Pure Chemical Components Research Articles

The antioxidant activity of Trachyspermum ammi essential oil and thymol in murine macrophages

Abstract Essential oils are abundant resources of several bioactive compounds, which possess therapeutic effects and have been at the heart attention of the research as the natural additives for the shelf-life extension of food products. This study aimed to investigate, in vitro radical scavenging and antioxidant capacities of Ttrachyspermum ammi (TEO) and its main components. Furthermore, this study sought to determine the antioxidant enzyme responses to TEO at the gene expression levels. The inhibitory effects of TEO and its main components on superoxide and nitric oxide production and NADH oxidase (NOX) and nitric oxide synthase (NOS) expression examined in lipopolysaccharide (LPS)-stimulated macrophages. TEO and thymol displayed a robust antioxidant activity while γ-terpinene and p-cymene have presented a few antioxidant activities. TEO at 10 μg/mL strongly reduced NO but potently increased reactive oxygen species (ROS) in LPS-stimulated macrophages. TEO significantly decreased inducible nitric oxide synthase (iNOS) mRNA expression but upregulated NOX mRNA in LPS-stimulated macrophages at 10 μg/mL. TEO had strong synergism with LPS to enhance ROS, a condition that is suitable against tumors propagation. Thymol at 10 μg/mL reduced NO, and ROS production and iNOS mRNA and NOX mRNA expression in LPS-stimulated macrophages but γ-terpinene and p-cymene did not show such activities. These differential activities between TEO and its pure chemical components strongly recommend that TEO antioxidant activity may be mainly due to the presence of thymol and also a strong synergism between all monoterpenes and monoterpenoids components of essential oils to the presentation a biological action.

Determining the number of pure chemical components in the mixed spectral data based on eigenvalue sequences transform

Determining the number of pure chemical components is an important step for various chemical data analysis methods like cluster analysis, principal component analysis, and spectral unmixing. In this paper, a method of eigenvalue sequences transform is proposed to improve the performance in determining the number of chemical components in spectral matrix. The proposed method converts the spectral data cube to eigenvalue sequences by applying the singular value decomposition technique firstly. Then, the method innovatively transforms the normalized eigenvalue sequences into a redefined coordinate system and detects the number of chemical components by searching the sequence of the highest point. Since the proposed method identifies the number of chemical components from the angle of geometry, all processes need not involve the use of time‐consuming iterations, extensive calibration tables, or pseudostatistical hypothesis. This paper also evaluates the applications of the proposed method with simulation and real‐world spectral data. The evaluation results show that the method has stronger robustness, better accuracy, and higher automaticity in estimating the number of chemical components by comparing with some calibration methods.

Raman Microspectroscopic Mapping with Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) Applied to the High-Pressure Polymorph of Titanium Dioxide, TiO2-II

The high-pressure, α-PbO2-structured polymorph of titanium dioxide (TiO2-II) was recently identified in micrometer-sized grains recovered from four Neoarchean spherule layers deposited between ∼2.65 and ∼2.54 billion years ago. Several lines of evidence support the interpretation that these layers represent distal impact ejecta layers. The presence of shock-induced TiO2-II provides physical evidence to further support an impact origin for these spherule layers. Detailed characterization of the distribution of TiO2-II in these grains may be useful for correlating the layers, estimating the paleodistances of the layers from their source craters, and providing insight into the formation of the TiO2-II. Here we report the investigation of TiO2-II-bearing grains from these four spherule layers using multivariate curve resolution-alternating least squares (MCR-ALS) applied to Raman microspectroscopic mapping. Raman spectra provide evidence of grains consisting primarily of rutile (TiO2) and TiO2-II, as shown by Raman bands at 174 cm-1 (TiO2-II), 426 cm-1 (TiO2-II), 443 cm-1 (rutile), and 610 cm-1 (rutile). Principal component analysis (PCA) yielded a predominantly three-phase system comprised of rutile, TiO2-II, and substrate-adhesive epoxy. Scanning electron microscopy (SEM) suggests heterogeneous grains containing polydispersed micrometer- and submicrometer-sized particles. Multivariate curve resolution-alternating least squares applied to the Raman microspectroscopic mapping yielded up to five distinct chemical components: three phases of TiO2 (rutile, TiO2-II, and anatase), quartz (SiO2), and substrate-adhesive epoxy. Spectral profiles and spatially resolved chemical maps of the pure chemical components were generated using MCR-ALS applied to the Raman microspectroscopic maps. The spatial resolution of the Raman microspectroscopic maps was enhanced in comparable, cost-effective analysis times by limiting spectral resolution and optimizing spectral acquisition parameters. Using the resolved spectra of TiO2-II generated from MCR-ALS analysis, a Raman spectrum for pure TiO2-II was estimated to further facilitate its identification.

Estimating the number of pure chemical components in a mixture by X-ray absorption spectroscopy.

Principal component analysis (PCA) is a multivariate data analysis approach commonly used in X-ray absorption spectroscopy to estimate the number of pure compounds in multicomponent mixtures. This approach seeks to describe a large number of multicomponent spectra as weighted sums of a smaller number of component spectra. These component spectra are in turn considered to be linear combinations of the spectra from the actual species present in the system from which the experimental spectra were taken. The dimension of the experimental dataset is given by the number of meaningful abstract components, as estimated by the cascade or variance of the eigenvalues (EVs), the factor indicator function (IND), or the F-test on reduced EVs. It is shown on synthetic and real spectral mixtures that the performance of the IND and F-test critically depends on the amount of noise in the data, and may result in considerable underestimation or overestimation of the number of components even for a signal-to-noise (s/n) ratio of the order of 80 (σ = 20) in a XANES dataset. For a given s/n ratio, the accuracy of the component recovery from a random mixture depends on the size of the dataset and number of components, which is not known in advance, and deteriorates for larger datasets because the analysis picks up more noise components. The scree plot of the EVs for the components yields one or two values close to the significant number of components, but the result can be ambiguous and its uncertainty is unknown. A new estimator, NSS-stat, which includes the experimental error to XANES data analysis, is introduced and tested. It is shown that NSS-stat produces superior results compared with the three traditional forms of PCA-based component-number estimation. A graphical user-friendly interface for the calculation of EVs, IND, F-test and NSS-stat from a XANES dataset has been developed under LabVIEW for Windows and is supplied in the supporting information. Its possible application to EXAFS data is discussed, and several XANES and EXAFS datasets are also included for download.

Chemical components determination via terahertz spectroscopic statistical analysis using microgenetic algorithm

In public security related applications, many suspicious samples may be a mixture of various chemical components that makes the usual spectral analysis difficult. In this paper, a terahertz spectroscopic statistical analysis method using a microgenetic algorithm (Micro-GA) has been proposed. Various chemical components in the mixture can be identified and the concentration of each component can be estimated based on the known spectral data of the pure chemical components. Five chemical mixtures have been tested using Micro-GA. The simulation results have shown agreement with other analytical methods. It is suggested that Micro-GA has potential applications for terahertz spectral identifications of chemical mixtures.

Short note: Estimating the number of pure chemical components in a mixture by maximum likelihood

AbstractComputations attributed to the Malinowski F‐test are incorrect because they were based on determining the equality of secondary eigenvalues instead of the equality of reduced eigenvalues as prescribed in the original work of Malinowski. When the correct expression was employed, the results were found to be in complete agreement with expectations. This does not, in any way, affect the validity of the proposed MLE methodology, which appears to be a valuable tool in the chemometric arsenal. Copyright © 2008 John Wiley & Sons, Ltd.

Effects of adlay hull extracts on uterine contraction and Ca2+mobilization in the rat

Dysmenorrhea is directly related to elevated PGF(2alpha) levels. It is treated with nonsteroid antiinflammatory drugs (NSAIDs) in Western medicine. Since NSAIDs produce many side effects, Chinese medicinal therapy is considered as a feasible alternative medicine. Adlay (Coix lachryma-jobi L. var. ma-yuen Stapf.) has been used as a traditional Chinese medicine for treating dysmenorrhea. However, the relationship between smooth muscle contraction and adlay extracts remains veiled. Therefore, we investigated this relationship in the rat uterus by measuring uterine contraction activity and recording the intrauterine pressure. We studied the in vivo and in vitro effects of the methanolic extracts of adlay hull (AHM) on uterine smooth muscle contraction. The extracts were fractionated using four different solvents: water, 1-butanol, ethyl acetate, and n-hexane; the four respective fractions were AHM-Wa, AHM-Bu, AHM-EA, and AHM-Hex. AHM-EA and its subfractions (175 microg/ml) inhibited uterine contractions induced by PGF(2alpha), the Ca(2+) channel activator Bay K 8644, and high K(+) in a concentration-dependent manner in vitro. AHM-EA also inhibited PGF(2alpha)-induced uterine contractions in vivo; furthermore, 375 microg/ml of AHM-EA inhibited the Ca(2+)-dependent uterine contractions. Thus 375 microg/ml of AHM-EA consistently suppressed the increases in intracellular Ca(2+) concentrations induced by PGF(2alpha) and high K(+). We also demonstrated that naringenin and quercetin are the major pure chemical components of AHM-EA that inhibit PGF(2alpha)-induced uterine contractions. Thus AHM-EA probably inhibited uterine contraction by blocking external Ca(2+) influx, leading to a decrease in intracellular Ca(2+) concentration. Thus adlay hull may be considered as a feasible alternative therapeutic agent for dysmenorrhea.

Rigorous Calculation of Critical Flow Conditions for Pressure Safety Devices

Sizing and verification of pressure relieving systems is an important topic in the design of process plants in order to assure equipments and people protection against malfunctions and hazards. The calculation of the critical flow (choke) condition is analysed with respect to existing standard calculations procedures (API and Omega methods) that implement approximate procedures and may not be extended to temperature/pressure regions near the thermodynamic critical point. These procedures may be replaced by a more rigorous calculation based on the evaluation of the local sonic velocity with equations of state. The method applies to systems composed of pure chemical components as well as to multi-component mixtures existing in the single phase and multi-phase regions. As a consequence of an exact calculation of the critical flow conditions, more accurate values of the discharged flowrate may be obtained. Comparisons with calculations performed using the standard API RP 520 procedure and Omega method are presented.

Estimating the number of pure chemical components in a mixture by maximum likelihood

AbstractThis paper addresses the problem of determining the number of pure chemical components in a mixture by applying the maximum likelihood estimator (MLE) of intrinsic dimension. The application here is to Raman spectroscopy data, although the method is general and can be applied to any type of data from a chemical mixture. We show that the MLE produces superior results compared to other methods on both simulated and real chemical mixtures, and is accurate even when minor components are present. Even if the signal‐to‐noise (SN) ratio is very low, accurate estimates can still be obtained by smoothing the data before applying the estimator, this approach is illustrated on two real datasets with high noise levels. Since the MLE is computed locally at every data point, we also show how the local estimates can be used for other applications, such as segmenting the specimen into homogeneous regions. Copyright © 2007 John Wiley & Sons, Ltd.

Component analysis of chemical mixtures using terahertz spectroscopic imaging

We demonstrate the possibility of inspection of chemical mixtures by terahertz (THz) spectroscopic imaging using known spectral data of the pure chemical components. This method, which is based on principle component analysis, can determine the existence of chemicals, identify them, map the spatial distribution of each component, and measure their content in a target. We determined qualitatively and quantitatively the components not only in pure chemical samples but also in mixtures. The content of each component determined experimentally comes in agreement with their respective known contents.

Correction of retention time shifts for chromatographic fingerprints of herbal medicines

In this study, the combination of chemometric resolution and cubic spline data interpolation was investigated as a method to correct the retention time shifts for chromatographic fingerprints of herbal medicines obtained by high-performance liquid chromatography–diode array detection (HPLC–DAD). With the help of the resolution approaches in chemometrics, it was easy to identify the purity of chromatographic peak clusters and then resolve the two-dimensional response matrix into chromatograms and spectra of pure chemical components so as to select multiple mark compounds involved in chromatographic fingerprints. With these mark components determined, the retention time shifts of chromatographic fingerprints might be then corrected effectively. After this correction, the cubic spline interpolation technique was then used to reconstruct new chromatographic fingerprints. The results in this work showed that, the purity identification of the chromatographic peak clusters together with the resolution of overlapping peaks into pure chromatograms and spectra by means of chemometric approaches could provide the sufficient chromatographic and spectral information for selecting multiple mark compounds to correct the retention time shifts. The cubic spline data interpolation technique was user-friendly to the reconstruction of new chromatographic fingerprints with correction. The successful application to the simulated and real chromatographic fingerprints of two Cortex cinnamomi, fifty Rhizoma chuanxiong, ten Radix angelicae and seventeen Herba menthae samples from different sources demonstrated the reliability and applicability of the approach investigated in this work. Pattern recognition based on principal component analysis for identifying inhomogenity in chromatographic fingerprints from real herbal medicines could further interpret it.

A method for the determination of reaction mechanisms and rate constants from two-way spectroscopic data

We propose a method for resolving spectroscopic reaction monitoring data into concentration profiles and spectra for the pure chemical components. It can be used if a valid mechanistic model for the reaction can be postulated. In the analysis, the parameters of the model, i.e. the rate constants, are also determined. If several mechanistic models are possible, these can be tested and the model best describing the data can be selected. The method is based on refinement of concentration profiles from the mechanistic model by target testing. Subsequent rotation of the loadings from principal component analysis (PCA) yields the pure component spectra. The only input used is the spectroscopic data, the mechanistic model(s) to be tested/fitted and possibly, but not necessarily, the initial concentrations of the species participating in the reaction. The performance of the method has been evaluated for synthetic and experimental data described by some common and simple reaction models. A comparison with techniques for non-linear least squares fitting and self-modelling curve resolution is included in the text.

Non-Chemical Potentials and Dissipative Forces in Thermoelastic Martensitic Transformation

*Institute of Metal Physics, National Academy of Sciences of the Ukraine,Department of Phase Transitions, 36 Vernadsky Str., 252142, Kiev, Ukraine**Departament de Fi´sica, Universitat Illes Balears. E-07071 Palma de Mallorca, Spain(Received October 16, 1997)(Accepted in revised form March 6, 1998)IntroductionDuring a martensitic transformation cycle, a shape memory alloy (SMA) runs a sequence of heterophasestates in which the fraction of martensitic phase increases continuously on cooling and decreases onheating showing a definite temperature hysteresis. At each step of both the forward and the reversetransformations the microstructure of SMA consists of a great number of plate-like martensite crystalsin an austenite matrix, appearing and growing on cooling and shrinking and disappearing on heating.Started with the works by Kurdjumov and Kurdjumov & Khandros (1–3), the problem of thermoelasticequilibrium in SMA remains still opened and has been the subject of extended discussions anddevelopments (4–13). Paskal & Monasevich (8, 9) postulated the existence of fraction dependent Gibbsfree energy potentials describing the direct and reverse martensitic transformation. They also derivedconstitutive thermodynamic force balance equations accounting for the temperature dependence of themartensite volume fraction in a global hysteresis cycle through minimisation of these, strictly speaking,non-equilibrium potentials. Some problems arise from this procedure: first, considering the configura-tion dependent thermodynamic functions as solely dependent on the martensite fraction is onlyphysically reasonable provided the microstructure sequence followed during the forward martensitictransformation is followed in the opposite sense during the reverse transformation. Such “microscopicreversibility” principle was qualitatively formulated by Olson & Cohen (5), who pointed out that growthand shrinkage of the martensitic plates take place in a well defined sequential order, the first platesformed on cooling being the last ones to disappear on heating. Second, introduction of non-equilibriumfree energies containing dissipative or frictional terms, and especially application of minimisationprinciple to them can be a source of misleading physical conclusions. Finally, the usual splitting of thefraction dependent thermodynamic potentials into pure chemical and non-chemical (elastic) compo-nents is practically useful, in the sense of their determination from the experiment, only if the purechemical equilibrium temperature T

Heuristic evolving latent projections: Resolving hyphenated chromatographic profiles by component stripping

A ‘component stripping’ technique is presented for unique resolution of multicomponent systems (with more than three overlapping components) by means of the heuristic evolving latent projections method. Partial chromatographic selectivity is utilized to resolve clusters of overlapping chromatographic peaks. Analytes with partial chromatographic selectivity are detected and their contributions in terms of estimated concentration profiles and spectra subsequently subtracted to obtain a data matrix of reduced rank. The procedure is repeated on the rank-reduced data until the variation corresponding to chemical components has been exhausted and the data matrix has zero ‘chemical’ rank in all regions. At this stage, the concentration profiles and spectra of all the pure chemical components should be obtained. The simplicity and efficiency of the proposed approach are proved on a simulated four-component mixture characterized by an unresolved profile of hypenated chromatographic data. The new method compares favourably with the so-called rank annihilation evolving factor analysis both with respect to preconditions and results.

Coexisting pyroxenes — a multivariate statistical approach

A population of 117 coexisting nonalkaline pyroxene pairs has been studied statistically to evaluate compositional and thermal effects on the element distribution. K DMg opx-cpx is influenced by the Fe/Mg-ratio, by the Ca content—especially of clinopyroxene—and by the content of tetrahedral Al. Fe and tetrahedral Al are found to be negatively correlated. A principal component analysis based on the variation of Si, Al IV, Al VI, Fe, Mg, Mn, Ca is performed. Dropping of highly correlated variables does not affect the result significantly. The first principal component reflects the major chemical variation in Fe and Mg. When using ferrous and ferric iron as separate entries of the analysis, either the second or the third component demonstrates a temperature dependence. It is, however, not possible to obtain pure temperature and chemical components due to the composition not being uncorrelated to temperature of formation. From these components a graph reflecting temperature of formation has been constructed.

Coexisting pyroxenes — a multivariate statistical approach

A population of 117 coexisting nonalkaline pyroxene pairs has been studied statistically to evaluate compositional and thermal effects on the element distribution. K D Mg opx−cpx is influenced by the Fe/Mg-ratio, by the Ca content—especially of clinopyroxene—and by the content of tetrahedral Al. Fe and tetrahedral Al are found to be negatively correlated. A principal component analysis based on the variation of Si, Al IV, Al IV, Fe, Mg, Mn, Ca is performed. Dropping of highly correlated variables does not affect the result significantly. The first principal component reflects the major chemical variation in Fe and Mg. When using Ferrous and ferric iron as separate entries of the analysis, either the second or the third component demonstrates a temperature dependence. It is, however, not possible to obtain pure temperature and chemical components due to the composition not being uncorrelated to temperature of formation. From these components a graph reflecting temperature of formation has been constructed.