Optimization of Proteins Recovery Process from Cheese Whey

The process to obtain a protein-containing ingredient from the remaining whey of Argentinean ＂Cuatriolo＂ cheese production was studied. In order to optimize the protein recovery, physical and chemical treatments were investigated. Two protocols with different sequences of application of heating step and acid addition were assayed in the presence and absence of fat and CaCl2. The results were evaluated by the yield, water retention and particle size. The results showed that the highest yield of the process （76.6%） and an increase in water retention （39.8 w/w） were achieved when the acid was added after boiled and incubated for 30 min at 90 ℃. In these working conditions, the presence of calcium shows a lower yield of recovery （72.8 %） and this behaviour correlates with a smaller particle size. Additionally, the presence of fat reduces the particle size and decreases the performance of the process （69.4%）. Thus, the yield of protein recovery is related to the particle size of the aggregates, i.e., the recovery of proteins increases when increase the particle size. The simultaneous presence of fat and CaCI2 increases the amount of water retained in the aggregated protein （47.62 w/w）. In conclusion, the process of aggregation in whey protein should take into account both the design of suitable protocol and the presence of fat and additives.

Detection of Glucomacropeptide in Raw Milk Adulterated with Cheese Whey in Ecuador

Milk is one of the products that can be adulterated in many ways affecting the quality of this and its derivatives. Glucomacropeptide (GMP) is a protein that is found only in the whey from the production of fresh cheese, enzymatically obtained from the coagulation of casein and which is commonly used to adulterate fresh or powdered milk. The aim of this study was to determine the adulteration of milk with cheese whey thought a molecular approach, where the glucomacropeptide was collected by sequential precipitation with trichloroacetic acid (ATC) and detected by polyacrylamidododecylsulfate gel electrophoresis (PAGE-SDS), using samples of fresh milk, intentionally adulterated with serum in the proportion of 0%, 1%, 5%, 10% and 15%. The results obtained showed that the detection of glucomacropeptide by electrophoresis was positive in all samples of adulterated milk, evidencing a band of 20.9 kDa in the reading, corresponding to the molecular weight of the GMP, showing that the technique used determines the adulteration in the milk, in a specific and sensitive way, also shows that in the evaluation of physical-chemical and microbiological parameters of milk, there are no significant differences between treatments, except for the pH that tends to decrease as the percentage of serum in the milk increases.

Valorisation of industrial hemp (Cannabis sativa L.) residues and cheese whey into volatile fatty acids for single cell protein production

The production of single cell protein(SCP)using lignocellulosic materials stands out as a promising route in the circular bioeconomy transition.However,multiple steps are necessary for lignocellulosics-to-SCP processes,involving chemical pretreatments and specific aerobic cultures.Whereas there are no studies that investigated the SCP production from lignocellulosics by using only biological processes and microbial biomass able to work both anaerobically and aerobically.In this view,the valorisation of industrial hemp(Cannabis sativa L.)biomass residues(HBRs),specifically hurds and a mix of leaves and inflorescences,combined with cheese whey(CW)was investigated through a semi-continuous acidogenic co-fermentation process(co-AF).The aim of this study was to maximise HBRs conversion into VFAs to be further used as carbon-rich substrates for SCP production.Different process conditions were tested by either removing CW or increasing the amount of HBRs in terms of VS(i.e.,two and four times)to evaluate the performance of the co-AF process.Increasing HBRs resulted in a proportional increase in VFA production up to 3115 mg HAc L^(-1),with experimental production nearly 40%higher than theoretical predictions.The synergy between HBRs and CW was demonstrated,proving the latter as essential to improve the biodegradability of the former.The produced VFAs were subsequently tested as substrates for SCP synthesis in batch aerobic tests.A biomass concentration of 2.43 g TSS L^(-1) was achieved with a C/N ratio of 5.0 and a pH of 9.0 after two days of aerobic fermentation,reaching a protein content of 42%(g protein per g TSS).These results demonstrate the overall feasibility of the VFA-mediated HBR-to-SCP valorisation process.

Some Properties of Traditional Whey Cheese （Mud Cheese） Produced in Turkey

In this study, the basic composition and microbiological properties of traditional Mud （Gamur） cheese produced in Tire region of Turkey was evaluated. The total solids, fat, protein, water soluble nitrogen （WSN）, acid value of cheese, fat, salt, titratable acidity, pH levels in Mud cheeses were variable according to whey characteristics used in their production. Wide variations were found in the total bacteria, Coliform bacteria, Staphylococcus aureus, yeast-mould, Lactobacillus spp. and Streptococcus spp. counts of cheese samples. The presence of coliforms, Staph. aureus and yeast-moulds indicate poor hygienic conditions of cheese production.

Hydrolysis of Lactose： Estimation of Kinetic Parameters Using Artificial Neural Networks

The analysis of any kinetic process involves the development of a mathematical model with predictive purposes. Generally, those models have characteristic parameters that should be estimated experimentally. A typical example is Michaelis-Menten model for enzymatic hydrolysis. Even though conventional kinetic models are very useful, they are only valid under certain experimental conditions. Besides, frequently large standard errors of estimated parameters are found due to the error of experimental determinations and/or insufficient number of assays. In this work, we developed an artificial neural network （ANN） to predict the performance of enzyme reactors at various operational conditions. The net was trained with experimental data obtained under different hydrolysis conditions of lactose solutions or cheese whey and different initial concentrations of enzymes or substrates. In all the experiments, commercial 13-galactosidase either free or immobilized in a chitosan support was used. The neural network developed in this study had an average absolute relative error of less than 5% even using few experimental data, which suggests that this tool provides an accurate prediction method for lactose hydrolysis.