Multivariate models for prediction of rheological characteristics of filamentous fermentation broth from the size distribution

Abstract

The main purpose of this article is to demonstrate that principal component analysis (PCA) and partial least squares regression (PLSR) can be used to extract information from particle size distribution data and predict rheological properties. Samples from commercially relevant Aspergillus oryzae fermentations conducted in 550 L pilot scale tanks were characterized with respect to particle size distribution, biomass concentration, and rheological properties. The rheological properties were described using the Herschel-Bulkley model. Estimation of all three parameters in the Herschel-Bulkley model (yield stress (tau(y)), consistency index (K), and flow behavior index (n)) resulted in a large standard deviation of the parameter estimates. The flow behavior index was not found to be correlated with any of the other measured variables and previous studies have suggested a constant value of the flow behavior index in filamentous fermentations. It was therefore chosen to fix this parameter to the average value thereby decreasing the standard deviation of the estimates of the remaining rheological parameters significantly. Using a PLSR model, a reasonable prediction of apparent viscosity (micro(app)), yield stress (tau(y)), and consistency index (K), could be made from the size distributions, biomass concentration, and process information. This provides a predictive method with a high predictive power for the rheology of fermentation broth, and with the advantages over previous models that tau(y) and K can be predicted as well as micro(app). Validation on an independent test set yielded a root mean square error of 1.21 Pa for tau(y), 0.209 Pa s(n) for K, and 0.0288 Pa s for micro(app), corresponding to R(2) = 0.95, R(2) = 0.94, and R(2) = 0.95 respectively.

Rheology of Lignocellulose Suspensions and Impact of Hydrolysis: A Review

“…Petersen et al [73] proposed, using principal components analysis, a prediction of apparent viscosity, yield stress and consistency index from the size distribution and biomass concentration. Based on Aspergillus orizae cultures, they assumed a constant power-law index around 0.4 and reported accurate correlations between predicted and experimental parameters (k and τ 0 ).…”

Pilot-scale process development and scale up for antifungal production

“…Although biomass was high at 130 g/L dcw, broth consisted of loosely aggregated mucoid clumps of mycelia, suspended in a black liquid containing unicellular bodies suspected to be conidia. A few atypical cultivations attained similar biomass levels but possessed unexpected markedly lower viscosities below 300 cP, suggesting a strong influence of factors not currently measured such as particle size distribution [53]. Broth supernatant was Newtonian and not significantly different in viscosity than uninoculated medium; thus secreted extracellular polysaccharides [54] or other such compounds were not believed present.…”

The Taming of the Shrew - Controlling the Morphology of Filamentous Eukaryotic and Prokaryotic Microorganisms

“…Laser diffraction was introduced for the quantification of fungal morphology because of its rapid, unbiased and robust results for particle size distributions as manual sample preparation is eliminated [224]. However, this method fails to report any other descriptive factors such as the roughness or aspect ratio of the particles.…”

“…It was found that laser diffraction resulted in similar particle size distributions to image analysis, whereas the standard deviations were generally higher, presumably because of discrepancies between the assumptions for the mathematical model and the biological sample and statistical artefacts based on the small number of particles analysed by image analysis. Studies on the application of laser diffraction for other filamentous organisms [55,198,224] as well as on other biological samples [226] indicate its applicability for the investigation of size distributions in process environments.…”