Proteolytic activity amongst selected Saprolegnia species

Saprolegia ssp. effectively utilized the protein casein as a sole source of carbon, nitrogen and sulphur, indicating considerable proteolytic activity. In the presence of a more simple carbon source such as glucose, which was readily assimilated, catabolite repression was not observed and casein exploitation was enhanced. Free proteinase activity was not detected by a number of methods, irrespective of culture conditions. However, clearing by mycelia of skimmed milk agar or agar amended with bacteria demonstrated a close association between proteinases and hyphae, suggestive of natural immobilization of proteinases. Casein breakdown was accompanied by release of individual amino acids and ammonia. The latter, indicative of amino acid assimilation and metabolism, was also associated with an increase in pH of culture medium. Single amino acids did not support growth of Saprolegnia but in combination with other amino acids, methionine encouraged greatest biomass production. Certain groupings of amino acids affected growth in a manner which departed from that expected, as assessed by multifactorial analysis of variance, and either enhanced or reduced growth.

Measurement of key compositional parameters in two species of energy grass by Fourier transform infrared spectroscopy

Two energy grass species, switch grass, a North American tuft grass, and reed canary grass, a European native, are likely to be important sources of biomass in Western Europe for the production of biorenewable energy. Matching chemical composition to conversion efficiency is a primary goal for improvement programmes and for determining the quality of biomass feed-stocks prior to use and there is a need for methods which allow cost effective characterisation of chemical composition at high rates of sample through-put. In this paper we demonstrate that nitrogen content and alkali index, parameters greatly influencing thermal conversion efficiency, can be accurately predicted in dried samples of these species grown under a range of agronomic conditions by partial least square regression of Fourier transform infrared spectra (R2 values for plots of predicted vs. measured values of 0.938 and 0.937, respectively). We also discuss the prediction of carbon and ash content in these samples and the application of infrared based predictive methods for the breeding improvement of energy grasses.