Lipase catalyzed modification of milkfat

Decreasing consumption of high fat milk and dairy products is driving the dairy industry to seek other uses for increasing surplus of milkfat. Enzyme catalyzed modification of milkfat using lipases is receiving particular attention. This review examines lipase-mediated modification of milkfat. Especial attention is given to industrial applications of lipases for producing structured and modified milkfat for improved physical properties and digestibility, reduced caloric value, and flavor enhancement. Features associated with reactions such as hydrolysis, transesterification, alcoholysis and acidolysis are presented with emphasis on industrial feasibility, marketability and environmental concerns. Future prospects for enzyme catalyzed modification of milk fat are discussed.     

Optimal temperature and concentration profiles in a cascade of CSTR's performing Michaelis-Menten reactions with first order enzyme deactivation

A necessary condition is found for the intermediate temperatures and substrate concentrations in a series of CSTR's performing an enzyme-catalyzed reaction which leads to the minimum overall volume of the cascade for given initial and final temperatures and substrate concentrations. The reaction is assumed to occur in a single phase under steady state conditions. The common case of Michaelis-Menten kinetics coupled with first order deactivation of the enzyme is considered. This analysis shows that intermediate stream temperatures play as important a role as intermediate substrate concentrations when optimizing in the presence of nonisothermal conditions. The general procedure is applied to a practical example involving a series of two reactors with reasonable values for the relevant five operating parameters. These parameters are defined as dimensionless ratios involving activation energies (or enthalpy changes of reaction), preexponential factors, and initial temperature and substrate concentration. For negligible rate of deactivation, the qptimality condition corresponds to having the ratio of any two consecutive concentrations as a single-parameter increasing function of the previous ratio of consecutive concentrations.List of Symbols C _E,0 mol.m^–3 Initial concentration of active enzyme - C _E,i mol.m^–3 Concentration of active enzyme at the outlet of the i-th reactor - C _S,0 mol.m^–3 Initial concentration of substrate - C _S,i mol.m^–3 Concentration of substrate at the outlet of the i-th reactor - Da _i Damköhler number associated with the i-th reactor ((V _i.k_v,0.C_E,0)/(Q.C_S,0)) - Da _min Minimum value of the overall Damköhler number - Da _tot Overall Damköhler number - E _d J.mol^–1 Activation energy of the step of deactivation of the enzyme - E _m J.mol^–1 Standard enthalpy change of the step of binding of substrate to the enzyme - E _v J.mol^–1 Activation energy of the step of enzymatic transformation of substrate - i Integer variable - j Dummy integer variable - k Dummy integer variable - k _d,i s^–1 Kinetic constant associated with the deactivation of enzyme in the i-th reactor (k _d,o·exp{–E _d/(R.T _i}) - k _d,0 s^–1 Preexponential factor of the kinetic constant associated with the deactivation of the enzyme - K _m,i mol.m^–3 Equilibrium constant associated with the binding of substrate to the enzyme in the i-th reactor, (k _m,o·exp{–E _m}(R.T _i}) - K _m,0 mol.m^–3 Preexponential factor of the Michaelis-Menten constant associated with the binding of substrate to the enzyme - k _v,i s^–1 Kinetic constant associated with the transformation of the substrate by the enzyme in the i-th reactor (k _v,o·exp{–E _v/(R.T _i})) - k _v,0 s^–1 Preexponential factor of the kinetic constant associated with the transformation of the substrate by the enzyme - N Number of reactors in the series - Q m³.s^–1 Volumetric flow rate of reacting liquid through the reactor network - R J.K^–1.mol^–1 Ideal gas constant - T _i K Absolute temperature at the outlet of the i-th reactor - T ₀ K Initial absolute temperature - V _i m³ Volume of the i-th reactor - v _max mol.m^–3.s^–1 Maximum rate of reaction under saturation conditions of substrate - x _i Normalized concentration of substrate (C_S,i/C_{S, 0}) - x _i,opt Optimum value of the normalized concentration of substrate - y _i Dimensionless temperature (exp{–T ₀/T _i}) - y _i,opt Optimum value of the dimensionless temperature Greek Symbols Dimensionless preexponential factor associated with the Michaelis-Menten constant (K _m,0/C_s,0) - Dimensionless activation energy of the step of enzymatic transformation of substrate (E _v/R.T₀)) - Dimensionless standard enthalpy change of the step of binding of substrate to the enzyme (E _m/(R.T₀)) - Dimensionless activation energy of the step of deactivation of the enzyme (E _d/(R.T₀)) - Dimensionless deactivation preexponential factor ((k _d,0.C_S,0)/(k_v,0.C_E,0)     

A heuristic approach for the economic optimization of a series of CSTR's performing Michaelis-Menten reactions

The estimation of the size of each reactor of a series of CSTR's performing a Michaelis-Menten reaction in the liquid phase can be obtained to advantage via an optimization technique leading to the minimum overall capital cost. The cost scaleup is assumed to be described by a power rule on the equipment capacity. Various contributions are lumped into the exponent, thus leading to values above unity. The analytical development leading to the optimal intermediate concentrations of substrate according to the foregoing criterion is presented. A short-cut method based on an empirical expression that approximates the numerical solution is reported. This correlation is found to be exact at the asymptotic behaviors, and to give accurate results within an acceptable error level for the range with physical interest. Therefore, it is particularly useful during the predesign steps of equipment for the biochemical industry.     

Hydrolysis of butteroil by immobilized lipase using a hollow-fiber reactor: part II. Uniresponse kinetic studies

A lipase from Aspergillus niger immobilized by adsorption on microporous, polypropylene hollow fibers was used to effect the hydrolysis of the glycerides of melted butterfat at 40 degrees C and pH 7.0. Mcllvane buffer was pumped through the lumen and melted butterfat was pumped courrently through the shell side of a shell-and-tube reactor. Nonlinear regression methods were employed to determine the kinetic parameters of three nested rate expressions derived from a Ping Pong Bi Bi enzymatic mechanism coupled with three nested rate expressions for the thermal deactivation of the enzyme. For the reaction conditions used in this research, a four-parameter rate expression (which includes a two-parameter deactivation rate expression and a two-parameter hydrolysis rate expression) is sufficient to model the overall release of free fatty acids from the triglycerides of butterfat as a function of space time and time elapsed after immobilization. At a space time of 3.7 h immediately after immobilization of lipase, 50% of the fatty acid residues esterified in the sn-1,3 positions of the triglycerides can be released in the hollow-fiber reactor.     

Microbiological profile in Serra ewes' cheese during ripening

The microflora of Serra cheese was monitored during a 35 d ripening period at three different periods within the ewe's lactation season. After 7 d ripening, the numbers of micro-organisms reached their maximum, and lactic acid bacteria (LAB) and coliforms were the predominant groups. Pseudomonads were not detected after 1 week of ripening. At all stages of ripening, cheeses manufactured in spring exhibited the lowest numbers of LAB and yeasts, whereas cheeses manufactured in winter showed the lowest numbers of coliforms and staphylococci.
Leuconostoc lactis was the most abundant LAB found in Serra cheese whereas Enterococcus faecium and Lactococcus lactis spp. lactis exhibited the highest decrease in percentage composition. Numbers of both Leuc. mesenteroides and Lactobacillus paracasei tended to increase throughout ripening. The most abundant coliform was Hafnia alvei. Klebsiella oxytoca was found in curd but declined in number during ripening. Staphylococcal flora of curd was mainly composed of Staphylococcus xylosus, Staph. aureus and Staph. epidermidis. Staphylococcus xylosus was the major species found at the end of ripening. Pseudomonas fluorescens , was the only Pseudomonas species isolated from the curd. Although a broad spectrum of yeasts were found in Serra cheese, Sporobolomyces roseus was the most abundant yeast isolated.     

Cost minimization in the predesign of an enzymatic CSTR: an overall approach

The balance equations pertaining to the modelling of a CSTR performing an enzyme-catalyzed reaction in the presence of enzyme deactivation are developed. Combination of heuristic correlations for the size-dependent cost of equipment and the purification-dependent cost of recovery of product with the mass balances was used as a basis for the development of expressions relating a (suitably defined) dimensionless economic parameter with the optimal outlet substrate concentration under the assumption that overall production costs per unit mass of product were to be minimized. The situation of Michaelis-Menten kinetics for the substrate depletion and first order kinetics for the deactivation of enzyme (considering that the free enzyme and the enzyme in the enzyme/substrate complex deactivate at different rates) was explored, and plots for several values of the parameters germane to the analysis are included.List of Symbols C _E mol m^–3 concentration of active enzyme - C _E,0 mol m^–3 initial concentration of active enzyme - C _p mol m^–3 concentration of product of interest - C _s mol m^–3 concentration of substrate - C _s,0 mol m^–3 initial concentration of substrate - I $ capital cost of equipment - k _d s^–1 deactivation constant of free enzyme - k _d s^–1 deactivation constant of enzyme in enzyme/substrate complex - K _m mol m^–3 Michaelis-Menten constant - K _m dimensionless counterpart of K _m - k _r s^–1 rate constant associated with conversion of enzyme/substrate complex into product - M _w kg mol^–1 molecular weight of product of interest - P $ kg^–1 cost of recovery of product of interest in pure form - Q m³s^–1 volumetric flow rate - V m³ volume of reactor - X $ kg^–1 global manufacture cost of product of interest in pure form - X dimensionless counterpart of X Greek Symbols ₁ $ m^–1.8 constant - ₂ $ m^–3 constant - t s useful life of CSTR - ₀ ratio of initial concentrations of enzyme and substrate - ratio of deactivation constant of free enzyme to rate constant of depletion of substrate - ratio of deactivation constants - univariate function expressing the dependence of the rate of enzyme deactivation on C _S - univariate function expressing the dependence of the rate of substrate depletion on C _S - dimensionless economic parameter     

Determination of the optimum operating time for batch isothermal performance of enzyme-catalyzed multisubstrate reactions

This communication consists of a mathematical analysis encompassing the maximization of the average rate of monomer production in a batch reactor performing an enzymatic reaction in a system consisting of a multiplicity of polymeric substrates which compete with one another for the active site of a soluble enzyme, under the assumption that the form of the rate expression is consistent with the Michaelis-Menten mechanism. The general form for the functional dependence of the various substrate concentrations on time is obtained in dimensionless form using matrix terminology; the optimum batch time is found for a simpler situation and the effect of various process and system variables thereon is discussed. The reasoning developed here emphasizes, in a quantitative fashion, the fact that the commonly used lumped substrate approaches lead to nonconservative decisions in industrial practice, and hence should be avoided when searching for trustworthy estimates of optimum operation.List of Symbols O 1/s row vector of zeros - a 1/s row vector of rate constants k _i(i = 2,...,N) - A 1/s matrix of rate constants k _i and k_–i (i=2,...,N) - b 1/s row vector of rate constant k ₂ and zeros - C mol/m³ molar concentration of S - C mol/m³ vector of molar concentrations of C _i (i=0, 1, 2, ..., N) - C ₀ mol/m³ column vector of initial molar concentrations of C _i(i=0, 1, 2,.., N) - C _–01 mol/m³ column vector of initial molar concentrations of C _i(i=2,..., N) - C _{E, tot} mol/m³ total molar concentration of enzyme molecules - C _i mol/m³ molar concentration of S _i (i=0,1,2,...,N) - C _{i, o} mol/m³ initial molar concentration of S _i(i=0, 1, 2, ..., N) - E enzyme molecule - I identity matrix - K 1/s matrix of lumped rate constants - k _i 1/s pseudo-first order lumped rate constant associated with the formation of S _i -1 (i=1, 2, ...,N) - k _{cat, i} 1/s first order rate constant associated with the formation of S _i-1 (i=1, 2, ..., N) - K _m mol/m³ Michaelis-Menten constant - L number of distinct eigenvalues - M _i multiplicity of the i-th eigenvalue - N maximum number of monomer residues in a single polymeric molecule - r ₁ mol/m³ s rate of formation of S ₀ - r _i mol/m³ s rate of release of S _i -1 - r _opt maximum average dimensionless rate of production of monomer S₀ - S lumped, pseudo substrate - S₁ inert moiety - S _i substrate containing i monomer residues, each labile to detachment as - S₀ by enzymatic action (i=1,2,...,N) - t s time elapsed since startup of batch reaction - t _lag s time interval required for cleaning, loading, and unloading the batch reactor - t _opt s time interval leading to the maximum average rate of monomer production - v _ij s^1-j eigenvectors associated with eigenvalue imi (i=1, 2, ..., L; j =1, 2, ..., M_i) Greek Symbols _ij mol/m³ arbitrary constant associated with eigenvalue _i (i=1, 2, ..., L; j=1, 2, ..., M _i ) - 1/s generic eigenvalue - _i 1/s i-th eigenvalue     

Strategy for the simulation of batch reactors when the enzyme-catalyzed reaction is accompanied by enzyme deactivation

The balance equations pertaining to the modelling of batch reactors performing an enzyme-catalyzed reaction in the presence of enzyme deactivation are developed. The functional form of the solution for the general situation where both the rate of the enzyme-catalyzed reaction and the rate of enzyme deactivation are dependent on the substrate concentration is obtained, as well as the condition that applies if a maximum conversion of substrate is sought. Finally, two examples of practical interest are explored to emphasize the usefulness of the analysis presented.List of Symbols C _E mol/m³ concentration of active enzyme - C _E,O mol/m³ initial concentration of active enzyme - C _S mol/m³ concentration of substrate - C _S,O mol/m³ initial concentration of substrate - C _S,min mol/m³ minimum value for the concentration of substrate - k 1/s first order rate constant associated with conversion of enzyme/substrate complex into product - k ₁ 1/s first order deactivation constant of enzyme (or free enzyme) - k ₂ 1/s first order deactivation constant of enzyme in enzyme/substrate complex form - K _m mol/m³ Michaelis-Menten constant - p mol/(m³s) time derivative of C _S - q mol/m³ auxiliary variable - t s time elapsed after reactor startup Greek Symbols 1/s univariate function expressing the dependence of the rate of enzyme deactivation on C _S - mol/m³ dummy variable of integration - mol/m³ dummy variable of integration - 1/s univariate function expressing the dependence of the rate of substrate depletion on C _S - m³/(mol s) derivative of with respect to C _S     

Microalgae and biofuels: a promising partnership?

Microalgae have much higher lipid yields than those of agricultural oleaginosous crops, and they do not compromise arable land. Despite this, current microalga-based processes suffer from several constraints pertaining to the biocatalyst and the bioreactor, which hamper technologically and economically feasible scale-up. Here, we briefly review recent active research and development efforts worldwide, and discuss the most relevant shortcomings of microalgal biofuels. This review goes one step further relative to related studies, because it tackles otherwise scarcely mentioned issues - for example, heterotrophic versus autotrophic metabolism, alkane versus glyceride synthesis, conduction versus bubbling of CO(2), and excretion versus accumulation of lipids. Besides promising solutions that have been hypothesized and arise from multidisciplinary approaches, we also consider less conventional ones. Microalgae and biofuels hold indeed a promising partnership, but a fully competitive technology is not expected to be available before the end of this decade, because the need for one order of magnitude increase in productivity requires development of novel apparatuses and transformed cells.