Extent of κ-casein hydrolysis during renneting of bovine milk: A critical assessment of the analytical and estimation approaches

Renneting is an enzymatic process that turns milk into curd which is then transformed into cheese. Rennet-induced coagulation of caseins (CNs) is the critical step during this process and the key is the primary hydrolysis of κ-CN's Phe105-Met106 bond by chymosin. This article comprehensively reviews the existing data on the extent/degree of κ-CN hydrolysis during renneting of bovine milk and critically evaluates its determination methods. The data show that under normal cheese-making conditions, milk gelation occurs at a degree of κ-CN hydrolysis <80%, which varies due to several factors including analytical and estimation approaches. The common approach involves isolating the macropeptides released, by precipitating whey proteins and residual CN in 1%-12% trichloroacetic acid (TCA), then assuming that the maximum amount obtained is 100% κ-CN hydrolysis. The drawback is that the estimated degree of κ-CN hydrolysis may be higher than the actual value as TCA partially precipitates the macropeptide fractions. Moreover, macropeptide isolation seems unnecessary based on current advances in chromatographic and electrophoretic techniques. The present work proposes a simple mass balance-based approach that will provide accurate estimates in future studies. The accuracy of measuring the degree of κ-CN hydrolysis has implications on the precision of the data in relation to its partitioning (% distribution between the curd and whey) which is essential for improving whey quality.

A method for the inline measurement of milk gel firmness using an optical sensor

At present, selection of cutting time during cheesemaking is made based on subjective methods, which has effects on product homogeneity and has prevented complete automation of cheesemaking. In this work, a new method for inline monitoring of curd firmness is presented. The method consisted of developing a model that correlates the backscatter ratio of near infrared light during milk coagulation with the rheological storage modulus. The model was developed through a factorial design with 2 factors: protein concentration (3.4 and 5.1%) and coagulation temperature (30 and 40°C). Each treatment was replicated 3 times; the model was calibrated with the first replicate and validated using the remaining 2 replicates. The coagulation process was simultaneously monitored using an optical sensor and small-amplitude oscillatory rheology. The model was calibrated and successfully validated at the different protein concentrations and coagulation temperatures studied, predicting the evolution of storage modulus during milk coagulation with coefficient of determination values >0.998 and standard error of prediction values <3.4 Pa. The results demonstrated that the proposed method allows inline monitoring of curd firming in cheesemaking and cutting the curd at a proper firmness to each type of cheese.

A new calculation of the kinetics of the renneting reaction

A detailed calculation of the growth of molecular weight during the renneting of milk is given, based on a first-order breakdown of κ-casein followed by development of instability caused either by a decrease in the intermicellar repulsive potential or by the formation of holes in the stabilizing surface layer of the micelles. Unlike most of the models which have been described, this model allows a complete analytical solution. The solution is, however, complex and difficult to use simply, although it is shown that the calculations are in accord with experimental observations of the dependence of the coagulation process upon the enzyme concentration and the concentration of the milk. The calculations are also compared with those from other models of the reaction.

A mathematical model for the description of chymosin action on casein micelles

A mathematical model for chymosin action on casein micelles is presented in a two-stage equation which results in a single curve demonstrating the lag time from enzyme addition to the end of coagulum firming. The model uses the Michaelis–Menten enzyme kinetics equation for the first reaction followed by an nth order reaction for the casein micelles agglomeration stage. The computer output using these equations shows that lag time is elongated as enzyme concentration is lowered. Regression analysis of time of gelation against l/E0 shows good correlation. Viscosity of the milk drops at the beginning of the κ-casein hydrolysis and increases thereafter, when the coagulum is being formed.

Application of numerical analysis to a number of models for chymosin-induced coagulation of casein micelles

Four models describing renneting kinetics are evaluated for their ability to describe well documented attributes of the coagulation of casein micelles. The first model is based on a constant flocculation rate parameter. In the second the flocculation rate constant is proportional to the product of the sizes of the aggregating particles. Both models fail to predict proper dependence of rennet coagulation time on enzyme concentration. The third model is based on an energy barrier being reduced in linear proportion to the degree of proteolysis. The enzyme dependency of this model only works when the initial energy barrier is larger than ∼ 50 kBT (where kB is Boltzmann's constant and T the absolute temperature), which does not seem feasible. The fourth model, based on functionality theory, is able to predict proper dependence of rennet coagulation time on enzyme concentration when functionality is ∼ 2.

Gel Formation of Peptides Produced by Extensive Enzymatic Hydrolysis of β-Lactoglobulin

The purpose of the present study was to identify which peptides were responsible for enzyme-induced gelation of extensively hydrolyzed beta-lactoglobulin with Alcalase in order to gain insight into the mechanism of gelation. Dynamic rheology, aggregation measurements, isoelectrofocusing as well as chromatography and mass spectrometry were used to understand the gel formation. A transparent gel was formed above a critical concentration of peptides while noncovalently linked aggregates appear with increasing time of hydrolysis. Extensive hydrolysis was needed for gelation to occur as indicated by the small size of the peptides. Isoelectrofocusing was successful at separating the complex mixture, and 19 main peptides were identified with molecular weight ranging from 265 to 1485 Da. Only one fragment came from a beta-sheet rich region of the beta-lactoglobulin molecule, and a high proportion of peptides had proline residues in their sequence.

Kinetics of rennet casein gelation at different cooling rates

A mathematical model was developed to quantitatively analyze the rheological data of rennet casein gelation at different cooling rates. Kinetic parameters were estimated and correlated with the microstructure development of the protein network. The kinetic model identified structure development upon cooling to be first order, and the network forming energies were estimated for four protein concentrations cooled at four rates. A lower energy for network formation was observed for a slower cooling rate and a higher protein concentration. This observation resulted from the availability of more flocs at a slower cooling rate and a higher casein concentration, simplifying floc cross-linking. By analyzing the kinetics during the aging process of casein gels, no difference in the reaction mechanism was observed. This study illustrated that structure formation resulted from the addition of flocs into the protein network: not all flocs were part of the network at a defined gel point. The incubation period following cooling integrated idle flocs into the network, thereby strengthening the gel. By understanding the gelation mechanism during cooling of rennet casein gels, the structure and thus quality of dairy products, such as processed cheese, may be better controlled.