H.S. Ramaswamy

Ultra high pressure treatment of orange juice: a kinetic study on inactivation of pectin methyl esterase

Abstract Ultra high pressure treatments ranging from 0 to 720 min were applied to freshly squeezed, non-pasteurized orange juice and reconstituted frozen orange juice at pressures in the range 100 to 400 MPa to investigate the pressure inactivation kinetics of pectin methyl esterase (PME) activity. Freshly squeezed orange juice was adjusted to pH 3.7 or 3.2 (12.6 ° Brix) while the reconstituted frozen concentrate orange juice was adjusted to 10, 20, 30 and 40 ° Brix at pH 3.7. The frozen concentrated orange juice was initially heat treated to inactivate the natural enzymes and then fortified with commercial PME prior to inactivation studies. Analyses of kinetic data revealed the presence of a dual-effect of pressure inactivation: the first one, designated as an instantaneous pressure kill (IPK), which depended only on the pressure level; and a second one, which depended on the holding time at each pressure level, described by first-order reaction kinetics (characterized by a rate constant, k , or by the more familiar decimal reduction time, D ). The associated IPK were higher and D values were lower at the lower pH and lower soluble solid contents. The pressure sensitivity of D values were adequately described by a z p value which represented the range of applied pressure between which the D values change by a factor of 10.

Neural network modeling of heat transfer to liquid particle mixtures in cans subjected to end-over-end processing

Artificial neural network (ANN) modeling was used for the overall heat transfer coefficient (U) and the fluid to particle heat transfer coefficient (hfp) associated with liquid particle mixtures, in cans subjected to end-over-end rotation. Both U and hfp are needed for modeling the time-temperature profiles of liquid and particles. Experimental data obtained for U and hfp under various test conditions were used for both training and evaluation. Multi-layer neural networks with seven input and two output neurons (for a single particle in a can), and six input and two outputs neuron (for multiple particles in a can) were trained. The optimal network was obtained by the varying number of hidden layers, number of neurons in each hidden layer and learning runs, using a back-propagation algorithm. Heat transfer coefficients were also predicted using dimensionless correlations developed earlier from the same data-set. Prediction errors associated with ANN were less than 3 and 5%, respectively, for U and hfp, which were about 50% better than those associated with dimensionless number models, indicating that the predictive performance of the ANN was far superior than that of dimensionless correlations. The ANN models were also more versatile than the dimensionless number models for predicting U and hfp.

Heat transfer and lethality considerations in aseptic processing of liquid/particle mixtures : A review

Consumer awareness and demand for nutritious yet inexpensive food products call for innovative processing techniques that have both safety and quality as primary objectives. These challenges appear to have been met by aseptic processing techniques, especially for liquid and high-acid foods. However, the extension of aseptic processing principles to low-acid foods containing discrete particles in viscous sauces has not been approved by regulatory agencies, particularly in North America. This apparent limitation is due primarily to the lack of adequate temperature monitoring devices to keep track of particles in dynamic motion, as well as to the residence time distribution of particles flowing in the continuous heat-hold-cool sections of the aseptic processing system. These problems have prompted active research to describe the phenomenal behavior of particulates through sound mathematical modeling and computer simulators. The accuracy of mathematical models depends heavily on how accurate input parametric values are. These parameters include the thermophysical properties of the carrier fluid and particles, as well as the aseptic processing system characteristics in relation to residence time distribution and the fluid-to-particle interfacial heat transfer coefficient. Apparently, several contradictory findings have been reported in the literature with respect to the effect of various processing parameters on the above-mentioned input parametric values. The need therefore arises for more collaborative studies involving the industry and academia. This review brings to perspective, the current status on the aseptic processing of particulate foods with respect to the critical processing parameters which affect the fluid-to-particle convective heat transfer coefficient associated with particulate laden products.

Evaluation Of Fluidized Versus Spouted Bed Drying Of Baker'S Yeast

ABSTRACT Drying of bakers yeast (Saccharomyces cerevisiae) was examined experimentally in laboratory scale fluidized bed and spouted bed dryers. Pressedyeast samples (70% moisture content, wb) were prepared in granular form (0.8–1 mm diameter) and dried first in a fluidized bed to characterize their fluidizationand drying properties. It was observed that high moisture yeast particles (up to 35% wb) belong to group C and D of Geldarts classification, especially for higher (>200 mm) bed heights making their fluidization very difficult. On the other hand, fluidization of pre-dried yeast particles with moisture content under 35%, wb (group B) was very easy. This characterization suggests a new concept for drying of bakers yeast i.e. spouted bed drying in the first stage and fluidized bed in the second stage or spouted bed drying for the entire process. The quality parameter examined during the drying process was viability of yeast cells at a standard moisture content (6–8%, wb).