## Assessment of Two-Dimensional Separative Systems Using Nearest-Neighbor Distances Approach. Part 1: Orthogonality Aspects

journal contribution

posted on 18.02.2016 by Witold Nowik, Sylvie Héron, Myriam Bonose, Mateusz Nowik, Alain Tchapla#### journal contribution

Any type of content formally published in an academic journal, usually following a peer-review process.

We propose here a new approach to
the evaluation of two-dimensional
and, more generally, multidimensional separations based on topological
methods. We consider the apex plot as a graph, which could further
be treated using a topological tool: the measure of distances between
the nearest neighbors (NND). Orthogonality can be thus defined as
the quality of peak dispersion in normalized separation space, which
is characterized by two factors describing the population of distances
between nearest neighbors: the lengths (di

_{(o)}) of distances and the degree of similarity of all lengths. Orthogonality grows with the increase of both factors. The NND values were used to calculate a number of new descriptors. They inform about the extent of peak distribution, like the arithmetic mean (*A̅*_{(o)}) of NNDs, as well as about the homogeneity of peak distribution, like the geometric mean (*G̅*_{(o)}) and the harmonic mean (*H̅*_{(o)}). Our new, NND-based approach was compared with another recently published method of orthogonality evaluation: the fractal dimensionality (*D*_{F}). The comparison shows that the geometric mean (*G̅*_{(o)}) is the descriptor behaving in the most similar way to dimensionality (*D*_{F}) and the harmonic mean (*H̅*_{(o)}) displays superior sensitivity to the shortest, critical distances between peaks. The latter descriptor (*H̅*_{(o)}) can be considered as sufficient to describe the degree of orthogonality based on NND. The method developed is precise, simple, easy to implement, and possible to use for the description of separations in a true or virtual system of any number of dimensions.