Application of quantitative chemometric analysis techniques to direct sampling mass spectrometry

This paper explores the use of direct sampling mass spectrometry coupled with multivariate chemometric analysis techniques for the analysis of sample mixtures containing analytes with similar mass spectra. Water samples containing varying mixtures of toluene, ethyl benzene, and cumene were analyzed by purge-and-trap/direct sampling mass spectrometry. Multivariate calibration models were built using partial least-squares regression (PLS), trilinear partial least-squares regression (tri-PLS), and parallel factor analysis (PARAFAC), with the latter two methods taking advantage of the differences in the temporal profiles of the analytes. The prediction errors for each model were compared to those obtained with simple univariate regression. Multivariate quantitative methods were found to be superior to univariate regression when a unique ion for quantitation could not be found. For prediction samples that contained unmodeled, interfering compounds, PARAFAC outperformed the other analysis methods. The uniqueness of the PARAFAC model allows for estimation of the mass spectra of the interfering compounds, which can be subsequently identified via visual inspection or a library search.     

New robust bilinear least squares method for the analysis of spectral-pH matrix data

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.     

PARAFAC2 and MCR-ALS quantification of Diltiazem antihypertensor based on a kinetic spectrophotometric methodology

A Diltiazem kinetic spectrophotometric UV–Vis method, based on a reaction of the Diltiazem with hidroxylamine and a ferric salt, was used for the quantification of Diltiazem in different pharmaceutical formulations. This method is based on the acquisition of three-way data structures [wavelength (nm) × time (s) × concentration (mg/L)] followed by chemometric analysis by an appropriate PARAFAC2 or MCR-ALS second-order calibration model. The results obtained are compared with those obtained by direct determination, at maximum wavelength, and by the United States Pharmacopeia (USP) standard chromatographic method. For all the pharmaceutical formulations analysed good quantification results were found with PARAFAC2 and MCR-ALS second-order calibration models. For bulk drug analysis, detection limits of 6 and 2 mg/L, and for pharmaceutical formulations analysis, an average detection limit of 41 and 39 mg/L were found, respectively with PARAFAC2 and MCR-ALS.     

A chemometric analysis for evaluation of early-stage cartilage degradation by infrared fiber-optic probe spectroscopy

In vivo identification of early-stage cartilage degradation could positively impact disease progression in osteoarthritis, but to date remains a challenge. The primary goal of this study was to develop an infrared fiber-optic probe (IFOP) chemometric method using partial least squares (PLS1) to objectively determine the degree of cartilage degradation. Arthritic human tibial plateaus (N = 61) were obtained during knee replacement surgery and analyzed by IFOP. IFOP data were collected from multiple regions of each specimen and the cartilage graded according to the Collins Visual Grading Scale of 0, 1, 2, or 3. These grades correspond to cartilage morphology that displayed normal, swelling or softening, superficially slight fibrillation, and deeper fibrillation or serious fibrillation, respectively. The model focused on detecting early cartilage degradation and therefore utilized data from grades 0, 1, and 2. The best PLS1 calibration utilized the spectral range 1733-984 cm(-1), and independent validation of the model utilizing 206 spectra to create a model and 105 independent test spectra resulted in a correlation between the predicted and actual Collins grade of R2 = 0.8228 with a standard error of prediction of 0.258 with a PLS1 rank of 15 PLS factors. A preliminary PLS1 calibration that utilized a cross-validation technique to investigate the possibility of correlation with histological tissue grade (33 spectra from 18 tissues) resulted in R2 = 0.8408 using only eight PLS factors, a very encouraging outcome. Thus, the groundwork for use of IFOP-based chemometric determination of early cartilage degradation has been established.     

Kinetic analysis of C.I. Acid Yellow 9 photooxidative decolorization by UV-visible and chemometrics

A kinetic study of the C.I. Acid Yellow 9 photooxidative decolorization process, using H(2)O(2) as oxidant, was carried out by chemometric analysis of the UV-visible data recorded during the process. The number of chemical species involved in the photooxidative decolorization process was established by singular value decomposition (SVD) and evolving factor analysis (EFA). Information about the different chemical species along the process was obtained from the spectral and concentration profiles recovered by soft multivariate curve resolution with alternating least squares (MCR-ALS). This information was complemented by mass spectrometry (MS) to postulate a reaction pathway. The dye photooxidative decolorization process involved consecutive and parallel reactions. The consecutive pathway consists of a first stage of dye oxidation followed by the rupture of the azo linkage to form smaller molecules that are degraded in a subsequent stage. The parallel reactions form products that are undetectable in the UV-visible spectra. Kinetic constants of the reactions postulated in the photooxidative process were retrieved by applying a hybrid hard and soft MCR-ALS resolution. All constants were similar for the consecutive stages and higher than those obtained for the parallel reactions.     

Possibilities of multivariate curve resolution and partial least squares in the resolution of coeluted peaks in liquid chromatography with electrochemical detection

Two different strategies are compared for the resolution of coeluted peaks in liquid chromatography with electrochemical detection. The first is voltammetric detection (VD) for the acquisition of currents as a function of potential and time with multivariate curve resolution by alternating least squares (MCR-ALS) for data analysis and quantification. The second is amperometric detection (AD), i.e. recording currents as a function of time for a fixed potential and calibration by partial least squares (PLS). Both approaches are used to analyse a model mixture of pyrocatechol, dopamine and epinephrine. The results show that for high analyte concentrations VD-MCR-ALS provides accurate quantification with minimal effort (a single injection of the sample and each standard). However, at lower concentrations the excessive proportion of noise and the predominance of the capacitive contribution decrease the performance of VD-MCR-ALS, thus making AD-PLS preferable as its greater accuracy counters the more time-consuming calibration, which involves the injection of a large number of mixtures of different composition.     

Comprehensive two-dimensional gas chromatography and chemometrics for the high-speed quantitative analysis of aromatic isomers in a jet fuel using the standard addition method and an objective retention time alignment algorithm

A high-speed quantitative analysis of aromatic isomers in a jet fuel sample is performed using comprehensive two-dimensional gas chromatography (GC x GC) and chemometrics. A GC x GC separation time of 2.8 min is achieved for three aromatic isomers in jet fuel, which is 5 times faster than a reference method in which a singlecolumn separation resolves two of the three isomers of interest. The high-speed GC x GC separation is more than 10 times faster than a recent GC x GC separation that fully resolves the three components of interest in gasoline. The high-speed GC x GC analysis of jet fuel is accomplished through short GC columns, high gas velocities, and partial chromatographic peak resolution followed by chemometric resolution of overlapped peaks. The standard addition method and an objective retention time alignment algorithm are used to correct for retention time variations prior to the chemometric data analysis. The standard addition method corrects for chemical matrix effects that cause analytes in complex samples to have peak shapes, widths, and retention times that differ considerably from those of calibration standards in pure solvents. The retention time alignment algorithm corrects for the relatively small retention time variations caused by fluctuating instrumental parameters such as flow rate and temperature. The use of data point interpolation in the retention time alignment algorithm results in a more accurate retention time correction then previously achieved. The generalized rank annihilation method (GRAM) is the chemometric technique used to resolve the overlapped GC x GC peaks. The correction of retention time variations allows for successful GRAM signal deconvolution. Using the retention time alignment algorithm, GRAM quantification accuracy and precision are improved by a factor of 4. The methodology used in this paper should be applicable to other comprehensive separation methods, such as two-dimensional liquid chromatography, liquid chromatography coupled with capillary electrophoresis, and liquid chromatography coupled with gas chromatography.     

Quantitative analysis of low-field NMR signals in the time domain

Two novel methods are described for direct quantitative analysis of NMR free induction decay (FID) signals. The methods use adaptations of the generalized rank annihilation method (GRAM) and the direct exponential curve resolution algorithm (DECRA). With FID-GRAM, the Hankel matrix of the sample signal is compared with that of a reference mixture to obtain quantitative data about the components. With FID-DECRA, a single-sample FID matrix is split into two matrices, allowing quantitative recovery of decay constants and the individual signals in the FID. Inaccurate results were obtained with FID-GRAM when there were differences between the frequency or transverse relaxation time of signals for the reference and test samples. This problem does not arise with FID-DECRA, because comparison with a reference signal is unnecessary. Application of FID-DECRA to 19F NMR data, which contained overlapping signals from three components, gave concentrations comparable to those derived from partial least squares (PLS) analysis of the Fourier transformed spectra. However, the main advantage of FID-DECRA was that accurate (<5% error) and precise (2.3% RSD) results were obtained using only one calibration sample, whereas with PLS, a training set of 10 standard mixtures was used to give comparable accuracy and precision.     

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.     

Combining self-modeling curve resolution methods and partial least squares to develop a quantitative reaction monitoring method with minimal reference data

An example of combining self-modeling curve resolution (SMCR) methods and partial least squares (PLS) to construct a quantitative model using minimal reference data is presented. The objective was to construct a quantitative calibration model to allow real-time in situ ultraviolet-attenuated total reflection (UV/ATR) measurements to determine the end-point during a chlorination reaction. Time restrictions for development combined with difficult reaction sampling conditions required the method to be developed using only a few key reference measurements. Utilizing evolving factor analysis (EFA) and the orthogonal projection approach (OPA), initial estimates of the concentration and spectral profiles for the intermediate and product were obtained. Further optimization by multivariate curve resolution-alternating least squares (MCR-ALS) led to refined estimates of the concentration profiles. A PLS2 model was then constructed using the calculated concentration profiles and the preprocessed UV spectra. Using a standard PLS model compatible with the spectrometer's standard process software facilitated real-time predictions for new batches. This method was applied to five 45 liter batches in a large-scale laboratory facility. The method successfully predicted the product concentration of batch 1 but exhibited larger prediction error for subsequent batches. The largest prediction error was attained during batch 3, for which a final concentration of 0.22 mole L(-1) was predicted, while the true measured value was 0.271 mole L(-1) (an error of 18.8%). However, the qualitative real-time profiles proved to be extremely useful as they allowed the end-point to be determined without sampling or performing off-line analysis. Furthermore, the concentration profile of the intermediate species, which could not be observed by the offline method, could also be observed in real-time and gave further confidence that the process was approaching the end-point. Another benefit of real-time reaction profiles was encountered during the manufacture when the formation of product in batch 3 appeared to be progressing slower than was observed in previous batches. This prompted a check of the batch temperature and it was found to be 10 degrees C lower than the required set-point. The temperature was corrected and the batch successfully reached completion in the expected time.     

Chemometric Modelling of Dissolution Rates of Griseofulvin From Solid Dispersions With Polymers

The quantitative relationship between the release rate of griseofulvin and the chemical and physical properties of a series of polymers, used for the preparations of solid dispersions, was investigated by the application of multiple regression analysis (MRA), partial least square analysis (PLS) and a new non linear chemometric procedure called CARSO (Computer Aided Response Surface Optimization).

It was confirmed that the degree of crystallinity of griseofulvin and the wettability of the powder samples are important in the dissolution mechanism and in the prediction of dissolution profiles of griseofulvin from these solid dispersions.     

On-line dissolution determination of Baicalin in solid dispersion based on near infrared spectroscopy and circulation dissolution system

Drug on-line circulation dissolution system with near infrared spectrophotometer for dissolution determination was reported in this paper and subsequently partial least squares (PLS) calibration model was established for concentration prediction of Baicalin in solid dispersion. When the main factor number in PLS calibration model was 6, the correlation coefficients of PLS calibration samples and prediction ones were all 0.9999 and the relative standard deviations were 0.69% and 1.10%, respectively, which showed good robustness and predictability. Combining drug circulation dissolution system with the PLS calibration model, dissolution of Baicalin in raw material drug and solid dispersion were obtained at different times. The results indicated that the dissolution property of Baicalin in solid dispersion (especially at the early time) had been significantly improved. The accumulated dissolution of Baicalin in the solid dispersion at 45 min reached nearly 40%, increasing by 15% compared with raw material drug (about 25%). The aforementioned PLS model associated with drug circulation dissolution system provided a simple, accurate and on-line support for dissolution determination of drug, especially at the early time of rapid dissolution.     

Precision trace gas analysis by FT-IR spectroscopy. 1. Simultaneous analysis of CO2, CH4, N2O, and CO in air

We report the development of a method of trace gas analysis based on 1-cm-1 resolution Fourier transform infrared (FT-IR) spectroscopy, deployable in both laboratory and field applications. Carbon dioxide, methane, nitrous oxide, and carbon monoxide may be analyzed simultaneously in a single air sample using this method. We have demonstrated that the method can provide analytical precision of the order of +/- 0.15 mumol mol-1 for CO2, +/- 0.9 nmol mol-1 for CH4, +/- 0.3 nmol mol-1 for N2O, and +/- 0.3 nmol mol-1 for CO, expressed as mole fractions in dry air. The analytical precision is in all cases competitive with or superior to that of the more usual methods of analysis for these trace gases, namely, nondispersive infrared spectroscopy for CO2 and gas chromatography-based techniques for CH4, N2O, and CO. The novel FT-IR method relies on calibration using synthetically calculated absorbance spectra and a chemometric multivariate calibration algorithm, classical least squares.     

Chemometric Modelling of Dissolution Rates of Griseofulvin From Solid Dispersions With Polymers

Abstract

The quantitative relationship between the release rate of griseofulvin and the chemical and physical properties of a series of polymers, used for the preparations of solid dispersions, was investigated by the application of multiple regression analysis (MRA), partial least square analysis (PLS) and a new non linear chemometric procedure called CARSO (Computer Aided Response Surface Optimization).

It was confirmed that the degree of crystallinity of griseofulvin and the wettability of the powder samples are important in the dissolution mechanism and in the prediction of dissolution profiles of griseofulvin from these solid dispersions.     

Combining PARAFAC analysis of HPLC-PDA profiles and structural characterization using HPLC-PDA-SPE-NMR-MS experiments: commercial preparations of St. John's Wort

Herbal preparations represent very complex mixtures, potentially containing multiple pharmacologically active entities. Methods for global characterization of the composition of such mixtures are therefore of pertinent interest. In this work, chemometric analysis of high-performance liquid chromatography with photodiode-array detection (HPLC-PDA) data from extracts of commercial preparations of Hypericum perforatum (St. John's wort) that originate from several continents is described. The spectral HPLC profiles were aligned in the elution mode using correlation optimized warping in order to remove peak misalignment caused by retention time shifts due to matrix effects. Furthermore, the warping was assisted by HPLC-PDA-SPE-NMR-MS (SPE = solid-phase extraction) experiments that yielded 1H NMR and 13C NMR data (from 1H-detected heteronuclear correlations), as well as ESI-MS and HRMS data, which enabled the identification of all major mixture constituents. The preprocessed HPLC-PDA data were subjected to parallel factor analysis (PARAFAC), a chemometric method that is a generalization of principal component analysis (PCA) to multi-way data arrays. PCA of the peak areas obtained from the PARAFAC analysis was used to facilitate sample comparison and allowed straightforward interpretation of constituents responsible for the differences in composition between individual preparations. In addition, loadings from the PARAFAC analysis provided pure elution profiles and pure UV spectra even for coeluting peaks, thus enabling the identification of chromatographically unresolved components. In conclusion, PARAFAC analysis of the readily accessible HPLC-PDA data provides the means for unsupervised and unbiased assessment of the composition of herbal preparations, of interest for assessment of their pharmacological activity and clinical efficacy.     

Chemometric determination of blood parameters using visible-near-infrared spectra

Visible and near-infrared (NIR) integrating sphere spectroscopy and chemometric multivariate linear regression were applied to determine hematocrit (HCT) and oxygen saturation (SatO2) of circulating human blood. Diffuse transmission, total transmission, and diffuse reflectance were measured and the partial least squares method (PLS) was used for calibration considering different wavelength ranges and selected optical measurement parameters. HCT and SatO2 were changed independently. Each parameter was adjusted to different levels and four designs with blood from different donors were carried out for the calibration with PLS. The calibration included the changes in hemolysis as well as inter-individual differences in cell dimensions and hemoglobin content. At a sample thickness of 0.1 mm the HCT and SatO2 were predicted with a root mean square error (PRMSE) of 1.4% and 2.5%, respectively, using transmission and reflectance spectra and the full Vis-NIR range. Using only diffuse NIR reflectance spectroscopy and a sample thickness of 1 mm, HCT and SatO2 could be predicted with a PRMSE of 1.9% and 2.8%, respectively. Prediction of hemolysis was also possible for one blood sample with a PRMSE of 0.8% and keeping HCT and SatO2 stable with a PRMSE of 0.03%.     

Calibration of multiplexed fiber-optic spectroscopy

Large-scale commercial bioprocesses that manufacture biopharmaceutical products such as monoclonal antibodies generally involve multiple bioreactors operated in parallel. Spectra recorded during in situ monitoring of multiple bioreactors by multiplexed fiber-optic spectroscopies contain not only spectral information of the chemical constituents but also contributions resulting from differences in the optical properties of the probes. Spectra with variations induced by probe differences cannot be efficiently modeled by the commonly used multivariate linear calibration models or effectively removed by popular empirical preprocessing methods. In this study, for the first time, a calibration model is proposed for the analysis of complex spectral data sets arising from multiplexed probes. In the proposed calibration model, the spectral variations introduced by probe differences are explicitly modeled by introducing a multiplicative parameter for each optical probe, and then their detrimental effects are effectively mitigated through a "dual calibration" strategy. The performance of the proposed multiplex calibration model has been tested on two multiplexed spectral data sets (i.e., MIR data of ternary mixtures and NIR data of bioprocesses). Experimental results suggest that the proposed calibration model can effectively mitigate the detrimental effects of probe differences and hence provide much more accurate predictions than commonly used multivariate linear calibration models (such as PLS) with and without empirical data preprocessing methods such as orthogonal signal correction, standard normal variate, or multiplicative signal correction.     

Applications of terahertz spectroscopy to pharmaceutical sciences

The application of terahertz pulsed spectroscopy within the US Food and Drug Administration's (FDA's) recent process analytical technology (PAT) initiative is considered. As a case study the potency levels in paracetamol (4-acetamidophenol) and aspirin (acetylsalicylic acid) test tablets have been recovered from the terahertz absorption spectra using a multivariate partial-least-squares (PLS) calibration model. Root-mean-square errors of cross-validation (RMSECVs) of 2.85% and 3.90% were obtained for paracetamol and aspirin, respectively. Information about other excipients can also be obtained; for example, using the strong lactose absorption lines in the tablets, RMSECVs of 3.65% and 4.30% could be recovered from the paracetamol and aspirin samples, respectively. As active ingredients may also change their solid-state form during formulation processing or storage and as this can adversely affect the final dosage performance, monitoring of pharmaceutical ingredients is essential for a 'right-first-time' philosophy within the industry. Terahertz pulse spectroscopy is a high-throughput technique with many areas of potential exploitation in the pharmaceutical industry; these issues are discussed in this paper.