Conformational analysis of model compounds of vitamin D by theoretical calculations

A conformational analysis of two model compounds of vitamin D was carried out by means of theoretical computations, Ab initio calculations were carried out using the standard 6-31G* basis set at the Hartree–Fock (HF) level of theory. In addition, the Møller–Plesset (MP2) correlation treatment was applied on the simplest model. Semiempirical calculations were also performed using the AM1 Hamiltonian. The results predict stable A-ring twist forms with energies in the order of 4–6 kcal/mol relative to the global minimum, significantly higher than those reported from molecular mechanics calculations. In addition, a folded conformation was found by the HF optimizations; however, its stability is predicted to be very poor. Comparison of the theoretical results with experimental data is discussed. © 1997 John Wiley & Sons, Inc. J Comput Chem 18 : 1647–1655, 1997     

Quantum chemistry of quantum size‐effects in semiconductors: Small clusters electronic structure calculations

Ab initio RHF SCF calculations are used for some small clusters M_xX_y, where M=Cd, Ag; X=S, I; and x, y≤7. Variation of electronic structure with size for some clusters with the bulklike tetrahedral coordination and with the lower symmetry allows one to predict their possible geometries which are compared with experimental data on the existence of the clusters. The chemical‐bonding factor (the chemical nature of bounded atoms, coordination number for metal and nonmetal atoms, hybridization, etc.) is of more importance for properties of the clusters than is the familiar quantum confinement effect of semiconductor clusters. The essential difference in regularities of small cluster formation is analyzed for CdS‐ and AgI‐based structures. ©1999 John Wiley & Sons, Inc. Int J Quant Chem 71: 337–341, 1999     

Principal component analysis versus fuzzy principal component analysis A case study: the quality of danube water (1985-1996)

Principal component analysis (PCA) is a favorite tool in environmetrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approach to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of the outliers. In this paper we discuss and apply a robust fuzzy PCA algorithm (FPCA). The efficiency of the new algorithm is illustrated on a data set concerning the water quality of the Danube River for a period of 11 consecutive years. Considering, for example, a two component model, FPCA accounts for 91.7% of the total variance and PCA accounts only for 39.8%. Much more, PCA showed only a partial separation of the variables and no separation of scores (samples) onto the plane described by the first two principal components, whereas a much sharper differentiation of the variables and scores is observed when FPCA is applied.     

Application of principal component analysis to a full profile correlative analysis of FTIR spectra

We have demonstrated an informatics methodology for finding correlations between the full profile Fourier transform infrared spectra of polycrystalline 3C‐silicon carbide (poly‐SiC) films and their growth conditions, thereby developing high‐throughput structure‐process relationships. Because SiC films are a structural element in photonic sensors, this paper focuses on the interpretation of their optical response, the multivariate tracking of critical processing pathways, and the identification of controlling processing mechanisms. Using principal component analysis, we have developed a data analysis tool to aid in the assessment of the relative contributions of experimental parameters in low‐pressure chemical vapor deposition processes to optical responses on the basis of the size of eigenvalues of the spectral data set. The applied methodology for identifying spectral relationships of stoichiometry, dopant chemistry, and microstructure of poly‐SiC provides more effective guidelines to manipulate optical responses by controlling multiple experimental parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

Thermogravimetric and principal component analyses in quality assessment of lubricating oils

The suitability of the derivative thermogravimetric and principal component analyses for the assessment of service performance of lubricating oils has been studied. A total sum of 179 samples has been examined, including M-20 Bp, MS-20 p, Marinol CB SAE-30 and DS-11 oils. The results indicate that principal component analysis greatly assisted in the analysis of the quality of lubricating oils by derivative thermogravimetric technique. Considering that, this multivariate statistical method can be applied to the differentiation of oil samples taking into account degree of their degradation in the oil system of an engine.     

A nonlinear principal component analysis to study archeometric data

Statistical techniques, when applied to data obtained by chemical investigations on ancient artworks, are usually expected to recognize groups of objects to classify the archeological finds, to attribute the provenance of items compared with earlier investigated ones, or to determine whether an archaelogical attribution is possible or not. The statistical technique most frequently used in archeometry is the principal component analysis (PCA), because of its simplicity in theory and implementation. However, the application of PCA to archeometric data showed severe limitations because of its linear feature. Indeed, PCA is inadequate to classify data whose behavior describe a curve or a curved subspace of the original data space. As a consequence of it, an amount of information is lost because the multi‐dimensional data space is compressed into a lower‐dimensional subspace including principal components. The aim of this work is then to test a novel statistical technique for archeometry. We propose a nonlinear PCA method to extract maximum chemical information by plotting data on the smallest number of principal components and to answer archeological questions. The higher accuracy and effectiveness of nonlinear PCA approach with respect to standard PCA for the analysis of archeometric data are shown through the study of Apulian red figured pottery (fifth–fourth century BC) coming from some of the most relevant archeological sites of ancient Apulia (Monte Sannace (Gioia del Colle), Egnatia (Fasano), Canosa, Altamura, Conversano, and Arpi(Foggia)). Copyright © 2016 John Wiley & Sons, Ltd.     

Cancer classification based on microarray gene expression data using a principal component accumulation method

The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subsp...     

The vibrational and NMR spectra,conformations and ab initio calculations of aminomethylene,propanedinitrile and itsN-methyl derivatives

The IR and Raman spectra of aminomethylene propanedinitrile (AM) [H₂N-CH=C(CN)₂], (methylamino)methylene propanedinitrile (MAM) [CH₃NH-CH=C(CN)₂] and (dimethylamino)methylene propanedinitrile (DMAM) [(CH₃)₂N-CH=C(CN)₂] as solids and solutes in various solvents have been recorded in the region 4000-50 cm^–1. AM and DMAM can exist only as one conformer. From the vibrational and NMR spectra of MAM in solutions, the existence of two conformers with the methyl group orientedanti andsyn toward the double C=C bond were confirmed. The enthalpy difference H ⁰ between the conformers was measured to be 3.7±1.4 kJ mol^–1 from the IR spectra in acetonitrile solution and 3.4±1.1 kJ mol^–1 from the NMR spectra in DMSO solution. Semiempirical (AM1, PM3, MNDO, MINDO3) and ab initio SCF calculations using a DZP basis set were carried out for all three compounds. The calculations support the existence of two conformersanti andsyn for MAM, withanti being 7.8 kJ mol^–1 more stable thansyn from ab initio and 8.6, 13.4, 11.6, and 10.8 kJ mor^–1 from AM1, PM3, MNDO, and MINDO3 calculations, respectively. Finally, complete assignments of the vibrational spectra for all three compounds were made with the aid of normal coordinate calculations employing scaled ab initio force constants. The same scale factors were optimized on the experimental frequencies of all three compounds, and a very good agreement between calculated and experimental frequencies was achieved.     

Independent component analysis and its applications in signal processing for analytical chemistry

Independent component analysis (ICA) is a statistical method the goal of which is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. In an ICA procedure, the estimated independent components (ICs) are identical to or highly correlated to the spectral profiles of the chemical components in mixtures under certain circumstances, so the latent variables obtained are chemically interpretable and useful for qualitative analysis of mixtures without prior information about the sources or reference materials, and the calculated demixing matrix is useful for simultaneous determination of polycomponents in mixtures. We review commonly used ICA algorithms and recent ICA applications in signal processing for qualitative and quantitative analysis. Furthermore, we also review the preprocessing method for ICA applications and the robustness of different ICA algorithms, and we give the empirical criterion for selection of ICA algorithms in signal processing for analytical chemistry.