of Inflammatory Lesions

Objective: The biochemical alterations between inflammatory fibrous hyperplasia (IFH) and normal tissues of buccal mucosa were probed by using the FT-Raman spectroscopy technique. The aim was to find the minimal set of Raman bands that would furnish the best discrimination. Background: Raman-based optical biopsy is a widely recognized potential technique for noninvasive real-time diagnosis. However, few studies had been devoted to the discrimination of very common subtle or early pathologic states as inflammatory processes that are always present on, for example, cancer lesion borders. Methods: Seventy spectra of IFH from 14 patients were compared with 30 spectra of normal tissues from six patients. The statistical analysis was performed with principal components analysis and soft independent modeling class analogy cross-validated, leave-one-out methods. Results: Bands close to 574, 1,100, 1,250 to 1,350, and 1,500 cm(-1) (mainly amino acids and collagen bands) showed the main intragroup variations that are due to the acanthosis process in the IFH epithelium. The 1,200 (C-C aromatic/DNA), 1,350 (CH(2) bending/collagen 1), and 1,730 cm(-1) (collagen III) regions presented the main intergroup variations. This finding was interpreted as originating in an extracellular matrix-degeneration process occurring in the inflammatory tissues. The statistical analysis results indicated that the best discrimination capability (sensitivity of 95% and specificity of 100%) was found by using the 530-580 cm(-1) spectral region. Conclusions: The existence of this narrow spectral window enabling normal and inflammatory diagnosis also had useful implications for an in vivo dispersive Raman setup for clinical applications.

Fluorescence and reflectance spectroscopy for identification of atherosclerosis in human carotid arteries using principal components analysis

Objectives: The aim of this work was to verify the differentiation between normal and pathological human carotid artery tissues by using fluorescence and reflectance spectroscopy in the 400- to 700-nm range and the spectral characterization by means of principal components analysis. Background Data: Atherosclerosis is the most common and serious pathology of the cardiovascular system. Principal components represent the main spectral characteristics that occur within the spectral data and could be used for tissue classification. Materials and Methods: Sixty postmortem carotid artery fragments (26 non-atherosclerotic and 34 atherosclerotic with non-calcified plaques) were studied. The excitation radiation consisted of a 488-nm argon laser. Two 600-mu m core optical fibers were used, one for excitation and one to collect the fluorescence radiation from the samples. The reflectance system was composed of a halogen lamp coupled to an excitation fiber positioned in one of the ports of an integrating sphere that delivered 5 mW to the sample. The photo-reflectance signal was coupled to a 1/4-m spectrograph via an optical fiber. Euclidean distance was then used to classify each principal component score into one of two classes, normal and atherosclerotic tissue, for both fluorescence and reflectance. Results: The principal components analysis allowed classification of the samples with 81% sensitivity and 88% specificity for fluorescence, and 81% sensitivity and 91% specificity for reflectance. Conclusions: Our results showed that principal components analysis could be applied to differentiate between normal and atherosclerotic tissue with high sensitivity and specificity.

Knoop Microhardness and FT-Raman Spectroscopic Evaluation of a Resin-Based Dental Material Light-Cured by an Argon Ion Laser and Halogen Lamp: An in Vitro Study

Objective: The purpose of this study was to evaluate in vitro the Knoop microhardness (Knoop hardness number [KHN]) and the degree of conversion using FT-Raman spectroscopy of a light-cured microhybrid resin composite (Z350-3M-ESPE) Vita shade A3 photopolymerized with a halogen lamp or an argon ion laser. Background Data: Optimal polymerization of resin-based dental materials is important for longevity of restorations in dentistry. Materials and Methods: Thirty specimens were prepared and inserted into a disc-shaped polytetrafluoroethylene mold that was 2.0 mm thick and 3 mm in diameter. The specimens were divided into three groups (n = 10 each). Group 1 (G1) was light-cured for 20 sec with an Optilux 501 halogen light with an intensity of 1000 mW/cm(2). Group 2 (G2) was photopolymerized with an argon laser with a power of 150 mW for 10 sec, and group 3 (G3) was photopolymerized with an argon laser at 200 mW of power for 10 sec. All specimens were stored in distilled water for 24 h at 37 degrees C and kept in lightproof containers. For the KHN test five indentations were made and a depth of 100 mu m was maintained in each specimen. One hundred and fifty readings were obtained using a 25-g load for 45 sec. The degree of conversion values were measured by Raman spectroscopy. KHN and degree of conversion values were obtained on opposite sides of the irradiated surface. KHN and degree of conversion data were analyzed by one-way ANOVA and Tukey tests with statistical significance set at p < 0.05. Results: The results of KHN testing were G1 = 37.428 +/- 4.765; G2 = 23.588 +/- 6.269; and G3 = 21.652 +/- 4.393. The calculated degrees of conversion (DC%) were G1 = 48.57 +/- 2.11; G2 = 43.71 +/- 3.93; and G3 = 44.19 +/- 2.71. Conclusions: Polymerization with the halogen lamp ( G1) attained higher microhardness values than polymerization with the argon laser at power levels of 150 and 200 mW; there was no difference in hardness between the two argon laser groups. The results showed no statistically significant different degrees of conversion for the polymerization of composite samples with the two light sources tested.

In-Line Monitoring of Particle Size during Emulsion Polymerization under Different Operational Conditions using NIR Spectroscopy

In the present work, the sensitivity of NIR spectroscopy toward the evolution of particle size was studied during emulsion homopolymerization of styrene (Sty) and emulsion copolymerization of vinyl acetate-butyl acrylate conducted in a semibatch stirred tank and a tubular pulsed sieve plate reactor, respectively. All NIR spectra were collected online with a transflectance probe immersed into the reaction medium. The spectral range used for the NIR monitoring was from 9 500 to 13 000 cm(-1), where the absorbance of the chemical components present is minimal and the changes in the NIR spectrum can be ascribed to the effects of light scattering by the polymer particles. Off-line measurements of the average diameter of the polymer particles by DLS were used as reference values for the development of the multi-variate NIR calibration models based on partial least squares. Results indicated that, in the spectral range studied, it is possible to monitor the evolution of the average size of the polymer particles during emulsion polymerization reactions. The inclusion of an additional spectral range, from 5 701 to 6 447 cm(-1), containing information on absorbances (""chemical information"") in the calibration models was also evaluated.

C-13-NMR, H-1-NMR, and FT-Raman study of radiation-induced modifications in radiation dosimetry polymer gels

H-1- and C-13-NMR spectroscopy and FT-Raman spectroscopy are used to investigate the properties of a polymer gel dosimeter post-irradiation. The polymer gel (PACT) is composed of acrylamide, N,N'-methylene-bisacrylamide, gelatin, and water. The formation of a polyacrylamide network within the gelatin matrix follows a dose dependence nonlinearly correlated to the disappearance of the double bonds from the dissolved monomers within the absorbed dose range of 0-50 Gy. The signal from the gelatin remains constant with irradiation. We show that the NMR spin-spin relaxation times (T-2) of PAGs irradiated to up to 50 Gy measured in a NMR spectrometer and a clinical magnetic resonance imaging scanner can be modeled using the spectroscopic intensity of the growing polymer network. More specifically, we show that the nonlinear T-2 dependence against dose can be understood in terms of the fraction of protons in three different proton pools. (C) 2000 John Wiley & Sons, Inc.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Independent component analysis applied to raman spectra for classification of in vitro human coronary arteries

Optical diagnostic methods, such as near-infrared Raman spectroscopy allow quantification and evaluation of human affecting diseases, which could be useful in identifying and diagnosing atherosclerosis in coronary arteries. The goal of the present work is to apply Independent Component Analysis (ICA) for data reduction and feature extraction of Raman spectra and to perform the Mahalanobis distance for group classification according to histopathology, obtaining feasible diagnostic information to detect atheromatous plaque. An 830nm Ti:sapphire laser pumped by an argon laser provides near-infrared excitation. A spectrograph disperses light scattered from arterial tissues over a liquid-nitrogen cooled CCD to detect the Raman spectra. A total of 111 spectra from arterial fragments were utilized.

Diagnostic model based on Raman spectra of normal, hyperplasia and prostate adenocarcinoma tissues in vitro

This study evaluated the use of Raman spectroscopy to identify the spectral differences between normal (N), benign hyperplasia (BPH) and adenocarcinoma (CaP) in fragments of prostate biopsies in vitro with the aim of developing a spectral diagnostic model for tissue classification. A dispersive Raman spectrometer was used with 830 nm wavelength and 80 mW excitation. Following Raman data collection and tissue histopathology (48 fragments diagnosed as N, 43 as BPH and 14 as CaP), two diagnostic models were developed in order to extract diagnostic information: the first using PCA and Mahalanobis analysis techniques and the second one a simplified biochemical model based on spectral features of cholesterol, collagen, smooth muscle cell and adipocyte. Spectral differences between N, BPH and CaP tissues, were observed mainly in the Raman bands associated with proteins, lipids, nucleic and amino acids. The PCA diagnostic model showed a sensitivity and specificity of 100%, which indicates the ability of PCA and Mahalanobis distance techniques to classify tissue changes in vitro. Also, it was found that the relative amount of collagen decreased while the amount of cholesterol and adipocyte increased with severity of the disease. Smooth muscle cell increased in BPH tissue. These characteristics were used for diagnostic purposes.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

Applications of a transportable Raman spectrometer for the in situ detection of controlled substances at border controls

A transportable Raman spectrometer was tested for the detection of illicit drugs seized during border controls. In a first step, the analysis methodology was optimized using reference substances such as diacetylmorphine (heroin), cocaine and amphetamine (as powder or liquid forms). Adequate focalisation distance and times of analysis, influence of daylight and artificial light sources, repeatability and limits of detection were studied. In a second step the applications and limitations of the technique to detect the illicit substances in different mixtures and containers was evaluated. Transportable Raman spectroscopy was found to be adequate for a rapid screen of liquids and powders for the detection and identification of controlled substances. Additionally, it had the advantage over other portable techniques, such as ion mobility spectrometry, of being non-destructive and capable of rapid analysis of large quantities of substances through containers such as plastic bags and glass bottles.

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.

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

Micro-Raman study of stress distribution in local isolation structures and correlation with transmission electron microscopy

Stress in local isolation structures is studied by micro‐Raman spectroscopy. The results are correlated with predictions of an analytical model for the stress distribution and with cross‐sectional transmission electron microscopy observations. The measurements are performed on structures on which the Si3N4 oxidation mask is still present. The influence of the pitch of the periodic local isolation pattern, consisting of parallel lines, the thickness of the mask, and the length of the bird"s beak on the stress distribution are studied. It is found that compressive stress is present in the Si substrate under the center of the oxidation mask lines, with a magnitude dependent on the width of the lines. Large tensile stress is concentrated under the bird"s beak and is found to increase with decreasing length of the bird"s beak and with increasing thickness of the Si3N4 film.