Modelling digestive hydrolysis of nutrients in fish using factorial designs and desirability function

Models simulating the in vitro digestive hydrolysis of nutrients by different animal species are frequently used to obtain a better understanding of factors affecting this process. Optimization algorithm of a model may be used to prospect the more favourable combination of selected factors resulting in the higher performance. This study was conducted to determine the combination of factors (pH, enzyme:substrate ratio, and reaction time) leading to highest bioavailability of proteins and carbohydrates in the gilthead seabream gastrointestinal tract. Besides, a novel multi-objective algorithm, desirability function, was introduced for optimization of the digestive hydrolysis of nutrients within the simulated gut of the species, using models based on the Response Surface Methodology. Design of experiment was defined based on the physiology and culture conditions of the species, and in vitro assays were performed in a two-phase (stomach ad intestine) digestion process, using the species-specific enzyme extract. According to results, intestinal phase of digestion makes the major contribution to the total protein hydrolysis, being the efficiency of the process directly correlated to all the three studied factors. In contrast, the efficiency of carbohydrate hydrolysis was directly correlated to the amount of substrate and inversely to the pH, while reaction time did not exert a significant effect. The physiological range of the factors studied in the assays favoured the hydrolysis of proteins over carbohydrates, a similar scenario to that observed in the live fish. Results from the mathematical models and their simultaneous optimization obtained from this work may have practical applications in design of feeds for this species.This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) with FEDER/ERDF contribution by project EFISHDIGEST (AGL2014-52888-R) to MY. Additional funding from project WISEFEED funded by the European Union's H2020 programme (Marie Skłodowska-Curie grant No 691150). NG is supported by a grant from MINECO (BES-2015-071662).Peer reviewe