State-dependent correlations of biochemical variables in plants

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Abstract

Distributions of biochemical variables in plant foliage are mainly similar to normal distributions. The correlations between two variables belonging to a given sampling time or a given physiological stage are regarded as state-dependent correlations. The conditions of existence of strong state-dependent correlation are the dependence of values of the variables from each other and the identity of their distributions. An assumed identity of distributions can manifest itself in the very strong similarity of their empirical distributions. Two distributions with finite number of elements are considered the same in their type when they approximate a characteristic distribution, for example, the normal distribution in the same manner. In this situation, the rank or normal score patterns of the data producing linear correlation are also very similar to each other. On the basis of identity criteria and the interdependence of the wariables, an equation for state-dependent correlation has been derived in theoretical way from standardization of the distributions. Thus, not only statistical but also physical meanings can be attached to the parameters of the regression of experimental results. In this paper, we show that the distinction of plant physiological and/or stress states from each other can be more effectiwe by using the correlation of biochemical variables for their characterizations than by separately statistical comparisons of means of the wariables. Covariance analysis (ANCOVA) of linear regressions of state-dependent correlations is able to distinguish actually various states that, on the basis of the means, seem undistinguishable.

Original languageEnglish
Pages (from-to)197-210
Number of pages14
JournalJournal of Chemometrics
Volume23
Issue number4
DOIs
Publication statusPublished - Apr 1 2009

Keywords

  • Carbohydrates
  • Enzyme correlation
  • Grape
  • Pendunculate oak
  • State-dependent correlation

ASJC Scopus subject areas

  • Analytical Chemistry
  • Applied Mathematics

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