Application to classification of mulberry leaves using multivariate analysis of proton NMR metabolomic data

Recently, NMR-based metabolomic analysis has been used to acquire information based on differentiation among biological samples. In the present study, we examined whether multivariate analysis was able to be applied to natural products and/or material field. Each extraction of 24 leaf samples, divided into six locations from the tip of the stem in each of four strains, was analyzed by pattern recognition methods, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). Twenty-four extracts from mulberry leaf showed independent spectra by 1H NMR. The separation of leaf extraction data due to the difference at six locations was achieved in the PCA score plot as correlation PC1 (86.1%) and PC3 (4.6%) and showed two loading plots, suggesting classification by leaf position as an independent variable in the loading plot. Moreover, the difference among six locations clarified the seven highest discrimination powers by the SIMCA method. Meanwhile, the PCA score plot obtained classification by the variety of mulberry strains with three loading plots, but the SIMCA method did not give a peak by classification.     

An application of cluster analysis and multivariate classification methods to spring water monitoring data

An optimized model of multivariate classification for the monitoring of eighteen spring waters in the land of Serra St. Bruno, Calabria, Italy, has been developed. Thirty analytical parameters for each water source were investigated and reduced to eight by means of Principal Component Analysis (PCA). Water springs were grouped in five distinct classes by cluster techniques (CA) and a model for their classification was built by a Partial Least Squares–Discriminant Analysis (PLS–DA) procedure. The model was optimized and validated and then applied to new data matrices, containing the analytical parameters carried out on the same sources during the successive years. This model proved to be able to notice deviations of the global analytical characteristics, by pointing out in the course of time a different distribution of the samples within the classes. The variation of nitrate concentration was demonstrated to be the major responsible for the observed class shifts. The shifting sources were localized in areas used as sowable lands and high variability of nitrate content was ascribed to the practice of crop rotation, involving a varying use of the nitrogenous chemical fertilizers.     

An application of cluster analysis and multivariate classification methods to spring water monitoring data

An optimized model of multivariate classification for the monitoring of eighteen spring waters in the land of Serra St. Bruno, Calabria, Italy, has been developed. Thirty analytical parameters for each water source were investigated and reduced to eight by means of Principal Component Analysis (PCA). Water springs were grouped in five distinct classes by cluster techniques (CA) and a model for their classification was built by a Partial Least Squares–Discriminant Analysis (PLS–DA) procedure. The model was optimized and validated and then applied to new data matrices, containing the analytical parameters carried out on the same sources during the successive years. This model proved to be able to notice deviations of the global analytical characteristics, by pointing out in the course of time a different distribution of the samples within the classes. The variation of nitrate concentration was demonstrated to be the major responsible for the observed class shifts. The shifting sources were localized in areas used as sowable lands and high variability of nitrate content was ascribed to the practice of crop rotation, involving a varying use of the nitrogenous chemical fertilizers.     

Comparing the performance of neural networks to well-established methods of multivariate data analysis: the classification of mass spectral data

Summary The performance of neural networks in classifying mass spectral data is evaluated and compared to methods of multivariate data analysis and pattern recognition. Back propagation networks are matched with linear discriminant analysis, Kohonen feature maps are compared to the knearest neighbour clustering algorithm. Eight classifiers were trained, in order to discriminate mass spectra of steroids from eight distinct classes of chemical compounds. The results obtained show slightly better performance of Kohonen networks compared to k-nearest neighbour clustering and equal performance of multi-layer perceptrons and discriminant analysis.     

Patterns in the precision of quantitative data from multicomponent gas chromatographic or gas chromatographic-mass spectrometric analysis

The dispersion of the quantitative results in the analysis of volatile compounds from multicomponent mixtures by different fractionation techniques (solid-phase microextraction and direct thermal desorption) followed by GC or GC-MS presents nonrandom patterns related to the existence of different factors in the fractionation process or in the chromatographic separation which affect, to a different extent, the recovery of the sample components. Statistical techniques have been used to show the relative importance of these factors. The improvement in data precision achieved by using volatile compound concentration ratios is discussed.     

Closure of analytical chemical data and multivariate classification

When it is not possible to analyze an exactly reproducible amount of sample (or whenever samples contain indefinite amounts of extraneous materials) it is customary to normalize the data by making, for example, the sum of the concentrations obtained for each sample equal to 100. Although the data normalized (or ;closed') in such a manner have been criticized, it is empirically shown that closure is appropriate in order to compare and classify samples of the type indicated above.     

Simple automated gas chromatographic analysis of amino acids and its application to brain tissue and urine

The separation of 21 free amino acids as their isobutyl N(O,S)-pentafluorobenzoyl derivatives by capillary gas chromatography with electron-capture detection is described. This method has been successfully applied to routine analysis of most of these amino acids in brain tissue and urine. The amino acids are converted to their isobutyl N(O,S)-pentafluorobenzoyl derivatives by acid-catalysed esterification followed by aqueous extractive pentafluorobenzoylation. The 21 amino acids can be separated on a single capillary column in 21 min. The high sensitivity permits analysis in minute quantities of sample and the high stability permits the use of an automatic sample injector at ambient temperatures. Thus, over 50 samples can be readily analyzed in a single day.     

Novel system for classifying chromatographic applications, exemplified by comprehensive two-dimensional gas chromatography and multivariate analysis

For practical chromatographers it is extremely difficult to judge the merits and limitations of new technological developments. On the other hand, it is nearly impossible for those at the forefront of technology to judge the implications of their efforts for all specific applications of chromatography. Both chromatographers and researchers can be aided by a classification of the numerous specific applications into a few well-defined categories. In this paper, we propose such a classification of all chemical analysis by chromatography into three generic types of applications, viz. target-compound analysis, group-type separation, and fingerprinting. The requirements for each type are discussed in general terms. The classification scheme is applied to assess the benefits and limitations of comprehensive two-dimensional gas chromatography (GC x GC) and the possible additional benefits of using multivariate-analysis (MVA) techniques for each type of application. The conclusions pertaining to the generic types of applications are indicative for the implications of new developments for specific chemical analysis by chromatography.     

Study of metabonomic profiles of human esophageal carcinoma by use of high-resolution magic-angle spinning 1H NMR spectroscopy and multivariate data analysis

Esophageal carcinoma (EC) is one of the most common malignant tumors. EC survival has remained disappointingly low because of the high malignancy of esophageal cancer and the lack of obvious clinical symptoms at an early stage. Early diagnosis is often difficult because the small tumor nodules are frequently missed. Metabonomics based on high-resolution magic-angle spinning (HRMAS) NMR has been popular for tumor detection because it is highly sensitive, provides rich biochemical information and requires no sample pretreatment. ¹H HRMAS spectra of non-involved adjacent esophageal tissues and of well differentiated and moderately differentiated esophageal carcinoma tumors were recorded and analyzed by use of multivariate and statistical analysis techniques. Moderately differentiated EC tumors were found to have increased total choline, alanine, and glutamate and reduced creatine, myo-inositol, and taurine compared with non-involved adjacent tissues. Moreover, clear differences between the metabonomic profiles of EC tissues enabled tumor differentiation. Furthermore, the integral Gly/MI ratio for samples of different tissue types were statistically significantly different; this was sufficient both for distinguishing non-involved tissues from esophageal carcinoma and for classification of well differentiated and moderately differentiated EC tumors.

Enantioselective gas chromatographic analysis of aqueous samples by on-line derivatisation. Application to enzymatic reactions

A new gas chromatography (GC) method is presented for analysing both the conversion and the enantiomeric excess (e.e.) of samples from alcohol dehydrogenase reactions. The chiral compounds studied were a series of saturated, straight chain alcohols, ranging from 2-butanol to 2-heptanol. The alcohols were converted to the corresponding trifluoroacetylated derivatives by injecting trifluoroacetic anhydride onto the column shortly after injection of the aqueous samples in split-injection mode (1:100) onto a Chiraldex G-TA capillary GC column. Injecting seven hundred aqueous enzymatic reaction mixtures according to the above-mentioned procedure revealed no noticeable loss of column performance. Using the new GC method, conventional sample work-up procedures such as extraction and off-line derivatisation are eliminated and throughput of samples is significantly enhanced.     

Factorial-design optimization of gas chromatographic analysis of tetrabrominated to decabrominated diphenyl ethers. Application to domestic dust

Gas chromatographic analysis of polybrominated diphenyl ethers (PBDEs) has been evaluated in an attempt to achieve better control of the separation process, especially for highly substituted congeners. Use of a narrow-bore capillary column enabled adequate determination of tetra, penta, hexa, hepta, octa, nona and decaBDE congeners in only one chromatographic run while maintaining resolution power similar to that of conventional columns. A micro electron-capture detector (GC–μECD) was used. Chromatographic conditions were optimized by multifactorial experimental design, with the objective of obtaining not only high sensitivity but also good precision. In this way two different approaches to maximizing response and minimizing variability were tested, and are fully discussed. These optimum chromatographic conditions were then used to determine PBDEs extracted from domestic dust samples by microwave-assisted solvent extraction (MASE). Quantitative recovery (90–108%) was achieved for all the PBDEs and method precision (RSD < 13%) was satisfactory. Accuracy was tested by use of the standard reference material SRM 2585, and sub-ng g⁻¹ limits of detection were obtained for all compounds except BDE-209 (1.44 ng g⁻¹). Finally, several samples of house dust were analysed by use of the proposed method and all the target PBDEs were detected in all the samples. BDE-209 was the predominant congener. Amounts varied from 58 to 1615 ng g⁻¹ and the average contribution to the total PBDE burden of 52%. The main congeners of the octaBDE mixture (BDE-183, BDE-197, BDE-207 and BDE-196) also made an important contribution (29%) to the total. These are the first data about the presence of these compounds in European house-dust samples. Finally, the sum of the main congeners in the pentaBDE commercial mixture (BDE-47, BDE-99, and BDE-100) contributed 14% to the total. Figure Polybrominated diphenyl ethers in House Dust     

A rapid and powerful method for the direct gas chromatographic analysis of alkanolamines; Application to ethanolamines

Summary The problems of derivative gas chromatography of the alkanolamines have been discussed. It has been demonstrated that the difficulties of direct GC, inherent in the high adsorption affinity and strong retentive interaction of this class of compounds with silaceous supports and stationary liquids, can be overcome by using organic polymer beads, having weakly interacting surface. From a short column containing Tenax-GC under programmed temperature conditions, better than base line resolution of the three ethanolamines can be achieved in less than 8 minutes, as against about an hour by derivative GC and several hours by chemical methods, based on amine functionality. Elution properties, such as retention index and its temperature coefficient, have been determined on Tenax-GC for the ethanolamines. A boric acid precolumn is reported for the first time to completely subtract the alkanolamines. The relative response factors, which are required for precision analysis, have been found to make an interesting comparison for the ethanolamines and their derivatives. Under optimised PTGC conditions, a 4 ft Tenax-GC column has revealed the presence of six impurity peaks with fairly good resolution in a commercial sample of triethanolamine.     

Methods of multivariate data analysis applied to the investigation of fen soils

Fen soils from two sites of the Rhin-Havel-Luch, a peatland in the north-east of Germany, have been investigated. The samples have been collected in two horizons, representing different degrees of degradation and mineralisation of peat. Gravimetric measurements, energy dispersive X-ray fluorescence (EDXRF), elemental analysis, and ¹H low resolution nuclear magnetic resonance (LR-NMR) of the fen soil samples have been performed. By multivariate analysis of all the experimental data, especially by the principal component analysis (PCA) and by the cluster analysis, respectively, it was possible to classify the fen soils, to identify their characteristic properties, to detect temporal and local variations, and to prove representative field sampling. Furthermore, the correlation between variables of the applied analytical methods could be interpreted in context to the composition of fen soils and mutual influences of their properties.     

Electron-capture gas chromatographic analysis of beta-phenylethylamine in tissues and body fluids using pentafluorobenzenesulfonyl chloride for derivatization

A gas chromatographic procedure is described for the analysis of beta-phenylethylamine (PEA) in tissues and body fluids. The method involves the use of pentafluorobenzenesulfonyl chloride for extraction and derivatization of PEA. This is followed by separation and analysis of the derivatized amine on a gas chromatograph equipped with a fused-silica capillary column and an electron-capture detector. The procedure is rapid, provides a stable and sensitive derivative, and has been applied to analysis of PEA in brain, heart, kidney, liver, lung, spleen and blood from the rat and urine from human subjects.     

Interpretation of geochemical data by the use of multivariate data analysis

Different strategies of multivariate data analysis are used to interpret a data base from geological samples. Cluster and correspondence analysis are applied to classify properly 34 chemical elements from 10 representative rock samples (volcanic series from Borovitsa, Rhodopa mountains, Bulgaria). Principal components analysis is also used as display method to visualize the relation between the variables and objects of interest. The multivariate data analysis applied makes it possible to interpret the origin and orogenesis of the samples.