Digital image analysis and fractal-based kinetic modelling for fungal biomass determination in solid-state fermentation

Abstract

This work deals with a non-destructive method involving image analysis and kinetic modelling to determine fungal biomass in solid-state fermentation (SSF). Fractal dimension, quantifying the morphological changes of mycelia-matrix from culture images, showed correlations with Penicillium decumbens biomass on lignocelluloses substrates. Kinetic models were constructed to describe the variation of fractal dimension of mycelia-matrix along with fungal growth. Fermentations on straw substrates with different particle lengths and moisture contents were carried out to validate the proposed models. Relative errors of the models were 0.541–5.221% for biomass and 0.454–3.885‰ for fractal dimension. Parameters δ and η in fractal kinetic models, which indicated the variation rates of fractal dimension, presented significant specificity for the specific growth rate of P. decumbens, thus can be used to predict fungal biomass in SSF. With advantages of low cost, reasonable accuracy and well adjustability, the coupling of dynamic imaging and computational modelling show potential in the on-line determination of fungal biomass in SSF.