Multivariate Principal Component Analysis Research Articles

Surface Electromyography Based Core Muscle Fatigue Analysis During Repetitive Plank Using Multivariate Dimensionality Reduction Methods in Boys Aged 12-14

The aims of the study were: 1. To analyse the discriminative power of neuromuscular components for classifying the pre and post muscle fatigued states. 2. To examine whether the modification of neural recruitment strategies become more/less heterogeneous due to fatigue. 3. To research the effect of Erector Spinae (ES) muscle activity collectively with Rectus Abdominis (RA) and External Oblique (EO) muscle activity to identify the reduced spine stability during fatiguing Plank. 
 Material and methods. Twelve boys (age – 12-14 years, height 148.75 ± 10 cm, body mass 38.9 ± 7.9 kg) participated in the study. Multivariate Discriminant Analysis (DA) and Principal Component Analysis (PCA) were applied to identify the changes in the pattern of the electromyographic signals during muscle fatigue. In DA the Wilks’ lambda, p-value, canonical correlation, classification percentage and structure matrix were used. To evaluate the component validity the standard limit for Kaiser-Meyer-Olkin (KMO) was set at ≥0.529 and the p-value of Bartlett’s test was ≤0.001. The eigenvalues ≥1 were used to determine the number of Principal Components (PCs). The satisfactory percentage of non-redundant residuals were set at ≤50% with standard value >0.05. The absolute value of average communality (x̄ h2) and component loadings were set at ≥0.6, ≥0.4 respectively. 
 Results. Standardized canonical discriminant analysis showed that pre and post fatigued conditions were significantly different (p = 0.000, Wilks’ lambda = 0.297, χ2 = 24.914, df=3). The structure matrix showed that the parameter that correlated highly with the discriminant function was ES ARV (0.514). The results showed that the classification accuracy was 95.8% between fatigued conditions. In PCA the KMO values were reduced [0.547Pre fatigue vs. 0.264Post fatigue]; the value of Bartlett’s sphericity test was in pre χ2 = 90.72 (p = 0.000) and post fatigue χ2 = 85.32 (p = 0.000); The Promax criterion with Kaiser Normalization was applied because the component rotation was non-orthogonal [Component Correlation Matrix (rCCM) = 0.520 Pre fatigue >0.3Absolute<0.357Post fatigue]. In pre fatigue two PCs (cumulative s2 – 80.159%) and post fatigue three PCs (cumulative s2 – 83.845%) had eigenvalues ≥1. The x̄ h2 increased [0.802 Pre fatigue vs. 0.838 Post fatigue] and the percentage of nonredundant residuals reduced [50% Pre fatigue vs. 44% Post fatigue] from pre to post fatigue. 
 Conclusions. The variability and heterogeneity increase in the myoelectric signals due to fatigue. The co-activity of antagonist ES muscle is significantly sensitive to identify the deteriorating spine stability during the fatiguing Plank. Highly correlated motor unit recruitment strategies between ES and RA, providing supportive evidence to the concept of shared agonist-antagonist motoneuron pool or “Common Drive” phenomenon during fatigue.

Altered amino acid metabolism between coronary heart disease patients with and without type 2 diabetes by quantitative 1H NMR based metabolomics

Coronary heart disease (CHD) is a multifactorial disease associated with complicated altered metabolic pathways, especially when it occurs with type 2 diabetes (T2D). Identification of metabolic changes could provide further understanding of the underlying pathological link between with CHD and T2D. In this study, 61 controls, 30 consecutive patients with only T2D, 188 with confirmed CHD among which 50 also having T2D were enrolled. Quantitative 1H nuclear magnetic resonance (1H NMR) based metabonomic approach was used to obtain the plasma metabolic profiles of the study samples. Multivariate principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to evaluate the differences of metabolites. Compared with controls, there were significant differences in CHD patient with T2D groups including BCAAs (isoleucine, leucine, and valine) and phenylalanine by using quantitative metabolomic profiling (all p < 0.05). With OPLS-DA, the results showed that, compared to CHD patients without T2D, there had higher levels of metabolites for leucine, valine, fumarate, tyrosine, and phenylalanine in the plasma of CHD patients with T2D. Moreover, the CHD patients with T2D showed modified phenylalanine, tyrosine and tryptophan biosynthesis, valine, leucine and isoleucine biosynthesis and degradation compared with CHD patients without T2D. By 1H NMR based quantitative metabolomic method, our findings suggest that T2D has important effects on BCAAs and AAAs metabolism in CHD patients indicating altered cardiac energy metabolism and activated signaling pathway could be potential targets for therapy in CHD disease combined with T2D.

ATR-FTIR spectroscopy-based differentiation of hepatitis C and dengue infection in human freeze-dried sera

In the present study, we evaluated attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy for the differential diagnosis hepatitis C virus (HCV) and Dengue virus (DENV) infected freeze-dried sera along with healthy freeze-dried sera. Spectral signatures were recorded from 50 freeze-dried sera samples of each sample type. To achieve improved classification accurateness between the three sample types, multivariate data classification algorithms principal component analysis (PCA) and principal component regression (PCR) were used. Outcomes show 96 % inter-class variation as depicted by PCA and a classification accuracy of 99.2 % using PCR as shown by the value of R2; therefore, we propose that ATR-FTIR coupled with chemo metric data classification algorithms has considerable potential to distinguishHCV and DENV infected freeze-dried sera samples.

Characterization of edaphic fauna in different monocultures in Savanna of Piauí.

Vegetation cover may show diversity and composition patterns of the soil invertebrate community, as a function of litter quantity and quality in a specific habitat. The objective of this work was to characterize the distribution of edaphic fauna in different monocultures. The study was carried out at Chapada Grande farm in Regeneração, PI. Four monoculture areas were chosen: no-tillage soybean, eucalyptus, pasture, and a preserved native cerrado forest. Soil fauna was collected in a dry and wet period by pitfall traps containing 4% formaldehyde. The edaphic fauna was evaluated by the number of individuals per trap per day, average richness and richness, Shannon diversity index and Pielou uniformity index. Data were submitted to analysis of variance and multivariate Principal Component Analysis (PCA). The highest number of individuals per day trap and of average richness were registered in the pasture, eucalyptus and forest areas in both periods, while soybean showed lower values with predominance of Coleoptera and Formicidae groups. The pasture and forest areas showed of higher Shannon index values in the two evaluated seasons, probably due to higher contributions organic residues in the soil that favors the shelter, feeding and reproduction conditions. Regarding the Pielou index, the soybean system showed higher values in this variable. The Aranae, Coleoptera, Formicidae and Diptera groups predominated in the humid period, while Coleoptera and Formicidae predominated in the dry period. Systems that generate greater accumulation of residues harbor a greater diversity of invertebrates of the edaphic fauna. Seasonality had an effect on all variables analyzed and the wet period showed more expressive values.

Sustainable water management in mineral processing by using multivariate variography to improve sampling procedures

Due to the rising pressure from governmental regulations, the water recycling rate has increased significantly in mining operations over the last decades, resulting in a high variation of process water quality, which could potentially impact the plant performance. The current effort to assess water quality in mining is shifting from managing water to fulfill environmental regulations (focus on the effluents) to controlling water quality to maintain the operating performance (focus on the water within the process). However, minimal effort has been made to design a dedicated sampling procedure for process water. This study investigates the use of multivariate variography and principal component analysis (PCA) for improving the process water sampling procedure at the Kevitsa Mine, Finland. The aim is to design a sampling procedure for evaluating water quality using two different types of datasets and illustrating the impact of the dataset structure on the sampling design. The results showed that the common spot sampling procedure generated a very high sampling error and was not the best practice for process water. The weekly sampling frequency used at the mine site, suitable for fulfilling environmental regulations was too low to capture the process water variation. Therefore, it is not recommended to use environmental water datasets for operating control purposes. The multivariate variographic analysis revealed the hidden cyclic variation through its ability to summarize the time variations and the correlation between multiple variables that were not visible through the classical univariate variogram approach. However, the number of increments recommended by the global multivariogram became impractically high. Hence, an alternative approach combining PCA to the mutivariogram was used to filter noise from the data and keep the relevant information. This study highlights the benefits of using multivariate variography to improve water sampling procedures in the mining industry and to reduce both operational and environmental risks associated with water quality variability. Thus, this method has the potential to be used in worldwide mining operations as a standard procedure for sampling water to provide reliable results.

Gas Chromatography-Mass Spectrometry Analysis of Compounds Emitted by Pepper Yellow Leaf Curl Virus-Infected Chili Plants: A Preliminary Study

Pepper yellow leaf curl virus (PYLCV) is a threat to chili plants and can significantly reduce yields. This study aimed as a pilot project to detect PYLCV by analyzing compounds emitted by chili plants using gas chromatography-mass spectrometry (GC-MS). The samples investigated in this research were PYLCV-infected and PYLCV-undetected chili plants taken from commercial chili fields. The infection status was validated by using a polymerase chain reaction (PCR) test. A headspace technique was used to extract the volatile organic compounds emitted by plants. The analysis of GC-MS results began with pre-processing, analyzing sample compound variability with a boxplot analysis, and sample classification by using a multivariate technique. Unsupervised multivariate technique principal component analysis (PCA) was performed to discover whether GC-MS could identify PYLCV-infected or not. The results showed that PYLCV-infected and PYLCV-undetected chili plants could be differentiated, with a total percent variance of the first three principal components reaching 91.32%, and successfully discriminated between PYLCV-infected and PYLCV-undetected chili plants. However, more comprehensive studies are needed to find the potential biomarkers of the infected plants.

Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of hbox {NO}_{{2}}, hbox {PM}_{{10}}, hbox {PM}_{2.5} and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the hbox {PM}_{{10}} and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

Serum Metabolomics Identifies Dysregulated Pathways and Potential Metabolic Biomarkers for Hyperuricemia and Gout.

To systematically profile metabolic alterations and dysregulated metabolic pathways in hyperuricemia and gout, and to identify potential metabolite biomarkers to discriminate gout from asymptomatic hyperuricemia. Serum samples from 330 participants, including 109 with gout, 102 with asymptomatic hyperuricemia, and 119 normouricemic controls, were analyzed by high-resolution mass spectrometry-based metabolomics. Multivariate principal components analysis and orthogonal partial least squares discriminant analysis were performed to explore differential metabolites and pathways. A multivariate methods with Unbiased Variable selection in R (MUVR) algorithm was performed to identify potential biomarkers and build multivariate diagnostic models using 3 machine learning algorithms: random forest, support vector machine, and logistic regression. Univariate analysis demonstrated that there was a greater difference between the metabolic profiles of patients with gout and normouricemic controls than between the metabolic profiles of individuals with hyperuricemia and normouricemic controls, while gout and hyperuricemia showed clear metabolomic differences. Pathway enrichment analysis found diverse significantly dysregulated pathways in individuals with hyperuricemia and patients with gout compared to normouricemic controls, among which arginine metabolism appeared to play a critical role. The multivariate diagnostic model using MUVR found 13 metabolites as potential biomarkers to differentiate hyperuricemia and gout from normouricemia. Two-thirds of the samples were randomly selected as a training set, and the remainder were used as a validation set. Receiver operating characteristic analysis of 7 metabolites yielded an area under the curve of 0.83-0.87 in the training set and 0.78-0.84 in the validation set for distinguishing gout from asymptomatic hyperuricemia by 3 machine learning algorithms. Gout and hyperuricemia have distinct serum metabolomic signatures. This diagnostic model has the potential to improve current gout care through early detection or prediction of progression to gout from hyperuricemia.

The methodology of the electrical conductivity test for Carthamus tinctorius L. seeds

ABSTRACT The electrical conductivity test stands out for generating quick responses for lot differentiation; however, studies are needed to improve this practice in safflower seeds. This study aimed to verify the efficiency and establish a methodology of the electrical conductivity test for safflower seeds. Initially, 12 lots were characterized regarding physical properties and physiological potential. For electrical conductivity, the experiment was carried out in three stages with the 12 lots: firstly, the soaking period was determined (2, 4, 6, 8, and 16 hours), then the volume was evaluated (50, 75, 100, 150, and 200 mL), and finally the number of seeds (25 and 50 seeds) was determined. The electrical conductivity test using 25 seeds in 50 mL of distilled water for six hours of soaking enables the differentiation of safflower seed lots. Multivariate analysis of principal components is efficient in discriminating the vigor of safflower seed lots.

MULTIVARIATE ANALYSIS APPLIED TO SPRAY DEPOSITION IN GROUND APPLICATION OF PHYTOSANITARY PRODUCTS IN COFFEE PLANTS

An adequate combination of factors involved in the technology used for phytosanitary product application contributes to an efficient spray deposition on the target. The objective of this study was to use multivariate analysis to characterize the magnitude of effects and the order of influence of three factors that interfere with the quality of phytosanitary product application in coffee plants. An entirely randomized design was adopted, with four repetitions, using a 2 × 2 × 3 factorial scheme, with two classes of droplets quality (fine and coarse), two application rates (250 and 400 L ha-1), and the use of adjuvants (with no adjuvant or with Fighter®and Aureo® adjuvants). The quality of the application was determined by jointly analyzing the spray deposition on three thirds of leaves, in their internal and external layers, the runoff to soil, coverage, droplet density, relative amplitude, and the volumetric median diameter. The results underwent analysis of variance (ANOVA) to measure the effect sizes (η2). After testing the assumptions of multivariate analysis, clustering and principal component analyses were performed. The class of droplets was found to be the most influential factor in the quality of the phytosanitary product application (spray deposition and runoff to soil). When focusing on spray deposition on leaves, the second-most influential factor was the application rate and the relation between the application rate and the adjuvants. For the other variables, the second-most influential factor was the application rate.

Stochastic Simulation of Tropical Cyclones for Risk Assessment at One Go: A Multivariate Functional PCA Approach

AbstractA multivariate functional principal component analysis approach to the full‐track simulation of tropical cyclones (TCs) is developed for risk assessment. Elemental variables of TC along the track necessary for risk assessment, such as center coordinates, maximum wind speed, minimum central pressure and ordinal dates, can be simulated simultaneously at one go, using solely the best‐track data with no data supplemented from any other sources. The simulation model is optimally determined by means of the ladle estimator. A TC occurrence model using the Conway–Maxwell–Poisson distribution is proposed as well, by which different dispersion features of annual occurrence can be represented in a unified manner. With the occurrence model, TCs can be simulated on an annual basis. The modeling and simulation process is programmed and fully automated such that little manual intervention is required, which greatly improves the modeling efficiency and reduces the turnaround time, especially when newly available TC data are incorporated periodically into the model. Comprehensive evaluation shows that this approach is capable of generating high‐performance synthetic TCs in terms of distributional and extreme value features, which can be used in conjunction with wind field and engineering vulnerability models to estimate economic and insurance losses for governments and insurance/reinsurance industry.

Prognostic Modeling of Parkinson's Disease Progression Using Early Longitudinal Patterns of Change.

Predicting Parkinson's disease (PD) progression may enable better adaptive and targeted treatment planning. Develop a prognostic model using multiple, easily acquired longitudinal measures to predict temporal clinical progression from Hoehn and Yahr (H&Y) stage 1 or 2 to stage 3 in early PD. Predictive longitudinal measures of PD progression were identified by the joint modeling method. Measures were extracted by multivariate functional principal component analysis methods and used as covariates in Cox proportional hazards models. The optimal model was developed from the Parkinson's Progression Marker Initiative (PPMI) data set and confirmed with external validation from the Longitudinal and Biomarker Study in PD (LABS-PD) study. The proposed prognostic model with longitudinal information of selected clinical measures showed significant advantages in predicting PD temporal progression in comparison to a model with only baseline information (iAUC=0.812 vs. 0.743). The modeling results allowed the development of a prognostic index for categorizing PD patients into low, mid, and high risk of progression to HY 3 that is offered to facilitate physician-patient discussion on prognosis. Incorporating longitudinal information of multiple clinical measures significantly enhances predictive performance of prognostic models. Furthermore, the proposed prognostic index enables clinicians to classify patients into different risk groups, which could be adaptively updated as new longitudinal information becomes available. Modeling of this type allows clinicians to utilize observational data sets that inform on disease natural history and specifically, for precision medicine, allows the insertion of a patient's clinical data to calculate prognostic estimates at the individual case level. © 2021 International Parkinson and Movement Disorder Society.

Potential of amyl alcohol mixtures derived from Scenedesmus quadricauda microalgae biomass as third generation bioenergy for compression ignition engine applications using multivariate-desirability analysis

ABSTRACT This study is aimed at synthesizing amyl alcohol from Scenedesmus quadricauda third-generation microalgae biomass which is cultivated sustainably using reactor combinations and Ehrlich biosynthetic pathway. Systematic experimentation was carried out on a compression ignition engine at maximum throttle pedal position condition to assess the potential of amyl alcohol-diesel mixture combinations and to assess its environmental impact at different loads. Optimum mixture proportion of amyl alcohol-diesel blends were identified statistically using multivariate principal component and desirability analysis based on the experimental data acquired from the engine testbed. From the study, it was inferred that a dry biomass yield rate of 4.2 g/L was achieved when the light intensity was maintained at ~51-56 μmol/m2/s in the in-situ raceway pond while cultivating Scenedesmus quadricauda. Experimentation showed that the fuel consumption reduced by 72.4% for AA20D80 at low-high load conditions. Improved combustion characteristics are achieved at higher concentrations of amyl alcohol operating at medium-high loads. The lowest smoke and NOX emission of 52.85 mg/m3 and 104 ppm was observed for AA40D60 at no-load condition. Statistical analysis revealed that the optimal amyl alcohol-diesel mixture combinations to be AA20D80 with combined desirability of 0.497.

Grain quality of common bean cultivars under low and high nitrogen dose

This study aimed to evaluate the effects of low and high topdressing doses of nitrogen (N) on the qualitative attributes of common beans (Phaseolus vulgaris L.) cultivars and indicate cultivars with better grain technological characteristics. The experimental design was randomized blocks in split plots, with four replicates. Plots comprised 16 common bean cultivars of the commercial grain ‘Carioca’, while subplots comprised two N doses: 20 and 120 kg ha-1 applied as topdressing. The following evaluations were carried out: sieve yield (SY), relative grain production on sieves (RGPS), crude protein content (PROT), final water volume absorbed (FVabs), time for maximum hydration (TMH), hydration ratio (HR), cooking time (CT) and resistance to cooking (RC). The results were subjected to analysis of variance by F test and means were grouped by the Scott-Knott test. Multivariate principal component analysis was used to identify processes. The grain quality of common bean is more dependent on the genotype studied than on agricultural management such as nitrogen fertilization. The increase in the N dose applied as topdressing increases the size of common bean grains and their protein content, but with little influence in grain hydration. FVabs and CT depend on the interaction between cultivar and N doses. CT had an inverse correlation with PROT, but it was little pronounced. The cultivars BRSMG Uai, IAC Alvorada, TAA Dama and TAA Bola Cheia have the best grain quality characteristics. Highlights: Cultivar interferes more in the common bean technological quality than nitrogen fertilization. Grains crude protein content had an inverse correlation with the cooking time. Nitrogen fertilization increases the grains size and crude protein content of common bean, but with little influence in grain hydration. The cultivars BRSMG Uai, IAC Alvorada, TAA Dama and TAA Bola Cheia have the best grain quality characteristics.

Distribution, source identification and potential ecological risk of heavy metals in surface sediments of the Mongla port area, Bangladesh

This study comprehensively investigated the distribution, source identification, and potential ecological risk of heavy metals (As, Ni, Pb, Cd, Cr, Cu, and Zn) in surface sediments of the Mongla port area of Bangladesh. The sediment samples were collected from ten sampling points during the wet and dry season and heavy metals were analyzed by atomic absorption spectrophotometer. The average concentration of heavy metal in sediments was higher during the dry season (11.36 to 118.55 mg/kg) as compared with the wet season (8.70 to 88.58 mg/kg). Certain indices, including the enrichment factor, geoaccumulation index, and contamination factor demonstrated that Cd was highly responsible for sediment pollution. The range of pollution load index values was 1.12 to 1.55 during wet and 1.59 to 2.27 during the dry season, indicating progressive deterioration of sediments by studied heavy metals. Multivariate principal component analysis showed that the source of As, Cd, Cr, Ni, Cu and, Zn in the port area surface sediment mainly due to anthropogenic activities. Considering the potential ecological risk, surface sediments of the Mongla port area showed moderate to considerable ecological risk. Potential acute toxicity results in most of the sampling points during the dry season was greater than 4, shows moderate to the severe toxicity of heavy metals in sediment. Further studies to assess the widespread risk of heavy metals are recommended to protect the aquatic ecosystem from heavy metal contamination.

Land-based sediment sources and transport to southwest Puerto Rico coral reefs after Hurricane Maria, May 2017 to June 2018

The effects of runoff from land on nearshore ecosystems, including coral reef communities, are influenced by both sediment supply and removal by coastal processes. Integrated studies across the land-sea interface describing sources and transport of terrestrial sediment and its nearshore fate allow reef protection initiatives to target key onshore and offshore areas. Geochemical signatures in the fine fraction of terrestrial sediment from watersheds in southwest Puerto Rico were determined by multivariate principal component analysis and used to identify terrestrial sources of sediment runoff to nearshore coral reefs. Sediment settling out of suspension at reefs was collected at approximately 2 month-long intervals in bottom-mounted sediment traps from May 2017 to June 2018, a period that included Hurricanes Irma and Maria. Bulk sediment accumulation rates in traps exceeded a 10 mg/cm2/d threshold found to stress corals at 5 of 7 reef sites throughout the 13 month-long study. Geochemical signatures showed that watersheds 10s km to the east were a predominant, year-round source of fine sediment to reefs offshore of Guánica Bay and could have introduced sediment-bound contaminants due to a higher degree of industrialization and urbanization than the local watershed. Sediment runoff from the local watershed appeared to be constrained to a narrow band close to shore. During the 2.5 months after Hurricanes Irma and Maria, bulk sediment accumulation rates increased substantially and fine sediment geochemical signatures were indicative of predominantly distal sources, except outside of the mouth of Guánica Bay, which was strongly impacted by local runoff. Mass wasting, sediment runoff, and coastal turbidity persisted for months after Hurricane Maria and could account for the appearance of a small fraction of geochemical variance from a distal sediment source that appeared in reef traps 4 months post-hurricane and persisted through the end of the study 9 months post-hurricane. Sediment geochemical sourcing in temporally resolved records from sediment traps showed how landscape-scale changes after a major hurricane affected both near-term and long-term sediment delivery to reef communities. In addition, the importance of fine sediment advection from distal sources indicates that successful reduction of land-based pressures on nearshore ecosystems will require cross-jurisdictional strategies.

Multivariate Neighborhood Trajectory Analysis: An Exploration of the Functional Data Analysis Approach

Recent neighborhood studies have focused on longitudinal aspects of neighborhood change and data‐mining methodologies that identify neighborhood trajectory patterns using time‐series multivariate census data. Existing neighborhood trajectory models capture neighborhood change by stacking cross‐sectional neighborhood clustering results across years and analyzing the discrete stepwise switching patterns between the clusters. Taking a different approach, we employ the functional data analysis (FDA) method to analyze longitudinal patterns of neighborhood change from mathematically represented multivariate time‐dependent curves to identify neighborhood trajectory clusters. This FDA‐based neighborhood trajectory model incorporates a multivariate functional principal component analysis and k‐means clustering. We have applied our model to neighborhoods in the Charlotte and Detroit metropolitan areas to identify ongoing racial and socioeconomic segregation patterns and the time dynamics of neighborhood change.

Comparison of physicochemical properties, amino acids, mineral elements, total phenolic compounds, and antioxidant capacity of Cuban fruit and rice wines.

Physicochemical characterization, amino acids contents, minerals composition, total phenolic compounds, and antioxidant capacity of Cuban wines from different raw materials were studied. The wines studied were grape wines, tropical fruit wines, and rice wines. Twenty‐one amino acids were identified and quantified, being Asp and Glu detected in all wines. The highest concentration of total amino acid content was found in wines elaborated from Cimarrona grape subjected to maceration with grape skins, while the raisined mixture grape wine presented the lowest values, probably caused by the amino acid degradation during the dehydration process by sun exposure. Minerals quantified were range amount limits of acceptable according to the OIV recommendation. Total phenolic compounds and antioxidant capacity showed the greatest values in wine from roasting rice. No statistical separation could be clearly observed by multivariate principal component analysis; however, 3 wine groups could be defined taking account the scores on the PC1.

Multivariate Analyses of Selected Hydro-bacterial Variables along the Longitudinal Gradient of Orani River, Philippines

There is paucity of environmental data for Orani river systems in Bataan (Philippines) despite its regional economic and ecological significance. The present study conducted multivariate analyses of various water quality parameters and selected bacterial communities to evaluate the river’s health integrity. River data exhibited spatial and seasonal variation, with observed significantly distinct heterotrophic and Gram-negative bacterial counts in the midstream and downstream against upstream samples. Fecal coliform concentrations were statistically different in the sequence: upstream < downstream < midstream. Multivariate Principal Component Analysis identified the most important variables affecting the variability in bacterial concentrations among the sampling groups (81.64% variation). Cluster analysis also revealed the associations and similarities of sampling groups based on abundance of bacterial loadings. Further, water quality indices were classified within “marginal” class (54.77 for wet season; 57.67 for dry season). The hydrological index estimated a score of 47.97 for wet season that described as “suitable with high restriction” class (30<(HI)c<55), and 62.24 for dry season in the “suitable with medium restriction” class (55<(HI)c<75).

Quantitative analysis of volatile compounds of four Chinese traditional liquors by SPME-GC-MS and determination of total phenolic contents and antioxidant activities

Abstract The aim of this work was to investigate the volatile compositions of four Chinese functional liquors. For this purpose, volatile compounds of four liquors were extracted with head-space solid-phase microextraction (HS-SPME) and analyzed with gas chromatography-mass spectrometry (GC-MS) along with the determination of odor activity value (OAV) and relative odor contribution (ROC). Sixty volatiles were tentatively identified and categorized into the following seven groups: alcohols, esters, fatty acids, carbonyl compound, hydrocarbons, phenols, and other components. The differences in chemical composition of volatile compounds were visualized with heat maps. Odorants were compared with different samples using a statistical analysis of Venn diagrams and a multivariate principal component analysis, and ethyl hexanoate, ethyl acetate, and ethyl octanoate were found to be the key odorants. Besides, abundant phenolic contents and high antioxidant ability of four Chinese functional liquors could potentially bring better health-boosting effects.