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Sarah A. White, Holly L. Scoggins, Richard P. Marini, and Joyce G. Latimer

fulfillment of the requirements for the MS degree. The authors gratefully acknowledge the technical assistance of Velva Groover. Plant material generously provided by Yoder Green Leaf, Lancaster, Pa. Multivariate repeated measures analysis of plant growth

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Jinshi Cui, Myongkyoon Yang, Daesik Son, Seongmin Park, and Seong-In Cho

study were to monitor the postharvest process at the farm and to develop multivariate analysis models using weight loss, firmness loss, and an assessment of bruising, together with fruit properties, impact force level, storage environment, and storage

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Givago Coutinho, Rafael Pio, Filipe Bittencourt Machado de Souza, Daniela da Hora Farias, Adriano Teodoro Bruzi, and Paulo Henrique Sales Guimarães

programs. According to Cruz et al. (2004) , comparing results from several multivariate analysis techniques provides a more accurate interpretation of the differences among cultivars, affording a more accurate interpretation of results with a low demand

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Adriano dos Santos, Francisco Eduardo Torres, Erina Vitório Rodrigues, Ariane de Andréa Pantaleão, Larissa Pereira Ribeiro Teodoro, Leonardo Lopes Bhering, and Paulo Eduardo Teodoro

the nonlinear regression analysis and multivariate analysis. Materials and Methods Experiments were performed in the municipalities of Aquidauana, Mato Grosso do Sul (lat. 20°27′12″ S; long. 55°40′06″ W; alt. 187 m asl.), Chapadão do Sul, Mato Grosso

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Mohsen Hesami and Mostafa Rahmati-Joneidabad

and their simple evaluation as well. Morphological features in accompany with multivariate statistical methods, such as PCA that was broadly used, and cluster analysis, are applicable means for screening individuals for many plants such as Cerasus

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A. Plotto, A. N. Azarenko, M. R. McDaniel, and J.P. Mattheis

`Gala' apples were harvested at weekly intervals for 6 weeks, refrigerated at 0C, and evaluated by a consumer panel monthly over a 6 month period for overall liking, firmness, sweetness, tartness and flavor intensities. Firmness, titratable acidity and soluble solids concentration were also measured. Initial analysis of sensory data revealed multicollinearity for overall liking, sweetness, and flavor. The five descriptors explained 75 % of the dataset variation in the first two factors. An orthogonal rotation separated overall liking, flavor and sweetness, and firmness and tartness into two independent factors. The distribution of mean scores along these independent factors showed that panelists could perceive changes due to ripening and maturation. The multivariate factor analysis was better than univariate ANOVA at illustrating how apple maturity stages were apparent to untrained panelists. Firmness was the only instrumental variable correlated to firmness ratings in the sensory tests. None of the analytical measurements could explain overall liking.

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Kwang-Chool Ko

Fifty nine morphological characters and isozyme band patterns of glutamate oxaloacetate transminase, peroxidase, glucose phosphate isomerase from fully expanded leaves were used for taxonomic study on 51 taxa consisted of Korean native and principal cultivars of the genus Pyrus. Taxonomic relationships were analyzed by complete cluster analysis method based on Euclidean taxonomic distance of IBM PC SPSS/PC+(ver. 3.0). Among the 39 qualitative morphological characters, a great deal of variations among 51 taxa were observed in immature fruit shape, skin lusterness, hair density on pedicel, anther color, shape of leaf apex and base, hair density on leaf surface, and leaf margin. Considerable variations were found in most tested quantitative characters except in the number of petals and styles. More reliable taxonomic results could be obtained by comparing morphological characters rather than examining isozyme band patterns. Even though there were considerable differences depending upon the methods of investigation, classification of the genus Pyrus by using isozyme band patterns was proved to be a good tool for rapid taxonomic studies.

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Eugene K. Blythe and Donald J. Merhaut

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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Calen McKenzie, Ivette Guzman, Ciro Velasco-Cruz, and Paul W. Bosland

encoding the key enzyme for chlorophyll b biosynthesis (CAO) from Arabidopsis thaliana Plant J. 21 305 310 doi: 10.1046/j.1365-313X.2000.00672.x Rencher, A.C. 2002 Methods of multivariate analysis Wiley Hoboken, NJ doi: 10