Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation

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dc.contributor.authorAlvarado, Ulises
dc.contributor.authorTacuriti, Juan
dc.contributor.authorColoma, Alejandro
dc.contributor.authorGallegos Rojas, Edgar
dc.contributor.authorCallo, Herbert
dc.contributor.authorValencia-Sullca, Cristina
dc.contributor.authorRafael, Nancy Curasi
dc.contributor.authorCastillo, Manuel
dc.date.accessioned2026-03-10T20:08:05Z
dc.date.available2026-03-10T20:08:05Z
dc.date.issued2025-08-01
dc.description.abstractMonitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labourintensive. This study developed artificial neural network (ANN) models to predict pH and titratable acidity during yoghurt fermentation using CIELAB colour parameters (L, a*, b*). Reconstituted milk powder with 12% total solids was prepared with varying protein levels (4.2–4.8%), inoculum concentrations (1–3%), and fermentation temperatures (36–44 ◦C). Data were collected every 10 min until pH 4.6 was reached. Forty models were trained for each output variable, using 90% of the data for training and 10% for validation. The first two phases of the fermentation process were clearly distinguishable, lasting between 4.5 and 7 h and exceeding 0.6% lactic acid in all treatments evaluated. The best pH model used two hidden layers with 28 neurons (R2 = 0.969; RMSE = 0.007), while the optimal acidity model had four hidden layers with 32 neurons (R2 = 0.868; RMSE = 0.002). The strong correlation between colour and physicochemical changes confirms the feasibility of this non-destructive approach. Integrating ANN models and colourimetry offers a practical solution for real-time monitoring, helping improve process control in industrial yoghurt production.
dc.description.sponsorshipConsejo Nacional de Ciencia, TecnologĂ­a e InnovaciĂłn TecnolĂłgica
dc.description.sponsorshipPrograma Nacional de InvestigaciĂłn CientĂ­fica y Estudios Avanzados PE501082973-2023
dc.formatapplication/pdf
dc.identifier.doihttps://doi.org/10.3390/dairy6040041
dc.identifier.urihttps://hdl.handle.net/20.500.12840/9883
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.publisher.countryPE
dc.relation.ispartofurn:issn:2624-862X
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectFermentation
dc.subjectPH
dc.subjectAcidity
dc.subjectCIELAB colour space
dc.subjectArtificial neural networks
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#3.03.00
dc.titleDevelopment of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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