Multivariate Data Analysis Approach Research Articles

Portable Mid-Infrared Device and Chemometrics for the Prediction of Low (0.5%) Total Trans Fat Content in Fast Foods.

The ruling that partially hydrogenated oils (PHO) are no longer "generally recognized as safe (GRAS)," has accelerated the replacement of PHO ingredients with fat alternatives having increasingly lower or no trans fat content. In the present study, we developed a Fourier-transform infrared (FTIR) spectroscopic procedure in conjunction with multivariate partial least squares regression (PLSR) and found it suitable for the accurate prediction of low (0.5%) total trans fat content, as percentage of total fat, measured as fatty acid methyl esters (FAME), in the lipids extracted from 24 representative fast foods. This multivariate data analysis approach is relevant because the precision of the current univariate FTIR official method (AOCS Cd 14-09) is reportedly poor below 2% of total fat, while PLSR has allowed us to accurately predict the concentration of low trans fat in fast foods. The performance of a portable FTIR device was also evaluated and compared to that of a benchtop FTIR spectrometer. For both infrared data sets, PLSR-predicted concentrations of total trans FAME, ranging from approximately 0.47% to 11.40% of total FAME, were in good agreement with those determined by a primary reference gas chromatography (GC) method (R2>0.99); high prediction accuracy was also evidenced by low root mean square error of cross-validation (RMSECV) values. The lowest RMSECV error of 0.12% was obtained with the portable device. The lowest total trans FAME concentration, determined by GC to be 0.42%, was accurately predicted by the portable FTIR/PLSR procedure as 0.47% of total FAME.

Secondary ion mass spectrometry imaging and multivariate data analysis reveal co-aggregation patterns of Populus trichocarpa leaf surface compounds on a micrometer scale.

Spatially resolved analysis of a multitude of compound classes has become feasible with the rapid advancement in mass spectrometry imaging strategies. In this study, we present a protocol that combines high lateral resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging with a multivariate data analysis (MVA) approach to probe the complex leaf surface chemistry of Populus trichocarpa. Here, epicuticular waxes (EWs) found on the adaxial leaf surface of P. trichocarpa were blotted on silicon wafers and imaged using TOF-SIMS at 10μm and 1μm lateral resolution. Intense M+● and M-● molecular ions were clearly visible, which made it possible to resolve the individual compound classes present in EWs. Series of long-chain aliphatic saturated alcohols (C21 -C30 ), hydrocarbons (C25 -C33 ) and wax esters (WEs; C44 -C48 ) were clearly observed. These data correlated with the 7 Li-chelation matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis, which yielded mostly molecular adduct ions of the analyzed compounds. Subsequently, MVA was used to interrogate the TOF-SIMS dataset for identifying hidden patterns on the leaf's surface based on its chemical profile. After the application of principal component analysis (PCA), a small number of principal components (PCs) were found to be sufficient to explain maximum variance in the data. To further confirm the contributions from pure components, a five-factor multivariate curve resolution (MCR) model was applied. Two distinct patterns of small islets, here termed 'crystals', were apparent from the resulting score plots. Based on PCA and MCR results, the crystals were found to be formed by C23 or C29 alcohols. Other less obvious patterns observed in the PCs revealed that the adaxial leaf surface is coated with a relatively homogenous layer of alcohols, hydrocarbons and WEs. The ultra-high-resolution TOF-SIMS imaging combined with the MVA approach helped to highlight the diverse patterns underlying the leaf's surface. Currently, the methods available to analyze the surface chemistry of waxes in conjunction with the spatial information related to the distribution of compounds are limited. This study uses tools that may provide important biological insights into the composition of the wax layer, how this layer is repaired after mechanical damage or insect feeding, and which transport mechanisms are involved in deploying wax constituents to specific regions on the leaf surface.

Qualitative and quantitative analysis of peanut adulteration in almond powder samples using multi-elemental fingerprinting combined with multivariate data analysis methods

In this study, adulteration of almond powder samples with peanut was analyzed using multi-elemental fingerprinting based on inductively coupled plasma optical emission measurements (ICP-OES) combined with chemometric methods. The ability of multivariate data analysis approaches, such as principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA), to achieve differentiation of samples and as partial least squares (PLS) and least squares support vector machine (LS-SVM), to quantify the adulteration based on the elemental contents has been investigated. Ten variables i.e. the contents of B, Na, Mg, K, Ca, Fe, Cu, Cu, Zn and Sr at μg g−1 level, determined by ICP-OES were used. Different almond and peanut samples were then mixed at various ratios to obtain mixtures ranging from 95/5 to 5/95 w/w and PCA-LDA was applied to classify the almonds, peanuts and adulterated samples. This method was able to differentiate peanut and almond samples from the adulterated samples. PLS and LS-SVM models were developed to quantify the adulteration ratios of almond using a training set and the constructed models were evaluated using a validation set. The root mean squared error of prediction (RMSEP) and the coefficient of determination (R2) of the validation set for PLS and LS-SVM were 3.81, 0.986 and 1.66, 0.997, respectively, which demonstrates the superiority of the LS-SVM model. The results show that the combination of multi-elemental fingerprinting with multivariate data analysis methods can be applied as an effective and feasible method for testing almond adulteration.

Numerical Taxonomy Helps Identification of Merliniidae and Telotylenchidae (Nematoda: Tylenchoidea) from Iran.

Numerical taxonomy was used for identification and grouping of the genera, species, and populations in the families Merliniidae and Telotylenchidae. The variability of each of 44 morphometric characters was evaluated by calculation of the coefficient of variability (CV) and the ratio of extremes (max/min) in the range of 1,020 measured females. Also correlation and regression analyses were made between characters to find potential collinearities. Hierarchical cluster analysis (HCA) was used for (i) grouping 21 genera in the superfamily Dolichodoroidea based on literature data coded for states of 18 diagnostic characters, and (ii) for grouping Iranian populations belonging to selected genera. Furthermore, STEPDISC analysis was used for (i) grouping 11 genera of Merliniidae and Telotylenchidae based on the measurements of 35 characters from 1,007 Iranian female specimens, and (ii) grouping measured females of eight species of Amplimerlinius and Pratylenchoides. The multivariate data analysis approach showed robust enough to summarize relationship between morphometric characters and group genera, species, and populations of the nematodes and in particular help to identify the genera and species of Amplimerlinius and Pratylenchoides.

Discrimination and Nitric Oxide Inhibitory Activity Correlation of Ajwa Dates from Different Grades and Origin.

This study was aimed at examining the variations in the metabolite constituents of the different Ajwa grades and farm origins. It is also targeted at establishing the correlations between the metabolite contents and the grades and further to the nitric oxide (NO) inhibitory activity. Identification of the metabolites was generated using 1H-NMR spectroscopy metabolomics analyses utilizing multivariate methods. The NO inhibitory activity was determined using a Griess assay. Multivariate data analysis, for both supervised and unsupervised approaches, showed clusters among different grades of Ajwa dates obtained from different farms. The compounds that contribute towards the observed separation between Ajwa samples were suggested to be phenolic compounds, ascorbic acid and phenylalanine. Ajwa dates were shown to have different metabolite compositions and exhibited a wide range of NO inhibitory activity. It is also revealed that Ajwa Grade 1 from the al-Aliah farm exhibited more than 90% NO inhibitory activity compared to the other grades and origins. Phenolic compounds were among the compounds that played a role towards the greater capacity of NO inhibitory activity shown by Ajwa Grade 1 from the al-Aliah farm.

Discriminating Brazilian crude oils using comprehensive two-dimensional gas chromatography–mass spectrometry and multiway principal component analysis

The geochemical characterization of petroleum is an essential task to develop new strategies and technologies when analyzing the commercial potential of crude oils for exploitation. Due to the chemical complexity of these samples, the use of modern analytical techniques along with multivariate exploratory data analysis approaches is an interesting strategy to extract relevant geochemical characteristics about the oils. In this work, important geochemical information obtained from crude oils from different production basins were obtained analyzing the maltene fraction of the oils by comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC×GC–QMS), and performing multiway principal component analysis (MPCA) of the chromatographic data. The results showed that four MPC explained 93.57% of the data variance, expressing mainly the differences on the profiles of the saturated hydrocarbon fraction of the oils (C13-C18 and C19-C30n-alkanes and the pristane/phytane ratio). The MPC1 grouped the samples severely biodegraded oils, while the type of the depositional paleoenvironments of the oils and its oxidation conditions (as well as their thermal maturity) could be inferred analysing others relevant MPC. Additionally, considerations about the source of the oil samples was also possible based on the overall distribution of relevant biomarkers such as the phenanthrene derivatives, tri-, tetra- and pentacyclic terpanes.

Classification of congenital disorders of glycosylation based on analysis of transferrin glycopeptides by capillary liquid chromatography-mass spectrometry

In this work, we describe a multivariate data analysis approach for data exploration and classification of the complex and large data sets generated to study the alteration of human transferrin (Tf) N-glycopeptides in patients with congenital disorders of glycosylation (CDG). Tf from healthy individuals and two types of CDG patients (CDG-I and CDG-II) is purified by immunoextraction from serum samples before trypsin digestion and separation by capillary liquid chromatography mass spectrometry (CapLC-MS). Following a targeted data analysis approach, partial least squares discriminant analysis (PLS-DA) is applied to the relative abundance of Tf glycopeptide glycoforms obtained after integration of the extracted ion chromatograms of the different samples. The performance of PLS-DA for classification of the different samples and for providing a novel insight into Tf glycopeptide glycoforms alteration in CDGs is demonstrated. Only six out of fourteen of the detected glycoforms are enough for an accurate classification. This small glycoform set may be considered a sensitive and specific novel biomarker panel for CDGs.

Time-of-flight secondary ion mass spectrometry as a screening method for the identification of degradation products in lithium-ion batteries—A multivariate data analysis approach

This paper presents a case study for the application of multivariate data analysis (MVA) to time-of-flight secondary ion mass spectrometry (ToF-SIMS) data from sample sets of mainly unknown surface composition. Aged lithium-ion battery (LIB) anodes were used as the test sample set due to their relatively complex composition. For example, LIB samples typically contain a large variety of different and often unidentified degradation products that complicate manual data processing. In this work, principal component analysis (PCA) was applied as a first step to find and classify relevant but unknown peaks in the ToF-SIMS mass spectra. As a result, peak identification was simplified in such a way that the chemical nature of 76% of the characteristic but previously unknown peaks was successfully identified. In a second step, multivariate curve resolution (MCR) was applied to depth profiles of the battery anodes for the first time, and a layered structure of the model samples was successfully determined. This approach also provided an efficient way to compare the layers' structure and the thickness across different samples. In addition to MCR, PCA was used on ToF-SIMS data to investigate all of the layer compositions of the complete sample set simultaneously. It is demonstrated that ToF-SIMS data from rarely characterized data sets can be processed successfully using MVA methods even if a priori knowledge of the sample sets is very limited. With respect to the test samples, the combination ToF-SIMS and MVA proved to be an attractive method to study the influence of different additives (vinylene carbonate, fluoroethylene carbonate, and ethylene sulfite) that appeared in the mass spectra, and was therefore helpful in understanding the formation of different degradation products in LiPF6-containing battery anodes.

A multivariate approach into physicochemical, biochemical and aromatic quality changes of purée based on Hayward kiwifruit during the final phase of ripening

The current work monitored a wide range of quality attributes of purée based on kiwifruit (Actinidia deliciosa, ‘Hayward’) during the final phase of ripening. A targeted approach was used to analyze texture, color, sugar, acidity and activity of specific enzymes. A headspace gas-chromatography mass-spectrometry analysis was used as an untargeted approach to fingerprint the volatile fractions. A multivariate data analysis approach was used combining all quality parameters (targeted and untargeted) in order to determine the most important attributes changing during late ripening. This work demonstrated that hardness, color, sugars, acids (malic and ascorbic acid) and esters and aldehyde headspace compounds were the quality indicators, changing most during kiwifruit late ripening. During late ripening, hardness decreased significantly and soluble solids content increased. The color of kiwifruit purée turned dark and less green. Sucrose decreased significantly with glucose and fructose increasing accordingly. Titratable acidity and major organic acids (quinic and citric acid) showed a similar trend increasing at start of late ripening stage and then decreasing, however malic and ascorbic acid consistently decreased during ripening. Pectin methylesterase and β-galactosidase increased and then declined, whereas peroxidase consistently increased. The formation of esters, such as methyl hexanoate and ethyl hexanoate, and a decrease of aldehydes, like hexanal, heptanal and (E)-2-hexenal, was observed. Therefore, the present work clearly demonstrated the potential of the multivariate approach to zoom into important quality indicators for the final phase of kiwifruit ripening. The outcome can provide useful information for ripening stage evaluation of kiwifruits in the processing industry.

Integrated mass spectrometric and multivariate data analysis approaches for the discrimination of organic and conventional strawberry (Fragaria ananassa Duch.) crops

In the present study, metabolic changes in secondary metabolites of Fragaria ananassa Duch. fruits during ripening were evaluated, and in particular, the differences between fruits obtained from either organic or conventional, non-organic crops.Extracts from ripe and unripe fruits of Fragaria ananassa var. Candonga were analyzed by direct infusion mass spectrometry using an instrument equipped with an ion trap analyzer (DI-ESI-IT-MS) and by liquid chromatography coupled to high resolution mass spectrometry using a mass spectrometer equipped with an ESI source and an Orbitrap analyzer (LC–ESI-FT-MS). Chromatograms were evaluated using the free software package MZmine and the resulting metabolomics data were processed. Data sets were analyzed by Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). All the differences between the fruits collected from organic crops and conventional crops were underlined. In addition, LC–ESI-FT-MS analyses were useful to achieve a comprehensive characterization of metabolic profiles of strawberries (flavonoids, anthocyanins and catechins were identified in the extracts). Flavonoids and anthocyanins were present with similar metabolic profile in F. ananassa obtained from organic and conventional, non-organic crops with differences from ripe and unripe fruits. Phenolic compounds thus are not responsive for the classification observed by untargeted metabolomic approach.

Lipidomic analysis of plasma, erythrocytes and lipoprotein fractions of cardiovascular disease patients using UHPLC/MS, MALDI-MS and multivariate data analysis.

Differences among lipidomic profiles of healthy volunteers, obese people and three groups of cardiovascular disease (CVD) patients are investigated with the goal to differentiate individual groups based on the multivariate data analysis (MDA) of lipidomic data from plasma, erythrocytes and lipoprotein fractions of more than 50 subjects. Hydrophilic interaction liquid chromatography on ultrahigh-performance liquid chromatography (HILIC-UHPLC) column coupled with electrospray ionization mass spectrometry (ESI-MS) is used for the quantitation of four classes of polar lipids (phosphatidylethanolamines, phosphatidylcholines, sphingomyelins and lysophosphatidylcholines), normal-phase UHPLC-atmospheric pressure chemical ionization MS (NP-UHPLC/APCI-MS) is applied for the quantitation of five classes of nonpolar lipids (cholesteryl esters, triacylglycerols, sterols, 1,3-diacylglycerols and 1,2-diacylglycerols) and the potential of matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is tested for the fast screening of all lipids without a chromatographic separation. Obtained results are processed by unsupervised (principal component analysis) and supervised (orthogonal partial least squares) MDA approaches to highlight the largest differences among individual groups and to identify lipid molecules with the highest impact on the group differentiation.

New and vintage solutions to enhance the plasma metabolome coverage by LC-ESI-MS untargeted metabolomics: the not-so-simple process of method performance evaluation.

Although LC-MS untargeted metabolomics continues to expand into exiting research domains, methodological issues have not been solved yet by the definition of unbiased, standardized and globally accepted analytical protocols. In the present study, the response of the plasma metabolome coverage to specific methodological choices of the sample preparation (two SPE technologies, three sample-to-solvent dilution ratios) and the LC-ESI-MS data acquisition steps of the metabolomics workflow (four RP columns, four elution solvent combinations, two solvent quality grades, postcolumn modification of the mobile phase) was investigated in a pragmatic and decision tree-like performance evaluation strategy. Quality control samples, reference plasma and human plasma from a real nutrimetabolomic study were used for intermethod comparisons. Uni- and multivariate data analysis approaches were independently applied. The highest method performance was obtained by combining the plasma hybrid extraction with the highest solvent proportion during sample preparation, the use of a RP column compatible with 100% aqueous polar phase (Atlantis T3), and the ESI enhancement by using UHPLC-MS purity grade methanol as both organic phase and postcolumn modifier. Results led to the following considerations: submit plasma samples to hybrid extraction for removal of interfering components to minimize the major sample-dependent matrix effects; avoid solvent evaporation following sample extraction if loss in detection and peak shape distortion of early eluting metabolites are not noticed; opt for a RP column for superior retention of highly polar species when analysis fractionation is not feasible; use ultrahigh quality grade solvents and "vintage" analytical tricks such as postcolumn organic enrichment of the mobile phase to enhance ESI efficiency. The final proposed protocol offers an example of how novel and old-fashioned analytical solutions may fruitfully cohabit in untargeted metabolomics protocols.

Reprint: A multivariate data analysis approach towards vibration analysis and vibration-based damage assessment: Application for delamination detection in a composite beam

This paper introduces a novel methodology for structural vibration analysis and vibration-based monitoring which utilises a special type of Principal Components Analysis (PCA), known as Singular Spectrum Analysis (SSA). In this study the methodology is introduced and demonstrated for the purposes of damage assessment in structures using their free decay response. The method׳s damage assessment properties are first demonstrated on a numerical example for a two-degree-of-freedom (2-DoF) spring–mass and damper system with nonlinear stiffness. The method is then applied to an experimental case study of a composite laminate beam. The method is based on the decomposition of the frequency domain structural variation response using new variables, the Principal Components (PCs). Only a certain number of the new variables are used to approximate the original vibration signal with very good accuracy. The presented results demonstrate the potential of the method for vibration based signal reconstruction and damage diagnosis. The healthy and the different damaged scenarios are clearly distinguishable in the new space of only two reconstructed components where a strong clustering effect is observed.

A multivariate data analysis approach towards vibration analysis and vibration-based damage assessment:: Application for delamination detection in a composite beam

This paper introduces a novel methodology for structural vibration analysis and vibration-based monitoring which utilises a special type of Principal Components Analysis (PCA), known as Singular Spectrum Analysis (SSA). In this study the methodology is introduced and demonstrated for the purposes of damage assessment in structures using their free decay response. The method׳s damage assessment properties are first demonstrated on a numerical example for a two degree-of-freedom (2DoF) spring–mass and damper system with nonlinear stiffness. The method is then applied to an experimental case study of a composite laminate beam. The method is based on the decomposition of the frequency domain structural variation response using new variables, the Principal Components (PCs). Only a certain number of new variables are used to approximate the original vibration signal with very good accuracy. The presented results demonstrate the potential of the method for vibration based signal reconstruction and damage diagnosis. The healthy and the different damaged scenarios are clearly distinguishable in the new space of only two reconstructed components where a strong clustering effect is observed.

Estimating the number of pure chemical components in a mixture by X-ray absorption spectroscopy.

Principal component analysis (PCA) is a multivariate data analysis approach commonly used in X-ray absorption spectroscopy to estimate the number of pure compounds in multicomponent mixtures. This approach seeks to describe a large number of multicomponent spectra as weighted sums of a smaller number of component spectra. These component spectra are in turn considered to be linear combinations of the spectra from the actual species present in the system from which the experimental spectra were taken. The dimension of the experimental dataset is given by the number of meaningful abstract components, as estimated by the cascade or variance of the eigenvalues (EVs), the factor indicator function (IND), or the F-test on reduced EVs. It is shown on synthetic and real spectral mixtures that the performance of the IND and F-test critically depends on the amount of noise in the data, and may result in considerable underestimation or overestimation of the number of components even for a signal-to-noise (s/n) ratio of the order of 80 (σ = 20) in a XANES dataset. For a given s/n ratio, the accuracy of the component recovery from a random mixture depends on the size of the dataset and number of components, which is not known in advance, and deteriorates for larger datasets because the analysis picks up more noise components. The scree plot of the EVs for the components yields one or two values close to the significant number of components, but the result can be ambiguous and its uncertainty is unknown. A new estimator, NSS-stat, which includes the experimental error to XANES data analysis, is introduced and tested. It is shown that NSS-stat produces superior results compared with the three traditional forms of PCA-based component-number estimation. A graphical user-friendly interface for the calculation of EVs, IND, F-test and NSS-stat from a XANES dataset has been developed under LabVIEW for Windows and is supplied in the supporting information. Its possible application to EXAFS data is discussed, and several XANES and EXAFS datasets are also included for download.

Analysis of Positive Control STR Experiments Reveals that Results Obtained for FGA, D3S1358, and D13S317 Condition the Success Rate of the Analysis of Routine Reference Samples

About 120,000 reference samples are analyzed each year in the Forensic Laboratory of Lyon. A total of 1640 positive control experiments used to validate and optimize the analytical method in the routine process were submitted to a multivariate exploratory data analysis approach with the aim of better understanding the underlying sources of variability. The peak heights of the 16 genetic markers targeted by the AmpFℓSTR(®) Identifiler(®) STR kit were used as variables of interest. Six different 3130xl genetic analyzers located in the same controlled environment were involved. Two major sources of variability were found: (i) the DNA load of the sample modulates all peak heights in a similar way so that the 16 markers are highly correlated, (ii) the genetic analyzer used with a locus-specific response for peak height and a better sensitivity for the most recently acquired. Three markers (FGA, D3S1358, and D13S317) were found to be of special interest to predict the success rate observed in the routine process.

Insights into the Motif Preference of APOBEC3 Enzymes

We used a multivariate data analysis approach to identify motifs associated with HIV hypermutation by different APOBEC3 enzymes. The analysis showed that APOBEC3G targets G mainly within GG, TG, TGG, GGG, TGGG and also GGGT. The G nucleotides flanked by a C at the 3′ end (in +1 and +2 positions) were indicated as disfavoured targets by APOBEC3G. The G nucleotides within GGGG were found to be targeted at a frequency much less than what is expected. We found that the infrequent G-to-A mutation within GGGG is not limited to the inaccessibility, to APOBEC3, of poly Gs in the central and 3′polypurine tracts (PPTs) which remain double stranded during the HIV reverse transcription. GGGG motifs outside the PPTs were also disfavoured. The motifs GGAG and GAGG were also found to be disfavoured targets for APOBEC3. The motif-dependent mutation of G within the HIV genome by members of the APOBEC3 family other than APOBEC3G was limited to GA→AA changes. The results did not show evidence of other types of context dependent G-to-A changes in the HIV genome.

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

Multivariate compositional data analysis methods were used to investigate geochemical data for water injected during hydraulic fracturing and for water produced from 19 Marcellus Shale gas wells in the northern Appalachian Basin. The data were originally published as part of an industry report. The analysis was adapted to consider the compositional nature of the data and avoid potentially spurious correlations present in raw concentration data through the application of log-ratio transformations. Techniques such as robust variation arrays, robust principal component analysis, and relative variation plots were applied to log-ratio transformed data. Results from this battery of multivariate tools indicate that two primary processes affect the chemical evolution of the water returned to the surface during the first 90days of production: mixing of injected water with formation brines of evaporated paleoseawater origin and injection of sulfate-rich water during hydraulic fracturing may stimulate sulfate reduction at some sites. Spatial variability in sulfate/alkalinity ratios appears to influence variations in geochemical controls on strontium versus barium with elevated proportions of strontium being found in more bicarbonate-poor environments, while barium is a larger proportion in sulfate-poor areas. Comparison of results using a log-ratio approach versus the more common analysis of concentration data reveals both similarities and some marked differences in the resulting interpretations. Results from this work are important in terms of both demonstrating methods to avoid mathematical inconsistencies from using raw brine geochemical data and to further investigate the geochemical controls on produced waters generated from shale gas reservoirs.

Measuring Subjective Well-Being in Taiwan

This paper explores the measurement of subjective well-being (SWB) in Taiwan through survey data as a result of 13 self-reported SWB questions. We illustrate the findings using multivariate data analysis approaches. First, by taking the first two principal component scores extracted from all SWB measurements, the biplot presents a relatively “even” society for SWBs, in which the plot depicts all data-points radiating from the center. Second, we employ factor analysis to juxtapose these 13 SWB measurements into three factors: health-related, prosperity-related, and social-related. Third and finally, this paper applies the seemingly unrelated regression model to verify the determinants of SWB. The SWB measurements are mostly increasing in higher education and (disposable) income, while falling with unemployment. Volunteering, donating more money to charities, having more leisure time, spending more hours on sports, and being involved in more arts-related activities all enhance an individual’s well-being. Gender and age may matter, but they are indecisive in the direction for various SWBs.

On the variability of the surface environment response to synoptic forcing over complex terrain: a multivariate data analysis approach

Synoptic climatology relates the atmospheric circulation with the surface environment. The aim of this study is to examine the variability of the surface meteorological patterns, which are developing under different synoptic scale categories over a suburban area with complex topography. Multivariate Data Analysis techniques were performed to a data set with surface meteorological elements. Three principal components related to the thermodynamic status of the surface environment and the two components of the wind speed were found. The variability of the surface flows was related with atmospheric circulation categories by applying Correspondence Analysis. Similar surface thermodynamic fields develop under cyclonic categories, which are contrasted with the anti-cyclonic category. A strong, steady wind flow characterized by high shear values develops under the cyclonic Closed Low and the anticyclonic H–L categories, in contrast to the variable weak flow under the anticyclonic Open Anticyclone category.