Analysis of large experimental datasets in electrochemical impedance spectroscopy

An approach for the analysis of large experimental datasets in electrochemical impedance spectroscopy (EIS) has been developed. The approach uses the idea of successive Bayesian estimation and splits the multidimensional EIS datasets into parts with reduced dimensionality. Afterwards, estimation of the parameters of the EIS-models is performed successively, from one part to another, using complex nonlinear least squares (CNLS) method. The results obtained on the previous step are used as a priori values (in the Bayesian form) for the analysis of the next part. To provide high stability of the sequential CNLS minimisation procedure, a new hybrid algorithm has been developed. This algorithm fits the datasets of reduced dimensionality to the selected EIS models, provides high stability of the fitting and allows semi-automatic data analysis on a reasonable timescale. The hybrid algorithm consists of two stages in which different zero-order optimisation strategies are used, reducing both the computational time and the probability to overlook the global optimum. The performance of the developed approach has been evaluated using (i) simulated large EIS dataset which represents a possible output of a scanning electrochemical impedance microscopy experiments, and (ii) experimental dataset, where EIS spectra were acquired as a function of the electrode potential and time. The developed data analysis strategy showed promise and can be further extended to other electroanalytical EIS applications which require multidimensional data analysis.     

Determination of Quercetin in Extracts of Ginkgo biloba L. Leaves by Near‐Infrared Reflectance Spectroscopy Based on Interval Partial Least‐Squares (iPLS) Model

Abstract

This paper developed a multivariate method of analysis of quercetin in Ginkgo biloba leaf extracts, based on reflectance NIR measurements and partial least squares regression. In order to give a better correlation with the results obtained by HPLC, multiplicative scatter correction (MSC) was utilized to correct scattering effect and interval partial least squares (iPLS) to select optimum wavelength region. In general, good calibration statistics were obtained for the prediction of quercetin content, as demonstrated by some figures of merit, namely linearity, repeatability, and accuracy. And the iPLS model was more reliable than the full model.     

Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.     

Sample cleanup-free determination of mycophenolic acid and its glucuronide in serum and plasma using the novel technology of ultra-performance liquid chromatography-electrospray ionization tandem mass spectrometry

Mycophenolic acid (MPA) is an immunosuppressant drug which powerfully inhibits lymphocyte proliferation. Since the early 1990s it has been used to prevent rejection in organ transplantation. The requirement of therapeutic drug monitoring shown in previous studies raises the necessity of acquiring accurate and sensitive methods to measure MPA and its major metabolite mycophenolic acid glucuronide (MPAG).The authors developed a sample cleanup-free, rapid, and highly specific method for simultaneous measurement of MPA and MPAG in human plasma and serum using the novel technology of ultra-performance liquid chromatography-electrospray ionization tandem mass spectrometry. MPA- and MPAG-determinations were performed during a 2.0-min run time. Multiple calibration curves for the analysis of MPA and MPAG exhibited consistent linearity and reproducibility in the range of 0.05-100 (r > 0.999) mg L⁻¹ and 4-4000 mg L⁻¹ (r > 0.999), respectively. Limits of Detection were 0.014 mg L⁻¹ for MPA and 1.85 mg L⁻¹ for MPAG. Lower Limits of Quantification were 0.05 mg L⁻¹ for MPA and 2.30 mg L⁻¹ for MPAG. Interassay imprecision was <10% for both substances. Mean recovery was 103.6% (range 78.1-129.7%) for MPA and 111.1% (range 73.0-139.6%) for MPAG. Agreement was good for MPA and MPAG between the presented method and a validated HPLC-MS/MS method. The Passing-Bablok regression line for MPA and MPAG was HPLC-MS/MS = 1.14 UPLC-MS/MS—0.14 [mg L⁻¹], r = 0.96, and HPLC-MS/MS = 0.77 UPLC-MS/MS + 0.50 [mg L⁻¹], r = 0.97, respectively. This sample cleanup-free and robust LC-MS/MS assay facilitates the rapid, accurate and simultaneous determination of MPA and MPAG in human body fluids.     

A study of the precision and accuracy of peak quantification in comprehensive two-dimensional liquid chromatography in time

Simulated chromatographic data were used to determine the precision and accuracy in the estimation of peak volumes (i.e., peak sizes) in comprehensive two-dimensional liquid chromatography in time (LC × LC). Peak volumes were determined both by summing the areas in the second dimension chromatograms and by fitting the second dimension areas to a Gaussian peak. The Gaussian method is better at predicting the peak volume than the moments method provided there are at least three second dimension injections above the limit of detection (LOD). However, when only two of the second dimension signals are substantially above baseline, the accuracy and precision of the Gaussian fit method become quite poor because the results from the fitting algorithm become indeterminate. Based on simulations in which the modulation ratio (M_R = 4¹σ/t_s) and sampling phase (?) were varied, we conclude for well-resolved peaks that the optimum precision in peak volumes in 2D separations will be obtained when the M_R is between two and five, such that there are typically four to ten second dimension peaks recorded over the eight σ width of the first dimension peak. This sampling rate is similar to that suggested by the Murphy–Schure–Foley criterion. This provides an RSD of approximately 2% for the signal-to-noise ratio used in the present simulations. The precision of the peak volume of experimental data was also assessed, and RSD values were in the range of 4–5%. We conclude that the poorer precision found in the LC × LC experimental data as compared to LC may be due to experimental imprecision in sampling the effluent from the first dimension column.     

Data fusion in multivariate calibration transfer

We report the use of stacked partial least-squares regression and stacked dual-domain regression analysis with four commonly used techniques for calibration transfer to improve predictive performance from transferred multivariate calibration models. The predictive performance from three conventional calibration transfer methods, piecewise direct standardization (PDS), orthogonal signal correction (OSC) and model updating (MUP), requiring standards measured on both instruments, was significantly improved from data fusion either by stacking of wavelet scales or by stacking of spectral intervals, as demonstrated by transfer of calibrations developed on near-infrared spectra of synthetic gasoline. Stacking did not produce as significant an improvement for calibration transfer using a finite impulse response (FIR) filter, but application of SPLS regression to FIR-transferred spectra improves predictive performance of the transferred model.     

Comments on the primary and scatter dose-spread kernels used for convolution methods

The basic primary and scatter dose-spread kernels used for convolution methods are usually produced by Monte Carlo simulations with the interaction point forced to the center of a large water phantom. However, it is still not clear whether such Monte Carlo based kernels allow accurate dose calculations with a wide range of field sizes and depths, especially in thorax phantoms. Using the differential primary and scatter concept, this paper proposes another type of basic kernel, with which perfectly accurate primary and scatter absorbed dose calculations can be performed under conditions that the beam is parallel, the incident beam intensity is uniform within and zero outside the field, and the primary beam attenuation coefficient along raylines is not a function of depth and off-axis distance.     

Simultaneous measurement of glucose and glutamine in aqueous solutions by near infrared spectroscopy

A method is described for measuring the concentrations of both glucose and glutamine in binary mixtures from near infrared (NIR) absorption spectra. Spectra are collected over the range from 5000–4000/cm (2.0–2.5μm) with a 1-mm optical path length. Glucose absorbance features at 4710, 4400, and 4300/cm and glutamine features at 4700, 4580, and 4390/cm provide the analytical information required for the measurement. Multivariate calibration models are generated by using partial least squares (PLS) regression alone and PLS regression combined with a preprocessing digital Fourier filtering step. The ideal number of PLS factors and spectral range are identified separately for each analyte. In addition, the optimum Fourier filter parameters are established for both compounds. The best overall analytical performance is obtained by combining Fourier filtering and PLS regression. Glucose measurements are established over the concentration range from 1.66–59.91 mM, with a standard error of prediction (SEP) of 0.32 mM and a mean percent error of 1.84%. Glutamine can be measured over the concentration range from 1.10–30.65 mM with a SEP of 0.75 mM and a mean percent error of 6.67%. These results demonstrate the analytical utility of NIR spectroscopy for monitoring glucose and glutamine levels in mammalian and insect cell cultures.