Quantitative Analysis Of Multicomponent Mixtures Research Articles

Quantitative analysis of multicomponent mixtures of rubber and additives based on terahertz time-domain spectroscopy and Krawtchouk moments

This paper presents a quantitative analytical method based on terahertz time-domain spectroscopy and chemometrics, which was used to detect the content of diphenyl-p-phenylenediamine in a five-component mixture comprising tire rubber and additives. We separately extracted the features of the spectra by using principal component analysis and Krawtchouk moments, and then used partial least squares regression and support vector regression to develop quantitative analysis models. The experimental results show that support vector regression outperforms partial least squares regression in the quantitative analysis of multicomponent mixtures, and the Krawtchouk moments method effectively enhances the prediction accuracy of the model. The Krawtchouk moment combined with support vector regression model shows excellent predictive ability. The correlation coefficient and root mean square error of Krawtchouk moment combined with support vector regression model for the calibration set were 0.9749 and 1.7256%, respectively, and for the prediction set were 0.9669 and 1.9777%, respectively. The results demonstrate that terahertz time-domain spectroscopy combined with Krawtchouk moments and support vector regression is an effective method for determining the antioxidant content in rubber and additive compounds.

Quantitative detection on metabolites of Haematococcus pluvialis by terahertz spectroscopy

Stressed Haematococcus Pluvialis cells would accumulate astaxanthin with the changes of lipid, pigment, and starch, which recently has been widely studied. Real-time dynamic monitoring of these components will have important guiding significance for screening high-yield and high-quality strains in the industry. Terahertz spectroscopy is not only suitable for measuring large flux samples, but also widely used for quantitative analysis of multicomponent mixtures. To investigate the potential of using terahertz spectroscopy to monitor and quantify metabolites in the algae, terahertz spectrum of Haematococcus Pluvialis under nitrogen starvation were captured and examined against those standard reagents of β-carotene, astaxanthin, and starch. The correlation between intensities of the characteristic peaks and the metabolite contents which were quantified by chemical analysis was established, and the results showed the correlation coefficient were higher than 0.92, indicating a relatively accurate performance to predict the metabolites concentration. Furthermore, to identify meaningful spectra range for quantification, the partial least squares combined with intensities of the interval characteristic peaks were established, where it demonstrated an accurate model with high prediction correlation coefficients (greater than0.98), low root-mean-square error of prediction (RMSEP), and high residual predictive deviation. The results approve the feasibility of using terahertz spectroscopy for synchronous detection on metabolites concentration in the microalgae production process.

Rietveld Refinement for Macromolecular Powder Diffraction

Powder X-ray diffraction (PXRD) has been employed extensively for the structural characterization of materials, quantitative analysis of multicomponent mixtures, phase identification, texture, and ...

The condition number of a matrix as an optimality criterion in the problems of parametric identification of linear equations systems

When solving applied problems, an important aspect of the analysis is the stability of the obtained solution with respect to experimental data errors. Empirical experimental data, although a priori inaccurate, can be represented by intervals of their range of values. In some cases, the limits of their variation may also be known. Obviously, the degree of inaccuracy of input data influences the solution of the parametric identification problem. Therefore, in the case when this solution is not the only one, methods for assessing the influence of experimental error on the stability of each potential solution are of interest as they may provide additional arguments in favour of choosing one of them. In the case of models formalized in the form of systems of linear algebraic equations, a similar effect can be investigated using the condition number. In a case of quantitative analysis of multicomponent mixtures, the paper presents an approach to the parametric identification of linear models based on the calculation of the maximum permissible parameter estimates in combination with the study of the coefficient stability of the system.

Quantitative Analysis of Multicomponent Mixtures of Over-the-Counter Pain Killer Drugs by Low-Field NMR Spectroscopy

Marketed pain relief drugs with one to three biologically active components, as well as mixtures of these ingredients, were qualitatively and quantitatively analyzed in an undergraduate student lab using a compact, low-field 1H NMR spectrometer. The students successfully analyzed more than 50 self-made sample mixtures with two or three components as well as the two marketed tablet formulations containing acetylsalicylic acid/l-ascorbic acid, or acetylsalicylic acid/paracetamol (acetaminophen)/caffeine. The NMR-based quantification is an attractive application of the technique, as well as a helpful introduction to NMR spectroscopic applications in life sciences. Problem-based learning on NMR techniques on commonly known drugs provided students the opportunity to develop and improve their skills in solving 1H NMR problems.

A simple multisensor detector based on tin dioxide in capillary gas chromatography

The use of an array of metal oxide film sensors as a detector for volatile organic compounds in capillary gas chromatography is described. The results of determination of the sensitivity, response time, and linearity range of the analytical signals from sensors are presented; these parameters are compared with similar characteristics of commercial gas chromatographic detectors. An approach is proposed to the identification of organic compounds by the shape and relative size of responses of individual sensors. It is demonstrated that the multisensor detector is applicable to solving typical analytical problems of the quantitative analysis of multicomponent mixtures.

Metabolic analysis of human biological fluids by 1H NMR spectroscopy

High-resolution NMR spectroscopy is widely used for the determination of the structure of complex molecules and the quantitative analysis of multicomponent mixtures in solutions. In the past decade, the extremely rapid development of NMR instrumentation resulted in an increase in the resolution and sensitivity of instruments by more than an order of magnitude. This has opened up new opportunities for the use of NMR spectroscopy in the quantitative analysis of the most important biological fluids (urine, blood plasma, cerebrospinal fluid, saliva, bile, tears, etc.), each of them can contain to several thousands of components in micromolar or lower concentrations. Clinicians and biologists from many countries used new capabilities of NMR spectroscopy to develop efficient methods for metabolic studies, medical diagnostics, and the development of new pharmaceutical preparations. This review briefly describes the status of studies in this new rapidly developing area of analytical chemistry and summarizes some of our experimental results.

Limitations to the use of solid-phase microextraction for quantitation of mixtures of volatile organic sulfur compounds.

A study of the range of volatile organic sulfur compounds produced by brassica plants has highlighted limitations to the use of Carboxen/PDMS fibers for their analysis by solid-phase microextraction (SPME). These fibers are sometimes advocated for the analysis of sulfur gases, but a quantitative comparison of analytical data derived by SPME and by direct gas sampling of standard mixtures of volatile low molecular weight sulfur compounds at 0.01-10 mg/L has identified potential errors associated with their use. Higher molecular compounds displace lower molecular weight compounds as a consequence of competition for active sites on the fiber, and the relative proportions of the components adsorbed onto the fiber depend on their ratio in the headspace. As their relative concentrations change from sample to sample, the varying interactions result in irregular analytical responses, reflected in erratic calibration curves. Standards containing single components are not valid; only a standard containing all components found in the sample to be analyzed, and at the same relative concentrations, is appropriate. In practice, this may preclude the use of the fibers for quantitative analysis of multicomponent mixtures.

Resolution of Overlapping Bands – An Idea for Quantitative Analysis of Undefined Mixturesa

Abstract A method for quantitative analysis of multicomponent mixtures with unknown individual spectra of the components, based on resolution of overlapping bands, is developed and its reliability is illustrated in analysis of both model and real mixtures. The two-step optimization procedure allows attainment of high precision, the molar parts and the individual spectra of the components. This approach could be used in molecular spectroscopy (UV-Vis-NIR, IR, Raman and CD spectroscopy) as well as in chromatography.

Fourier transform infrared least-squares methods for the quantitative analysis of multicomponent mixtures of airborne vapors of industrial hygiene concern.

Air monitoring methods suitable for use in the workplace, though accurate for monitoring individual compounds or classes of compounds, cannot be used to monitor several compounds or classes of compounds simultaneously. In the past few years, Fourier transform infrared (FT-IR) spectroscopy has been investigated for use as a method for multicomponent quantitative analysis. This work focuses on quantitative analysis of six mixtures in ambient air. The concentration ranges of the two- to six-component mixtures are from 50 ppm to 100 ppb. The optimal least-squares fit (LSF) method selected, background reference file chosen, and quantitative peak windows picked were evaluated in this effort. The quantitative results of six mixtures were accurate at the 50, 10, and 1 ppm levels. There were some components for which the analysis was also accurate at the 0.1 ppm level. The data indicate that the LSF program could be used to quantify strongly overlapping multicomponent mixtures. The results support the conclusion that the FT-IR spectrometer is appropriate for the direct quantification of multicomponent mixtures of many airborne gases and vapors of industrial hygiene concern.

Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat

This paper is concerned with the quantitative analysis of multicomponent mixtures by diffuse reflectance spectroscopy. Near-infrared reflectance (NIRR) measurements are related to chemical composition but in a nonlinear way, and light scatter distorts the data. Various response linearizations of reflectance (R) are compared ( R with Saunderson correction for internal reflectance, log 1/ R, and Kubelka-Munk transformations and its inverse). A multi-wavelength concept for optical correction (Multiplicative Scatter Correction, MSC) is proposed for separating the chemical light absorption from the physical light scatter. Partial Least Squares (PLS) regression is used as the multivariate linear calibration method for predicting fat in meat from linearized and scatter-corrected NIRR data over a broad concentration range. All the response linearization methods improved fat prediction when used with the MSC; corrected log 1/ R and inverse Kubelka-Munk transformations yielded the best results. The MSC provided simpler calibration models with good correspondence to the expected physical model of meat. The scatter coefficients obtained from the MSC correlated with fat content, indicating that fat affects the NIRR of meat with an additive absorption component and a multiplicative scatter component.

Quantitative analysis of multicomponent mixtures using their absorption spectra and linear programming

Quantitative analysis of multicomponent mixtures using their absorption spectra and linear programming

On the quantitative analysis of multicomponent mixtures by gas-liquid chromatography

The thermal conductivity cell response to some heterocyclic and hydrocarbon compounds was determined relatively to pyridine and was used to calculate with accuracy the molar as well as the weight concentration of analysed multicomponent mixtures from peak area values. From these and other results obtained by correcting data that have been previously reported in literature it was shown that by applying the appropriate factor it was possible to obtain the composition of the unknown mixture either as the mole or the weight per cent with accuracy.