Capillary gas chromatography-mass spectrometry of all 93 acyclic octenes and their identification in fluid catalytic cracked gasoline

The Kováts retention indices of all 93 acyclic octenes on polydimethylsiloxane and squalane as stationary phases as well as their mass spectra were measured. The means of gas chromatography-mass spectrometry (GC-MS) were used for confirmation of GC identification as well as for mass spectrometric deconvolution of the majority of gas chromatographic unseparated isomeric octene peaks. The distinction between corresponding E and Z acyclic octenes, that is either difficult or even impossible by means of GC-MS, was obtained on the basis of larger temperature coefficients of retention indices for Z isomeric octenes than for corresponding E isomers. The retention data expressed as homomorphy factors were correlated with the degree of branching, position of double bond, and position of alkyl group with respect to the double bond of acyclic octenes, and the structure-retention relationships were formulated. The 81 acyclic octenes were identified in FCC gasoline.     

Monitoring and classification of wastewater quality using supervised pattern recognition techniques and deterministic resolution of molecular absorption spectra based on multiwavelength UV spectra deconvolution

A field flow approach for the in situ monitoring of wastewater quality is developed and assessed in this work, based on a combination of methods employing deconvolution of molecular absorption spectra and in situ/on-line analysis of wastewater effluent of various origin. The approach involves in situ immersion probes to monitor basic physicochemical parameters followed by UV spectrum deconvolution in order to provide a rapid estimate of organic matter, suspended solids and nitrate and on-line analysis of phosphates in a fully automated setup. The collected data are then treated with a series of supervised pattern recognition techniques in order to classify wastewater effluent according to their origin in three major categories namely municipal, industrial and hospital. The results suggest that the method affords a good approximation of realistic concentrations, as determined by reference methods, while it affords a good classification among various wastewater effluents of different origin. In that manner, the method enables a rapid inference of treated wastewater quality and a robust assessment of treatment process state, especially with regards to violations of effluent quality parameters.     

Synthesis and anion recognition properties of pyrrole-bearing acyclic receptors

A new series of acyclic anion receptors (1-4) based on methyl 5-(aminomethyl)-1H-pyrrole-2-carboxylate were designed and synthesized. The anion recognition properties of these receptors were examined by ¹H NMR spectroscopy and rationalized by density functional theoretical calculation. Receptor 1 displays the highest affinity and selectivity for anionic guests mainly due to the intramolecular hydrogen bonds and rigid molecular geometry.     

Determination of cocaine contamination on banknotes using tandem mass spectrometry and pattern recognition

Tandem mass spectrometry is used to monitor the contamination of banknotes by cocaine. By introducing a series of banknotes into an instrument a distribution of contamination can be obtained. The distribution of samples arising from defendants where the banknotes have been in close proximity to cocaine should differ from the distribution from the general background population. Peak picking and integration is used to produce a series of intensity readings for a batch of banknotes. By visually inspecting these distribution, and applying a variety of chemometric methods (principal components analysis, cluster analysis and class modelling via Mahalanobis distance) it is possible to discriminate effectively between the two classes of distribution (7157 background notes and 4826 case notes alleged to be from drug dealers). By calculating the Mahalonobis distance over 100 bootstrap iterations, background samples were correctly classified 96.48% of the time, while case samples were correctly classified 89.37% of the time.     

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates.     

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates.     

Molecular recognition properties of acyclic cucurbiturils toward amino acids,peptides, and a protein

The binding of 1 and 2 toward 19 amino acid amides by ¹H NMR and ITC is reported. Hosts 1 and 2 bind to aromatic or hydrophobic residues by cavity inclusion leaving the cationic residues at the C=O portals. K_a values range from 10² to >10⁶ M^?1 with H-Phe-NH₂, H-Trp-NH₂, and H-Tyr-NH₂ displaying sub-micromolar K_d values. Hosts 1 and 2 bind tightly to dicationic H-Lys-NH₂ and H-Arg-NH₂ which are poor guests for CB[7]. Comparison of the affinity of 1 and 2 toward the amino acid amide, N-acetyl-amino-acid amide, and amino acid forms of Phe revealed that the removal of the NH₃⁺ to O=C and SO₃^? electrostatic interactions costs 3.8 kcal/mol whereas the introduction of an unfavourable CO₂^? to O=C and SO₃^? electrostatic interactions costs 2.1 kcal/mol. Hosts 1 and 2 bind to insulin with low micromolar affinity. Acyclic CB[n] display high affinity toward a wider range of N-terminal amino acids residues than CB[n] which suggests a broad range of applications.     

Development of a fast analytical tool to identify oil spillages employing infrared spectral indexes and pattern recognition techniques

A fast analytical tool based on attenuated total reflectance mid-IR spectrometry is presented to evaluate the origin of spilled hydrocarbons and to monitor their fate on the environment. Ten spectral band ratios are employed in univariate and multivariate studies (principal components analysis, cluster analysis, density functions - potential curves - and Kohonen self organizing maps). Two indexes monitor typical photooxidation processes, five are related to aromatic characteristics and three study aliphatic and branched chains. The case study considered here comprises 45 samples taken on beaches (from 2002 to 2005) after the Prestige carrier accident off the Galician coast and 104 samples corresponding to weathering studies deployed for the Prestige's fuel, four typical crude oils and a fuel oil. The univariate studies yield insightful views on the gross chemical evolution whereas the multivariate studies allow for simple and straightforward elucidations on whether the unknown samples match the Prestige's fuel. Besides, a good differentiation on the weathering patterns of light and heavy products is obtained.     

Recognizing chromatographic peaks with pattern recognition methods III. Application of the algorithm for peak recognition in trace analysis

Summary The application of a newly developed peak recognition algorithm is shown. This algorith is based on the KNN method, one of the pattern recognition methods. It is shown that peaks with a S/N-ratio down to one can be safely recognized. This is also possible if the baseline has not only detector noise, but has other disturbances, e.g., noise signals which are generated by a reaction detector. The recognition ability of the algorithm is demonstrated by a standard chromatogram with three different concentrations and with two different sampling rates. The improvement against the classical algorithm is demonstrated. Some properties of the algorithm are discussed.     

Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis

Two clinical data sets were applied for pattern recognition in order to discover the correlation between urinary nucleoside profiles and tumours. One data set contains 168 clinical urinary samples, of which 84 specimens are from female thyroid cancer patients (malignant tumour group), and the other samples were collected from healthy women (normal group). However, 168 clinical urinary samples comprised the second data set, too. In all the specimens, each number of the samples for both uterine cervical cancer patients (malignant tumour group) and healthy females (normal group) is 60, and the other 48 samples were collected from uterine myoma patients (benign tumour group). For the two data sets, the separation and quantitative determination of the clinical urinary nucleosides were performed by capillary electrophoresis (CE). The pattern recognition was achieved applying multiple layer perceptron artificial neural networks (MLP ANN) based on conjugate gradient descent training algorithm. Moreover, applying the proposed principal component analysis (PCA) input selection scheme to MLP ANN, the accuracy rate of the pattern recognition was improved to some extent (or without any deterioration) even by much simpler structure of MLP ANN. The study showed that MLP ANN based on PCA input selection was a promising tool for pattern recognition.     

Treatment of a class-in-class modelling problem in chemical pattern recognition

A supervised learning procedure is described for the detection and characterization of classes which overlap or are located within other classes. The UNEQ classifier proposed by Derde and Massart is extended by applying tests for multivariate homogeneity and homoscedasticity, principal components analysis of each class box, and Boolean-type decision rules. The extended algorithm is suitable for class-in-class modelling. The procedure is applied to chemical data for an environmental problem involving an industrial source of emission and its immission effects.     

Neutral losses: A type of important variables in prediction of branching degree for acyclic alkenes from mass spectra

Neutral losses are a type of important variables in mass spectral interpretation. Since it is hard to calculate or extract neutral losses from mass spectra, they are usually discarded. In this study, dissimilarity analysis was employed to extract mass spectral characteristics for predicting branching degree of acyclic alkenes. The relationships between branching degree and neutral loss were constructed under direction of experimental observation and mass spectral fragmentations. A branching degree predictor of acyclic alkenes was subsequently built based on the above relationships. After tested by the experimental data in previous studies, the predictor could correctly provide the branching degree from abundant ions of mass spectra. More importantly, this predictor was able to point out which acyclic alkenes could be predicted correctly or not.     

Toxicological classification of urine samples using pattern recognition techniques and capillary electrophoresis

In toxicology, hazardous substances detected in organisms may often lead to different pathological conditions depending on the type of exposure and level of dosage; hence, further analysis on this can suggest the best cure. Urine profiling may serve the purpose because samples typically contain hundreds of compounds representing an effective metabolic fingerprint. This paper proposes a pattern recognition procedure for determining the type of cadmium dosage, acute or chronic, administrated to laboratory rats, where urinary profiles are detected using capillary electrophoresis. The procedure is based on the composition of a sample data matrix consisting of areas of common peaks, with appropriate pre-processing aimed at reducing the lack of reproducibility and enhancing the potential contribution of low-level metabolites in discrimination. The matrix is then used for pattern recognition including principal components analysis, cluster analysis, discriminant analysis and support vector machines. Attention is particularly focussed on the last of these techniques, because of its novelty and some attractive features such as its suitability to work with datasets that are small and/or have low samples/variable ratios. The type of cadmium administration is detected as a relevant feature that contributes to the structure of the sample matrix, and samples are classified according to the class membership, with discriminant analysis and support vector machines performing complementarily on a training and on a test set.     

A pattern recognition approach to 14-epi-hydrophenanthrene core of the morphine alkaloids based on shikimic acid

An expeditious and stereoselective construction of C-14-epimer of the tetracyclic hydrophenanthrene framework of the morphine alkaloids is described. The core structure is obtained in nine steps in 11% overall yield from shikimic acid via three key transformations: (i) coupling of shikimic acid with 2-iodoisovanillin at C-3 by double S_N2 inversion to form the aryl ether 5; (ii) an intramolecular Heck reaction to construct the dihydrobenzofuran ring and (iii) a McMurry coupling to furnish the hydrophenanthrene core.     

Capillary electrophoretic profiling and pattern recognition analysis of urinary nucleosides from thyroid cancer patients

Metabolic profiling analysis by capillary electrophoresis (CE) was combined with pattern recognition methods to see some correlation between urinary nucleoside levels and thyroid cancer. A total of 15 nucleosides were identified in urines from 12 female thyroid cancer patients and 12 healthy females studied. From the scatter plot evaluation, inosine showed the highest estimated diagnostic power with ca. 97.725% confidence level, followed by N²-methylguanosine. Star symbol graphs showed differences in levels of both minor and major nucleosides between cancer and normal groups more efficiently, compared with histogram. The stepwise discriminant analysis (SDA) selected N²-methylguanosine, N²,N²-dimethylguanosine and 1-methylguanosine as the most discriminating variables between thyroid cancer and normal groups. The canonical discriminant analysis (CDA) correctly classified all urine specimens studied into two separate clusters of cancer and normal groups in a canonical plot. The principal component analysis (PCA) distinguished cancer patients from normal controls in a principal component plot. The cluster analysis (CA) yielded a dendrogram displaying group separation without any single wrong linkage.     

Application of pattern recognition methods in secondary ion mass spectrometry

Pattern recognition methods were applied to SIMS spectra of aluminum and manganese borides to test if proper feature selection and distinction between the various compounds on the basis of the evaluation of cluster ions is possible. It was found that the features selected on an empirical basis could be reduced for practical application from 13 and 11 to 2–3 and 5–8 for aluminum borides and manganese borides, respectively, and that identification of the different compounds is possible by evaluation of the fragmentation pattern. Furthermore, there is evidence that this approach does not only work for pure compounds but also for specimens, where the interesting species is only a minor fraction in the analysed volume.     

Characterization of fatty oils by pattern recognition of triglycerides profiles

The triglyceride profiles for corn, soybean, sunflower, grapestone, olive and olive husks oils were obtained by high temperature capillary gas chromatography on HT-FS capillary columns coated with OV-17-OH. The data were treated by methods of computerized pattern analysis, involving principal component analysis, discriminant analysis, and hierarchical clustering. A graphical representation of results as “star symbol plots” allows prompt visual recognition of the characteristic profiles and straightforward qualitative identification of unknowns as well as recognition of adulterations.     

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.