Quantification and handling of nonlinearity in Raman micro-spectrometry of pharmaceuticals (original) (raw)
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Journal of Pharmaceutical and Biomedical Analysis, 2012
In this study, pharmaceutical grade sorbitol was used as a model system for comparison of Raman based library spectral correlation methods with more sophisticated methods of chemometric data analysis. Both crystallizing sorbitol (CS) and non-crystallizing sorbitol (NCS) from several manufacturers were examined. The Raman spectrum of each sample was collected and identified by correlation with a spectral library that included the CS spectrum but not the NCS spectrum. The average hit quality index (HQI) for the measured NCS spectra and the library CS spectrum was 0.966 whereas the average HQI for the measured CS spectra was 0.991. Both HQIs exceeded the 0.950 threshold that is commonly used for material verification. To enhance the discrimination between CS and NCS, a CS/NCS classification model was constructed using soft independent modeling of class analogies (SIMCA). SIMCA was able to positively identify CS and NCS solutions with no mis-classifications. When CS was adulterated with low levels (0-5%) of ethylene glycol (EG) and diethylene glycol (DEG), the HQI values of the measured spectra and the CS library spectrum were still above 0.950. When the CS SIMCA model was applied to adulterated CS spectra, it determined that CS samples with adulterant levels as low as 2% were outside of the CS class. A quantitative PLS model was also applied to EG adulterated CS and resulted in a detection limit of 0.9% for EG. The results obtained from these studies highlight the importance of selecting an appropriate data analysis process for the detection of low level adulterants in pharmaceutical raw materials using Raman spectroscopic screening methods.
Drug stability analysis by Raman spectroscopy
Pharmaceutics, 2014
Pharmaceutical drugs are available to astronauts to help them overcome the deleterious effects of weightlessness, sickness and injuries. Unfortunately, recent studies have shown that some of the drugs currently used may degrade more rapidly in space, losing their potency before their expiration dates. To complicate matters, the degradation products of some drugs can be toxic. Here, we present a preliminary investigation of the ability of Raman spectroscopy to quantify mixtures of four drugs; acetaminophen, azithromycin, epinephrine, and lidocaine, with their primary degradation products. The Raman spectra for the mixtures were replicated by adding the pure spectra of the drug and its degradant to determine the relative percent contributions using classical least squares. This multivariate approach allowed determining concentrations in ~10 min with a limit of detection of ~4% of the degradant. These results suggest that a Raman analyzer could be used to assess drug potency, nondestru...
Journal of Pharmaceutical and Biomedical Analysis, 2019
Highlight Raman spectral variations of the pharmaceutical materials were observed due to the presence of water. Unwanted spectral baseline change due to moisture variation caused spectral inconsistency across calibration and test samples which degraded quantitative model performance. Including moisture variations into the calibration set significantly improved Raman spectroscopic model performance. performance for API. The work demonstrated that accounting for moisture variation during method development reduced the prediction error of the multivariate prediction model.
The Analyst, 2010
We present the first quantitative study of polymorphic content in a model pharmaceutical formulation using transmission Raman spectroscopy (TRS), and compare the results obtained with those from traditional backscattering geometry. The transmission method is shown to provide a true bulk measurement of the composition, being unaffected by systematic or stochastic sub-sampling issues that can plague traditional backscattering geometries. The accuracy of the quantification of the polymorphs using TRS was shown to surpass considerably that achieved using conventional backscattering mode. For a model-free fit, the TRS method yielded R 2 of 0.996 compared to the backscattering value of 0.802; for a partial least squares fit with a single component the TRS method accounted for 98.09% of the variance in the data and yielded an R 2 of 0.985, compared to 89.65% of the variance and R 2 of 0.804 for the backscattering method.
Multivariate data analysis for Raman imaging of a model pharmaceutical tablet
Analytica Chimica Acta, 2005
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 m × 400 m 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.
Applied Spectroscopy, 2006
Identification and quantification of molecular species are central applications of molecular spectroscopy. In complex multicomponent systems like tissue samples, linear parametric models are often used to estimate the relative concentrations of the biochemical components of the sample. In situations where not all of the components of the sample are known or modeled, such parametric models can suffer from omitted variable bias and result in skewed estimates of component concentrations. We propose a semi-parametric approach that tries to avoid this omitted variable bias by effectively including unknown covariates as a non-parametric term in the regression equation. Constituent concentrations estimated with such partial linear models should outperform strict parametric linear models when the user has limited information on the composition of a multi-constituent system.
Raman spectroscopy for quantitative analysis of pharmaceutical solids
Journal of Pharmacy and Pharmacology, 2007
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 gran...