Raman Spectroscopy For Quantitative Analysis Research Articles

Surface-Enhanced Raman Spectroscopy for Quantitative Analysis of Anesthetic Agents: Toward Personalized Drug Monitoring in Anesthesia

Surface-Enhanced Raman Spectroscopy for Quantitative Analysis of Anesthetic Agents: Toward Personalized Drug Monitoring in Anesthesia

A simple and efficient alginate hydrogel combined with surface-enhanced Raman spectroscopy for quantitative analysis of sodium nitrite in meat products.

Sodium nitrite is a commonly used preservative and color protectant in the food industry. Conventional analytical methods are highly susceptible to food matrix interference, time-consuming and costly. In this study, the ion cross-linking method was employed to prepare alginate hydrogel substrates, and phenosafranin was chosen as a single-molecule probe to analyze sodium nitrite. Our investigation centered on elucidating the effects of alginate and cross-linking ion concentrations on Raman signal characteristics. The optimal Raman response was observed in the precursor solution with 1% sodium alginate and 0.1 mol L-1 cross-linking ions. The relative standard deviations (RSDs) of the feature peaks from the three substrate batches ranged from 1.22% to 16.30%, attesting the robustness and consistency of the substrates. The signal reduction of the substrates after a four-week storage period remained below 10%, indicating that the substrates had good reproducibility and stability. The limits of detection (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced ham were determined to range from 3.75 mg kg-1 to 8.11 mg kg-1, with low interference from the food matrix. The support vector machine algorithm was utilized to train and predict the data, which proved to be more accurate (98.6%-99.8% recovery) than the traditional linear regression model (81.9%-112.7% recovery) in predicting the spiked samples. The application of hydrogel-based surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in food, combined with machine learning for regression prediction in data processing, collectively augmented the potential of SERS technology in the field of food analysis.

Quantitative analysis of cephalexin in solid dosage form by Raman spectroscopy and chemometric tools

Objective To use Raman Spectroscopy for qualitative and quantitative evaluation of pharmaceutical formulations of active pharmaceutical ingredient (API) of Cephalexin. Significance Raman Spectroscopy is a noninvasive, nondestructive, reliable and rapid detection technique used for various pharmaceutical drugs quantification. The present study explores the potential of Raman Spectroscopy for quantitative analysis of pharmaceutical drugs. Method For qualitative and quantitative analysis of Cephalexin API, various standard samples containing less and more concentration of API than commercial tablet was prepared. To study spectral differences, the mean plot of all the samples was prepared. For qualitative analysis, Principal Component Analysis (PCA) and for quantitative analysis Partial Least Square Regression analysis (PLSR) was used. Both of these are Multivariate data analysis techniques and give reliable results as published in previous literature. Results PCA model distinguished all the Raman Spectral data related to the various Cephalexin solid dosage formulations whereas the PLSR model was used to calculate the concentration of different unknown formulations. For the PLSR model, RMSEC and RMSEP were determined to be 3.3953 and 3.8972, respectively. The prediction efficiency of this built PLSR model was found to be very good with a goodness of the model value (R2) of 0.98. The PLSR model also predicted the concentrations of Cephalexin formulations in the blind or unknown sample. Conclusion These findings demonstrate that the Raman spectroscopy coupled to PLSR analysis could be regarded as a fast and effectively reliable tool for quantitative analysis of pharmaceutical drugs.

Advance application of Raman spectroscopy for quantitative analysis of noncrystalline components in thin films of poly(ε‐caprolactone)/poly(butadiene) blends

AbstractA quantitative analysis method for the distribution of noncrystalline poly(butadiene) component in poly(ε‐caprolactone)/poly(butadiene) (PCL/PB) binary blends have been analyzed by advance application of Raman spectroscopy, optical microscopy, and differential scanning calorimetry (DSC) techniques. Thin films of different compositions of PCL/PB binary blends were prepared from solution and isothermally crystallized at a certain temperature. After calibration with real data, quantitative analyses by Raman spectroscopy revealed the amorphous PB are trapped inside the PCL crystals. Polarized optical microscopy and real time atomic force microscopy were used to collect data for the crystal morphology and crystal growth rate. For pure PCL crystals, a morphology of truncated lozenge shape was observed, independent of crystallization temperature and regardless of the blends compositions. For the pure PCL and their blends, almost unique crystal growth rate was found. The miscibility behaviors using DSC were drawn through melting point depression method. The Hoffman‐Weeks extrapolations of the blends were found to be linear and identical with those of the neat PCL. The interaction parameter for the blends indicating that the PCL and PB blends have no intermolecular interaction, confirming the blends are immiscible. Despite the immiscibility of the blend, the PCL crystals do not bend during the growth process and do not reduce the growth rate as they do for miscible blend systems.

A Comparative Approach to Screen the Capability of Raman and Infrared (Mid- and Near-) Spectroscopy for Quantification of Low-Active Pharmaceutical Ingredient Content Solid Dosage Forms: The Case of Alprazolam.

Content uniformity is a critical attribute for potent and low-dosage formulations of active pharmaceutical ingredient (API) that, in addition to the formulation parameters, plays pivotal role during pharmaceutical development and production. However, when API content is low, implementing a vibrational spectroscopic analytical tool to monitor the content and blend uniformity remains a challenging task. The aim of this study was to showcase the potentials of mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy for quantitative analysis of alprazolam (ALZ) in a low-content powder blends with lactose, which is used as a common diluent for tablets produced by direct compression. The offered approach might be further scaled up and exploited for potential application in the process analytical technology (PAT). Partial least square and orthogonal PLS (OPLS) methodologies were employed to build the calibration models from raw and processed spectral data (standard normal variate, first and second derivatives). The models were further compared regarding their main statistical indicators: correlation coefficients, predictivity, root mean square error of estimation (RMSEE), and root mean square error of cross-validation (RMSEEcv). All statistical models presented high regression and predictivity coefficients. The RMSEEcv for the optimal models was 1.118, 0.08, and 0.059% for MIR, NIR, and Raman spectroscopy, respectively. The scarce information content extracted from the ALZ NIR spectra and the major band overlapping with those from lactose monohydrate was the main culprit of poor accuracy in the NIR model, whereas the subsampling instrumental setup (resulting in a non-representative spectral acquisition of the sample) was regarded as a main limitation for the MIR-based calibration model. The OPLS models of the Raman spectra of the powder blends manifested favorable statistical indicators for the accuracy of the calibration model, probably due to the distinctive ALZ Raman pattern resulting in the largest number of predictive spectral points that were used for the mathematical modeling. Furthermore, the Raman scattering calibration model was optimized in narrower scanning range (1700-700 cm-1) and its prediction power was evaluated (root mean square error of prediction, RMSEP = 0.03%). Thus, the Raman spectroscopy presented the most favorable statistical indicators in this comparative study and therefore should be further considered as a PAT for the quantitative determination of ALZ in low-content powder blends.

Raman Spectroscopy for Quantitative Analysis in the Pharmaceutical Industry.

Raman spectroscopy is a very promising technique increasingly used in the pharmaceutical industry. Due to its development and improved instrumental versatility achieved over recent decades and through the application of chemometric methods, this technique has become highly precise and sensitive for the quantification of drug substances. Thus, it has become fundamental in identifying critical variables and their clinical relevance in the development of new drugs. In process monitoring, it has been used to highlight in-line real-time analysis, and it has been used more commonly since 2004 when the Food and Drug Administration (FDA) launched Process Analytical Technology (PAT), integrated with the concepts of Pharmaceutical Current Good Manufacturing Practices (CGMPs) for the 21st Century. The present review presents advances in the application of this tool in the development of pharmaceutical products and processes in the last six years.

Raman Spectroscopy of Novel UHMW Polyethylene-Based Nanocomposites with Nanographite and Nanoclay

We analyze the Raman spectra of nanocomposites based on ultrahigh-molecular-weight polyethylene with nanoclay, thermoexpanded graphite, and reduced graphite oxide fillers. We discuss the potential of Raman spectroscopy for quantitative analysis of the nanocomposite structure, the influence of the fillers on the phase and conformation compositions of the polymer matrix, as well as for the monitoring of dispersion of the nanographite fillers in the nanocomposites.

Raman Spectroscopy for Quantitative Analysis of Point Defects and Defect Clusters in Irradiated Graphite

We report the development of Raman spectroscopy as a powerful tool for quantitative analysis of point defect and defect clusters in irradiated graphite. Highly oriented pyrolytic graphite (HOPG) was irradiated by 25 keV He+ and 20 keV D+ ions. Raman spectroscopy and transmission electron microscopy revealed a transformation of irradiated graphite into amorphous state. Annealing experiment indicated a close relation between Raman intensity ratio and vacancy concentration. The change of Raman spectra under irradiation was empirically analyzed by “disordered-region model,” which assumes the transformation from vacancy-contained region to disordered region. The model well explains the change of Raman spectra and predicts the critical dose of amorphization, but the nature of the disordered region is unclear. Then, we advanced the model into “dislocation accumulation model,” assigning the disordered region to dislocation dipole. Dislocation accumulation model can simulate the irradiation time dependencies of Raman intensity ratio and the c-axis expansion under irradiation, giving a relation between the absolute concentration of vacancy and Raman intensity ratio, suggesting an existence of the barrier on the mutual annihilation of vacancy and interstitial.

Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry

Discrete wavelet transform (DWT) provides a well-established means for spectral denoising and baseline elimination to enhance resolution and improve the performance of calibration and classification models. However, the limitation of a fixed filter bank can prevent the optimal application of conventional DWT for the multiresolution analysis of spectra of arbitrarily varying noise and background. This paper presents a novel methodology based on an improved, second-generation adaptive wavelet transform (AWT) algorithm. This AWT methodology uses a spectrally adapted lifting scheme to generate an infinite basis of wavelet filters from a single conventional wavelet, and then finds the optimal one. Such pretreatment combined with a multivariate calibration approach such as partial least squares can greatly enhance the utility of Raman spectroscopy for quantitative analysis. The present work demonstrates this methodology using two dispersive Raman spectral data sets, incorporating lactic acid and melamine in pure water and in milk solutions. The results indicate that AWT can separate spectral background and noise from signals of interest more efficiently than conventional DWT, thus improving the effectiveness of Raman spectroscopy for quantitative analysis and classification.

Raman spectroscopy for quantitative analysis of pharmaceutical solids

Raman spectroscopy is experiencing a surge in interest in solid-state pharmaceutical applications. It is rapid, non-destructive, no sample preparation is required and measurements can be made in aqueous environments. It can be used for not only qualitative, but also quantitative, analysis. In this paper, the use of Raman spectroscopy for quantitative analysis of pharmaceutical solids is reviewed. The technique has been used for chemical and physical form analysis. Physical form analysis has involved quantification of polymorphism, hydrates, the amorphous form and, recently, protein conformation. Initially, simple powder systems were quantified, although this has since extended to complex pharmaceutical formulations, including tablets, capsules, microspheres and suspensions. Formulations have also been analysed through packaging. The characteristics of the technique make it ideal for process monitoring and it has been used to quantify changes in-situ during processes such as wet granulation and batch crystallisation. The theoretical basis of quantitative Raman spectroscopy, common data analysis approaches, including multivariate analysis, and sources of error in quantitative analysis are also discussed.

Determination of Phenols in Water Using Raman Spectroscopy

The potential of Raman spectroscopy for quantitative analysis of phenols in water solution has been investigated. A group of six compounds—phenol, o-chlorophenol, 2, 4-dichlorophenol, 2, 4, 6-trichlorophenol, 2-chloro-S-methylphenol and 2-chloro-4-nitrophenol—was studied with the use of the 514.5, 488.0, and 457.9 nanometer lines of an argon ion laser. Attention was given to the effects of source intensity, optical alignment, and background fluctuations on quantitative results. It was found that the use of an internal standard with each measurement made a significant improvement in the accuracy and precision of results. Two methods of quantitation, peak area measurement and cross-correlation, were used. Results were somewhat better for cross-correlation, presumably because of more effective exclusion of background interference. Limits of detection were calculated based upon the slope and the standard deviation of the intercept of the standard curve. These varied from the range of 100 ppm to 0.3 ppm, depending on the compound. The most important factor controlling sensitivity is occurrence of resonance enhancement.

A kinetic study of crosslinking vinyl polymerization by laser Raman spectroscopy

Kinetic studies of crosslinking vinyl polymerizations are usually only applicable to the initial stages of the reaction, prior to the gel point, due to the subsequent insolubility of the polymer and the great increase in viscosity. We have demonstrated the suitability of laser Raman spectroscopy for quantitative analysis over the entire conversion range in an investigation of the polymerization of diethylene glycol bis (allyl carbonate) in bulk, with benzoyl peroxide initiation, between 70 and 85°C. The rate of consumption of vinyl bonds could be approximated by first-order kinetics up to ca. 80% depletion. Lower rates were observed at higher conversions.