Red yeast strains are ubiquitous microorganisms which accumulate substantial amounts of lipids and lipid-soluble metabolites. Red yeasts utilize many waste substrates of different origin. In this work red yeasts strains (Rhodotorula, Sporobolomyces, Cystofilobasidium) were used for screening of growth and metabolic activity. As a carbon source waste animal fat and its hydrolysis products were used. Hydrolysis of animal fat was tested in alkaline as well as acidic conditions. As the substrate glucose (control), glycerol, crude animal fat, acid fat hydrolyzate and hydrolysate: glucose 1:2 were used. Screening of growth and metabolic activity of red yeasts was performed by flow cytometry. Extracellular lipase production was monitored as adaptation mechanism. Carotenoids, ergosterol and ubiquinone were quantified by HPLC/PDA/MS/ESI and the biomass was evaluated gravimetrically. All tested strains utilized fat hydrolysate and produced red coloured biomass. Cultivation in media containing non-hydrolysed fat led to strain specific induction of extracellular lipase. Amount of lipid metabolites produced by individual strains was depended on glycerol content in medium. The highest increase of lipase production was observed in Cystofilobasidium macerans and Sporobolomyces shibatanus. Valorisation of animal fat can lead to production of unsaturated fatty acids, single cell oils, carotenoid pigments, sterols and enriched red yeast biomass.

This paper deals with the design of a neural network-based biomass concentration estimation system. This system is enhanced by the incorporation of information about the actual metabolism of the microorganism cultivated, which is taken from an on-line knowledge-based system. Two different design approaches have been investigated using the fed-batch cultivation of baker's yeast as the model process. In the first, metabolic state (MS) data were passed as additional input to the neural network; in the second, these data were used to select a neural network suitable for the specific MS. Two neural network types--feed-forward (Levenberg-Marquardt) and cascade correlation--were applied to this system and tested, and the performances of these neural networks were compared.

Red yeast Cystofilobasidium capitatum autofluorescence was studied by means of confocal laser scanning microscopy (CLSM) to reveal distribution of carotenoids inside the cells. Yeasts were cultivated in 2L fermentor on glucose medium at permanent light exposure and aeration. Samples were collected at different times for CLSM, gravimetric determination of biomass and HPLC determination of pigments. To compare FLIM (Fluorescence Lifetime Imaging Microscopy) images and coupled data (obtained by CLSM) with model systems, FLIM analysis was performed on micelles of SDS:ergosterol and SDS:coenzyme Q with different content of ergosterol and coenzyme Q, respectively, and with constant addition of beta-carotene. Liposomes lecithin:ergosterol:beta-carotene were investigated too. Two different intracellular forms of carotenoids were observed during most of cultivations, with third form appeared at the beginning of stationary phase. Observed behavior is probably due to formation of some kind of carotenoid protective system in membranes of different compartments of yeast cell, especially cytoplasmic membrane.

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