Multiobjective optimization of Murta (Ugni molinae Turcz) juice lactic acid fermentation with monocultures and cocultures of Lactobacillus acidophilus and Lactiplantibacillus plantarum

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dc.article.number100996
dc.catalogadorpva
dc.contributor.authorGomes Lobo, Cristian Jesús
dc.contributor.authorLuna, R.
dc.contributor.authorRodríguez Machado, Adrián
dc.contributor.authorPérez C., José Ricardo
dc.contributor.authorFranco, Wendy
dc.date.accessioned2025-06-03T21:26:13Z
dc.date.available2025-06-03T21:26:13Z
dc.date.issued2025
dc.description.abstractResponse surface methodology (RSM) and the desirability function (DF) approach have been widely used for multiresponse optimization in lactic acid fermentation processes. However, multiobjective optimization (MOO) can provide a broader range of optimal cultivation conditions that compromise between multiple objectives. This study applied multiobjective optimization of murta (Ugni molinae Turcz) juice lactic fermentation under monoculture and coculture conditions. The total phenolic content (TPC), Lactic Acid Bacteria (LAB) count, and Lactic Acid (LA) concentration were optimized simultaneously. Decision variables were temperature, initial pH, and inoculation percentage. Several multicriteria decision-making (MCDM) methods were applied to select the optimal compromising responses, considering 80% priority for TPC, 15% for LAB, and 5% for LA. The highest TPC (723 ± 15 mg/L) was achieved at 35.3 ◦ C, pH 5.9, and 2% (v/v) inoculum in monoculture. The highest LAB count (6.7 ± 0.0 log CFU/mL) was obtained in monoculture at 34.6 ◦ highest LA (0.48 ± 0.03 g/L) was achieved in coculture at 35.6 ◦ C, pH 6, and 2.7% (v/v) inoculum. The C, pH 5.9, and 2.1% (v/v) inoculum. The simple additive weighting (SAW) method generated optimal solutions that aligned better with our expected priorities. Experimental validation confirmed that monocultures closely matched predicted optima, yielding higher TPC and LAB values than cocultures. While MOO and DF produced similar Pareto fronts, MOO required less computational time and generated more diverse solutions. These findings highlight the practical utility of MOO- MCDM for designing efficient multiobjective fermentation processes in functional food development.
dc.fechaingreso.objetodigital2025-06-03
dc.format.extent9 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.afres.2025.100996
dc.identifier.issn2772-5022
dc.identifier.urihttps://doi.org/10.1016/j.afres.2025.100996
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/104565
dc.information.autorucEscuela de Ingeniería; Gomes Lobo, Cristian Jesús; S/I; 1186434
dc.information.autorucEscuela de Ingeniería; Rodríguez Machado, Adrián; S/I; 1092265
dc.information.autorucEscuela de Ingeniería; Pérez C., José Ricardo; 0000-0002-1278-7782; 100130
dc.information.autorucEscuela de Ingeniería; Franco, Wendy; 0000-0001-5858-8554; 219464
dc.issue.numero1
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherElsevier B.V.
dc.revistaApplied Food Research
dc.rightsacceso abierto
dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMultiobjective optimization
dc.subjectMulticriteria decision-making
dc.subjectNSGA-II
dc.subjectDesirability function
dc.subjectLactic acid fermentation
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleMultiobjective optimization of Murta (Ugni molinae Turcz) juice lactic acid fermentation with monocultures and cocultures of Lactobacillus acidophilus and Lactiplantibacillus plantarum
dc.typeartículo
dc.volumen5
sipa.codpersvinculados1186434
sipa.codpersvinculados1092265
sipa.codpersvinculados100130
sipa.codpersvinculados219464
sipa.trazabilidadORCID;2025-06-03
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