Artificial neuronal networks (ANN) to model the hydrolysis of goat milk protein by subtilisin and trypsin.

The enzymatic hydrolysis of milk proteins yield final products with improved properties and reduced allergenicity. The degree of hydrolysis (DH) influences both technological (e.g.

Artificial neural networks to model the production of blood protein hydrolysates for plant fertilisation.

Background: Amino acid-based fertilisers increase the bioavailability of nitrogen in plants and help withstand stress conditions. Additionally, porcine blood protein hydrolysates are able to supply iron, which is involved in chlorophyll synthesis and improves the availability of nutrients in soil. A high degree of hydrolysis is desirable when producing a protein hydrolysate intended for fertilisation, since it assures a high supply of free amino acids.

Production of goat milk protein hydrolysate enriched in ACE-inhibitory peptides by ultrafiltration.

A global process for the production of goat milk hydrolysates enriched in angiotensin converting enzyme (ACE) inhibitory peptides was proposed. Firstly, the protein fractions (caseins and whey proteins) were separated by ultrafiltration through a 0·14 μm ceramic membrane. The casein fraction obtained in the retentate stream of the above filtration step was subsequently hydrolysed with a combination of subtilisin and trypsin.

Optimisation of the hydrolysis of goat milk protein for the production of ACE-inhibitory peptides.

Goat milk protein was hydrolysed with subtilisin and trypsin. As input variables, temperature was assayed in the interval 45-70 °C for subtilisin and 30-55 °C for trypsin, while the enzyme-substrate ratio varied from 1 to 5%. The effect of the input variables on the degree of hydrolysis and ACE-inhibitory activity (output variables) was modelled by second order polynomials, which were able to fit the experimental data with deviations below 10%.