Application Of Hierarchical Cluster Analysis Research Articles

Application of hierarchical cluster analysis and principal component analysis to identify compositional trends of brine in crystalline basements and sedimentary basins

The present study identifies distinct geochemical signatures of deep brines originating from crystalline basements and sedimentary basins. A corresponding conceptual hydrogeochemical model is developed outlining the evolution of crystalline and sedimentary brines. The combined application of Hierarchical Cluster Analysis and Principal Component Analysis allowed the identification of evolutionary trends of groundwater towards crystalline and sedimentary brines. Using 308 brine samples from the existing literature, eight chemical species were considered in the analysis: sodium, magnesium, potassium, calcium, strontium, chlorine, sulfate, and bromine, from which two salinity evolution pathways towards common Ca–Cl brines were derived: (1) the evolution of crystalline brines, which evolved from recharge water to brine through hydration of silicates, is described by the linear equation Ca = −1610 + 0.4*Cl; and (2) the evolution of sedimentary brines, which evolved from recharge water to brine through dissolution and precipitation of halite, is described by the linear equation Cl/Br = 103 + Na/Br. We also discuss differences in water isotopes, and in lithium and boron concentrations which directly reflect various temperature-dependent processes that control the evolution of sedimentary and crystalline brines. This leads to development of a discriminatory graphical tool using B/Br and Li/Br ratios, with crystalline brines having B/Br and Li/Br ratios below 0.01 and sedimentary brines above 0.01. Our results improve our understanding of deep crustal brine evolution through brine-rock interaction.

Application of hierarchical cluster analysis in age segmentation for thermal comfort differentiation of elderly people in summer

Thermophysiological functions should be considered in the age segmentation of elderly people for thermal comfort differentiation. This paper aims to establish a scientific age segmentation criterion for elderly people based on thermal physiological demands. Firstly, field measurements and questionnaires are conducted in two pensioners’ buildings, and hierarchical cluster analysis is adopted to segment the age of elderly people based on thermophysiological functions in summer. Secondly, the effects of the basal metabolic rate (BMR), body fat percentage (BF%) and cardiac output (CO) on the physiological indexes are analyzed, then the thermal sensation deviation degree and the environmental sensitivity of the elderly people at different ages are analyzed. Finally, subjective perception and physiological parameter are combined to explore summer indoor environmental comfort zones for elderly people at different ages. The results show that the age of elderly people is segmented into [60, 69], [70, 89] and [90, 99]. With the increase of age, BMR and CO decrease, and BF% increases; the sensitivity of subjective perception to temperature, relative humidity and air speed decreases, and the sensitivity of MST to temperature and relative humidity also decreases. For the three age groups, the air temperature comfort zones are 23.25–27.41°C, 19.50–28.67°C and 20.72–30.73°C, respectively; the humidity comfort zones are 42.50–60.83%, 40.50–68.00% and 35.00–71.67%, respectively; and the air speed comfort zones are 0.02–0.31 m/s, 0–0.27 m/s and 0–0.29 m/s, respectively. This study can provide theoretical support for thermophysiological age segmentation criteria and differentiated environment design for elderly people in summer.

Geographical and entomological differentiation of Philippine honey by multivariate analysis of FTIR spectra

Honey is a product that is sought after by consumers because of its desirable properties. However, due to the high demand and limited supply, honey is often the target of falsification. The properties of honey are ultimately tied to its botanical, geographical, and entomological origins. Thus, accurate knowledge of these information is a step towards identifying honey adulteration. This work reports the successful discrimination of Philippine honey samples based on their geographical and entomological origins through the multivariate analysis of their FTIR spectra. Application of the principal component analysis (PCA) led to the distinct separation of samples. In the presented method, no other preprocessing of the FTIR spectral data was performed, yet the observed discrimination is comparable to works with spectral preprocessing. Application of hierarchical clustering analysis (HCA) also led to the separation of the honey samples into distinct clusters. The observed clustering in the dendrogram was found to correspond more on the species difference rather than the geographical location. This work has successfully demonstrated the discrimination of Philippine honey samples in terms of their geographical and entomological origins using FTIR spectroscopy and chemometrics.

Application of Statistical Methods to Biomarkers for Geochemical Evaluation and Genetic Type Determination of Gas Condensates in the Persian Gulf and Coastal Fars, Southern Iran

AbstractIn a comprehensive geochemical study, the genetic relationships among 14 samples of gas condensates from the Persian Gulf were investigated for the purpose of evaluating the respective source rocks in terms of both age and sedimentary paleoenvironment. Chemometric analysis was used for categorization and determination of a certitude range to determine the genetic type of the condensate families in the studied basin. The samples were collected from Late Permian – Triassic reservoirs (Dalan and Kangan formations) located in 6 gasfields (gas condensate) hosting some of Iran's most important gas/gas condensate reserves. Obtained by gas chromatography‐mass spectrometry (GC‐MS), a total of 16 biomarker parameters (10 maturity‐related parameters and 6 sedimentary environment‐related parameters) were used to evaluate the samples in terms of thermal maturity (and hence their positions in the maturity chart), the sedimentary environment of the source rock and the lithology. Application of Hierarchical Clustering Analysis (HCA) and Principal Component Analysis (PCA) to the collected data led to the categorization of the samples into three main genetic groups, I–III. Groups I and III were found to be located in the east and the west of the Persian Gulf, respectively, while Group II was developed between the two other groups.

Detecting a structure in the European energy transition policy instrument mix: What mix successfully drives the energy transition?

European Union countries sharing the same goal of carbon reduction have created a complex array of different strategies, policies and instruments to provide a supportive framework for navigating and accelerating the energy transition. The paper assumes that different mixes of policy instruments influence the European Union's efforts to become a low-carbon economy. However, instead of examining a policy instrument mix in isolation at the country-level, this paper uses hierarchical cluster analysis to detect the extent of similarities and patterns across European countries, considering the types of policy instruments that target energy efficiency and renewable energy. Fuzzy-set qualitative comparative analysis is then applied to explore different combinations of specific policy instruments for each cluster in managing the energy transition. The application of hierarchical cluster analysis detected the existence of three country clusters, which makes the European policy landscape less complex and suitable for developing more effective energy transition policies. The application of fuzzy-set qualitative comparative analysis confirmed that there is no single instrument necessary to make the energy transition a success. On the contrary, only a configuration of specific policy instruments targeting both energy efficiency and renewable energy can promote sustained success in the energy transition.

Co-gasification of refuse-derived fuels and bituminous coal with oxygen/steam blend to hydrogen rich gas

The gasification technology of refuse-derived fuels (RDF) can represent a future alternative to the global hydrogen production and a pathway for the development of the circular economy. The paper presents an innovative way of utilizing RDF through their oxygen/steam co-gasification with bituminous coal to hydrogen rich gas. Five different RDF samples (RDF1÷RDF5) were investigated. The in-depth analyses of the co-gasification of bituminous coal blends with different amounts of RDF (10, 15 and 20%w/w) under various temperature conditions were conducted with the application of Hierarchical Clustering Analysis (HCA). The results of the research study revealed a decrease in the total gas yield as well as in the hydrogen yield observed with the increase in the RDF fraction in the fuel blend. The lowest hydrogen yield and the highest carbon conversion were noted for the co-gasification tests of coal blends with 20%w/w for all the studied RDFs. The SEM-EDS (Scanning Electron Microscopy with Energy Dispersive Spectroscopy) and WDXRF (Wavelength Dispersive X-ray Fluorescence) results showed a significantly higher H2 yield in RDF2 co-gasification with coal in comparison with all the remaining RDFs, due to the higher concentration of calcium in the sample. The molecular structure analysis of polymers using Fourier transform infrared spectroscopy (FTIR) demonstrated that the most prevalent synthetic polymers in RDF2 are polyethylene terephthalate and polyvinyl chloride characterized by the lowest thermal stability compared to polyethylene and polypropylene.

Characterization of Regional Groundwater System Based on Aquifer Response to Recharge–Discharge Phenomenon and Hierarchical Clustering Analysis

The investigations of groundwater hydrograph reasonably reflect the aquifer response to recharge–discharge phenomenon and its characteristics. A better understanding of aquifer characteristics such as regional aquifer classification, recharge and discharge patterns, aquifer geology and flow patterns are the surface indicators that may be more effective and less costly for interpreting basic regional hydrogeological conditions and assessments. This study deals with the application of Hierarchical Clustering Analysis to understand the groundwater spatio-temporal patterns and to visualize/classify the nature of the aquifer in the regional area of Kaohsiung City, Taiwan. Groundwater level fluctuation patterns and slopes of rising and recession limbs are used to identify the pumping effects and classify aquifers. The results of clustering analysis show that the groundwater observation wells in the study area can be divided into five major characteristics along with the upstream to downstream of Kaoping River. The clusters are consistent with basic lithology distribution and age of sedimentary, which represents the characteristics of groundwater level fluctuation. The identified groundwater hydrographs patterns provide newer insights related to aquifer response to recharge–discharge phenomenon, types of aquifers and their behaviors. The knowledge of water level fluctuations in the observation wells provides a piece of prior information about the abstraction of groundwater. The proposed aquifer classification and pumping effect have great potential for applied use in groundwater management e.g., save drilling cost.

Analysis of the Impact of Physicochemical Parameters Characterizing Coal Mine Waste on the Initialization of Self-Ignition Process with Application of Cluster Analysis

Purpose The subject of the research presented in this paper is the analysis of physicochemical parameters characterizing coal mine waste, in terms of their impact on the initialization of the self-ignition phenomenon. Methods The model was constructed with the application of Hierarchical Cluster Analysis complemented with a colour data map enabling the tracing of similarities between the samples of coal mine waste in the space of parameters and between the examined physicochemical parameters in the space of samples. The data set analysed included parameters characterizing coal mine waste collected from 12 various coal mine waste dumps, either in operation or closed, and where thermal effects either took place or were not reported. Results The HCA model constructed and complemented with a colour data map revealed that the tendency of coal mine waste to self-ignite is primarily affected by the contents of moisture, ash, volatile matter, C and S, values of heat of combustion, calorific value and contents of SiO2, Al2O3, K2O, SO3, TiO2, Co, Ni and Rb. Practical implications One of the major environmental hazards associated with the storage of coal mine waste is the possibility of self-ignition. At present, there are no applicable methods of assessment of this risk. The application of Hierarchical Clustering Analysis complemented with a colour data map enabled the analysis of data structures organized in matrix X(m × n) by tracing the similarities between the examined objects in the parameter space and between the measured parameters in the object space, and therefore contributed to the development of procedures of coal mine waste self-ignition risk assessment. Originality/value The originality of the study presented in this paper comes from finding the parameters affecting the tendency of coal mine waste to self-ignite.

Analysis of the Impact of Physicochemical Parameters Characterizing Coal Mine Waste on the Initialization of Self-Ignition Process with Application of Cluster Analysis

Purpose The subject of the research presented in this paper is the analysis of physicochemical parameters characterizing coal mine waste, in terms of their impact on the initialization of the self-ignition phenomenon. Methods The model was constructed with the application of Hierarchical Cluster Analysis complemented with a colour data map enabling the tracing of similarities between the samples of coal mine waste in the space of parameters and between the examined physicochemical parameters in the space of samples. The data set analysed included parameters characterizing coal mine waste collected from 12 various coal mine waste dumps, either in operation or closed, and where thermal effects either took place or were not reported. Results The HCA model constructed and complemented with a colour data map revealed that the tendency of coal mine waste to self-ignite is primarily affected by the contents of moisture, ash, volatile matter, C and S, values of heat of combustion, calorific value and contents of SiO2, Al2O3, K2O, SO3, TiO2, Co, Ni and Rb. Practical implications One of the major environmental hazards associated with the storage of coal mine waste is the possibility of self-ignition. At present, there are no applicable methods of assessment of this risk. The application of Hierarchical Clustering Analysis complemented with a colour data map enabled the analysis of data structures organized in matrix X(m × n) by tracing the similarities between the examined objects in the parameter space and between the measured parameters in the object space, and therefore contributed to the development of procedures of coal mine waste self-ignition risk assessment. Originality/value The originality of the study presented in this paper comes from finding the parameters affecting the tendency of coal mine waste to self-ignite.

The application of hierarchical clustering to analyzing ashes from the combustion of wood pellets mixed with waste materials.

Air pollution constitutes the greatest environmental threat to human health in the European Union. In Poland, the emission of particulate matter and harmful gases originating from local coal based boiler plants and the combustion of fuels in residential heating appliances is a considerable source of air pollution. The combustion of fuel in home furnaces is inefficient due to the use of cheap fuels of low heating parameters and the frequent addition of waste. For the purpose of the research, deciduous tree wood pellets were selected as the basic fuel with the admixture of plastic waste, rubber, waste paper, wood residues, diapers, textile waste, multi-material packaging, construction waste, biomass and alternative fuel (RDF). Examining ash samples to confirm the practices of combusting or co-combusting waste materials in heating appliances is considered to be one of the most reliable detection methods; however, the results of direct research require further data processing. The application of hierarchical clustering analysis to the obtained results arranged into a matrix enabled in a simple way to demonstrate the similarities between the examined samples of fuel and the samples of fuel mixed with waste materials in the parameters space as well as to analyze the similarities among the measured parameters (the content of particular elements in ash) in the space of the examined samples. The application of chemometric methods for the purpose of identifying the combusted fuels, and, in particular the co-combusted waste complements the currently used monitoring tools which control the use of low quality fuels or the combustion of waste of different origin.

Diabetes mellitus type 2: Exploratory data analysis based on clinical reading

AbstractDiabetes mellitus type 2 (DMT2) is a severe and complex health problem. It is the most common type of diabetes. DMT2 is a chronic metabolic disorder that affects the way your body metabolizes sugar. With DMT2, your body either resists the effects of insulin or does not produce sufficient insulin to continue normal glucose levels. DMT2 is a disease that requires a multifactorial approach of controlling that includes lifestyle change and pharmacotherapy. Less than ideal management increases the risk of developing complications and comorbidities such as cardiovascular disease and numerous social and economic penalties. That is why the studies dedicated to the pathophysiological mechanisms and the treatment of DMT2 are extremely numerous and diverse. In this study, exploratory data analysis approaches are applied for the treatment of clinical and anthropometric readings of patients with DMT2. Since multivariate statistics is a well-known method for classification, modeling and interpretation of large collections of data, the major aim of the present study was to reveal latent relations between the objects of the investigation (group of patients and control group) and the variables describing the objects (clinical and anthropometric parameters). In the proposed method by the application of hierarchical cluster analysis and principal component analysis it is possible to identify reduced number of parameters which appear to be the most significant discriminant parameters to distinguish between four patterns of patients with DMT2. However, there is still lack of multivariate statistical studies using DMT2 data sets to assess different aspects of the problem like optimal rapid monitoring of the patients or specific separation of patients into patterns of similarity related to their health status which could be of help in preparation of data bases for DMT2 patients. The outcome from the study could be of custom for the selection of significant tests for rapid monitoring of patients and more detailed approach to the health status of DMT2 patients.

Application of Water Quality Index and Multivariate Statistical Analysis for Assessing Coastal Water Quality

Contamination of coastal water by seawater is a global phenomenon, which deteriorates the water quality. The present study aims to assess the suitability of coastal water for drinking and irrigation purposes with regard to seawater contamination. Conventional hydrochemical data have been utilized in conjunction with mathematical and statistical methods. Sixty groundwater and nine surface water samples were collected from the eastern coastal plains of Odisha, India and were analyzed for various physico-chemical parameters. A significant influence of seawater on water quality has been observed. Additionally, ion-exchange process has been identified to play a vital role in controlling the water chemistry. Application of hierarchical cluster analysis on water quality data produces two clusters of samples (C-1 and C-2) that are well-explained by the factors of principal component analysis. Higher dissolved salt contents in a majority of samples in the C-1 cluster have resulted in higher water quality indices that make them unsuitable for drinking purposes. Further, their placement in the Wilcox and USSLS diagrams indicated that these samples are even unsuitable for irrigation purposes. The C-2 cluster comprises 38% of water samples, which can be utilized for both drinking and irrigation purposes. The spatial distribution of water quality shows that the drinkable and irrigational water is mainly confined to a few patches in the area. These patches may be utilized for supplying the drinking water to the coastal population.

Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping out and Hierarchical Cluster Analysis.

School personnel currently lack an effective method to pattern and visually interpret disaggregated achievement data collected on students as a means to help inform decision making. This study, through the examination of longitudinal K-12 teacher assigned grading histories for entire cohorts of students from a school district (n=188), demonstrates a novel application of hierarchical cluster analysis and pattern visualization in which all data points collected on every student in a cohort can be patterned, visualized and interpreted to aid in data driven decision making by teachers and administrators. Additionally, as a proof-of-concept study, overall schooling outcomes, such as student dropout or taking a college entrance exam, are identified from the data patterns and compared to past methods of dropout identification as one example of the usefulness of the method. Hierarchical cluster analysis correctly identified over 80% of the students who dropped out using the entire student grade history patterns from either K-12 or K-8.

Application of multivariate statistical techniques in alongshore differentiation of coastal barriers

This paper investigates the existence of statistical correlations and relationships between geological and oceanographic parameters along a coastal barrier system in southern Brazil. The existence of few statistical studies on coastal barriers motivated the use of multivariate statistical techniques that take environmental parameters such as coastal morphological-sedimentary features and their morphometric measurements into account. The Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were used to assess the spatial variations of fourteen parameters driving the current and past behavior of this coastal barrier system at fourteen locations. Through the application of HCA, these locations were divided into four groups (Groups 1, 2, 3, and 4). The HCA results were analyzed using PCA. The first (PC1) and the second (PC2) principal components together explained at least 76.4% of the variability of all four groups. The most important geological and oceanographic parameters that were correlated to PC1 and PC2 were different in each group. In general, the shoreface slope and shoreface dimensions were common to all groups as PC1-correlated parameters, being the most important ones regarding data variability.In addition, for the progradational (Groups 1 and 4), retrogradational (Group 2) and aggradational barriers (Group 3) PCA indicated that shoreline orientation (wave exposure) and beach type are secondary, but relevant factors controlling the past and current behavior of coastal barriers in southern Brazil.

What Should Cognitive Science Look Like? Neither a Tree Nor Physics.

While pointing out important features of cognitive science, Núñez et al. (2019) also argue prematurely for the end of cognitive science. I discuss problematic analytic features in the application of hierarchical cluster analysis to journal citation data. On the conceptual side, I argue that the research programs framework of Lakatos may not be so wisely applied to cognitive science. Further, the diversity of structure in cognitive science departments may represent a rational, strategic adaptation by an interdisciplinary department to cognitive and other resource challenges rather than the sign of low progress on a discipline.

Abstract P367: Application of Hierarchical Cluster Analysis on Five Risk Factors Scores (RFS) to Estimate Individual Patient Risk in Case Where There is No Official Country Specific RFS: Example From Bosnia and Herzegovina

Objective: The accuracy and applicability of various cardiovascular disease (CVD) risk calculators may not be same in different populations. Wecompared 5 RFS for CVD risk on patients from Bosnia and Herzegovina to explore relations between different RFS and estimate use of theircombination. Methods: We utilized demographic, medical history, and lipid profile data gathered from patients seen in primary care clinic in 2013. FiveRFS namely, 10 year Framingham CHD score, QRISK 2, AHA/ACC ASCVD risk score, Framingham ATP III score, and EU SCORE were calculatedusing demographic, history of diabetes and hypertension, taking any hypertension medication, and lipid profile variables. Additionally, we entered these scores as dimensions in hierarchical cluster analysis to group people based on their risk of developing CVD. Results: There were 1277 patients in thisstudy and majority (65%) were women. The mean (SD) age of the sample was 56.2 (11.4) years. Correlation between these scores are presented inTable 1. We obtained 4 clusters with significant different cluster centers. Clusters were ordered from lowest to highest risk; cluster 1 containing patients with lowest and cluster 4 with highest mean RFS. Cluster centers, which represents mean values for all the RFS are given in Table 2. Conclusion: All risk scores performed well in this population. Only EU SCORE correlated less with other RFS. Obtained clusters are more homogenous and were ableto classify patients better. This novel method of using calculated RFCs as dimensions in clustering produced very good estimates of patient’s risk ofdeveloping CVD by combining several RFS, even in a case where originally RFS were created for and from different population.

Comparison between counterfeit and authentic medicines: A novel approach using differential scanning calorimetry and hierarchical cluster analysis

Erectile dysfunction medicines such as Cialis and Viagra are very popular worldwide and are between the most prevalent counterfeit medicines in Brazil. A range of analytical methods has been used to analyze Cialis and Viagra, such as ATR-FTIR, GCMS and UPLC–MS. Until now, there are no data available of DSC methods for analysis of counterfeit medicines of Cialis and Viagra. DSC is a thermal analysis that provides useful information of physico-chemical events, and however is almost not used for forensic purposes. In this study, thermal analysis of 25 counterfeit Viagra and Cialis seized by Brazilian Federal Police were performed by DSC and compared to their authentic medicines and analytical standards, along with chemometric tools. Authentic samples of Viagra displayed a similar thermal profile with the API, while Cialis were different with additional endothermic peaks, that could be related to excipients interference. Thermograms of Viagra counterfeit samples were similar to authentic samples, while Cialis showed an enlargement and displacement of endothermic peaks. Also, some Cialis counterfeit samples showed melting peaks attributed to sildenafil, the API of Viagra, instead tadalafil, confirming previous results obtained by UPLC-MS. Multivariate analysis with application of Hierarchical Cluster Analysis classified different groups of samples, including a cluster with counterfeit Cialis and Viagra, indicating the use of same API for both counterfeit medicines and possibly the same illicit production; and a cluster with authentic Viagra and counterfeit Cialis, confirming the addition of sildenafil instead tadalafil to Cialis counterfeit samples. Here for the first time we described the use of DSC for chemical profiling of Cialis and Viagra and showed that even when applied to a small group of samples, DSC along with chemometric tools can be considered as a good auxiliary method in forensic casework samples. DSC provided useful data to perform the identification of counterfeit and authentic medicines, with low cost and a simple method.

Phytoavailability of potentially toxic elements from industrially contaminated soils to wild grass

Topsoil and grass samples from 14 sites located in different distances from three copper mining factories and a copper smelter were collected in Srednogorie, Bulgaria. The paper discusses results of studies on the mobility of potentially toxic elements (As, Cd, Cr, Cu, Mn, Ni, Pb, Zn) from contaminated soils to wild grass Cynodon dactylon in terms of transfer (TF: soil to plant) and phytoavailability (PF: EDTA (ethylenediaminetetraacetic acid)-soluble to plant) factors. Soils located close to mine and smelter factories were heavily contaminated by As and Cu reaching levels up to 500 mg/kg and 2400 mg/kg, respectively for As and Cu. At seven sites arsenic and copper in grass were over the normal levels (1 mg/kg As; 20 mg/kg Cu). The content of Cu in grass averaged 63 mg/kg exceeding the maximum allowable for sheep. Relationship between PF and TF values was found for As, Cd, Cu, Ni, Pb, Zn but not for Mn and Cr. To some extent the soil pH, TOM (total organic matter) and CaCO3 content are related to the phytoavailability of As, Cd and Cu since for Zn and Pb this relation is attributed to Al- and Fe-content of soil. Statistically significant correlation coefficients were determined between the EDTA-soluble fraction of soil and content in grass in the case of As, Cd, Cr, Cu, Mn and Pb indicating the suitability of the short procedure for phytoavailability studies of those elements. The application of hierarchical cluster analysis and self-organizing maps of Kohonen made it possible to reveal specific hidden relationships between the soil variables and transfer factors as well as between the sampling locations. This additional information helps in more detailed interpretation of phytoavailability and transfer processes in the region of interest.The novelty in this study is achieved by careful consideration of the possibility of using EDTA extracts of the toxic metals in investigating of the phytoavailability and transfer processes soil/plant. Additionally, chemometric expertise used makes it possible to differentiate the behavior of each toxic metal in the processes studied. The combination of easy option for rapid extraction and intelligent data analysis gives a new perspective for contributions in explanation of the complex interactions between soils and plants when assessing pollution events in a certain environment.

Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics

Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in all data sets. This work highlights the importance of technical replication in mass spectrometry-based studies, and presents a new application of HCA as a tool for evaluating the effectiveness of data filtering prior to statistical analysis.

Hierarchical clustering analysis framework of mutually exclusive crash causation parameters for regional road safety strategies

ABSTRACTHierarchical clustering analysis framework is developed to identify benchmark and critical regions for effective road safety strategies. The regions are grouped based on agglomeration coefficient of mutually exclusive crash causation parameters. Subsequently, regions from groups with lower than a threshold index value are selected as benchmark for the poorly performing critical counterparts. Euclidean distance-based Ward's, median and centroid clustering techniques are explored through a case study of Indian states and Union Territories. As per data between 2006 and 2015, fatal crash percentages of driving under influence of drug and alcohol, excessive speeding, vehicle malfunction and road conditions related crash causation parameters, severity index and its growth rate are assessed based on respective threshold values of 6.35%, 43.28%, 2.42%, 1.79%, 26.7 and 3.1%. These are the national average of respective indices. It demonstrated the unique application of hierarchical clustering analysis in benchmark and critical region identification.