Publication

Orthogonality of Two-Dimensional Separations Based on Conditional Entropy

Hubert Girault
2011
Journal Articles

Abstract

A new approach to assess the orthogonality of two-dimensional (2-D) separation systems based on conditional entropy is developed. It considers the quantitative distribution of peaks in the entire separation space such that the orthogonality obtained is independent of the number of peaks observed for each separation technique. Therefore, it can be used to compare the orthogonality of different 2-D separation protocols for a given sample. Herein, the developed method has been employed to estimate the orthogonality of peptide separation by off-gel electrophoresis (OGE) hyphenated to capillary zone electrophoresis (CZE).

Official source

https://infoscience.epfl.ch/entities/publication/73113cba-7540-4a56-a097-cedfbb1c5bef

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Hubert Girault
2011
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/73113cba-7540-4a56-a097-cedfbb1c5bef

About this result

Related concepts (32)

Conditional entropy

In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable given that the value of another random variable is known. Here, information is measured in shannons, nats, or hartleys. The entropy of conditioned on is written as . The conditional entropy of given is defined as where and denote the support sets of and . Note: Here, the convention is that the expression should be treated as being equal to zero. This is because .

Rényi entropy

In information theory, the Rényi entropy is a quantity that generalizes various notions of entropy, including Hartley entropy, Shannon entropy, collision entropy, and min-entropy. The Rényi entropy is named after Alfréd Rényi, who looked for the most general way to quantify information while preserving additivity for independent events. In the context of fractal dimension estimation, the Rényi entropy forms the basis of the concept of generalized dimensions. The Rényi entropy is important in ecology and statistics as index of diversity.

Min-entropy

The min-entropy, in information theory, is the smallest of the Rényi family of entropies, corresponding to the most conservative way of measuring the unpredictability of a set of outcomes, as the negative logarithm of the probability of the most likely outcome. The various Rényi entropies are all equal for a uniform distribution, but measure the unpredictability of a nonuniform distribution in different ways.