Chromatographic data for pharmacological classification of imidazol(in)e drugs.

A set of eighteen imidazol(in)e derivative drugs of various pharmacological activity were analysed under different high-performance liquid chromatographic (HPLC) conditions. Capacity factors were determined employing methanol-buffer eluents at seven volume ratios and at pH 10.9, 7.0 and 2.9. The use of an alkaline buffer was possible owing to the application of poly(butadiene)-coated alumina (PBCA) as the stationary phase. Two systems employing octadecylsilica (ODS) columns were applied, one operated at pH 7.0 and the other at pH 2.9. Capacity factors of the test solute drugs were determined in 21 chromatographic systems. All the data were subjected to chemometric analysis despite the fact that, except for the PBCA systems, only a limited range of linearity of the logarithm of capacity factor versus volume fraction of methanol in mobile phase was observed. The matrix of 21 x 18 capacity factors was statistically analysed by the principal component method. The first two principal components accounted for 80% of the variance in the capacity factors studied. The principal component object scores clearly separated the agents into groups in accordance with their pharmacological classification. It was concluded that diverse retention data can provide more information relevant to the bioactivity of solutes than just a one-dimensional hydrophobicity scale.     

模式识别用于压电晶体传感器阵列定量检测有机蒸汽混合物

本文应用9个压电晶体组成传感器阵列, 每片晶体上分别涂有不同种类的冠醚衍生物, 用它来定量检测二元及三元有机蒸汽混合物, 在数据处理中比较了两种模式识别方法---偏最小二乘法(PLS)和人工神经网络法(ANN), 实验证明, ANN法在预测准确度上明显优于PLS法, 本文还讨论了解决神经网络训练过拟合现象的方法。     

根据动力学-光谱数据解析高锰酸钾氧化草酸钠的反应过程

通过在线测量硫酸介质中高锰酸钾氧化草酸钠反应过程的光谱变化，获得了动力学—光谱二维数据。借助主成分分析法，确定了高锰酸钾的单信息区，并确认反应过程中产生了一种有吸收的中间体。根据最小二乘回归等化学计量学方法，结合相关组分间存在等吸收点这一光谱特征及质量平衡原理，成功地解折出反应体系中相关组分的实际动力学谱及纯光谱。根据Savitsky-Golay求导法，获得了各组分的速率曲线。对解析结果的进一步研究表明，该反应符合自催化模式。     

Chemometric analysis of skeletal data from non-fused and non-pi-complexed pentafulvenes

A broad chemometric study was made on structural data from non-fused and non-pi-complexed pentafulvenes obtained both from the Cambridge structural database (CSD) and from several studies to synthesise new fulvene compounds. Three main differentiated pentafulvene groups can be established considering bond distances extracted from the CSD database. Structural data for the new 1-mono and 1,4-disubstituted 2,3,6-trioxypentafulvenes and 1,4-disubstituted-6-amino-2, 3-dioxypentafulvenes reveal different structural behaviours due to their high functionality. The chemometric techniques employed comprise principal component analysis, cluster analysis, selection of essential variables (Procrustes rotation) and isoprobability curves, all of them giving essentially the same general chemical conclusions.     

Multicomponent Chemometric Determination of Colorant Mixtures by Voltammetry

Abstract

A quantitative chemometric approach, iterative target transformation factor analysis (ITTFA), was applied to the simultaneous determination of mixtures of four food colorants (Amaranth, Sunset Yellow. Tartrazine and Ponceau 4R) by adsorptive voltammetry. The conventional and first-derivative voltammograms of the quaternary mixtures were used to perform the optimization of the calibration procedure by the ITTFA models. The proposed method was applied satisfactorily to the determination of a set of synthetic quaternary mixtures of colorant in phosphate/citric acid buffers (pH 5.7) and acceptable results were obtained. No significant advantages were found with the application of derivative technique in this voltammetric work.     

Application of modern chemometric methods to the study of equilibria in solutions

The possibility of the spectrometric-chemometric study of equilibria in solutions is demonstrated for substances with strongly overlapping spectra, in particular, using the independent component analysis (MILCA and SIMPLISMA algorithms) and the alternating least squares algorithm (MCR-ALS). Using the chemometric approach allows one to resolve spectral curves, identify species present in the solution, and calculate the characteristics of equilibria. The proposed approach is illustrated on a series of examples (study of a tautomeric equilibrium and complexation reactions).     

Multivariate evaluation of factors influencing the performance of a formic acid biosensor for use in air monitoring.

A formic acid biosensor for air monitoring has been evaluated using chemometric methods. Using experimental design eleven factors that could influence the performance of the biosensor were examined. The response matrices consisted of six parameters (steady state currents at three different formic acid concentrations and response rates during changes in formic acid concentrations) describing the performance of the biosensor. The data were evaluated using a combination of principal component analysis (PCA) and multiple linear regression (MLR). To confirm the conclusions from the PCA-MLR partial least squares (PLS) was also used. The most important factor for the biosensor performance was found to be the enzyme concentration. Using the information from the chemometric analyses the optimum operation conditions for the biosensor were determined. The steady state currents were increased by 18-30% and the initial two response rates increased by 47-89% compared with a biosensor that had not been optimised.     

Application of factor analysis for resolving thermogravimetric-mass spectrometric analysis spectra

Factor Analysis was used for extracting information out of the mixture mass spectra recorded in a thermogravimetric-mass spectrometric analysis. Principal component analysis (PCA) and a special diagram, the contour variance diagram (ContVarDia), were used for performing the factor analysis. The method was applied for studying the thermal decomposition of Kraton 1107 copolymer. Pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) was used for identification of the pyrolysis products of Kraton 1107. The application of factor analysis resulted in the determination of the main thermal decomposition steps and the prediction of the mass spectrum corresponding to each step. Those mass spectra were either pure spectra corresponding to main evolved gases or average spectra corresponding to multiple gases evolved in one decomposition step. The advantages and the limitations of the chemometric approach were discussed.     

Processing of NMR,UV, and IR spectrometric data prior to chemometric simulation by independent component and principal component analysis

We consider methods for the mathematical preprocessing of signals in the spectrometric analysis of multicomponent mixtures using chemometric algorithms aimed at adjusting the baseline, experimental noise, and random shift of spectral bands. Practical examples of using simple mathematical operations (scaling, centering, derivatization) are given. The effectiveness of algorithms is illustrated by a wide range of spectroscopic signals (electronic absorption, IR, and NMR spectra) combined with chemometric methods of principal component analysis and independent component analysis.     

Preparation and properties of water‐absorbent composites of chloroprene rubber,starch, and sodium acrylate

The effects of the amounts of starch, sodium acrylate (NaAA) and dicumyl peroxide (DCP) on the properties of chloroprene rubber (CR)/starch/NaAA composites prepared by melting method were investigated. The results showed that the addition of starch improved the mechanical properties, but decreased the water‐absorbing capacity of the composite, most likely due to the decrease in the local concentration of the main water‐absorbing material sodium polyacrylate and the increase in crosslinking density of the composite resulting from the reaction between starch and CR. This reaction was verified by the vulcanized curves, DSC curves, and the cut surface morphology. The as‐prepared composite demonstrated higher water‐absorbing capacity, resulting from the incorporation of NaAA. The mechanical properties decreased with increasing the DCP loading, and the water‐absorbing ratio is the maximum at 1.0 phr DCP. The tensile strength of the composite decreased significantly after water immersion, due to the absorbed water acting as a plasticizer. The extracted component from composites after water immersion is mainly sodium polyacrylate according to Fourier transform infrared (FT‐IR) spectroscopy analysis. The morphology of the composites before and after water immersion was observed by optical transmission microscopy (OTM). The results indicated that the starch exhibits a good dispersion state, and the water‐absorbing capacity results primarily from sodium polyacrylate. Copyright © 2010 John Wiley & Sons, Ltd.     

Determination of volatile components in peptic powder by gas chromatography-mass spectrometry and chemometric resolution

Gas chromatography-mass spectrometry (GC-MS) coupled with chemometric resolution upon two-dimensional data was proposed as a method for the analysis of volatile components in a traditional Chinese medicinal preparation peptic powder which contains Rhizoma Atractylodis, Pericarpium Citri Reticulatae, Cortex Magnoliae Officinalis and Radix Glycyrrhizae. Ninety-three components were separated and 65 of them were qualitatively and quantitatively analyzed which represented about 90.28% of the total content. With the help of chemometric resolution, the data were resolved into a pure chromatogram and a mass spectrum of each chemical component. The accuracy of qualitative and quantitative results was greatly improved by using the two-dimensional comprehensive information of chromatograms and mass spectra. The example showed that chemometric resolution could greatly enhance separation ability. This makes it possible to analyze complicated practical systems like traditional Chinese medicinal preparations with the help of coupled instruments and chemometric resolution methods.     

Multivariate statistical analysis of aerosol immission data — An exemplary investigation on the North Frisian island of Pellworm

Element concentrations of 23 elements in different particle size fractions of aerosol samples from the island of Pellworm in the German Bight were analyzed by the help of total reflection X-ray fluorescence analysis. These immission data were investigated with multivariate statistical methods. Multivariate correlation analysis, as a newer chemometric method, demonstrates the autocorrelation of the immission state for different particle size fractions of dustlike aerosols. The immission of smaller particles is more strongly autocorrelated than that of larger particles. The factor analysis of each particle size fraction allows the extraction of two pollution factors for each fraction. The weights of these factors, anthropogenic and sea spray, change with size. The anthropogenic factor is more highly weighed for smaller particles, the sea spray factor is stronger in larger particles. The dependence of factor scores for smaller particles on wind direction indicates the sources of the extracted factors.     

Towards the identification of plant and animal binders on Australian stone knives

There is limited information regarding the nature of plant and animal residues used as adhesives, fixatives and pigments found on Australian Aboriginal artefacts. This paper reports the use of FTIR in combination with the chemometric tools principal component analysis (PCA) and hierarchical clustering (HC) for the analysis and identification of Australian plant and animal fixatives on Australian stone artefacts. Ten different plant and animal residues were able to be discriminated from each other at a species level by combining FTIR spectroscopy with the chemometric data analysis methods, principal component analysis (PCA) and hierarchical clustering (HC). Application of this method to residues from three broken stone knives from the collections of the South Australian Museum indicated that two of the handles of knives were likely to have contained beeswax as the fixative whilst Spinifex resin was the probable binder on the third.     

Spectrophotometric multicomponent determination of sunset yellow, tartrazine and allura red in soft drink powder by double divisor-ratio spectra derivative, inverse least-squares and principal component regression methods

Double divisor-ratio spectra derivative (graphical method), classical least-squares and principal component regression (two numerical methods) methods were developed for the spectrophotometric multicomponent analysis of soft drink powders and synthetic mixtures containing three colorants without any chemical separation. The graphical method is based on the use of derivative signals of the ratio spectra using double divisor. In this method, the linear determination ranges were 2-8 mug ml(-1) sunset yellow, 4-18 mug ml(-1) tartrazine and 2-8 mug ml(-1) allura red in 0.1 M HCl. In the numerical methods, a training set was randomly prepared by using 18 samples containing between 0 and 8 mug ml(-1) of sunset yellow, 0-18 mug ml(-1) of tartrazine and 0-8 mug ml(-1) of allura red. The chemometric calibrations were calculated by using the prepared training set and its absorbances at seven points (from 375.0 to 550.0 nm) in the spectral region 325-584 nm. The proposed methods were validated by using synthetic ternary mixtures and applied to the simultaneous determination of three colorants in soft drink powders. The obtained results were statistically compared with each other.     

Trace analysis of acids and bases by conductometric titration with multiparametric non-linear regression

A chemometric method for analysis of conductometric titration data was introduced to extend its applicability to lower concentrations and more complex acid-base systems. Auxiliary pH measurements were made during the titration to assist the calculation of the distribution of protonable species on base of known or guessed equilibrium constants. Conductivity values of each ionized or ionizable species possibly present in the sample were introduced in a general equation where the only unknown parameters were the total concentrations of (conjugated) bases and of strong electrolytes not involved in acid-base equilibria. All these concentrations were adjusted by a multiparametric nonlinear regression (NLR) method, based on the Levenberg-Marquardt algorithm. This first conductometric titration method with NLR analysis (CT-NLR) was successfully applied to simulated conductometric titration data and to synthetic samples with multiple components at concentrations as low as those found in rainwater (∼10 μmol L⁻¹). It was possible to resolve and quantify mixtures containing a strong acid, formic acid, acetic acid, ammonium ion, bicarbonate and inert electrolyte with accuracy of 5% or better.     

独立成分分析结合在线红外技术研究3,4-双(4'-氨基呋咱基-3')氧化呋咱的合成反应机理

利用在线红外技术监测3,4-双(4'-氨基呋咱基-3')氧化呋咱(DATF)的合成过程,并结合核独立成分分析算法对反应过程中获得的实时红外光谱数据进行解析,得到了反应物、中间体及产物各组分纯物质的红外光谱图.采用密度泛函理论B3LYP法,在6-31+G(d,p)基组水平上求得中间体的红外振动光谱,验证了所分离红外光谱图的正确性,从而推导出合理的合成反应机理.结果表明,核独立成分分析算法能合理地解析红外光谱在线数据,并有效捕捉合成反应的中间体,对合成反应机理的研究具有重要的指导意义.     

Classification of biodiesel using NIR spectrometry and multivariate techniques

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm⁻¹. The data were preprocessed by selecting a spectral range of 5000-4500 cm⁻¹, and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.     

pKa determinations of xanthene derivates in aqueous solutions by multivariate analysis applied to UV-Vis spectrophotometric data

Xanthenes form to an important class of dyes which are widely used. Most of them present three acid-base groups: two phenolic sites and one carboxylic site. Therefore, the pKa determination and the attribution of each group to the corresponding pKa value is a very important feature. Attempts to obtain reliable pKa through the potentiometry titration and the electronic absorption spectrophotometry using the first and second orders derivative failed. Due to the close pKa values allied to strong UV-Vis spectral overlap, multivariate analysis, a powerful chemometric method, is applied in this work. The determination was performed for eosin Y, erythrosin B, and bengal rose B, and also for other synthesized derivatives such as 2-(3,6-dihydroxy-9-acridinyl) benzoic acid, 2,4,5,7-tetranitrofluorescein, eosin methyl ester, and erythrosin methyl ester in water. These last two compounds (esters) permitted to attribute the pKa of the phenolic group, which is not easily recognizable for some investigated dyes. Besides the pKa determination, the chemometry allowed for estimating the electronic spectrum of some prevalent protolytic species and the substituents effects evaluation.     

Classification of ancient proteinaceous painting media by the joint use of pattern recognition and factor analysis on GC/MS data

Reference proteinaceous binding media of the kinds historically used in painting (specially prepared for this study) and samples collected from ancient polychrome works of art were analysed by gas chromatography – mass spectrometry. The data have been processed by several multivariate chemometric techniques, such as cluster analysis, principal component analysis, factor analysis and SIMCA technique in order to classify the binding media of the ancient works of art on the basis of the proteinaceous material used. The characterisation of ancient samples was possible by the joint use of SIMCA technique and factor analysis. The latter, in particular, has made it possible to point into evidence the presence of at least two latent factors. The first factor seems to be connected with the chemical composition of the painting media, while the second factor is likely to be connected with ageing processes. Received: 14 October 1998 / Revised: 23 April 1999 / Accepted: 20 May 1999     

Classification of ancient proteinaceous painting media by the joint use of pattern recognition and factor analysis on GC/MS data

Reference proteinaceous binding media of the kinds historically used in painting (specially prepared for this study) and samples collected from ancient polychrome works of art were analysed by gas chromatography – mass spectrometry. The data have been processed by several multivariate chemometric techniques, such as cluster analysis, principal component analysis, factor analysis and SIMCA technique in order to classify the binding media of the ancient works of art on the basis of the proteinaceous material used. The characterisation of ancient samples was possible by the joint use of SIMCA technique and factor analysis. The latter, in particular, has made it possible to point into evidence the presence of at least two latent factors. The first factor seems to be connected with the chemical composition of the painting media, while the second factor is likely to be connected with ageing processes. Received: 14 October 1998 / Revised: 23 April 1999 / Accepted: 20 May 1999