Particular Principal Component Analysis Research Articles

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.

Empowering cotton breeding programs through the strategic exploration and exploitation of phenotypic diversity of genetic resources under climate change conditions

Plant phenotype assessment approaches have facilitated crop improvement in recent years by providing opportunities for the dissection of complex nature quantitative traits. Fiber quality and yield are significant economic traits with a complex nature mainly due to interactions between its genetic architecture and the surrounding environment. These economic traits showed stagnant performance in recent decades due to eroded genetic diversity via the selection process. It necessitates exploiting conserved germplasm resources to rebuild genetic diversity within cotton cultivars. Compared to the conventional univariate selection methods using a single trait at a time, the multivariate analysis extends its selection to multiple variables for the selection of genotypes. In evaluating a core collection of cotton over two years using various phenotyping methods, multivariate analysis, particularly principal component analysis (PCA), was employed to assess genetic diversity. The PCA analysis demonstrated substantial variability of 39.83% and 45.37% across two years, highlighting a significant impact on yield and fiber quality attributes. The cumulative eigenvalues of principal components exceeding 80% supported the classification of accessions into six distinct groups based on their trait variability. Both PCA and cluster analysis successfully categorized accessions into six groups, indicating high diversity and suggesting their potential utility in breeding programs. The diverse genotypes identified in this study would be potential resources for variety development and accelerated breeding programs, particularly focusing on stress resilience under climate change scenarios. By utilizing these diverse genotypes, there is an opportunity to enhance breeding strategies and accelerate the development of improved cotton varieties with enhanced yield and fiber quality attributes. The significance of our study lies in its demonstration of the importance of genetic diversity in improving yield and fiber quality attributes, thus underscoring its relevance to cotton breeding programs.

Chestnut Episperm as a Promising Natural Source of Phenolics from Agri-Food Processing by-Products: Optimisation of a Sustainable Extraction Protocol by Ultrasounds

Chestnut processing has increasingly grown in recent years. All the processes involved in the chestnut supply chain are characterized by the production of high levels of by-products that cause several environmental and disposal issues. The Castanea spp. fruit production is related to a high number of chestnut episperm. This underutilized agricultural by-product may be evaluated as a good resource for the extraction of health-promoting natural molecules, such as phenolics. This preliminary study aimed to develop and optimize, using a multivariate statistical approach, a sustainable protocol for the ultrasound-assisted extraction (UAE) of the main phenolics from chestnut episperm (cv Marsol, C. sativa × C. crenata). A design of experiment (DoE) approach was employed. This approach focused on the two quantitative UAE process factors: the extraction time (X1), within a timeframe ranging from 10 to 30 min, and the sample-to-solvent (w/v) ratio (X2), ranging from 1/30 to 1/10. These variables were investigated to determine their impact on phenol extraction yield. Exploratory analysis, in particular principal component analysis (PCA) and multiple linear regression (MLR), were carried out on the studied responses. The phenolic characterization of ten different extracts was also performed using high-performance liquid chromatography (HPLC), both to define the levels of specific phenolics selected for their health-promoting properties and to evaluate some important features, such as the total antioxidant capacity. The values of total polyphenolic content (TPC) obtained in the different experiments ranged between 97 (extract 4) and 142 (extract 6) mg GAE/g of dried weight (DW). Moreover, results from the ferric reducing antioxidant power (FRAP) test confirmed the high TPC values, highlighting that all the ultrasound extracts contained excellent levels of molecules with good antioxidant properties. In particular, extracts 2 and 3 showed the highest AOC values (about 490–505 mmol Fe2+/Kg of dried weight). The proposed optimized protocol allowed for obtaining formulations characterized by high levels of tannins, phenolic acids, and catechins. Indeed, episperm extracts contained high levels of chlorogenic acid (15–25 mg/100 g DW), ferulic acid (80–120 mg/100 g DW), castalagin (20–80 mg/100 g DW), and vescalagin (40–75 mg/100 g). Finally, in this research study, the potential of chestnut episperm as a source of polyphenolic molecules to be extracted by green technologies and used in several food and/or pharmaceutical applications was evaluated to valorize a sustainable reuse strategy of agri-food processing by-products, also reducing the environmental impact of this waste derived from chestnut processing.

Psychometric evaluation of the Malay version of the Individual Community-Related Empowerment scale among older adults in Malaysia.

Malaysia is projected to become an aged country by 2030, with the older age group comprising 10 percent of the total population. To address this demographic shift, comprehensive plans and initiatives are being implemented at various levels, ranging from the government to local communities. A crucial aspect of these efforts is community empowerment, which requires a reliable and validated tool for measurement. This study aimed to validate the Individual Community Related Empowerment (ICRE) scale in alignment with the national language of Malaysia. This cross-sectional study was conducted from May to December 2019. Back-to-back translation from the English to Malay version of the ICRE scale was done. A total of 328 older persons aged 60 years old and above who attended clinics and understood Malay had been randomly selected. Exploratory factor analysis (EFA), particularly Principal Component Analysis (PCA) with Varimax rotation and Kaiser Normalization, was performed in this study using IBM SPSS version 27 Amos graphic. The findings revealed that the Malay version of the Individual Community Related Empowerment (ICRE-m) scale consists of five components: self-efficacy, intention, participation, motivation, and critical awareness, which collectively accounted for 92.3% of total variance. All five components demonstrated Cronbach's alpha values greater than 0.7, indicating the reliability of the selected items for field studies. The ICRE-m scale is acceptable for field studies and valid for measuring individual-related community empowerment. Nurses and other healthcare professionals can employ this scale specifically within the Malay-speaking population, particularly in the Asian region. Future studies on community empowerment among older individuals can utilize this tool to assess community readiness for participating in community health interventions.

HyperPCA: A powerful tool to extract elemental maps from noisy data obtained in LIBS mapping of materials

Laser-induced breakdown spectroscopy (LIBS) is a preferred technique for fast and direct multi-elemental mapping of samples under ambient pressure, without any limitation on the targeted element. However, LIBS mapping data have two peculiarities: an intrinsically low signal-to-noise ratio due to single-shot measurements, and a high dimensionality due to the high number of spectra acquired for imaging. This is all the truer as lateral resolution gets higher: in this case, the ablation spot diameter is reduced, as well as the ablated mass and the emission signal, while the number of spectra for a given surface increases. Therefore, efficient extraction of physico-chemical information from a noisy and large dataset is a major issue. Multivariate approaches were introduced by several authors as a means to cope with such data, particularly Principal Component Analysis (PCA). PCA is useful to analyze correlations between different elements, but it is limited to low signal-to-noise ratios.In this paper, we introduce HyperPCA, a new analysis tool for hyperspectral images based on a sparse representation of the data using Discrete Wavelet Transform (DWT) and kernel-based sparse PCA (kSPCA) to reduce the impact of noise on the data and to consistently extract the spectroscopic signal, with a particular emphasis on LIBS data. The method is first illustrated using simulated LIBS mapping datasets to emphasize its performances with an extremely low shot-to-shot signal-to-noise ratio, and with a variable degree of spectral interference. Comparisons to standard PCA and to traditional univariate data analyses are provided. Finally, it is used to process real data in two cases that clearly illustrate the potential of the proposed algorithm. We show that the method presents advantages both in quantity and quality of the information recovered, thus improving the physico-chemical characterization of analysed surfaces.Recovering the principal components of noisy spectroscopic data is difficult Current PCA approaches show limited efficacy for LIBS mapping data Improvements are introduced through sparse signal representation and kernel-based PCA Noise is reduced by using a kernel function on the entries of the covariance matrix Wavelets mostly group lines of given elements in the same principal components.

Detection of adulteration activities in edible bird's nest using untargeted 1H-NMR metabolomics with chemometrics

Edible bird's nests (EBNs) are the nests of swiftlets, made from the saliva of the male swiftlet (Aerodramus fuciphagus). Due to their nutritional value, EBNs are recognized as a premium food and highly in demand among the Chinese community. EBNs are commonly adulterated with cheaper ingredients and efforts are being made to combat these activities using different analytical techniques. In this study, nuclear magnetic resonance (1H-NMR) metabolomic fingerprinting combined with chemometrics, particularly principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA), was employed to detect adulteration in EBNs. Authentic EBN samples from different locations in Malaysia were used and adulteration was simulated using nutrient agar, collagen, gelatine, karaya gum and melamine at 1, 5 and 10% w/w, respectively. Overall, unsupervised PCA was able to distinguish authentic EBNs from those adulterated with nutrient agar, collagen and gelatine down to 5% w/w adulteration level. As for EBN adulterated with karaya gum and melamine, a distinct peak can be observed at 1.91 ppm and 6.10 ppm, respectively. The supervised OPLS-DA predictive model was able to differentiate authentic EBNs from simulated adulterated EBNs with 100% accuracy. Conclusively, 1H-NMR metabolomics combined with chemometrics could be a potential tool for the detection of adulteration in EBN.

Dummy Molecularly Imprinted Polymers Using DNP as a Template Molecule for Explosive Sensing and Nitroaromatic Compound Discrimination

This work reports a rapid, simple and low-cost voltammetric sensor based on a dummy molecularly imprinted polymer (MIP) that uses 2,4-dinitrophenol (DNP) as a template for the quantification of 2,4,6-trinitrotoluene (TNT) and DNP, and the identification of related substances. Once the polymer was synthesised by thermal precipitation polymerisation, it was integrated onto a graphite epoxy composite (GEC) electrode via sol–gel immobilisation. Scanning electron microscopy (SEM) was performed in order to characterise the polymer and the sensor surface. Responses towards DNP and TNT were evaluated, displaying a linear response range of 1.5 to 8.0 µmol L−1 for DNP and 1.3 to 6.5 µmol L−1 for TNT; the estimated limits of detection were 0.59 µmol L−1 and 0.29 µmol L−1, for DNP and TNT, respectively. Chemometric tools, in particular principal component analysis (PCA), demonstrated the possibilities of the MIP-modified electrodes in nitroaromatic and potential interfering species discrimination with multiple potential applications in the environmental field.

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning

To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a convolutional neural network (CNN) that classifies breast lesions as benign and malignant. The proposed CNN adopts a modified Inception-v3 architecture to provide efficient feature extraction in ABUS imaging. Because the ABUS images can be visualized in transverse and coronal views, the proposed CNN provides an efficient way to extract multiview features from both views. The proposed CNN was trained and evaluated on 316 breast lesions (135 malignant and 181 benign). An observer performance test was conducted to compare five human reviewers' diagnostic performance before and after referring to the predicting outcomes of the proposed CNN. Our method achieved an area under the curve (AUC) value of 0.9468 with five-folder cross-validation, for which the sensitivity and specificity were 0.886 and 0.876, respectively. Compared with conventional machine learning-based feature extraction schemes, particularly principal component analysis (PCA) and histogram of oriented gradients (HOG), our method achieved a significant improvement in classification performance. The proposed CNN achieved a >10% increased AUC value compared with PCA and HOG. During the observer performance test, the diagnostic results of all human reviewers had increased AUC values and sensitivities after referring to the classification results of the proposed CNN, and four of the five human reviewers’ AUCs were significantly improved. The proposed CNN employing a multiview strategy showed promise for the diagnosis of breast cancer, and could be used as a second reviewer for increasing diagnostic reliability.

Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods.

Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant-pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography-mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).

A GIS methodology to support sea water quality assessment in coastal areas

The development of methodologies for assessing water quality in coastal areas including mapping of eutrophication levels is a research area of high interest. A wide range of methodological approaches can be found in the literature, including multivariate techniques, since marine eutrophication is a multi-parametric phenomenon. In this context, statistical analysis and in particular Principal Component Analysis (PCA) have been widely applied. However, no attempt has been presented so far for mapping eutrophication levels based on information acquired from PCA results in integration with spatial analysis methods. The rapid development of Geographical Information Systems provides the appropriate framework for the development and application of methodologies integrating statistical analysis, spatial analysis methods and mapping techniques. This paper proposes such a methodological approach for assessing sea water quality in coastal areas. The methodology is clearly described and the Strait of Mytilene at the east of the Island of Lesvos in the NE Aegean Sea, Greece is used as a case study.

A Metabolomics-Guided Exploration of the Phytochemical Constituents of Vernonia fastigiata with the Aid of Pressurized Hot Water Extraction and Liquid Chromatography-Mass Spectrometry.

Vernonia fastigiata is a multi-purpose nutraceutical plant with interesting biological properties. However, very little is known about its phytochemical composition and, thus the need for its phytochemical characterization. In the current study, an environmentally friendly method, pressurized hot water extraction (PHWE), was used to extract metabolites from the leaves of V. fastigiata at various temperatures (50 °C, 100 °C, 150 °C and 200 °C). Ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UHPLC-qTOF-MS) analysis in combination with chemometric methods, particularly principal component analysis (PCA) and liquid/gas chromatography mass spectrometry (XCMS) cloud plots, were used to descriptively visualize the data and identify significant metabolites extracted at various temperatures. A total of 25 different metabolites, including hydroxycinnamic acid derivatives, clovamide, deoxy-clovamide and flavonoids, were noted for the first time in this plant. Overall, an increase in extraction temperature resulted in an increase in metabolite extraction during PHWE. This study is the first scientific report on the phytochemical composition of V. fastigiata, providing insight into the components of the chemo-diversity of this important plant.

A new adaptive PCA based thresholding scheme for fault detection in complex systems

For large scale and complex processes, data-driven analysis methods are receiving increasing attention for fault detection and diagnosis to improve process operation by detecting when abnormal process operations exist and diagnosing the sources of the abnormalities. Common methods based on multivariate statistical analysis are widely used and particularly principal component analysis (PCA), fault detection indices used along with PCA including the Hotelling T² statistic and the sum of squared prediction error (SPE) known as the Q statistic can be used to identify faults. This paper develops a new adaptive thresholding scheme based on a modified exponentially weighted moving average (EWMA) control chart statistic, which is effective in detecting small changes and abrupt shifts in the process operation. The aim is to enhance the performance of PCA methods for process monitoring, while maintaining a low false alarm rate with good sensitivity of anomalies. The performance of the developed scheme is compared to a conventional fixed thresholding technique by evaluating the detection performance across various types of faults that occurred in the Tennessee Eastman Process, The results demonstrate the promising capabilities of our proposed scheme.

The potential of spectral and hyperspectral-imaging techniques for bacterial detection in food: A case study on lactic acid bacteria

Official methods for the detection of bacteria are based on culture techniques. These methods have limitations such as time consumption, cost, detection limits and the impossibility to analyse a large number of samples. For these reasons, the development of rapid, low-cost and non-destructive analytical methods is a task of growing interest.In the present study, the capability of spectral and hyperspectral techniques to detect bacterial surface contamination was investigated preliminarily on gel cultures, and subsequently on sliced cooked ham. In more detail, two species of lactic acid bacteria (LAB) were considered, namely Lactobacillus curvatus and Lactobacillus sakei, both of which are responsible for common alterations in sliced cooked ham.Three techniques were investigated, with different equipment, respectively: a macroscopic hyperspectral scanner operating in the NIR (10,470–5880cm−1) region, a FT-NIR spectrophotometer equipped with a transmission arm as the sampling tool, working in the 12,500–5800cm−1 region, and a FT-MIR microscopy operating in the 4000–675cm−1 region.Multivariate exploratory data analysis, in particular principal component analysis (PCA), was applied in order to extract useful information from original data and from hyperspectrograms. The results obtained demonstrate that the spectroscopic and imaging techniques investigated can represent an effective and sensitive tool to detect surface bacterial contamination in samples and, in particular, to recognise species to which bacteria belong.

Analytical evaluation, by GC/MS, of gelatine removal from ancient papers induced by wet cleaning: A comparison between immersion treatment and application of rigid Gellan gum gel

In paper conservation, wet cleaning treatments are usually performed to remove harmful degradation substances. Although cleaning treatments are fundamental in this field, the use of free water, besides causing an excessive swelling of cellulose fibres and sometimes also strong alterations of graphic media, could induce the extraction of the original sizing agents, resulting in a weakening of the paper structure. Rigid polysaccharide hydrogels (i.e. Gellan gum) can be effectively used to minimize the impact of water on paper. The present study evaluates the rate of gelatine removal from paper artworks comparing two different wet cleaning treatments: washing by immersion in free deionized water and by means of Gellan gum application. After a preliminary characterization of the paper samples through Hertzberg stain test and Fourier transform infrared spectroscopy (FT-IR), the study is carried out using gas chromatography–mass spectrometry analysis (GC–MS). The results showed that Gellan gum was less invasive than immersion treatment since it caused the extraction of lower amounts of gelatine from paper. The gelatine removal was also tested by statistical analysis, in particular principal component analysis (PCA) of amino acids.

Non-invasive identification of organic materials in wall paintings by fiber optic reflectance infrared spectroscopy: a statistical multivariate approach

The aim of this study is to develop a method for the non-invasive and in situ identification of organic binders in wall paintings by fiber optic mid-FTIR reflectance spectroscopy. The non-invasive point analysis methodology was set-up working on a wide set of wall painting replicas of known composition and using statistical multivariate methods, in particular principal component analysis (PCA), for the interpretation, understanding, and management of data acquired with reflectance mid-FTIR spectroscopy. Results show that PCA can be helpful in managing and preliminary sorting of the large amount of spectra typically collected during non-invasive measurement campaigns and highlight further avenues for research. The developed PCA model was finally applied to the case of a Renaissance wall painting by Perugino assessing it predictability as compared to the interpretation of the single spectrum.

Noise correlations and SNR in phased-array MRS

The acquisition of magnetic resonance spectroscopy (MRS) signals by multiple receiver coils can improve the signal-to-noise ratio (SNR) or alternatively can reduce the scan time maintaining a reliable SNR. However, using phased array coils in MRS studies requires efficient data processing and data combination techniques in order to exploit the sensitivity improvement of the phased array coil acquisition method. This paper describes a novel method for the combination of MRS signals acquired by phased array coils, even in presence of correlated noise between the acquisition channels. In fact, although it has been shown that electric and magnetic coupling mechanisms produce correlated noise in the coils, previous algorithms developed for MRS data combination have ignored this effect. The proposed approach takes advantage of a noise decorrelation stage to maximize the SNR of the combined spectra. In particular Principal Component Analysis (PCA) was exploited to project the acquired spectra in a subspace where the noise vectors are orthogonal. In this subspace the SNR weighting method will provide the optimal overall SNR. Performance evaluation of the proposed method is carried out on simulated (1)H-MRS signals and experimental results are obtained on phantom (1)H-MR spectra using a commercially available 8-element phased array coil. Noise correlations between elements were generally low due to the optimal coil design, leading to a fair SNR gain (about 0.5%) in the center of the field of view (FOV). A greater SNR improvement was found in the peripheral FOV regions.

Multivariate data analysis as a fast tool in evaluation of solid state phenomena

A thorough understanding of solid state properties is of growing importance. It is often necessary to apply multiple techniques offering complementary information to fully understand the solid state behavior of a given compound and the relations between various polymorphic forms. The vast amount of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets analyzed covered dehydration phenomena of a set of hydrates followed by variable temperature X-ray powder diffractometry and Raman spectroscopy and the crystallization of amorphous lactose monitored by Raman spectroscopy. Identification of different transitional states upon the dehydration enabled the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information.

Characterisation of vegetable oils by surface acoustic wave sensing electronic nose

Flavour analysis is typically performed by human organoleptic analysis, which is often expensive and subjective. A novel approach using a surface acoustic wave sensing electronic nose (zNose TM) for flavour analysis was explored to characterise 16 types of vegetable oils. Fatty acid composition, iodine value, peroxide value, p-anisidine value and free fatty acid analyses were conducted to determine the quality and characteristics of vegetable oils. The zNose TM was employed successfully for qualitative distinction of flavour in different vegetable oils. This is achieved using a visual fragrance pattern, called a VaporPrint TM, derived from the frequency of the SAW detector. VaporPrint TM was shown to be particularly useful for assessing vegetable oil aroma profile in its entirety. This image is created by transforming the time variable to a radial angle with the beginning and end of the analysis occurring at 0°, or vertical. A Chemometric method, particularly principal component analysis (PCA), was conducted for electronic nose data processing and identification. Analysis of the score plot of the PCA for the zNose TM measurement showed that 97% of the total variance in the data was described by PC 1 and PC 2. The loading plot revealed that five compounds ( m, k, n, s, and p) were important for differentiate the vegetable oils.

Polycyclic aromatic hydrocarbons in bulk deposition at a suburban site: assessment by principal component analysis of the influence of meteorological parameters

Abstract Bulk deposition (wet+dry) of 14 polycyclic aromatic hydrocarbons (PAHs) was measured every week from 5 March 2001 to 4 March 2002 on a suburban site at Evreux located 100 km West of Paris (France). Measured concentrations in bulk depositions ranged from 1.1 to 222 ng l−1 with an average of 71.5 ng l−1. The most prevalent PAHs are phenanthrene, fluoranthene and pyrene, which represent 60% of the total concentration on average. The potentially carcinogenic PAHs represent 22% of the PAH concentrations. Statistical analysis, in particular principal component analysis (PCA), allowed us to analyse this data set, highlighting the influence of meteorological parameters (temperature, precipitation amount) on PAH concentration, both quantitatively and qualitatively. The temperature played an important role for PAH concentration in bulk deposition and influenced the efficiency of washout, while rainfall amounts seemed to have a minor influence. Thus, we found seasonal trends in PAH concentration. High rainfall correlated with high proportions of light PAHs. Finally, the study of wind directions allowed us to localize sources of PAHs and to estimate their respective contribution. The influence of the urban area of Rouen on PAH concentrations measured at Evreux is emphasized.

Euclidean Distance as a Similarity Metric for Principal Component Analysis

Abstract Eigentechniques, in particular principal component analysis (PCA), have been widely used in meteorological analyses since the early 1950s. Traditionally, choices for the parent similarity matrix, which are diagonalized, have been limited to correlation, covariance, or, rarely, cross products. Whereas each matrix has unique characteristic benefits, all essentially identify parameters that vary together. Depending on what underlying structure the analyst wishes to reveal, similarity matrices can be employed, other than the aforementioned, to yield different results. In this work, a similarity matrix based upon Euclidean distance, commonly used in cluster analysis, is developed as a viable alternative. For PCA, Euclidean distance is converted into Euclidean similarity. Unlike the variance-based similarity matrices, a PCA performed using Euclidean similarity identifies parameters that are close to each other in a Euclidean distance sense. Rather than identifying parameters that change together, the r...