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for Examination of Inkjet Prints in Forensic Analysis, 7th International Symposium of Information and Graphic Arts Technology, Slovenia, June 5-6. Hofer R (2004) Journal of Forensic Science 49: 1353-1357. Jolliffe IT (2002) Principal Component Analysis, Second Edition. Springer-Verlag New York, ISBN 0-387-95442-2. Lavine BK (1998) Anal. Chem 70: 209-228. Marida KV, Kent JT, Bibby JM (1979) Academic Press Inc. 24: 502-1982. McClure FW (2003) NIR Publications 11: 487-518. Meloun M, Militký J, Hill M (2005) Computer analysis of multivariate data in examples (in Czech

References Chiang L. H. and Colegrove L. F. (2007). Industrial implementation of on-line multivariate quality control, Chemometrics and Intelligent Laboratory Systems 88(2): 143-153. Croux C., Filzmoser P. and Oliveira M. (2007). Algorithms for projection-pursuit robust principal component analysis, Chemometrics and Intelligent Laboratory Systems 87(2): 218-225. Croux C. and Ruiz-Gazen A. (2005). High breakdown estimators for principal components: The projection-pursuit approach revisited, Journal of Multivariate Analysis 95(1): 206-226. Dunia R. and Qin S

.H. (2005). (2D)(2)PCA: Two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing , 69, 224-231. [12] Kiatpanichagij, K., Afzulpurkar, N. (2009). Use of supervised discretization with PCA in wavelet packet transformation-based surface electromyrogram classification. Biomedical Signal Processing and Control , 4, 127-138. [13] Jolliffe, I.T., Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of The Royal Society A – Mathematical Physical and Engineering Sciences , 374


The usability of various single extractors in the chemical analysis of composts was evaluated using principal component analysis. Ten different single extractors were used to determine the contents of microelements obtained in the chemical extraction of four different composts. It was found that principal component analysis is a satisfactory statistical method enabling the comparison of different solutions in terms of efficiency of extraction of microelements from composts of different composition. The results showed that 1mol dm-3 HCL and 10% HNO3 solutions had the highest extraction strength, and 0.01mol dm-3 CaCl2 and 1mol dm-3 NH4NO3 the lowest.


A new method for predicting correlation between fabric parameters and the drape is developed. This method utilises a Principal Component Analysis (PCA) of intercorrelated influencing parameters (bending rigidity, weight, thickness) and the drape parameters (drape coefficient and the node number). This paper describes the PCA procedure and presents the similarities and contrasts between variables.


There is a unique link between leadership qualities and organizational success. Leadership is the problem of many organizations but little attention is given to leadership-related research. This paper aimed to examine the qualities of a good leader using principal component analysis (PCA). The study adopted a quantitative research approach by eliciting perceptions of respondents on the qualities of a good leader through structured questionnaire. One hundred and fifty (150) questionnaires were administered to top management of companies within the construction industry, banking industry, food industry, and information technology industry. One hundred and twenty-seven (127) were retrieved and considered for further analysis. The data obtained were analyzed using PCA. The findings revealed the principal qualities of a good leader to be: 1) accessibility and dedication, 2) neutrality and modesty, 3) aspiration and attentiveness, 4) believe and aptitude, 5) dignity and amiability, 6) insight and confidence, 7) vitality and concentration, 8) originality and honesty, 9) responsibility and team spirit, 10) decency and self-assurance, 11) charitable, 12) comical and maintenance culture, and 13) reliability. It is recommended that leaders should demonstrate these leadership qualities to enhance organizational effectiveness and efficiency.


Three plant species were assessed in this study - ozone-sensitive and -resistant tobacco, ozone-sensitive petunia and bean. Plants were exposed to ambient air conditions for several weeks in two sites differing in tropospheric ozone concentrations in the growing season of 2009. Every week chlorophyll contents were analysed. Cumulative ozone effects on the chlorophyll content in relation to other meteorological parameters were evaluated using principal component analysis, while the relation between certain days of measurements of the plants were analysed using multivariate analysis of variance. Results revealed variability between plant species response. However, some similarities were noted. Positive relations of all chlorophyll forms to cumulative ozone concentration (AOT 40) were found for all the plant species that were examined. The chlorophyll b/a ratio revealed an opposite position to ozone concentration only in the ozone-resistant tobacco cultivar. In all the plant species the highest average chlorophyll content was noted after the 7th day of the experiment. Afterwards, the plants usually revealed various responses. Ozone-sensitive tobacco revealed decrease of chlorophyll content, and after few weeks of decline again an increase was observed. Probably, due to the accommodation for the stress factor. While during first three weeks relatively high levels of chlorophyll contents were noted in ozone-resistant tobacco. Petunia revealed a slow decrease of chlorophyll content and the lowest values at the end of the experiment. A comparison between the plant species revealed the highest level of chlorophyll contents in ozone-resistant tobacco.

17 Spanish autonomous regions (individuals). The first 16 columns are the active variables (main trip activities) and the last 8 are supplementary variables (sub-variables from sports). Active variables define the components extracted from the correlation matrix. Supplementary variables should be interpreted according to those components and they do not increase data variability. ( Dazy, Le Barzic 1996 : 20). The Principal Component Analysis enables us to do the following: – identify the components (relevant dimensions) related to tourist activities carried out by


Landraces of spinach in Iran have not been sufficiently characterised for their morpho-agronomic traits. Such characterisation would be helpful in the development of new genetically improved cultivars. In this study 54 spinach accessions collected from the major spinach growing areas of Iran were evaluated to determine their phenotypic diversity profile of spinach genotypes on the basis of 10 quantitative and 9 qualitative morpho-agronomic traits. High coefficients of variation were recorded in some quantitative traits (dry yield and leaf area) and all of the qualitative traits. Using principal component analysis, the first four principal components with eigen-values more than 1 contributed 87% of the variability among accessions for quantitative traits, whereas the first four principal components with eigen-values more than 0.8 contributed 79% of the variability among accessions for qualitative traits. The most important relations observed on the first two principal components were a strong positive association between leaf width and petiole length; between leaf length and leaf numbers in flowering; and among fresh yield, dry yield and petiole diameter; a near zero correlation between days to flowering with leaf width and petiole length. Prickly seeds, high percentage of female plants, smooth leaf texture, high numbers of leaves at flowering, greygreen leaves, erect petiole attitude and long petiole length are important characters for spinach breeding programmes.

Pooling of low flow regimes using cluster and principal component analysis

This article deals with the regionalization of low flow regimes lower than Q95 in Slovakia. For the regionalization of 219 small and medium-sized catchments, we used a catchment area running from 4 to 500 km2 and observation periods longer than 20 years. The relative frequency of low flows lower than Q95 was calculated. For the regionalization, the nonhierarchical clustering K-means method was applied. The Silhouette coefficient was used to determine the right number of clusters. The principal components were found from the pooling variables on the principal components. The K-means clustering method was applied. Next, we compared the differences between the two methods of pooling data into regional types. The results were compared using an association coefficient.