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.

Applying Principal Component Analysis (PCA) to the Selection of Forensic Analysis Methodologies

2021 ◽  
pp. 105937
Author(s):  
Nigel K. Booker ◽  
Peter Knights ◽  
J.D. Gates ◽  
Richard E. Clegg
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Forensic Analysis ◽  
Selection Of

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.

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.

The Evaluation of Comprehensive Transportation System in Inner Mongolia Based on Principal Component Analysis

2014 ◽  
Vol 505-506 ◽  
pp. 782-786
Author(s):  
Chun Mei Zhang ◽  
Zhan Xin Ma ◽  
Lu Lu Zhai ◽  
Xin Yu Cui ◽  
Xiao Biao Zhao
Keyword(s):  
Principal Component Analysis ◽  
Inner Mongolia ◽  
Rapid Development ◽  
Principal Component ◽  
Component Analysis ◽  
Transportation System ◽  
Autonomous Region ◽  
Economic Output ◽  
Transportation Development ◽  
Inner Mongolia Autonomous Region

Based on the relevant data of comprehensive transportation system in Inner Mongolia Autonomous Region from 1990 to 2011, the transport equipment, transport mileage, transport capacity, and the transport share of the total economic output in four aspects are studied. Then we select 13 indicators to build the evaluation of comprehensive transportation system in Inner Mongolia Autonomous Region. Using SPSS17.0 software to perform the principal component analysis could get the evaluation of the development of comprehensive transportation system in Inner Mongolia, which has maintained rapid development in the past 22 years, especially after 2003, higher than previous years. It is in accordance with the current transportation development of Inner Mongolia Autonomous Region, next we verify the feasibility of the Principal Component Analysis (PCA) on transportation problem. The method also has theoretical significance of research on relevant aspects of other areas.

Comparison of several variants of principal component analysis (PCA) on forensic analysis of paper based on IR spectrum

2016 ◽  
Author(s):  
Loong-Chuen Lee ◽  
Choong-Yeun Liong ◽  
Khairul Osman ◽  
Abdul Aziz Jemain
Keyword(s):  
Principal Component Analysis ◽  
Ir Spectrum ◽  
Principal Component ◽  
Component Analysis ◽  
Forensic Analysis

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 Principal Component Analysis as a Statistical Tool for Concrete Mix Design

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2668
Author(s):  
Janusz Kobaka
Keyword(s):  
Principal Component Analysis ◽  
Rapid Development ◽  
Principal Component ◽  
Component Analysis ◽  
Mix Design ◽  
Statistical Tool ◽  
Concrete Mix Design ◽  
Concrete Mix ◽  
2D And 3D ◽  
Economic Considerations

With the recent and rapid development of concrete technologies and the ever-increasing use of concrete, adapting concrete to the specific needs and applications of civil engineering is necessary. Due to economic considerations and care for the natural environment, improving the methods currently used in concrete design is also necessary. In this study, the author used principal component analysis as a statistical tool in the concrete mix design process. Using a combination of PCA variables and 2D and 3D factors has made it possible to refine concrete recipes. Thirty-eight concrete mixes of different aggregate grades were analyzed using this method. The applied statistical analysis showed many interesting relationships between the properties of concrete and the content of its components such as the clustering of certain properties, showing dependence between the properties and the quantities of certain ingredients in concrete, and reducing noise in the data, which most importantly simplifies interpretation. This method of analysis can be used as an aid for concrete mix design.

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 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.

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