Noninvasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for noninvasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform noninvasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.     

Determination of the polymorphic forms of bicifadine hydrochloride by differential scanning calorimetry-thermogravimetric analysis, X-ray powder diffraction, attenuated total reflectance-infrared spectroscopy, and attenuated total reflectance-near-infrared spectroscopy

The pharmaceutical compound bicifadine hydrochloride, which has been found to crystallize in two polymorphic forms, has been characterized by thermal analysis, X-ray powder diffraction (XRPD), infrared (IR) spectroscopy, and near-infrared (NIR) spectroscopy. A series of 22 sample mixtures of polymorph 1 and polymorph 2 were prepared and calibration models for the quantitation of these binary mixtures have been developed for each of the XRPD, attenuated total reflectance (ATR)-IR, and ATR-NIR analytical techniques. The quantitative results were obtained using a partial least squares (PLS) algorithm, which predicted the concentration of polymorph 1 from the XRPD spectra with a root mean standard error of prediction (RMSEP) of 4.4%, from the IR spectra with a RMSEP of 3.8%, and from the NIR spectra with a RMSEP of 1.4%. The studies indicate that when analyses are carried out on equivalent sets of spectra, NIR spectroscopy offers significant advantages in quantitative accuracy as a tool for the determination of polymorphs in the solid state and is also more convenient to use than both the ATR-IR and XRPD methods. Density functional theory (DFT) B3LYP calculations and IR spectral simulation have been used to determine the nature of the vibrational modes that are the most sensitive in the analysis.     

Determination of glucose in human aqueous humor using Raman spectroscopy and designed-solution calibration

Glucose concentrations of in vitro human aqueous humor (HAH) samples from cataract patients were determined using 785 nm Raman spectra and partial least squares (PLS) calibration. PLS models were created from spectra of prepared calibration solutions rather than aqueous humor samples. Spectra were obtained with an excitation energy (100 mW for 150 s), which was higher than can be applied in vivo, to decrease the models' contribution to prediction uncertainty. The solutions contained experimentally designed levels of glucose, bicarbonate, lactate, urea, and ascorbate. Multiplicative signal correction of spectra helped compensate for the +/-20% drift in laser power observed at the sample over six noncontiguous days of data collection. Seventeen HAH samples containing 38-775 mg/dL of glucose exhibited a root-mean-square error (RMSEP) of 22 mg/dL, coefficient of determination (r(2)) of 0.989, and bias of 6 mg/dL when predicted from lower energy (30 s) spectra collected contemporaneously with fifty calibration spectra. Similar results were obtained even when spectral data were gathered separately for human aqueous humor samples and calibration samples: 10 HAH samples, calibrated on 25 solutions measured 3.6 weeks earlier, exhibited an RMSEP of 23 mg/dL, r(2) of 0.992, and bias of 9 mg/dL. The results demonstrate progress toward the determination of glucose levels in patient-derived aqueous humor using laboratory-derived "artificial aqueous humor" calibration solutions.     

Characterization and quantitation of aprepitant drug substance polymorphs by attenuated total reflectance fourier transform infrared spectroscopy

In this study, we report the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FT-IR) for the identification and quantitation of two polymorphs of Aprepitant, a substance P antagonist for chemotherapy-induced emesis. Mixtures of the polymorph pair were prepared by weight and ATR-FT-IR spectra of the powdered samples were obtained over the wavelength range of 700-1500 cm(-1). Significant spectral differences between the two polymorphs at 1140 cm(-1) show that ATR-FT-IR can provide definitive identification of the polymorphs. To investigate the feasibility of ATR-FT-IR for quantitation of polymorphic forms of Aprepitant, a calibration plot was constructed with known mixtures of the two polymorphs by plotting the peak ratio of the second derivative of absorbance spectra against the weight percent of form II in the polymorphic mixture. Using this novel approach, 3 wt % of one crystal form could be detected in mixtures of the two polymorphs. The accuracy of ATR-FT-IR in determining polymorph purity of the drug substance was tested by comparing the results with those obtained by X-ray powder diffractometry (XRPD). Indeed, polymorphic purity results obtained by ATR-FT-IR were found to be in good agreement with the predictions made by XRPD and compared favorably with actual values in the known mixtures. The present study clearly demonstrates the potential of ATR-FT-IR as a quick, easy, and inexpensive alternative to XRPD for the determination of polymorphic identity and purity of solid drug substances. The technique is ideally suited for polymorph analysis, because it is precise, accurate, and requires minimal sample preparation.     

Evaluation of infrared-reflection absorption spectroscopy measurement and locally weighted partial least-squares for rapid analysis of residual drug substances in cleaning processes

The usefulness of infrared-reflection absorption spectroscopy (IR-RAS) for the rapid measurement of residual drug substances without sampling was evaluated. In order to realize the highly accurate rapid measurement, locally weighted partial least-squares (LW-PLS) with a new weighting technique was developed. LW-PLS is an adaptive method that builds a calibration model on demand by using a database whenever prediction is required. By adding more weight to samples closer to a query, LW-PLS can achieve higher prediction accuracy than PLS. In this study, a new weighting technique is proposed to further improve the prediction accuracy of LW-PLS. The root-mean-square error of prediction (RMSEP) of the IR-RAS spectra analyzed by LW-PLS with the new weighting technique was compared with that analyzed by PLS and locally weighted regression (LWR). The RMSEP of LW-PLS with the proposed weighting technique was about 36% and 14% smaller than that of PLS and LWR, respectively, when ibuprofen was a residual drug substance. Similarly, LW-PLS with the weighting technique was about 39% and 24% better than PLS and LWR in RMSEP, respectively, when magnesium stearate was a residual excipient. The combination of IR-RAS and LW-PLS with the proposed weighting technique is a very useful rapid measurement technique of the residual drug substances.     

Quantitative whole spectrum analysis with MALDI-TOF MS, Part II: Determining the concentration of milk in mixtures

The work summarised in this paper presents the second part of a two-paper series on quantitative whole spectrum analysis with MALDI-TOF MS on skimmed milk. In Part I experiments were carried out to search for optimal sample preparation and instrumental settings in terms of signal-to-noise ratios and repeatability. The results were utilised in the present study when trying to predict concentrations of cow, goat and ewe milk in mixed milk samples. Partial least squares regression was combined with suitable pre- and post-processing of spectra and concentration responses. A plotting method was used where predictions are visualised as a mixture design. The objective was to show that MALDI-TOF MS had potential for being used in quantitative analysis without involving peak comparison or other types of expert guided research. Predictions of a validation data set gave promising results with the best RMSEP values ranging from 5.4% (w/w) to 6.5% (w/w), for the different milk types used, and corresponding R²_pred values ranging from 94.5% to 96.2%. This indicates that MALDI-TOF is sufficiently accurate and repeatable to be used in practical application for quantitative analysis. Three variable selection strategies based on visual inspections and regression modelling were also evaluated. These were all outperformed, with regard to prediction error, by the use of whole spectra and multivariate regression. The results indicate that multivariate regression on whole spectra can be far more effective than using a few selected variables.     

Nondestructive direct determination of heroin in seized illicit street drugs by diffuse reflectance near-infrared spectroscopy

A new method has been developed for the fast and nondestructive direct determination of heroin in seized street illicit drugs using partial least-squares regression analysis of diffuse reflectance near-infrared spectra. Data were obtained from untreated samples placed in standard glass chromatography vials. A heterogeneous population of 31 samples, previously analyzed by a reference method, was employed to build the calibration model and to have a separated validation set. Based on the use of zero-order data for a calibration set of 21 samples, after standard normal variate and quadratic linear removed baseline correction (detrending), in the wavelength range from 1111 to 1647 nm, 8 PLS factors were enough to obtain a root-mean-square error of prediction of 1.3% w/w, with a quality coefficient of 10% for the estimation of the accuracy error in the prediction of heroin concentration in unknown samples and a residual predictive deviation of 5.4.     

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path Fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

The use of multiple calibration sets in partial least squares (PLS) regression was proposed to improve the quantitative determination of NH(3) over wide concentration ranges from open-path Fourier transform infrared (OP/FT-IR) spectra. The spectra were measured near animal farms, where the path-integrated concentration of NH(3) can fluctuate from nearly zero to as high as approximately 1000 ppm-m. PLS regression with a single calibration set did not cover such a large concentration range effectively, and the quantitative accuracy was degraded due to the nonlinear relationship between concentration and absorbance for spectra measured at low resolution (1 cm(-1) and poorer.) In PLS regression with multiple calibration sets, each calibration set covers a part of the entire concentration range, which significantly decreases the serious nonlinearity problem in PLS regression occurring when only a single calibration set is used. The relative error was reduced from approximately 6% to below 2%, and the best results were obtained with four calibration sets, each covering one quarter of the entire concentration range. It was also found that it was possible to build the multiple calibration sets easily and efficiently without extra measurements.     

Multivariate calibration and instrument standardization for the rapid detection of diethylene glycol in glycerin by Raman spectroscopy

The transfer of a multivariate calibration model for quantitative determination of diethylene glycol (DEG) contaminant in pharmaceutical-grade glycerin between five portable Raman spectrometers was accomplished using piecewise direct standardization (PDS). The calibration set was developed using a multi-range ternary mixture design with successively reduced impurity concentration ranges. It was found that optimal selection of calibration transfer standards using the Kennard-Stone algorithm also required application of the algorithm to multiple successively reduced impurity concentration ranges. Partial least squares (PLS) calibration models were developed using the calibration set measured independently on each of the five spectrometers. The performance of the models was evaluated based on the root mean square error of prediction (RMSEP), calculated using independent validation samples. An F-test showed that no statistical differences in the variances were observed between models developed on different instruments. Direct cross-instrument prediction without standardization was performed between a single primary instrument and each of the four secondary instruments to evaluate the robustness of the primary instrument calibration model. Significant increases in the RMSEP values for the secondary instruments were observed due to instrument variability. Application of piecewise direct standardization using the optimal calibration transfer subset resulted in the lowest values of RMSEP for the secondary instruments. Using the optimal calibration transfer subset, an optimized calibration model was developed using a subset of the original calibration set, resulting in a DEG detection limit of 0.32% across all five instruments.     

Analysis of the effect of particle size on polymorphic quantitation by Raman spectroscopy

Raman spectroscopy has been widely used to monitor various aspects of the crystallization process. Although it has long been known that particle size can influence Raman signal, relatively little research has been conducted in this area, in particular for mixtures of organic materials. The aim of this study was to investigate the effect of particle size on quantification of polymorphic mixtures. Several sets of calibration samples containing different particle size fractions were prepared and Raman spectra were collected with different probes. Calibration models were built using both univariate and multivariate analysis. It was found that, for a single component system, Raman intensity decreased with increasing particle size. For mixtures, calibration models generated from the same particle size distribution as the sample yielded relatively good predictions of the actual sample composition. However, if the particle sizes of the calibration and unknown samples were different, prediction errors resulted. For extreme differences in particle sizes, prediction errors of up to 20% were observed. Prediction errors could be minimized by changing the sampling optics employed.     

Blank augmentation protocol for improving the robustness of multivariate calibrations

An updating procedure is described for improving the robustness of multivariate calibration models based on near-infrared spectroscopy. Employing a single blank sample containing no analyte, repeated spectra are acquired during the instrumental warm-up period. These spectra are used to capture the instrumental profile on the analysis day in a way that can be used to update a previously computed calibration model. By augmenting the original spectra of the calibration samples with a group of spectra collected from the blank sample, an updated model can be computed that incorporates any instrumental drift that has occurred. This protocol is evaluated in the context of an analysis of physiological levels of glucose in a simulated biological matrix designed to mimic blood plasma. Employing data of calibration and prediction samples acquired over approximately six months, procedures are studied for implementing the algorithm in conjunction with calibration models based on partial least squares (PLS) regression. Over the range of 1-20 mM glucose, the final algorithm achieves a standard error of prediction (SEP) of 0.79 mM when the augmented PLS model is applied to data collected 176 days after the collection of the calibration spectra. Without updating, the original PLS model produces a seriously degraded SEP of 13.4 mM.     

Indirect spectral hard modeling for the analysis of reactive and interacting mixtures

We present an indirect hard modeling (IHM) approach for the quantitative analysis of reactive multicomponent mixtures with intermolecular interaction. It can be used when it is not possible to obtain calibration data in the composition region of interest. The goal of this work, specifically, is to analyze reactive systems, although the validation of the method is done with nonreactive systems. Compared to conventional hard modeling, the new approach reduces the manual work required for modeling and renders unnecessary the assignment of bands in mixture spectra to individual components. It is based on parametric models of the pure component spectra that are made just flexible enough to fit the spectra of the unknown mixtures, and it only requires small calibration data sets that may lie in different regions of the composition space. The application to infrared (IR) and Raman spectra of multicomponent systems is discussed.     

Noninvasive method for monitoring ethanol in fermentation processes using fiber-optic near-infrared spectroscopy

Short-wavelength near-infrared (SW-near-IR) spectroscopy (700-1100 nm) is used for the determination of ethanol during the time course of a fermentation. Measurements are performed noninvasively by means of a photodiode array spectrometer equipped with a fiber-optic probe placed on the outside of the glass-wall fermentation vessel. Pure ethanol/water and ethanol/yeast/water mixtures are studied to establish the spectral features that characterize ethanol and to show that determination of ethanol is independent of the yeast concentration. Analysis of the second-derivative data is accomplished with multilinear regression (MLR). The standard error of prediction (SEP) of ethanol in ethanol/water solutions is approximately 0.2% over a range of 0-15%; the SEP of ethanol in ethanol/yeast/water solutions is 0.27% (w/w). Results from the mixture experiments are then applied to actual yeast fermentations of glucose to ethanol. By use of a gas chromatographic method for validation, a good correlation is found between the intensity of backscattered light at 905 nm and the actual ethanol. Additional experiments show that a calibration model created for one fermentation can be used to predict ethanol production during the time course of others with a prediction error of 0.4%.     

单基发射药生产过程中驱水棉水分含量的快速检测方法?

由于驱水棉的水分含量对单基发射药成型工艺有着较大的影响,采用近红外光谱分析技术对驱水棉水分含量进行快速检测。通过对比分析纯水、硝化棉(NC)、乙醇以及驱水棉的光谱图,确定了水分含量检测建模区域为5 015.6~5 224.8 cm~(-1)和6 525.9~7 008.7 cm~(-1)。比较不同光谱预处理方法,发现标准正态变量校正(SNV)、一阶导数和平滑的组合方法对驱水棉的光谱进行预处理效果最好。采用偏最小二乘法对水分含量建立定量校正模型,并对预测集样本进行预测和对模型进行重复性验证。试验结果表明:校正集和交互验证相关系数R2分别为0.995 4和0.994 4,预测均方根误差RMSEP值为0.039 0,对预测集的药料样本预测的平均相对误差为0.997%,模型的重复性良好,检测时间小于20 s,能满足单基发射药连续自动化生产工艺的要求。     

Attenuated total reflection infrared difference spectroscopy (ATR-IRDS) for quantitative reaction monitoring

Monitoring of chemical reactors is key to optimizing yield and efficiency of chemical transformation processes. Aside from tracking pressure and temperature, the measurement of the chemical composition is essential in this context. We present an infrared difference spectroscopy approach for determining the reactant (cyclooctene) and product (cyclooctane) concentrations during a catalytic hydrogenation reaction in the solvent cyclohexane, which is present in large excess. Subtracting the spectrum of the pure solvent from the reactor mixture spectra yields infrared (IR) spectra, which can ultimately be evaluated using a curve-fitting procedure based on spectral soft modeling. An important feature of our evaluation approach is that the calibration only requires recording the pure component spectra of the reactants, products, and solvent. Hence, no time-consuming preparation of mixtures for calibration is necessary. The IR concentration results are in good agreement with gas chromatography measurements.     

Prediction of Drug Content and Hardness of Intact Tablets Using Artificial Neural Network and Near-Infrared Spectroscopy

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel® PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer™ was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer™ (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision® software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2%w/w), prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.     

On-line fermentation monitoring by mid-infrared spectroscopy

A new method for on-line monitoring of fermentations using mid-infrared (MIR) spectroscopy has been developed. The method has been used to predict the concentrations of glucose and ethanol during a baker's yeast fermentations. A completely automated flow system was employed as an interface between the bioprocess under study and the Fourier transform infrared (FT-IR) spectrometer, which was equipped with a flow cell housing a diamond attenuated total reflection (ATR) element. By using the automated flow system, experimental problems related to adherence of CO(2) bubbles to the ATR surface, as well as formation of biofilms on the ATR surface, could be efficiently eliminated. Gas bubbles were removed during sampling, and by using rinsing steps any biofilm could be removed from the ATR surface. In this way, constant measuring conditions could be guaranteed throughout prolonged fermentation times (approximately 8 h). As a reference method, high-performance liquid chromatography (HPLC) with refractive index detection was used. The recorded data from different fermentations were modeled by partial least-squares (PLS) regression comparing two different strategies for the calibration. On the one hand, calibration sets were constructed from spectra recorded from either synthetic standards or from samples drawn during fermentation. On the other hand, spectra from fermentation samples and synthetic standards were combined to form a calibration set. Differences in the kinetics of the studied fermentation processes used for calibration and prediction, as well as the precision of the HPLC reference method, were identified as the main chemometric sources of error. The optimal PLS regression method was obtained using the mixed calibration set of samples from fermentations and synthetic standards. The root mean square errors of prediction in this case were 0.267 and 0.336 g/L for glucose and ethanol concentration, respectively.     

Quantitative thin-layer chromatography/mass spectrometry analysis of caffeine using a surface sampling probe electrospray ionization tandem mass spectrometry system

Quantitative determination of caffeine on reversed-phase C8 thin-layer chromatography plates using a surface sampling electrospray ionization system with tandem mass spectrometry detection is reported. The thin-layer chromatography/electrospray tandem mass spectrometry method employed a deuterium-labeled caffeine internal standard and selected reaction monitoring detection. Up to nine parallel caffeine bands on a single plate were sampled in a single surface scanning experiment requiring 35 min at a surface scan rate of 44 mum/s. A reversed-phase HPLC/UV caffeine assay was developed in parallel to assess the mass spectrometry method performance. Limits of detection for the HPLC/UV and thin-layer chromatography/electrospray tandem mass spectrometry methods determined from the calibration curve statistics were 0.20 ng injected (0.50 muL) and 1.0 ng spotted on the plate, respectively. Spike recoveries with standards and real samples ranged between 97 and 106% for both methods. The caffeine content of three diet soft drinks (Diet Coke, Diet Cherry Coke, Diet Pepsi) and three diet sport drinks (Diet Turbo Tea, Speed Stack Grape, Speed Stack Fruit Punch) was measured. The HPLC/UV and mass spectrometry determinations were in general agreement, and these values were consistent with the quoted values for two of the three diet colas. In the case of Diet Cherry Coke and the diet sports drinks, the determined caffeine amounts using both methods were consistently higher (by approximately 8% or more) than the literature values.     

Temperature-independent near-infrared analysis of lysozyme aqueous solutions

Digital Fourier filtering is used to produce a temperature-insensitive univariate calibration model for measuring lysozyme in aqueous solutions. Absorbance spectra over the 5000-4000 cm-1 spectral range are collected for lysozyme standards maintained at 14 degrees C. These spectra are used to compute the calibration model while a set of spectra collected at temperatures ranging from 4 to 24 degrees C are used to validate the accuracy of this model. The root-mean-square error of prediction (RMSEP) is 0.279 mg/mL over a tested lysozyme concentration range of 0.036-51.6 mg/mL. The detection limit is 0.68 mg/mL. In addition, multivariate calibration models based on partial least-squares regression (PLS) are evaluated and compared to the results from the univariate model. PLS outperforms the univariate model by providing a RMSEP of 0.090 mg/mL. Analysis of variance showed that both calibration methods effectively eliminate the adverse affects created by variations in solution temperature.