Breeding potential of acrid elephant foot yam genotypes for yield and nutritional quality using multivariate analysis

The purpose of this study was to evaluate the monthly milk production and quality of buffaloes from two milk production systems in the Brazilian northeast using the multivariate analysis: principal component analysis (PCA). A total of 2,506 individual milk recordings were performed in two production systems, containing information on milk production (kg day-1), fat, protein, lactose and total solids counts and somatic cell count (SCC). There were positive correlations between the fat content and the contents of total solids (TS) and protein, and of TS and protein. From the PCA, two main components (PC1 and PC2) were identified, explaining 67.71% of the total variation. The fat, protein, lactose and ST level, represented by PC1, explain 46.18% of the total variance, and were an indicator of milk nutritional quality. The CP2, composed of milk production, SCC and production systems, explains 21.53% of the total variance, and was indicative of herd health. PCA results may be useful in dairy buffalo breeding programs, and a reduced number of variables are necessary to assess the nutritional quality of milk and herd health.

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Breeding potential of cultivated eggplant genotypes for bacterial wilt disease tolerance using multivariate analysis

International Journal of Vegetable Science ◽

10.1080/19315260.2021.1947935 ◽

2021 ◽

pp. 1-21

Author(s):

Tridip Bhattacharjee ◽

Praveen Kumar Maurya ◽

Swadesh Banerjee ◽

Asit Kumar Mandal ◽

Imtinungsang Jamir ◽

...

Keyword(s):

Multivariate Analysis ◽

Bacterial Wilt ◽

Wilt Disease ◽

Disease Tolerance ◽

Breeding Potential ◽

Bacterial Wilt Disease

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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