QUANTIFICATION OF THE CONTRIBUTION OF FILLER CHARACTERISTICS TO NATURAL RUBBER REINFORCEMENT USING PRINCIPAL COMPONENT ANALYSIS

2018 ◽  
Vol 91 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Cindy S. Barrera ◽  
Alfred B. O. Soboyejo ◽  
Katrina Cornish
Keyword(s):  
Principal Component Analysis ◽  
Carbon Black ◽  
Natural Rubber ◽  
Polymer Composites ◽  
Principal Component ◽  
Component Analysis ◽  
Specific Product ◽  
Industrial Residues ◽  
Explanatory Variables ◽  
Wide Range

ABSTRACT Practical statistical models were developed to quantify individual contributions from characteristics of conventional and non-conventional fillers and predict resulting mechanical properties of both hevea and guayule natural rubber composites. Carbon black N330 and four different agro-industrial residues, namely, eggshells, carbon fly ash, processing tomato peels, and guayule bagasse, were used in this study. Filler characteristics were used as explanatory variables in multiple linear regression analyses. Principal component analysis was used to evaluate correlations among explanatory variables based on their correlation matrices and to transform them into a new set of independent variables, which were then used to generate reliable regression models. Surface area, dispersive component of surface energy, carbon black, and waste-derived filler loading were found to have almost equal importance in the prediction of composite properties. However, models developed for ultimate elongation poorly explained variability, indicating the dependence of this property on other variables. Agro-industrial residues could potentially serve as more sustainable fillers for polymer composites than conventional fillers. This new modeling approach for polymer composites allows the performance of a wide range of different waste-derived fillers to be predicted with minimum laboratory work, facilitating the optimization of compound recipes to address specific product requirements.

scholarly journals Forensic analysis on printer inks via chemometrics

2021 ◽  
Vol 1192 (1) ◽  
pp. 012029
Author(s):  
L H Mohd Zawawi ◽  
N F Mohamed Azmin ◽  
M F Abd. Wahab ◽  
S I Ibrahim ◽  
M Y Mohd Yunus
Keyword(s):  
Principal Component Analysis ◽  
Digital Media ◽  
Graphical Representation ◽  
Rapid Development ◽  
Principal Component ◽  
Component Analysis ◽  
Forensic Analysis ◽  
Criminal Cases ◽  
Trial Court ◽  
Wide Range

Abstract Printer inks are becoming necessary for utilization for wide range of purposes by society in current times with rapid development in technology and digital media area. Thus, forgery and counterfeiting becoming easier for the criminals. It is dangerous as some criminals will misused the technology by mean of addition and adulteration of parts of text or numbers on document as the inks and document can be made as an evidence in the trial court. Thus, the characterization and differentiation of the printed inks in the suspected documents (civil or criminal cases) may provide important information about the authenticity of the printer inks. The focus of this study to differentiate the chemical component of three different types of sample inks by incorporation of FTIR spectrophotometer with principal component analysis. The unique features of the ink samples were unmasked from the score plots of the principal component analysis. Thus, the graphical representation provided by the FTIR spectra with principal component analysis enabled the discrimination certain chemical in the printer inks.

Evaluating the Performance Degradation of Centrifugal Pumps Using the Principal Component Analysis

2021 ◽  
Author(s):  
Andrew Eaton ◽  
Wael Ahmed ◽  
Marwan A. Hassan
Keyword(s):  
Principal Component Analysis ◽  
Distribution Systems ◽  
Water Distribution ◽  
Principal Component ◽  
Component Analysis ◽  
Performance Degradation ◽  
Centrifugal Pumps ◽  
Pump Performance ◽  
Hydraulic Machines ◽  
Wide Range

Abstract Centrifugal pumps are used in a variety of engineering applications, such as power production, heating, cooling, and water distribution systems. Although centrifugal pumps are considered to be highly reliable hydraulic machines, they are susceptible to a wide range of damage due to several degradation mechanisms, which make them operate away from their best efficiency range. Therefore, evaluating the energy efficiency and performance degradation of pumps is an important consideration to the operation of these systems. In the present study, the hydraulic performance along with the vibration response of an industrial scale centrifugal pump (7.5KW) subjected to different levels of impeller unbalance were experimentally investigated. Extensive testing of pump performance along with vibration measurements were carried. Both time and frequency domain techniques coupled with principal component analysis (PCA) were used in this evaluation. The effect of unbalance on the pump performance was found to be mainly on the shaft power, while no change in the flow rate and the pump head were observed. As the level of unbalance increased, the power required to operate the pump at the designated speed increased by as much as 12%. The PCA found to be a useful tool in comparing the pump vibrations in the field in order to determine the presence of unbalance as well as the degree of damage. The results of this work can be used to evaluate and monitor pump performance under prescribed degradation in order to enhance preventative maintenance programs.

scholarly journals Assessment of the genetic components and trait associations in diverse set of fieldpea (Pisum sativum L.) genotypes

2015 ◽  
Vol 43 (3) ◽  
pp. 323-330 ◽  
Author(s):  
AK Parihar ◽  
GP Dixit ◽  
V Pathak ◽  
D Singh
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Components ◽  
Agronomic Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Genetic Components ◽  
Wide Range ◽  
Cluster Distance ◽  
Biological Yield

One hundred and 40 genotypes of fieldpea were used to assess the genetic divergence for various agronomic traits. The study revealed that all the accessions were significantly different for the traits and a wide range of variability exists for most of the traits. Correlation studies exhibited that seed yield had positive significant correlation with most of the traits. Cluster analysis classified 140 genotypes into 12 distinct groups. A large number of genotypes (30) were placed in cluster IV followed by cluster III with 24 genotypes. The maximum inter-cluster distance was observed between clusters III and IV indicating the possibility of high heterotic effect if the individuals from these clusters are cross-bred. Principal component analysis yielded 12 Eigen vectors and PCA analysis revealed significant variations among traits with seven major principal components explaining about 90% of variations. The estimates of Eigen value associated with the principal components and their respective relative and accumulated variances explained 50.16% of total variation in the first two components. The characters with highest weight in component first were biological yield, pods/plant, yield/plant and branches/plant which explained 34.22% of the total variance. The results of principal component analysis were closely in line with those of the cluster analysis. The grouping of genotypes and hybridization among genetically diverse genotypes of different cluster would be helpful in broadening the genetic base of fieldpea and producing desirable recombinants for developing new fieldpea varieties. DOI: http://dx.doi.org/10.3329/bjb.v43i3.21605 Bangladesh J. Bot. 43(3): 323-330, 2014 (December)

scholarly journals Capacitive facial activity measurement

ACTA IMEKO ◽  
2014 ◽  
Vol 2 (2) ◽  
pp. 78 ◽  
Author(s):  
Ville Rantanen ◽  
Pekka Kumpulainen ◽  
Hanna Venesvirta ◽  
Jarmo Verho ◽  
Oleg Spakov ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Principal Component ◽  
Component Analysis ◽  
Measured Data ◽  
Activity Measurement ◽  
Facial Movements ◽  
Wide Range ◽  
The Face ◽  
Facial Activity

A wide range of applications can benefit from the measurement of facial activity. The current study presents a method that can be used to detect and classify the movements of different parts of the face and the expressions the movements form. The method is based on capacitive measurement of facial movements. It uses principal component analysis on the measured data to identify active areas of the face in offline analysis, and hierarchical clustering as a basis for classifying the movements offline and in real-time. Experiments involving a set of voluntary facial movements were carried out with 10 participants. The results show that the principal component analysis of the measured data could be applied with almost perfect performance to offline mapping of the vertical location of the facial activity of movements such as raising and lowering eyebrows, opening mouth, raising mouth corners, and lowering mouth corners. The presented classification method also performed very well in classifying the same movements both with the offline and the real-time implementations.

scholarly journals Study of the Influence of Different Yeast Strains on Red Wine Fermentation with NIR Spectroscopy and Principal Component Analysis

J ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 133-147 ◽  
Author(s):  
Antonio Marsico ◽  
Rocco Perniola ◽  
Maria Cardone ◽  
Matteo Velenosi ◽  
Donato Antonacci ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Sensory Properties ◽  
Mixed Fermentation ◽  
Wine Production ◽  
Yeast Cultures ◽  
Wide Range

Alcoholic fermentation is a key step in wine production. Indeed, a wide range of compounds, which strongly affect the sensory properties of wine, is produced during this process. While Saccharomyces cerevisiae yeast cultures are commonly employed in winemaking to carry on the fermentation process, some non-Saccharomyces species have recently gained attention due to their ability to produce various metabolites of oenological interest. The use of different yeasts strains usually results in wines with different sensory properties, despite being obtained from the same grape variety. In this paper, we tested the feasibility of using near-infrared spectroscopy (NIR) to discriminate among red wines from three different grape varieties produced with pure S. cerevisiae or by mixed fermentation with a promising non-Saccharomyces yeast, namely the Starmeriella bacillaris, which usually yields wines with significant amounts of glycerol and low levels of ethanol, acetic acid, and acetaldehyde. A principal component analysis (PCA) performed on the NIR spectra was used to search for differences in the samples. The NIR results have been compared with both basic wine parameters and sensory analysis data.

scholarly journals Surface soil metal elements variability affected by environmental and soil properties

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254928
Author(s):  
Wei Wu ◽  
Yu Li ◽  
Mingshu Yan ◽  
Lechao Yang ◽  
Jiali Lei ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Soil Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Critical Parameters ◽  
Explanatory Variables ◽  
Metal Elements ◽  
Classical Statistics ◽  
Soil Metal

Identifying the factors controlling the spatial variability of soil metal elements could be a challenge task due to the interaction of environmental attributes and human activities. This study aimed to investigate the critical explanatory variables controlling total Ca, Cd, Cr, Cu, Zn, Fe, Mn, Mg, Pb, and Zn variations in the arable topsoil using classical statistics, principal component analysis, and random forest techniques. The work was conducted in the core region of the Three Gorges Reservoir of China. The explanatory variables included soil, topography, climate, vegetation, land use type, and distance-related parameters. Average concentrations of the metal elements were in order of Fe > Mg > Ca > Mn > Zn > Cr > Ni > Pb > Cu > Cd. Soil Cr, Fe, and Pb showed low variability while others presented medium variability. Average concentrations of Cr, Fe, Cd, and Mg exceeded their corresponding background values. There were highly positive correlations between all metal elements except Pb, Cd and Cr. The principal component analysis further demonstrated that the sources of Pb, Cd, and Cr differed with other elements. The results of random forest suggested that soil properties followed by topography were critical parameters affecting the variations of Ca, Mg, Mn, Fe, Ni, Zn, and Cu. Agricultural activities and soil properties were major factors controlling the variations of Pb, Cr, and Cd. Further study should be conducted to understand the relations between the metal elements and soil properties.

scholarly journals Evaluation of drought resistance in Iris germanica L. based on subordination function and principal component analysis

2017 ◽  
pp. 770 ◽  
Author(s):  
Wei Bo ◽  
Baochun Fu ◽  
Guojie Qin ◽  
Guoming Xing ◽  
Yuguo Wang
Keyword(s):  
Principal Component Analysis ◽  
Drought Resistance ◽  
Principal Component ◽  
Component Analysis ◽  
Drought Treatment ◽  
Sod Activity ◽  
Moisture Condition ◽  
Wide Range ◽  
Iris Germanica ◽  
Physiological Indexes

Drought is one of the major environment stresses that have a wide range of impact on plants. In this study, seven physiological indexes including the content of soluble protein (SP), chlorophyll (Chl) and malondialdehyde (MDA), superoxide dismutase (SOD) and peroxidase (POD) activities, leaf relative water content (RWC), rate of water loss (RWL) from excised leaves were measured in leaves of Iris germanica before and after the drought treatment. It was found that the content of MDA and SP, POD and SOD activity increased, while RWL and RWC decreased in response to drought stress. Based on the subordinate function values of seven physiological indexes, seven single indexes were transformed into three principal components namely damage degree, active oxygen removal ability and moisture condition and the composite score (F value) of each iris variety was calculated by principal component analysis (PCA). Based on the F values, 10 iris cultivars could be divided into three groups by cluster analysis (CA): drought-resistance (2 varieties), medium drought-resistance (5 varieties), and low drought-resistance (3 varieties). Meanwhile, optimum regression equation was constructed. Therefore, this work provides a comprehensive and reliable method for evaluating drought resistance in the varieties of Iris germanica.

scholarly journals Factors Driving University Choice: A Principal Component Analysis on Italian Institutions

2018 ◽  
Author(s):  
Mara Soncin ◽  
Giovanni Azzone
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geographical Proximity ◽  
University Choice ◽  
Multiple Factors ◽  
University Reputation ◽  
Job Opportunities ◽  
Wide Range ◽  
Ease Of Access

When investigating students’ motivations to enroll in university, a wide range of elements related to the overall student experience concerning both the institution and the surrounding context should be taken into account. The current study moves from this point to analyse students’ choice factors from a survey completed by 27,705 students across 23 Italian institutions by means of a logistic Principal Component Analysis. Results confirm the presence of multiple factors jointly influencing students’ choice, with geographical proximity, job opportunities in the region, university reputation and ease of access opposing one another. Aggregating results at institutional level, students’ distribution prove to be highly heterogeneous across universities. From this, a managerial tool is provided to position student population and derive strategic implications.  Finally, policy considerations are reported.

Classification of Wines Using Principal Component Analysis

2021 ◽  
Vol 16 (1) ◽  
pp. 56-67 ◽  
Author(s):  
Jackson Barth ◽  
Duwani Katumullage ◽  
Chenyu Yang ◽  
Jing Cao
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Nearest Neighbor ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Explanatory Variables ◽  
Highly Correlated

AbstractClassification of wines with a large number of correlated covariates may lead to classification results that are difficult to interpret. In this study, we use a publicly available dataset on wines from three known cultivars, where there are 13 highly correlated variables measuring chemical compounds of wines. The goal is to produce an efficient classifier with straightforward interpretation to shed light on the important features of wines in the classification. To achieve the goal, we incorporate principal component analysis (PCA) in the k-nearest neighbor (kNN) classification to deal with the serious multicollinearity among the explanatory variables. PCA can identify the underlying dominant features and provide a more succinct and straightforward summary over the correlated covariates. The study shows that kNN combined with PCA yields a much simpler and interpretable classifier that has comparable performance with kNN based on all the 13 variables. The appropriate number of principal components is chosen to strike a balance between predictive accuracy and simplicity of interpretation. Our final classifier is based on only two principal components, which can be interpreted as the strength of taste and level of alcohol and fermentation in wines, respectively. (JEL Classifications: C10, Cl4, D83)

Principal Component Analysis for Predicting the Response of Nonlinear Base-Isolated Buildings

2009 ◽  
Vol 25 (1) ◽  
pp. 93-115 ◽  
Author(s):  
Sriram Narasimhan ◽  
Min Wang ◽  
Mahesh Pandey
Keyword(s):  
Principal Component Analysis ◽  
Nonlinear Dynamic Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Standard Errors ◽  
Important Conclusion ◽  
Intensity Measures ◽  
Peak Displacement ◽  
Wide Range ◽  
Selection Of

In the design of base-isolated buildings, a critical parameter governing the selection of isolator parameters is the peak displacement of the isolation layer. In this study, a model is developed in order to predict the peak base displacements utilizing multiple ground motion parameters, called intensity measures (IMs), as the inputs. The issue of correlation between various IMs is addressed through principal component analysis (PCA). This method also lends itself to dimensionality reduction, as those components that do not contribute significantly to the variance are discarded. The prediction intervals from the model are compared with the results from nonlinear dynamic analysis. An important conclusion is that by using the PCA based model, the standard errors remain relatively small and constant for a wide range of isolation periods. It is therefore clear that by utilizing multiple IMs and accounting for their correlation effects, it is possible to estimate the responses of base-isolated buildings with good confidence.

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