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Abstract

Principal component analysis, a technique which reduces the dimensionality of multivariate data by removing intercorrelations among variables, has a number of potentially useful applications in horticultural research. It can be used to order multivariate commodity quality data in 1 or 2 orthogonal dimensions called principal components, which express most of the variance of the original data. Scores on these principal components can be used as an index of commodity quality to replace subjective visual quality ratings in conventional statistical analyses. Interpretation of the pattern of variable loadings on these principal components may aid in the elucidation of interactions among variables in the data. Plotting of multivariate data in 2 or 3 dimensional principal component space can be useful for displaying relationships among cultivars or species in taxonomic studies.

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2 years that share at least two cultivars (because standards were present in most years for both mummy blight and fruit rot data sets, typically many cultivars were shared for randomly selected pairs of years). We use a principal components analysis

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Abstract

Principal component analysis of soil and foliar analysis and plant quality data for field-grown Salvia splendens Sello cv. Red Pillar was useful for pointing out relationships among these variables and suggested possible growth limiting factors. Soil P and foliar P, Ca, Cu, Zn, and N were found to be positively related to plant quality on the first principal component, whereas soil K, Ca, Mg, and NO3 and foliar Fe were negatively related to quality. The former elements are thought to be limiting growth in this situation, while the latter elements in some way suppress the uptake or utilization of the deficient elements. The third and fourth components described well known relationships of soil pH with soil and foliar concentrations of several elements.

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significantly greater than that in the other regions. Principal component analysis. Principal component analysis is a statistical analysis method that simplifies multiple indicators into a few comprehensive indicators and uses a few variables to reflect

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largest, up to 33.21%, indicating a considerable variation in W5S content among the germplasms. Principal component analysis. Thirty-five fruit quality indicators of 15 germplasms were measured, and the CV between the qualities were calculated

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Abbreviation: PCA, principal component analysis. 1 Station de Biométrie. We thank F. Lescourret, L. Pages, and reviewers for helpful comments on this paper, and S. Hamilton and M. Jones for improving the English translation. The PACA Region is also

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further examination and comparison of container substrates. Two such exploratory multivariate methods that require no distributional assumptions are principal components analysis (PCA) and cluster analysis (CLA). PCA, which reduces the dimensionality of

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selected variable categories was also reduced by the Pearson correlation coefficient and by principal components analysis (PCA) ( Iezzoni and Pritts, 1991 ). These analyses were performed on the correlation matrix obtained with the frequencies, turning the

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vegetable species and tends to accumulate in plant leaves ( Vila et al., 1997 ; Zoghbi et al., 1995 ). Fig. 2. A principal component analysis of the correlation between volatile compounds and aerial vegetative tissues of G. bicolor (n = 3). 25CL

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