Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

In this paper, we propose tools for studying biophysical characteristics of a person by processing polygraph charts. We present the results produced by two methods of multivariate data analysis: the principal component analysis (PCA) and projection on latent structures (PLS). Data of biophysical state of a person during the polygraph testing - galvanic skin response (GSP), photoplethysmogram, blood pressure, and other biophysical characteristics – are processed and analyzed.It is shown that load diagrams are the preferable method to identify the relationship between the biophysical characteristics of a person. The principal component analysis provides an opportunity to assess minor changes in biophysical characteristics of a person and to take into account situational significant identifiers. They can attest to the fact that a possibly significant physical-chemical process runs in a human body.It is demonstrated that the projection on latent structures should be used to develop regression models that can constantly be used in polygraph testing.It is concluded that proposed methods of multivariate data analysis can help in an investigation of biophysical characteristics of a person using polygraph data. Also, the proposed methods are able to detect possible malfunctions in the responsible physical-chemical processes. The obtained results enable solving practical problems of information security matters, for example, the identification of an insider.DOI 10.14258/izvasu(2018)1-05

Multivariate data analysis of key pollutants in sewage samples: a case study

Five multivariate data analysis methods, namely principal components analysis, non-linear mapping, hierarchical clustering, minimal spanning tree and mode analysis, were used to analyse the natural structure of red wines from Tarragona (Catalonia, Spain). Seventeen parameters were analysed for each sample to determine the multi-dimensional spectrum in which the wines are studied. In spite of the homogeneity of the wine samples which resulted from the similarity of the edaphic, varietal factors as well as the cultural conditions of the producing areas, two different clusters can be distinguished on the basis of all the methods used. The groups of wine samples obtained coincide with two viticultural zones that differ with regard to climate and geographical conditions. Therefore, a natural distinction can bei made between the wines produced in each adjacent zone, forming the basis for future differentiation according to brand name.

The purpose of the research was to identify the method of preparation and the composition of aceclofenac based nanoparticles using various multivariate data analysis methods. Top down and bottom up approaches were used for the preparation of the nanoparticles and polymers like hydroxylpropyl methylcellulose and polyvinyl pyrrolidone, surfactants like sodium lauryl sulphate and Tween 80 were screened for the purpose of formulation. Different combinations of these factors were used for designing of the experiments and the obtained products were evaluated for particle size, poly dispersibility index, zeta potential, drug content and entrapment efficiency. Principal component analysis, agglomerative hierarchical clustering, composite desirability index aided to identify the best possible combination. Nanoprecipitation along with sonication as process and poly vinyl pyrrolidone as stabilizer gave the highest values of desirability index. The formulation composition of nanosuspension was oscillated around the settings for the zeroing in on optimum quantitative formulation. The nanosuspension was gelled using Carbopol. The gel was studied for in vitro and ex vivo permeation characteristics. The optimized formulation was found to be stable and had acceptable levels of irritation as studied in wistar rats. Thus various data analysis methods could help identify the right combination of process and formulation composition.

In this work, multivariate data analysis methods were applied to the analysis and interpretation of micro‐Raman spectra, collected from a broad set of historical iron‐based ink samples, previously characterised for the content of organic acids (gallic acid, ellagic acid and protocatechuic acid). The proposed method relies on principal component analysis of the noisy spectra typically obtained on original, degraded, organic samples, where fluorescence could affect the Raman signal. The signal components could be distinguished from the noise components and then used to build a linear discriminant analysis (LDA) model, achieving separation of the spectra into three classes. Selection of pure signal factors also improved effectiveness and performances of partial least square regression (PLS) algorithms, allowing quantification of condensed tannic acid residuals. Application of multivariate methods to discriminate signal from noise removes the need for spectral data manipulation (filtering, smoothing and differentiating). The obtained classification method for discrimination of historic inks and the regression method for determination of condensed tannic acid residuals supports the use of Raman analysis of fluorescing organic materials, and may provide information to scholars on ink composition and potentially on its provenance. Copyright © 2013 John Wiley & Sons, Ltd.

Variation patterns of nitric oxide in Catalonia during the period from 2001 to 2006 using multivariate data analysis methods

The role of permutation tests in exploratory multivariate data analysis

The goal of this work is the comparison of differences in the lipidomic compositions of human cell lines derived from normal and cancerous breast tissues, and tumor vs. normal tissues obtained after the surgery of breast cancer patients. Hydrophilic interaction liquid chromatography/electrospray ionization mass spectrometry (HILIC/ESI-MS) using the single internal standard approach and response factors is used for the determination of relative abundances of individual lipid species from five lipid classes in total lipid extracts of cell lines and tissues. The supplementary information on the fatty acyl composition is obtained by gas chromatography/mass spectrometry (GC/MS) of fatty acid methyl esters. Multivariate data analysis (MDA) methods, such as nonsupervised principal component analysis (PCA), hierarchical clustering analysis (HCA) and supervised orthogonal partial least-squares discriminant analysis (OPLS-DA), are used for the visualization of differences between normal and tumor samples and the correlation of similarity between cell lines and tissues either for tumor or normal samples. MDA methods are used for differentiation of sample groups and also for identification of the most up- and downregulated lipids in tumor samples in comparison to normal samples. Observed changes are subsequently generalized and correlated with data from tumor and normal tissues of breast cancer patients. In total, 123 lipid species are identified based on their retention behavior in HILIC and observed ions in ESI mass spectra, and relative abundances are determined. MDA methods are applied for a clear differentiation between tumor and normal samples both for cell lines and tissues. The most upregulated lipids are phospholipids (PL) with a low degree of unsaturation (e.g., 32:1 and 34:1) and also some highly polyunsaturated PL (e.g., 40:6), while the most downregulated lipids are PL containing polyunsaturated fatty acyls (e.g., 20:4), plasmalogens and ether lipids. Copyright © 2016 John Wiley & Sons, Ltd.

Lipidomic differentiation between human kidney tumors and surrounding normal tissues using HILIC-HPLC/ESI-MS and multivariate data analysis.

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.

A novel method for calculating biomagnification factors is presented and demonstrated using contaminant concentration data from the Swedish national monitoring program regarding organochlorine contaminants (OCs) in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg, sampled from 1996 to 1999 from the Baltic Sea. With this randomly sampled ratios (RSR) method, biomagnification factors (BMF(RSR)) were generated and denoted with standard deviation (SD) as a measure of the variation. The BMFRsR were calculated by randomly selecting one guillemot egg out of a total of 29 and one herring out of a total of 74, and the ratio was determined between the concentration of a given OC in that egg and the concentration of the same OC in that herring. With the resampling technique, this was performed 50 000 times for any given OC, and from this new distribution of ratios, BMF(RSR) for each OC were calculated and given as geometric mean (GM) with GM standard deviation (GMSD) range, arithmetic mean (AM) with AMSD range, and minimum (BMF(MIN)) as well as maximum (BMF(MAX)) biomagnification factors. The 14 analyzed OCs were p,p'DDT and its metabolites p,p'DDE and p,p'DDD, polychlorinated biphenyls (PCB congeners: CB28, CB52, CB101, CB118, CB138, CB153, and CB180), hexachlorocyclohexane isomers (alpha-, beta-, and gammaHCH), and hexachlorobenzene (HCB). Multivariate data analysis (MVDA) methods, including principal components analysis (PCA), partial least squares regression (PLS), and PLS discriminant analyses (PLS-DA), were first used to extract information from the complex biological and chemical data generated from each individual animal. MVDA were used to model similarities/dissimilarities regarding species (PCA, PLS-DA), sample years (PLS), and sample location (PLS-DA) to give a deeper understanding of the data that the BMF modeling was based upon. Contaminants that biomagnify, that had BMF(RSR) significantly higher than one, were p,p'DDE, CB118, HCB, CB138, CB180, CB153, ,betaHCH, and CB28. The contaminants that did not biomagnifywere p,p'DDT, p,p'DDD, alphaHCH, CB101, and CB52. Eventual biomagnification for gammaHCH could not be determined. The BMF(RSR) for OCs present in herring muscle and guillemot egg showed a broad span with large variations for each contaminant. To be able to make reliable calculations of BMFs for different contaminants, we emphasize the importance of using data based upon large numbers of, as well as well-defined, individuals.

Multivariate data analysis methods, although widely diffused since the 1960’s, are really much older. Hotelling founded both the Principal Component Analysis and the Canonical Analysis in the 30’s, by further developing previous studies made by Spearman and Pearson at the beginning of the century. These methods were subsequently expanded and enriched, but remained beyond the reach of the common practitioner because of the massive calculations they required. With the birth, and subsequent dramatic development of computers, multivariate data analysis methods have finally become practicable (Bouroche and Saporta, 1983).KeywordsFactor ScoremUltivariate MethodMultivariate Data AnalysisAmes TestHydrazine SulfateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A multivariate data analysis was performed on responses of an impedimetric biosensor to quantify Salmonella typhimurium. To develop the impedimetric biosensor, an interdigitated microelectrode (IME) was fabricated by using a semiconductor fabrication process. Anti-Salmonella antibodies were immobilized based on streptavidin–biotin binding on the surface of the IME to form an active sensing layer. The impedance spectra of the biosensor were measured at frequencies ranging between 10 Hz and 1 MHz with different concentrations of S. typhimurium in PBS and pork samples. Partial-least squares (PLS) regression models were developed and evaluated as a multivariate data analysis method. The best PLS regression model developed from impedance spectra that were acquired with different individual biosensors had the correlation coefficient of determination (R2) 0.75 and 0.59 for PBS and pork samples, respectively. The PLS models estimated the concentration of S. typhimurium with 1.49 and 1.96 Log CFU mL−1 of root mean square of error in prediction (RMSEP) for PBS and pork samples, respectively. The PLS model built with impedance data from the same biosensor improved R2 to 0.85 and 0.89, and reduced the RMSEPs to 1.13 and 1.02 Log CFU mL−1 for PBS and pork samples, respectively. This study showed the feasibility of quantifying S. typhimurium by performing multivariate analysis on responses from the impedimetric biosensor.

Geochemical evolution of produced waters from hydraulic fracturing of the Marcellus Shale, northern Appalachian Basin: A multivariate compositional data analysis approach

SummaryMethods for shape comparisons among groups originally developed in zoological studies are found to be useful in examining variation in leaf shape. These methods (sampling outlines by means of truss networks connecting landmarks or pseudolandmarks, and sheared principal components analysis) take advantage of electronic methods of data capture and multivariate data analysis. They have the desirable properties of discriminating between shape variation and variation in size, and of permitting their results to be related directly to the original measurements from which they were obtained. The utility of these methods is demonstrated with respect to a search for possible leaf shape intermediacy in putative Crataegus hybrids, and an examination of geographic variation in the cross‐sectional shape of Larix laricina needles.