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A population balance model was developed for wheat starch hydrolysis to simulate the performance parameters of a viscosity-based device, known as the Falling Number instrument. The instrument is widely used as an indirect means to gauge the level of preharvest sprout activity in cereal grains such as wheat and barley. The model consists of three competing kinetics: starch gelatinization, enzymatic hydrolysis, and enzyme thermal deactivation. Using established principles of starch rheology and fluid mechanics, the model simulates the velocity profiles of the falling stirrer, starch gel viscosity, and the Falling Number readings at various levels of alpha-amylase. Model predictions for the velocity of the stirrer at any time during the downward fall, as well as the prediction of the total time needed for the fall, defined as the Falling Number, were in fair agreement with experimental measurements. There was better agreement between the modeled viscosity and the final viscosity of the starch gel as measured by a precision rheometer than there was with the measured Falling Number., (Copyright 2002 Wiley Periodicals, Inc.)

Microbial cells are more readily rendered nonviable by the combined action of air sparging and mechanical agitation than by either action along. A. bubble breakup/coalescence model that incorporates the cell-bubble encounter rate, bubble breakup rate, and death probability is proposed to describe cell inactivation in the presence of bubbles maintained through the joint action of agitation and air, which is continually fed into the impeller stream region via passive vortex entrainment from the surface above or via active sparging from below. Experimental results obtained from a fragile algal (Ochromonas malhamensis) culture are consistent with the model prediction. In particular, the specific cell death rate is linearly related to the specific bubble interfacial surface area. It is shown that cells exhibit sparging-sensitive characteristics when agitation is mild, but become sensitive to surface vortexing when agitation turns vigorous enough to introduce air entrainment. Experimental data obtained from different stirrer sizes are in good agreement with the model., ((c) 1992 John Wiley & Sons, Inc.)

This article compares backpropagation neural networks (BNN) with partial least squares (PLS) techniques in terms of their ability to deconvolute fluorescence spectra. Both actual experimental and simulated spectral data are studied for 2 binary systems. These systems consist of mixtures of tryptophan and tyrosine, and NADH and tryptophan over a total concentration range of 10(-7) to 10(-4) M. It is shown that BNN is superior to PLS for both systems.

A biosensor model was developed for a commercial NADH fluorescence probe to describe the single-frequency excitation and emission fluorescence behavior of an aqueous mixture of fluorophores. This model is essential in correlating the measured signals to the concentrations of fluorescent compounds in a bioreactor. In addition to the concentrations of fluorescent components, the relevant parameters of the model are the absorbance at both the excitation and the emission frequencies by the solvent and other absorbing species, the background signals, the light path length of the bioreactor vessel, the fluorescence yield, and the lampdetector configuration. Due to inner-filter effects and other interferences, the probe signal is intrinsically nonlinear in both the fluorophore concentration and the path length. An important parameter in the model is the geometric constant, S, which accounts for variations in the monitoring efficiency throughout the sample because fluorescent light is emitted in all directions. Previous models, derived from an unrealistic assumption that fluorescent light is emitted only in one direction parallel to the probe axis, are shown to be seriously deficient. The validity of the model was verified experimentally for a single-component solution in which both the fluorophore concentration and path length were varied.

An extensive fluorescence database for binary tyrosinetryptophan mixtures utilizing 280 nm excitation was collected. The database spanned three orders of magnitude (10(-6)M-10(-3)M) and covered all compositions within this range. A generalized model for describing the multicomponent fluorescence signals as a function of emission wavelength, excitation wavelength, and sample composition was derived. A geometric integral that contained all the geometric factors affecting fluorescence was introduced; thus the model was applicable to various configurations, including the three used in this study: an NADH probe, a backscatter laser-induced fluorescence setup, and a commercial spectroflurometer. A correction factor was proposed that allowed linearization of the fluorescence signals with respect to fluorophore concentrations. The effect of the water Raman on fluorescence spectra was also modeled. The model contains only two wavelength-dependent parameters for each of the components present in a sample, one specifying absorption of the excitation energy and the other specifying the species' fluorescence tendency. These wavelength-dependent parameters were correlated with polynomials. The average prediction error at each wavelength was 10-20%, a major portion of which was attributed to experimental uncertainties.

A systematic method is presented which is capable of both detecting the presence of grossly biased measurement errors and locating the source of these errors in a bioreactor through statistical hypothesis testing. Equality constraints derived from material and energy balances are employed for the detection of data inconsistencies and for the subsequent identification of the suspect measurements by a process of data analysis and rectification. Maximum likelihood techniques are applied to the estimation of the states and parameters of the bioreactor after the suspect measurements have been eliminated. The level of significance is specified by the experimenter while the measurments are assumed to be randomly, normally distributed with zero mean and known variances. Two different approaches of data analysis, batchwise and sequential, that lead to a consistent set of adjustments on the experimental values, are discussed. Several examples based on the fermentation data taken from literature sources are presented to demonstrate the utility of the proposed method, and one set of data is solved numerically to illustrate the computational aspect of the algorithm.