Artificial neural networks in the retention of anthocyanins and total phenolics in the osmotic pre-treatment of Biloxi variety blueberry (Vaccinium corymbosum L.) jam

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dc.creatorJesús Alfredo Obregón Domínguez
dc.creatorCarlos Alberto Minchón Medina
dc.creatorGabriela del Carmen Barraza-Jáuregui
dc.date2024
dc.date.accessioned2026-07-07T03:30:56Z
dc.descriptionBlueberries are a fruit that is an important source of bioactive components beneficial to the human diet, such as anthocyanins and total phenolics, which are altered by the use of high temperatures during processing. This study aimed to evaluate the use of artificial neural networks in the optimization of sucrose concentration and time for the osmotic pre-treatment of blueberries of the Biloxi variety, to retain the greatest amount of anthocyanins and total phenolics in the subsequent preparation of jam. Artificial neural networks of the feedforward type were used, with a Backpropagation training algorithm with Levenberg-Marquardt weight adjustment, to achieve the optimal predicted combination that maximizes the retention of these bioactive components. The model achieved its best performance with 11 neurons in the hidden layer, achieving an R2 coefficient of 0.98 and a mean square error of 4.76, indicating a strong ability for generalization. Artificial neural networks allowed to obtain the best optimal combination of predicted multiple responses of factors consisting of a sucrose concentration of 1.64 M and a time of 211.52 min, which retained a higher content of total monomeric anthocyanins with 70.98 mg cyanidin-3-O-glucoside 100 g-1 of jam and total phenolics with 110.54 mg GAE g-1 of jam. On the other hand, through single-response optimization was obtained that the combination of experimental factors that maximized total anthocyanins (71.59 mg cyanidin-3-O-glucoside 100 g-1 of jam) was 1.54 M of sucrose and 232.73 min and for total phenols (111.06 mg GAE g-1 of jam) 1.79 M of sucrose and 196.36 min. The use of artificial neural networks is an excellent alternative for modeling phenomena, compared to traditional methods.
dc.formatapplication/pdf
dc.identifier0304-2847
dc.identifierhttps://www.redalyc.org/articulo.oa?id=179980940008
dc.identifierhttps://www.redalyc.org/journal/1799/179980940008/
dc.identifierhttps://www.redalyc.org/journal/1799/179980940008/html/
dc.identifierhttps://www.redalyc.org/journal/1799/179980940008/179980940008.epub
dc.identifierhttps://www.redalyc.org/journal/1799/179980940008/movil
dc.identifier10.15446/rfnam.v77n3.107488
dc.identifier.urihttp://hdl.handle.net/123456789/436073
dc.languageen
dc.publisherUniversidad Nacional de Colombia
dc.relationhttp://www.redalyc.org/revista.oa?id=1799
dc.rightsRevista Facultad Nacional de Agronomía - Medellín
dc.sourceRevista Facultad Nacional de Agronomía - Medellín (Colombia) Num.3 Vol.77
dc.subjectAgrociencias
dc.titleArtificial neural networks in the retention of anthocyanins and total phenolics in the osmotic pre-treatment of Biloxi variety blueberry (Vaccinium corymbosum L.) jam
dc.typeartículo científico

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Revista Facultad Nacional de Agronomía - Universidad Nacional de Colombia