Multivariate Analysis Tools Research Articles

Optimization of cyber-physical urban mobility systems in developing countries: A dependency structure matrix approach with advanced artificial intelligence techniques

Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.

Metabolite profiling in ten bread wheat (Triticum aestivum L.) genotypes in response to drought stress

Wheat is frequently constrained by extreme environmental conditions such as drought. Improving drought tolerance in wheat genotypes is crucial for ensuring food security, especially considering the challenges posed by climate change. To reveal the involvement of metabolites in drought response, ten diverse wheat genotypes were investigated under control and water scarcity conditions. The field experiments were set-up, using a 5 × 2 alpha lattice design, with two replicates per treatment, in the 2022 and 2023 growing seasons. Metabolites associated with drought tolerance were analysed using ultra-high performance liquid chromatography, coupled with a quadruple time of flight mass spectrometry (UHPLC-qTOF-MS). Multivariate statistical analysis (MVDA) tools, viz. principal component analysis (PCA) and the orthogonal projection to latent structures-discriminant analysis (OPLS-DA) loading scatter plot were used to identify the metabolites that are positively and negatively correlated to drought stress. Significant variation (p < 0.05) among genotypes was observed, with 58 metabolites annotated, including phenolic acids, carbohydrates, and fatty acids. The annotated compounds were linked to thirteen most significant pathways, with one carbon metabolism, cutin, suberin and wax synthesis and starch and sucrose metabolism being significantly affected by water stress, based on the KEGG pathway analysis. The two high-yielding wheat genotypes (LM48 and BW140) under drought stress displayed significant upregulation of key metabolites such as sinapoyl hydroxyagmatine, 7-oxostigmasterol, 1-O-caffeoyl-3-O-p-coumaroylglycerol, and 3-beta-3-lupanol, when compared to the non-stressed conditions. This study demonstrates the prospects of applied metabolomics for chemotaxonomic classification, phenotyping and selection in plant breeding, as well as potential use in crop improvement.

Study of Multimodal Identification Algorithms using Modern Methods and Tools of Multivariate Analysis

This article aims to comprehensively investigate the theoretical and practical foundations, as well as the distinctive characteristics, underpinning the study of multimodal identification algorithms. This investigation will be conducted using state-of-the-art methods and tools of multidimensional analysis. The development of a multimodal algorithm using the method of modality fusion at the feature level encompasses the integration of various algorithms rooted in multivariate analysis. These include a combined voice activity detector, a face detector utilizing the MTCNN (multi-task cascade convolutional networks) architecture, fine-frequency cepstral coefficients, facial image features, and a decision-making module. To construct a multimodal identification algorithm, a framework for combining these algorithms based on multivariate analysis is proposed. Analysis of the acquired data indicates that “Test 1”, utilizing facial image data, exhibits the highest performance indicators, approaching nearly 100%. Tests 2 and 3 involving voice signals exhibit a minor error in the pre-processing stage, attributed to the inherent delay experienced by participants during the video conference. The proposed multimodal algorithm, integrated within a biometric identification system, enables successful user verification research through the utilization of a combined multidimensional analysis algorithm. Furthermore, the algorithm showcases superior research outcomes in comparison to other analogous multimodal identification algorithms, as it yields precise results.

Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

BackgroundRapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance.ResultsWe develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model.ConclusionsWe demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

Forensic Discrimination of White Architectural Paints via ATR FT-IR Spectroscopy and Chemometrics

The present work entailed the comparative examination and discrimination of white architectural paint samples of different brands/manufacturers. The analysis of this evidence can facilitate exhibiting a linkage between the victim, and the crime scene and assist in the reconstruction of the crime scene. Architectural paint traces have evidential value, especially in cases of burglary, acts of vandalism, property crimes, etc. In the present study, a total of 102 samples of 34 distinct brands (3 samples of each brand) of white commercially available architectural paints were purchased and analysed using ATR FT-IR spectroscopy and multivariate analytical tools. The use of visual analysis of FTIR spectral data resulted in 12 undifferentiated/ indistinguishable sample pairs with a discrimination power of 97.86%. Adding PCA and PLS-DA in concert with visual analysis of FTIR spectra resulted in 99.4% (3 sample pairs undifferentiated) and 100% discrimination power, respectively. In conclusion, samples of each brand were completely differentiated using ATR FT-IR spectra subjected to the PLS-DA model. To assess the impact of different substrates on the examination of paint chips, simulated samples were produced on different substrates and the obtained results from the substrates study have been summarized. The ATR-FTIR spectroscopy therefore suggests an objective and rapid approach for the discrimination of white architectural paints for forensic purposes.

Compositional nutrient diagnosis (CND) applied to strawberry soilless

Soluble fertilizers are among the main costs in strawberry production, so knowing the nutrients balance in plant tissue is essential. In this sense, the Compositional Nutrient Diagnosis (CND) method is presented as a multivariate analysis tool for the nutritional diagnosis of the crop. The main goal of this paper was to establish CND standards and critical nutrient levels in strawberries cultivated in a soilless system. This study uses the Compositional Nutrient Diagnosis (CND) method to analyze nutrient levels grown in a soilless system. Commercial production greenhouses were evaluated for yield and leaf nutrients contents to develop the standards. Data were collected from 52 plots, each consisting of diagnostic leaves from 10 plants, and subsequently analyzed to determine critical nutrient deficiencies and excesses. The CND methodology was sensitive in diagnosing the differences between high and low-yield populations. Thus, adjusting the critical nutrient levels for soilless strawberry cultivation was possible. Due to deficiency, the most limiting nutrients for strawberry production were N, Mg, and S, and Zn and Cu due to excess. Results indicate specific nutrient thresholds, with significant findings from the integration of all indices, it was possible to calculate the sufficiency ranges for soilless strawberry: N = 26–31, P = 4–7, K = 8–11, Ca = 10–15, Mg = 3–4, S = 0.3–0.5 in g kg−1, and B = 18–95, Cu = 1–2, Fe = 46–96, Mn = 80–203, Zn = 1–40, Mo = 1.3–2.7, Ni = 0.1–0.4 in mg kg−1.

Optimization of HS-SPME/GC-MS Method for Determining Volatile Organic Compounds and Sensory Profile in Cocoa Honey from Different Cocoa Varieties (Theobroma cacao L.).

This study aimed to develop an analytical method using HS-SPME/GC-MS to determine the volatile organic compound (VOC) profiles and evaluate the sensory attributes of cocoa honey from four cocoa varieties (CCN51, PS1319, SJ02, and Parazinho). Using a multivariate factorial experimental design, the HS-SPME/GC-MS method was optimized to determine the VOC profiles. Twenty previously trained tasters participated in the ranking descriptive analysis, while 108 consumers participated in the acceptance and purchase intention tests. A total of 84 volatile organic compounds were identified from various chemical classes, including acids, alcohols, aldehydes, esters, ketones, monoterpenes, oxygenated monoterpenoids, sesquiterpenes, and oxygenated sesquiterpenoids. Palmitic acid was the compound found in the highest concentration in all varieties (5.13-13.10%). Multivariate analysis tools identified key compounds for differentiation and grouping of the samples. The results revealed that the variety significantly influenced both the VOCs' concentrations and sensory profiles. The CCN51, PS1319, and SJ02 varieties exhibited the highest diversity of VOCs and sensory attributes. Notably, the SJ02 and CCN51 varieties demonstrated superior acceptability and purchase intention, with means ranging from 7.21 and 7.08 to 3.71 and 3.56, respectively. These results indicate their potential as promising sources of cocoa honey for the food industry.

High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry.

Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT. We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex. Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD. This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

Identification of metabolic components of carotid plaque in high-risk patients utilizing liquid chromatography-tandem mass spectrometry.

Carotid atherosclerosis is a chronic progressive vascular disease that can be complicated by stroke in severe cases. Prompt diagnosis and treatment of high-risk patients are quite difficult due to the lack of reliable clinical biomarkers. This study aimed to explore potential plaque metabolic markers of stroke-prone risk and relevant targets for pharmacological intervention. Carotid intima and plaque sample tissues were obtained from 20 patients with cerebrovascular symptoms of carotid origin. An untargeted metabolomics approach based on liquid chromatography-tandem mass spectrometry was utilized to characterize the metabolic profiles of the tissues. Multivariate and univariate analysis tools were used. A total of 154 metabolites were significantly altered in carotid plaque when compared with thickened intima. Of these, 62 metabolites were upregulated, whereas 92 metabolites were downregulated. Support vector machines identified the 15 most important metabolites, such as N-(cyclopropylmethyl)-N'-phenylurea, 9(S)-HOTrE, ACar 12:2, quinoxaline-2,3-dithiol, and l-thyroxine, as biomarkers for high-risk plaques. Metabolic pathway analysis showed that abnormal purine and nucleotide metabolism, amino acid metabolism, glutathione metabolism, and vitamin metabolism may contribute to the occurrence and progression of carotid atherosclerotic plaque. Our study identifies the biomarkers and related metabolic mechanisms of carotid plaque, which is stroke-prone, and provides insights and ideas for the precise prevention and targeted intervention of the disease.

Mapping soil trace metal distribution using remote sensing and multivariate analysis.

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Surface-Enhanced Raman Spectroscopy for Monitoring the Biochemical Changes Due to DNA Mutations Induced by CRISPR-Cas9 Genome Editing in the Aspergillus niger Fungus.

In this study, surface-enhanced Raman spectroscopy (SERS) technique, along with principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA), is used as a simple, quick, and cost-effective analysis method for identifying biochemical changes occurring due to induced mutations in the Aspergillus niger fungus strain. The goal of this study is to identify the biochemical changes in the mutated fungal cells (cell mass) as compared to the control/nonmutated cells. Furthermore, multivariate data analysis tools, including PCA and PLS-DA, are used to further confirm the differentiating SERS spectral features among fungal samples. The mutations are caused in A. niger by the clustered regularly interspaced palindromic repeat CRISPR-Cas9 genomic editing method to improve their biotechnological potential for the production of cellulase enzyme. SERS was employed to detect the changes in the cells of mutated A. niger fungal strains, including one mutant producing low levels of an enzyme and another mutant producing high levels of the enzyme as a result of mutation as compared with an unmutated fungal strain as a control sample. The distinctive features of SERS corresponding to nucleic acids and proteins appear at 546, 622, 655, 738, 802, 835, 959, 1025, 1157, 1245, 1331, 1398, and 1469 cm-1. Furthermore, PLS-DA is used to confirm the 89% accuracy, 87.7% precision, 87% sensitivity, and 88.9% specificity of this method, and the value of the area under the curve (AUROC) is 0.67. It has been shown that surface-enhanced Raman spectroscopy is an effective method for identifying and differentiating biochemical changes in genome-modified fungal samples.

Multivariate analysis for data mining to characterize poultry house environment in winter

The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into three growth phases (weeks 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All three clustering methods were found to be suitable for grouping data, with FCM outperforming the other two. Using data clustering, the broiler house spaces were divided into three, two, and two subspaces (clusters) for weeks 1–3, 4–6, and 7–9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.

Quality Characterization of Fava Bean-Fortified Bread Using Hyperspectral Imaging.

As the demand for alternative protein sources and nutritional improvement in baked goods grows, integrating legume-based ingredients, such as fava beans, into wheat flour presents an innovative alternative. This study investigates the potential of hyperspectral imaging (HSI) to predict the protein content (short-wave infrared (SWIR) range)) of fava bean-fortified bread and classify them based on their color characteristics (visible-near-infrared (Vis-NIR) range). Different multivariate analysis tools, such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and partial least square regression (PLSR), were utilized to assess the protein distribution and color quality parameters of bread samples. The result of the PLS-DA in the SWIR range yielded a classification accuracy of ˃99%, successfully classifying the samples based on their protein contents (low protein and high protein). The PLSR model showed an RMSEC of 0.086% and an RMSECV of 0.094%. Also, the external validation resulted in an RMSEP of 0.064%. The PLSR model possessed the capability to efficiently predict the protein content of the bread samples. The results suggest that HSI can be successfully used to classify bread samples based on their protein content and for the prediction of protein composition. Hyperspectral imaging can therefore be reliably implemented for the quality monitoring of baked goods in commercial bakeries.

Quality by design fostered fabrication of cabazitaxel loaded pH-responsive Improved nanotherapeutics against prostate cancer

Cabazitaxel has been approved for the treatment of prostate cancer since 2010. However, its poor solubility and permeability pitfalls prevent its accumulation at the target site and promote severe adverse effects. About 90% of prostate cancer (PCa) patients suffer from bone metastasis. This advent reports the development of CBZ-loaded pH-responsive polydopamine nanoparticles (CBZ NP) against metastatic PCa cells. Quality by design (QbD) and multivariate analysis tools were employed for the optimization of CBZ NP. Amorphisation of CBZ along with metastatic microenvironment responsive release was observed thereby imparting spatial release and circumventing solubility pitfalls. CBZ NP retained its cytotoxic potential, with a significant increase in quantitative cellular uptake. Apoptotic markers observed from nuclear staining with elevated reactive oxygen species (ROS) and mitochondrial damage revealed by JC-1 staining demonstrated the efficacy of CBZ NP against PC-3 cells with good serum stability and diminished hemolysis. Cell cycle analysis revealed substantial S and G2/M phase arrest with enhancement in apoptosis was observed. Western blot studies revealed an elevation in caspase-1 and suppression in Bcl-2 indicating enhanced apoptosis compared to the control group. Substantial reduction in the diameter of 3D-Tumoroid and enhanced cell proliferation inhibition indicated the efficacy of CBZ NP in PCa. Thus, we conclude that CBZ NP could be a promising Nanotherapeutic approach for PCa.

«Цифровой двойник» населения Арктики в демографических исследованиях и управлении развитием территорий

The article considers “digital twins” of the population as a tool for socio-demographic research and territorial management. The experience of creating digital twins of the population and interactive web-sites devoted to demographic issues is systematized. A methodology for creating a digital twin of the Arctic population is proposed, based on three methodological principles: consideration of the hierarchy of territories, spatial representation of data, and combining demographic statistics with new digital data sources. The author has developed the Digital twin of the Arctic population — an interactive website (dashboard) containing detailed data on the Arctic population, including municipal and settlement levels. It includes demographic statistics, census results and data from digital platforms. The Digital twin of the Arctic population covers such issues as the size, dynamics and composition of the population, resettlement, natural and migration movement, labor and employment, transport movements, science and education, and the impact of the pandemic on demographic processes. Tools of ranking, multivariate analysis, clustering and forecasting of indicator values are implemented for researchers. From the viewpoint of state and municipal management, the main interest is the demographic profiles of regions and territories, reflecting up-to-date information about the demographic situation. Using the Digital twin of the Arctic population, the author draws conclusions about the spatial patterns of the demographic development of the Russian Arctic.

Quality assessment of promising garlic (Allium sativum L.) varieties based on principal component analysis

Physicochemical properties, pungency, anti-nutritional factors, cutting strength, and colour attributes of seven promising garlic varieties were assessed for their potential application. The garlic varieties were found to be slightly acidic (pH 6.08 - 6.77), and contained varying amounts of crude protein, carbohydrate, crude fat, ash, crude fibre, acidity, and total soluble solids (TSS). The pungency factor as pyruvic acid was found to be in the range of 34.84 - 86.69 μmol/mL, whereas anti-nutritional factors such as saponin (6.63 - 13.98 g), phytic acid (0.03 - 0.6 g), and tannin (0.18 - 0.39 gCE) per 100 g on a fresh weight basis were present. The Bhima purple (BP) variety with a purple tinge showed higher saponin content than the other varieties with a whitish tinge. The cutting strength of garlic cloves varied between 17.20 and 104.61 N. Physical and gravimetric properties were estimated and found to be variety-dependent (p ≤ 0.05). Colour attributes like L* (lightness), a* (redness), b* (yellowness), chroma, hue, and browning index were significantly different (p ≤ 0.05) for all the garlic varieties. The minimum hue value, and maximum a* and b* values, browning index (BI), and chroma were found in the BP variety. Principal component analysis as a significant multivariate analysis tool was applied to assess the existence of correlation among the estimated parameters. It was found to be 75.41% of the total variance when considering the first three principals. Haryana garlic-17 (HG-17) variety showed significant quality as it yielded positive correlation with the maximum number of quality attributes.

Clinical applications of spectroscopic techniques in conjunction with multivariate analysis in virus diagnosis

In view of the global pandemic that started in 2020, caused by COVID-19, the importance of the existence of fast, reliable, cheap diagnostic techniques capable of detecting the virus even in the first days of infection became evident. This review discusses studies involving the use of spectroscopic techniques in the detection of viruses in clinical samples. Techniques based on mid-infrared, near-infrared, Raman, and molecular fluorescence are explained and it was demonstrated how they can be used in conjunction with computational tools of multivariate analysis to build models capable of detecting viruses. Studies that used real clinical samples from 2011 to 2021 were analyzed. The results demonstrate the potential of the techniques in detecting viruses. Spectroscopic techniques, as well as chemometric techniques, were also explained. Viral diagnosis based on spectroscopy has interesting advantages compared to standard techniques such as: fast results, no need for reagents, non-destructiveness for the sample, no need for sample preparation, relatively low cost, among others. Several studies have corroborated the real possibility that, in the near future, we may have spectroscopic tools being successfully applied in viral diagnosis.

Identifying quasi-periodic variability using multivariate empirical mode decomposition: a case of the tropical Pacific

Abstract. A variety of statistical tools have been used in climate science to gain a better understanding of the climate system's variability on various temporal and spatial scales. However, these tools are mostly linear, stationary, or both. In this study, we use a recently developed nonlinear and nonstationary multivariate time series analysis tool – multivariate empirical mode decomposition (MEMD). MEMD is a powerful tool for objectively identifying (intrinsic) timescales of variability within a given spatio-temporal system without any timescale pre-selection. Additionally, a red noise significance test is developed to robustly extract quasi-periodic modes of variability. We apply these tools to reanalysis and observational data of the tropical Pacific. This reveals a quasi-periodic variability in the tropical Pacific on timescales ∼ 1.5–4.5 years, which is consistent with El Niño–Southern Oscillation (ENSO) – one of the most prominent quasi-periodic modes of variability in the Earth’s climate system. The approach successfully confirms the well-known out-of-phase relationship of the tropical Pacific mean thermocline depth with sea surface temperature in the eastern tropical Pacific (recharge–discharge process). Furthermore, we find a co-variability between zonal wind stress in the western tropical Pacific and the tropical Pacific mean thermocline depth, which only occurs on the quasi-periodic timescale. MEMD coupled with a red noise test can therefore successfully extract (nonstationary) quasi-periodic variability from the spatio-temporal data and could be used in the future for identifying potential (new) relationships between different variables in the climate system.

A statistical method for source approtionment of soil pollution

The present study was conducted to evaluate the source apportionment of soil pollution and its impact on soil quality using multivariate statistical analysis tools. Multivariable statistical techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and correlation offers superior interpretation of complicated data sets to better understand the soil quality. Soil samples were analyzed for physicochemical parameters (pH, temperature, electrical conductivity, organic carbon, moisture content, phosphate, potassium and sulphar) and for some important heavy metals (zinc, iron, manganese and copper). Sampling stations have been classified into three groups using Cluster Analysis (CA) in a convincing way. Results revealed that among all the sampling stations, three significantly different groups: (1) stations having highest pollution sources (HW and MW), (2) Moderate polluted area (SW and IW) &amp; (3) control site (VW) of the study stretch. The PCA generated 2 significant factors having eigenvalues &gt;1 which explain 82.78% of the variance in the dataset. Each soil quality parameter with strong correlation coefficient value was considered to be significant (p&lt;0.01). Therefore, the present study revealed that mining activities, dumping of municipal sewage, disposal of biomedical waste and industrial effluent including solid waste (e.g fly ash) have greater influence on the soil quality. Results also showed that the concentrations of heavy metals (Cu, Mn and Zn) in highly polluted soil are beyond the permissible limit. The pollutant levels were significantly different in the three groups of soil pollution character samples, which were confirmed by ANOVA analysis.