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

Published online by Cambridge University Press:  29 August 2018

Francisco Javier Espejo-Carpio ,
Raúl Pérez-Gálvez ,
Antonio Guadix  and
Emilia María Guadix
Francisco Javier Espejo-Carpio
Affiliation:
Department of Chemical Engineering, University of Granada, 18071 Granada, Spain
Raúl Pérez-Gálvez*
Affiliation:
Department of Chemical Engineering, University of Granada, 18071 Granada, Spain
Antonio Guadix
Affiliation:
Department of Chemical Engineering, University of Granada, 18071 Granada, Spain
Emilia María Guadix
Affiliation:
Department of Chemical Engineering, University of Granada, 18071 Granada, Spain
*
*For correspondence; e-mail: rperezga@ugr.es
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Abstract

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., solubility, water binding capacity) and biological (e.g., angiotensin-converting enzyme (ACE) inhibition, antioxidation) properties of the resulting hydrolysate. Phenomenological models are unable to reproduce the complexity of enzymatic reactions in dairy systems. However, empirical approaches offer high predictability and can be easily transposed to different substrates and enzymes. In this work, the DH of goat milk protein by subtilisin and trypsin was modelled by feedforward artificial neural networks (ANN). To this end, we produced a set of protein hydrolysates, employing various reaction temperatures and enzyme/substrate ratios, based on an experimental design. The time evolution of the DH was monitored and processed to generate the ANN models. Extensive hydrolysis is desirable because a high DH enhances some bioactivities in the final hydrolysate, such as antioxidant or antihypertensive. The optimization of both ANN models led to a maximal DH of 23·47% at 56·4 °C and enzyme–substrate ratio of 5% for subtilisin, while hydrolysis with trypsin reached a maximum of 21·3% at 35 °C and an enzyme–substrate ratio of 4%.

Keywords

Enzymatic hydrolysisgoat milk hydrolysatesartificial neural networksproteases; optimization
Type
Research Article
Information
Journal of Dairy Research , Volume 85 , Issue 3 , August 2018 , pp. 339 - 346
Copyright
Copyright © Hannah Dairy Research Foundation 2018 

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References

Abakarov, A, Teixeira, A, Simpson, R, Pinto, M & Almonacid, S 2011 Modeling of squid protein hydrolysis: artificial neural network approach. Journal of Food Process Engineering 34 2026–2046Google Scholar
Adler Nissen, J 1986 Enzymic Hydrolysis of Food Proteins. London: Elsevier Applied Science Publishers LTDGoogle Scholar
Ahmed, AS, El-Bassiony, T, Elmalt, LM & Ibrahim, HR 2015 Identification of potent antioxidant bioactive peptides from goat milk proteins. Food Research International 74 80–88Google Scholar
Ba, D & Boyaci, IH 2007 Modeling and optimization II: comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction. Journal of Food Engineering 78 846–854Google Scholar
Baş, D, Dudak, FC & Boyacı, İH 2007 Modeling and optimization III: reaction rate estimation using artificial neural network (ANN) without a kinetic model. Journal of Food Engineering 79 622–628Google Scholar
Bernacka, H 2011 Health-promoting properties of goat milk. Medycyna Weterynaryjna 67 507–511Google Scholar
Bryjak, J, Murlikiewicz, K, Zbiciński, I & Stawczyk, J 2000 Application of artificial neural networks to modelling of starch hydrolysis by glucoamylase. Bioprocess Engineering 23 351–357Google Scholar
Buciński, A, Karamać, M, Amarowicz, R & Pegg, RB 2008 Modeling the tryptic hydrolysis of pea proteins using an artificial neural network. LWT – Food Science and Technology 41 942Google Scholar
Capriotti, AL, Cavaliere, C, Piovesana, S, Samperi, R & Laganà, A 2016 Recent trends in the analysis of bioactive peptides in milk and dairy products. Analytical and Bioanalytical Chemistry 408 2677–2685Google Scholar
de Castro, RJS, Bagagli, MP & Sato, HH 2015 Improving the functional properties of milk proteins: focus on the specificities of proteolytic enzymes. Current Opinion in Food Science 1 64–69Google Scholar
Duan, C-C, Yang, L-J, Li, A-L, Zhao, R & Huo, G-C 2014 Effects of enzymatic hydrolysis on the allergenicity of whey protein concentrates. Iranian Journal of Allergy, Asthma and Immunology 13 26–32Google Scholar
Dupont, C, Hol, J, Nieuwenhuis, EES, De Jongste, JC, Samsom, JN, Van Leer, EHG, Elink Schuurman, BEE, De Ruiter, LF, Neijens, HJ, Versteegh, FGA, Groeneweg, M, Van Veen, LN, Vaessen-Verberne, AA, Smit, MJM, Vriesman, AW, Roosen, YM & Den Exter, GL 2015 An extensively hydrolysed casein-based formula for infants with cows’ milk protein allergy: tolerance/hypo-allergenicity and growth catch-up. British Journal of Nutrition 113 1102–1112Google Scholar
El-Salam, MHA & El-Shibiny, S 2013 Bioactive peptides of buffalo, camel, goat, sheep, mare, and yak milks and milk products. Food Reviews International 29 1–23Google Scholar
Fatiha, B, Sameh, B, Youcef, S, Zeineddine, D & Nacer, R 2013 Comparison of artificial neural network (ANN) and response surface methodology (RSM) in optimization of the immobilization conditions for lipase from Candida rugosa on amberjet® 4200-Cl. Preparative Biochemistry and Biotechnology 43 33–47Google Scholar
Foophow, T, Foophow, T, Tanaka, S, Koga, Y, Takano, K & Kanaya, S 2010 Subtilisin-like serine protease from hyperthermophilic archaeon Thermococcus kodakaraensis with N- and C-terminal propeptides. Protein Engineering, Design and Selection 23 347–355Google Scholar
Pérez-Gálvez, R, Espejo-Carpio, FJ, Guadix, EM & Guadix, A 2016 Artificial neural networks to model the production of blood protein hydrolysates for plant fertilisation. Journal of the Science of Food and Agriculture 96 207–214Google Scholar
García-Moreno, PJ, Pérez-Gálvez, R, Espejo-Carpio, FJ, Ruiz-Quesada, C, Pérez-Morilla, AI, Martínez-Agustín, O, Guadix, A & Guadix, EM 2016 Functional, bioactive and antigenicity properties of blue whiting protein hydrolysates: effect of enzymatic treatment and degree of hydrolysis. Journal of the Science of Food and Agriculture 97 299–308Google Scholar
Gobbetti, M, Minervini, F & Rizzello, CG 2004 Angiotensin I-converting-enzyme-inhibitory and antimicrobial bioactive peptides. International Journal of Dairy Technology 57 173–188Google Scholar
Hernández-Ledesma, B, García-Nebot, MJ, Fernández-Tomé, S, Amigo, L & Recio, I 2014 Dairy protein hydrolysates: peptides for health benefits. International Dairy Journal 38 82–100Google Scholar
Li, G-H, Le, G-W, Shi, Y-H & Shrestha, S 2004 Angiotensin I–converting enzyme inhibitory peptides derived from food proteins and their physiological and pharmacological effects. Nutrition Research 24 469–486Google Scholar
Li, L, Wang, J, Zhao, M, Cui, C & Jiang, Y 2006 Artificial neural network for production of antioxidant peptides derived from bighead carp muscles with alcalase. Food Technology and Biotechnology 44 441–448Google Scholar
Li, S, Hu, Y, Hong, Y, Xu, L, Zhou, M, Fu, C, Wang, C, Xu, N & Li, D 2016 Analysis of the hydrolytic capacities of Aspergillus oryzae proteases on soybean protein using artificial neural networks. Journal of Food Processing and Preservation 40 918–924Google Scholar
López-Fandiño, R, Otte, J & van Camp, J 2006 Physiological, chemical and technological aspects of milk-protein-derived peptides with antihypertensive and ACE-inhibitory activity. International Dairy Journal 16 1277–1293Google Scholar
Ma, Y, Sun, X & Wang, L 2015 Study on optimal conditions of alcalase enzymatic hydrolysis of soybean protein isolate. Advance Journal of Food Science and Technology 9 154–158Google Scholar
Miner-Williams, WM, Stevens, BR & Moughan, PJ 2014 Are intact peptides absorbed from the healthy gut in the adult human? Nutrition Research Reviews 27 308–329Google Scholar
Morales-Medina, R, Pérez-Gálvez, R, Guadix, A & Guadix, EM 2016 Artificial neuronal network modeling of the enzymatic hydrolysis of horse mackerel protein using protease mixtures. Biochemical Engineering Journal 105 3890–3891Google Scholar
Moreno-Montoro, M, Olalla-Herrera, M, Rufián-Henares, JÁ, Martínez, RG, Miralles, B, Bergillos, T, Navarro-Alarcón, M & Jauregi, P 2017 Antioxidant, ACE-inhibitory and antimicrobial activity of fermented goat milk: activity and physicochemical property relationship of the peptide components. Food and Function 8 2783–2791Google Scholar
Muro Urista, C, Álvarez Fernández, R, Riera Rodriguez, F, Arana Cuenca, A & Téllez Jurado, A 2011 Review: production and functionality of active peptides from milk. Food Science and Technology International 17 293–317Google Scholar
Nagodawithana, T & Reed, G 2013 Enzymes in Food Processing. Elsevier Applied Science Publishers LTD, LondonGoogle Scholar
Olsen, JV, Ong, S-E & Mann, M 2004 Trypsin cleaves exclusively C-terminal to arginine and lysine residues. Molecular & Cellular Proteomics: MCP 3 608–614Google Scholar
Phelan, M & Kerins, D 2011 The potential role of milk-derived peptides in cardiovascular disease. Food and Function 2 153–167Google Scholar
Pihlanto, A 2006 Antioxidative peptides derived from milk proteins. International Dairy Journal 16 1306–1314Google Scholar
Pinto, GA, Giordano, RLC & Giordano, RC 2007 Neural network inference of molar mass distributions of peptides during tailor-made enzymatic hydrolysis of cheese whey: effects of pH and temperature. Applied Biochemistry and Biotechnology 143 142–152Google Scholar
Samaranayaka, AGP & Li-Chan, ECY 2011 Food-derived peptidic antioxidants: A review of their production, assessment, and potential applications. Journal of Functional Foods 3 229–254Google Scholar
Severin, S & Xia, WS 2006 Enzymatic hydrolysis of whey proteins by two different proteases and their effect on the functional properties of resulting protein hydrolysates. Journal of Food Biochemistry 30 77–97Google Scholar
Tavano, OL 2013 Protein hydrolysis using proteases: an important tool for food biotechnology. Journal of Molecular Catalysis B: Enzymatic 90 1–11Google Scholar
Van der Ven, C, Gruppen, H, De Bont, DBA & Voragen, AGJ 2001 Emulsion properties of casein and whey protein hydrolysates and the relation with other hydrolysate characteristics. Journal of Agricultural and Food Chemistry 49 5005–5012Google Scholar
Valencia, P, Pinto, M & Almonacid, S 2014 Identification of the key mechanisms involved in the hydrolysis of fish protein by Alcalase. Process Biochemistry 49 258–264Google Scholar