title: Artificial neural networks to model the production of blood protein hydrolysates for plant fertilisation.
abstract: 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. Given the complexity of enzyme reactions, empirical approaches such as artificial neural networks (ANNs) are preferred for modelisation.
  
  
  RESULTS
  Porcine blood meal was hydrolysed for 3 h with subtilisin. The time evolution of the degree of hydrolysis was successfully modelled by means of a feedforward ANN comprising 10 neurons in the hidden layer and trained by the Levenberg-Marquardt algorithm. The ANN model described adequately the influence of pH, temperature, enzyme concentration and reaction time upon the degree of hydrolysis, and was used to estimate the optimal operation conditions (pH 6.67, 56.9 °C, enzyme to substrate ratio of 10 g kg(-1) and 3 h of reaction) leading to the maximum degree of hydrolysis (35.12%).
  
  
  CONCLUSIONS
  ANN modelling was a useful tool to model enzymatic reactions and was successfully employed to optimise the degree of hydrolysis.
authors.affiliation: Departamento de Ingeniería Química, Universidad de Granada, 18071 Granada, Spain.
authors.forename: Raúl Pérez
authors.initials: RP
authors.lastname: Gálvez
authors.affiliation: Departamento de Ingeniería Química, Universidad de Granada, 18071 Granada, Spain.
authors.forename: Francisco Javier Espejo
authors.initials: FJ
authors.lastname: Carpio
authors.affiliation: Departamento de Ingeniería Química, Universidad de Granada, 18071 Granada, Spain.
authors.forename: Emilia M
authors.initials: EM
authors.lastname: Guadix
authors.affiliation: Departamento de Ingeniería Química, Universidad de Granada, 18071 Granada, Spain.
authors.forename: Antonio
authors.initials: A
authors.lastname: Guadix
mesh_terms: D000383:Agriculture; D000465:Algorithms; D000596:Amino Acids / Q000378:metabolism*; D000818:Animals; D001682:Biological Availability; D001798:Blood Proteins / Q000378:metabolism*; D005308:Fertilizers; D006863:Hydrogen-Ion Concentration; D006868:Hydrolysis; D007501:Iron / Q000378:metabolism; D008954:Models, Biological; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000378:metabolism*; D011492:Protein Hydrolysates / Q000096:biosynthesis* / Q000378:metabolism; D015203:Reproducibility of Results; D020860:Subtilisin / Q000378:metabolism; D013552:Swine / Q000097:blood; D013696:Temperature
chemical_list: D000596:Amino Acids; D001798:Blood Proteins; D005308:Fertilizers; D011492:Protein Hydrolysates; D007501:Iron; D020860:Subtilisin; D009584:Nitrogen
keywords: artificial neural networks; blood protein; degree of hydrolysis; enzymatic hydrolysis; fertilisation; modelisation