Clustering and nonlinear regression analysis applied to the in vitro growing of Leucoagaricus gongylophorus (Singer) Möller in a culture environment enriched with different botanical extracts

Abstract

The study aimed to evaluate the in vitro growing of Leucoagaricus gongylophorus in a culture environment enriched with different extracts from botanical species, via multivariate statistical analysis of data clustering, besides identifying the fungus behavior through nonlinear regression equations. The fungus was inoculated into a BDA medium enriched with eight botanical extracts, along with two controls (with and without the addition of 20mL distilled water), in Petri plates. The plates were kept in a climatized chamber at a 25±1ºC temperature in the dark, during a 42 days period and the fungus growing was evaluated at weekly intervals. It was proved that the clustering analysis and the Chapman-Richards model are effi cient to verify the similarity of different botanical extracts in the fungus development, as well as to estimate and describe the growing of L. gongylophorus.

Keywords

• Fungus
• Mathematical model
• Leaf-cutting ants
• Linear regression