Leaf anatomical study of Onosma (Section Onosma , Subsection Haplotricha ) Boraginaceae in Iran

Objectives The determination of chemical components is usually used in the quality control of propolis. However, chemical components from different types of propolis are similar. The objective of this investigation was to establish a method based on a specific chemical fingerprint profile and a multivariate mixed model statistical analysis which could easily distinguish propolis of different origins and promote the quality control of propolis. Methods A novel approach using high performance liquid chromatography (HPLC) coupled with multivariate statistical analysis was established for profiling and distinguishing Chinese and Brazilian green propolis. A batch of 22 propolis samples was analyzed, and the datasets on retention time, peak area and sample codes were subjected to mixed multivariate statistical analysis consisting of principal component analysis (PCA) and a self-organization mapping net (SOM). Results The fingerprints were profiled. PCA score plots showed Chinese and Brazilian green propolis clearly classified into two groups. The visualized SOM results showed data from the two groups projected to the adjacent neurons clearly separated from each other. Artepillin C, which contributed greatly to the differentiation, was screened out and identified as the reference compound. Artepillin C is the characteristic component in Brazilian propolis which can be used as chemical marker to distinguish propolis of different origins. Conclusions In this study, fingerprints coupled with multivariate statistical analysis have been successfully applied to distinguish Chinese from Brazilian green propolis. The research identified a chemical marker, and thus helps to investigate and promote the quality control of propolis.

Monitoring of chemical and physical-chemical parameters of surface water is a very important factor in the quality control and management. Surface water quality is largely determined by atmospherics (natural process) and the discharge of industrial and municipal waste water (anthropogenic process). By applying different statistical methods (multivariate statistical analysis) can significantly reduce the bulkiness of the available data obtained by monitoring, and therefore the correct interpretation of the results of the quality and the ecological status of water. In this paper, using multivariate statistical analysis (cluster analysis, factor analysis and principal components analysis) processed the results of the analysis of surface water in AP Vojvodina during the 2011 year. Based on the results of statistical analysis it could by identified the main factors that have an impact on the ecological status and ecological potential of water flows. In this way it can improve the existing monitoring network.

This study was carried out to monitor water quality in Uzuncayir Dam Lake from May 2013 to March 2014 based on monthly sample monitoring from ten stations. Temperature, dissolved oxygen, pH, biological oxygen demand (BOD), electrical conductivity, total dissolved solids, total hardness, total alkalinity, specific conductance, calcium (Ca), magnesium (Mg), orthophosphate-phosphorus (PO 4 -P), ammonium (NH 4 ), nitrate (NO 3 ), nitrite (NO 2 ), chlorophyll a (Chl-a) parameters were investigated during study. Data obtained were evaluated by using multivariate statistical analysis, cluster analysis, principal component analysis and factor analysis. Uzuncayir Dam Lake is defined as a hard water lake because of the high pH and high electrical conductivity and total solids content as the result of the high content of dissolved anions and cations. Principal components (PCA) analysis revealed in two different clusters to the similarities among the stations reflected different physico-chemical properties and the level of pollution in Uzuncayir Dam Lake. Three potential factors were determined explaining 100% of the total variation of the data. In component 1, 17.7 % of all variation had Mg, NH 4 and NO 2 . According to the component 2 and 3 the results showed that 34.95 % of all variation stem from agricultural drainage and nutrient, from physical parameters of 68.2 %. In analyses of cluster, one group (10 station) was found to affect directly the activities of domestic and agricultural land. Based on the Surface Water Quality Management (A and B group), it was concluded that Uzuncayir Dam Lake had high quality and slightly contaminated and the appropriate structure for many activities such as agricultural irrigation, drinking water supply, trout and other fish production.

Water quality deterioration due to industrialization and urbanization is a growing environmental concern, particularly in developing regions. This study assesses the surface water quality of Kurichi and Big Lakes in the Ukkadam area, Coimbatore, India, using multivariate statistical techniques to identify key pollution sources and evaluate contamination levels. Despite prior research on water quality in urban lakes, limited studies have systematically analyzed multiple contaminants using advanced statistical approaches. A total of 12 water samples were collected between June 2023-June 2024 and analyzed for physicochemical, microbiological, and anionic parameters. Principal Component Analysis (PCA) and Factor Analysis (FA) revealed three dominant components explaining 68.42% and 42.81% of the total variance in Kurichi and Big Lakes, respectively. The Piper plot classified water types, while Cluster Analysis (CA) grouped sampling sites based on contamination levels. The Pearson correlation matrix determined pollutant interdependencies, and the Water Quality Index (WQI) categorized pollution severity against WHO and BIS standards. The results indicate that organic matter, industrial discharge, fertilizer runoff, and untreated wastewater are the primary contributors to water pollution. High pollution levels were detected near industrial zones, with Kurichi Lake exhibiting significantly poorer water quality than Big Lake. The findings highlight the urgent need for improved wastewater management and pollution control policies to safeguard aquatic ecosystems and public health. PRACTITIONER POINTS: Multivariate Statistical Analysis: Applied PCA, FA, Piper plot, CA, and Pearson correlation matrix to assess water quality. Water Quality Index (WQI) Classification: Identified pollution sources and categorized water quality based on WHO and BIS standards. Principal Component Analysis (PCA) Findings: Three major components explained 68.42% and 42.81% of the total variation in Kurichi and Big Lakes, respectively. Major Pollution Sources: Factor analysis identified organic compounds, human activity, fertilizers, chemical waste, and wastewater discharge as primary contaminants. Industrial Area Impact: CA and WQI results highlighted high pollution sensitivity near industrial zones.

In today’s society, under the demand for high-quality talents, the professionals cultivated in colleges and universities should not only master professional skills but also need to have the spirit of innovation and entrepreneurial ability in professional aspects. In this paper, firstly, the multivariate statistical analysis method is explained, and the basic principles of the factor analysis model, principal component analysis method and systematic clustering method are given to analyze the data in the following article. Secondly, starting from the necessity of the integration of bi-initiative education and professional education of teacher training majors in colleges and universities, we constructed the 4344 specialized and innovative integrated talent cultivation model of teacher training majors and constructed an evaluation system to evaluate the effectiveness. Finally, based on the evaluation system, the data analysis of the specialized and creative integrated talent cultivation model was carried out using factor analysis, principal component analysis and systematic clustering method. The results show that in the factor analysis, the highest loading value of the first principal factor is 0.917, the contribution rate of the first principal factor is 39.67%, and the loading value of the principal component factor reaches the highest value of 0.925. The clustering analysis is based on the results of the factor analysis, and the respondents are divided into 4 clusters. The number of people in the 2nd category is more than 30 people, which accounts for about 31.41% of the total number of people. The method of multivariate statistical analysis can be used to analyze the data effectively for the specialized and integrated personnel training mode of teacher training in colleges and universities and also gives the path of specialized and integrated personnel training for teacher training.

The Shatt Al Arab River (SAAR) is a major source of raw water for most water treatment plants (WTP’s) located along with it in Basrah province. This study aims to determine the effects of different variables on water quality of the SAAR, using multivariate statistical analysis. Seventeen variables were measured in nine WTP’s during 2017, these sites are Al Hussain (1), Awaissan (2), Al Abass (3), Al Garma (4), Mhaigran (5), Al Asmaee (6), Al Jubaila (7), Al Baradia (8), Al Lebani (9). The dataset is treated using principal component analysis (PCA) / factor analysis (FA), cluster analysis (CA) to the most important factors affecting water quality, sources of contamination and the suitability of water for drinking and irrigation. Three factors are responsible for the data structure representing 88.86% of the total variance in the dataset. CA shows three different groups of similarity between the sampling stations, in which station 5 (Mhaigran) is more contami-nated than others, while station 3 (Al Abass) and 6 (Al Asmaee) are less contaminated. Electrical conductivity (EC) and sodium adsorption ratio (SAR) are plotted on Richard diagram. It is shown that the samples of water of Mhaigran are locat-ed in the class of C4-S3 of very high salinity and sodium, water samples of Al Abass station, are located in the class of C3-S1 of high salinity and low sodium, and others are located in the class of C4-S2 of high salinity and medium sodium. Generally, the results of most water quality parameters reveal that SAAR is not within the permissible levels of drinking and irrigation.

ABSTRACTIn order to assess heavy metal pollution of sediment samples collected from Shuangtaizi estuary, contamination factor (CF) and multivariate statistical analyses, including principal component analysis (PCA), cluster analysis (CA) and correlation analysis, are carried out in this paper. CF confirms that Pb, Cu and Hg concentrations are very low and all fall within the range of background, while Zn and Cd demonstrate moderate contamination (in A10, A13, A15, A16 and A17 sites) and very high contamination (in A10, A13, A15 and A17 sites), respectively. The PCA indicates that four significant principal components (PCs) are extracted, explaining 88.959% of total variance, which suggests that Pb, Cu and Zn are mainly associated with organic carbon (OC). The result from CA is consistent with that obtained from PCA, classifying that the heavy metals in two clusters derive from different sources. Correlation analysis supports the conclusions from PCA and CA, elucidating the relationships between heavy metals and particle sizes of the sediments.

Objectives : 14 reservoirs in the Geum river watershed were clustered and classified using the results of factor analysis based on water quality characteristics. Also, correlation analysis between pollutants (land system, living system, livestock system) and water quality characteristics was performed to elucidate the effect of pollutants on water quality.Methods : Cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed during the last 5 years (2014-2018) were performed to derive the principal components. Then, correlation analysis between principal components and pollutants was performed to verify the feasibility of clustering.Results and Discussion : From the factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed, three to six principal components (PCs) were extracted and extracted PCs explained approximately 74% of overall variations in water quality. As a result of clustering reservoirs based on the extracted PCs, the reservoirs clustered by nitrogen and seasonal PCs were Ganwol, Geumgang, and Sapgyo, the reservoirs clustered by organic pollution and internal production PCs were Tapjung, Dae, Seokmun, and Yongdam, the reservoirs clustered by organic pollution, internal production, and phosphorus are Bunam, Yedang, and Cheongcheon, and finally the remaining Boryeong, Daecheong, Chopyeong, and Songak were clustered as other factors. From the correlation analysis between principal components and pollutants, significant correlation between the land, living, and livestock pollutants and water quality characteristics was found in Ganwol, Topjeong, Daeho, Bunam, and Daecheong. These reservoirs are considered to require continuous and careful management of specific (land, living, livestock) pollutants. In terms of water quality and pollutant management, the Ganwol, Sapgyo, and Seokmunho are considered to implement intensive measures to improve water quality and to reduce the input of various pollutants.Conclusions : Although the water quality of the reservoir is a result of complex interactions such as influent water factors, morphological and hydrological factors, internal production factors, and various pollutants, optimized watershed and water quality management measures can be implemented through multivariate statistical analysis.

In order to avoid the frequent severe crashes, it is crucial to find out the main influential factors of these crashes, according to previous crash data. The paper analyzed the importance of various factors based on the human-vehicle-road system, using two methods of multivariate statistical analysis (cluster analysis and factor analysis). The data of 166 severe traffic crashes, each causing 10 deaths and above (hereinafter referred to as severe traffic crashes), in China from 2008 to 2014 was collected. Descriptive statistical analysis of the collected data was performed, from which we extracted 23 main factors, involving human, vehicle, and road. Cluster analysis was used to classify the 23 factors. The similarities between different variables were calculated by Ward's method. With hierarchical clustering of variables, this paper formed a detailed classification system and drew the tree diagram according to the clustering process. Next, taking relevant experience into account, the importance classification results of the factors were obtained. On the basis of cluster analysis, this paper used factor analysis method to extract the common factors from the variables. We calculated the factor loading matrix by principal component analysis, rotated the matrix by maximum deviation method, and then calculated the factor scores by regression method. Finally, this paper figured out the weight of every influential factor through the variance contribution rate of each common factor and the factor score matrix, from which the 23 factors were ranked. The comprehensive analysis shows that, among all affecting factors, the main factors and their weights as follows: speeding (8.0%), ramp (6.3%), weather (6.1%), road surface (5.9%), driving experience (5.6%), road alignment (5.4%), etc. Based on this result, the following suggestions can be put forward to improve the traffic safety situation: To curb speeding and other illegal driving behaviors; To improve protection facilities at curves and ramps; To maintain the road surface condition in adverse weather; To strengthen the management and training of novice drivers; To enhance the traffic supervision during holidays. The statistical analysis methods and models used in this paper are expected to play a role in the application of big data in traffic in future.

BackgroundLeaf hydraulic and economics traits are critical for balancing plant water and CO2 exchange, and their relationship has been widely studied. Leaf anatomical traits determine the efficiency of CO2 diffusion within mesophyll structure. However, it remains unclear whether leaf anatomical traits are associated with leaf hydraulic and economics traits acclimation to long-term drought.ResultsTo address this knowledge gap, eight hydraulic traits, including stomatal and venation structures, four economics traits, including leaf dry mass per area (LMA) and the ratio between palisade and spongy mesophyll thickness (PT/ST), and four anatomical traits related to CO2 diffusion were measured in tomato seedlings under the long-term drought conditions. Redundancy analysis indicated that the long-term drought decreased stomatal conductance (gs) mainly due to a synchronized reduction in hydraulic structure such as leaf hydraulic conductance (Kleaf) and major vein width. Simultaneously, stomatal aperture on the adaxial surface and minor vein density (VDminor) also contributed a lot to this reduction. The decreases in mesophyll thickness (Tmes) and chlorophyll surface area exposed to leaf intercellular air spaces (Sc/S) were primarily responsible for the decline of mesophyll conductance (gm) thereby affecting photosynthesis. Drought increased leaf density (LD) thus limited CO2 diffusion. In addition, LMA may not be important in regulating gm in tomato under drought. Principal component analysis revealed that main anatomical traits such as Tmes and Sc/S were positively correlated to Kleaf, VDminor and leaf thickness (LT), while negatively associated with PT/ST.ConclusionsThese findings indicated that leaf anatomy plays an important role in maintaining the balance between water supply and CO2 diffusion responses to drought. There was a strong coordination between leaf hydraulic, anatomical, and economical traits in tomato seedlings acclimation to long-term drought.

The study has been carried out to assess surface water quality of Old Brahmaputra River from September 2015 to March 2016. DO, BOD5, COD, pH, EC, Chloride, Alkalinity, and Hardness concentrations in water samples have been found to range within 0.66-2.9 mg/L, 21-138 mg/L, 45-250 mg/L, 7.1-7.8, 185-1080 uS/cm, 10-98 mg/L, 85-197 mg/L, and 84-148 mg/L, respectively. Multivariate statistical analyses such as Principal Component Analysis (PCA) and Correlation Matrix (CM) reveal significant anthropogenic intrusion of pollutants in water, while Cluster Analysis (CA) gives decent results that render three different groups of resemblance between the two sampling sites, reflecting the different water quality indicators of the river system. Very strong positive linear relations have been found between Alkalinity vs. Chloride (0.998), COD vs. BOD (0.994), Chloride vs. EC (0.981), Alkalinity vs. EC (0.976), and Hardness vs. EC (0.952) at the significance level of 0.01, which direct their common origin from industrial effluents, municipal wastes, and agricultural activities. River Pollution Index (RPI) indicates that the water of the Old Brahmaputra River varies from low to high pollution.

Multivariate statistical analysis explains the huge and complicated current situation of the original data efficiently, concisely, and explicitly. It simplifies the original data into representative factors, or bases on the similarity between data to cluster and identify clustering outcome. In this study, the statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate characteristics of groundwater quality at an industrial park site located in Kaohsiung, Taiwan. Results from the principal component analysis (PCA) and factor analyses (FA) show that seven principal components could be compiled from 20 groundwater quality indicators obtained from groundwater analyses, which included background factor, salt residua factor, hardness factor, ethylene chloride factor, alkalinity factor, organic pollutant factor, and chloroform factor. Among the seven principal components, the major influencing components were salinization factor and acid-base factor. Results show that the seven principal component factors were able to represent 89.6% of the total variability for 20 different groundwater quality indicators. Groundwater monitoring wells were classified into seven groups according to the partition of homogeneity and similarity using the two-phase cluster analysis (CA). The clustering results indicate that chlorides such as 1,1-dichloroethylene, 1,1-dichloroethane, and cis-1,2-dichloroethylene had the highest concentrations among the clusters. This indicates that groundwater at nearby areas may be polluted by chlorinated organic compounds. Results from the correlation analysis by Fisher coefficient formula show that the cluster results of seven groups of groundwater wells had 100 and 80% accuracies using discriminant and cross-validation analyses, respectively. This implies that high accuracy can be obtained when discriminant and cluster analyses are applied for data evaluation.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review

Identifying the data structure including trends and groups/clusters in geochemical problems is essential to discuss the origin of sources and processes from the observed variability of data. An increasing number and high dimensionality of recent geochemical data require efficient and accurate multivariate statistical analysis methods. In this paper, we show the relationship and complementary roles of k‐means cluster analysis (KCA), principal component analysis (PCA), and independent component analysis (ICA) to capture the true data structure. When the data are preprocessed by primary standardization (i.e., with the zero mean and normalized by the standard deviation), KCA and PCA provide essentially the same results, although the former returns the solution in a discretized space. When the data are preprocessed by whitening (i.e., normalized by eigenvalues along the principal components), KCA and ICA may identify a set of independent trends and groups, irrespective of the amplitude (power) of variance. As an example, basalt isotopic compositions have been analyzed with KCA on the whitened data, demonstrating clear rock type/tectonic occurrence/mantle end‐member discrimination. Therefore, the combination of these methods, particularly KCA on whitened data, is useful to capture and discuss the data structure of various geochemical systems, for which an Excel program is provided.

Differentiation of Czech wines using multielement composition – A comparison with vineyard soil