{
  "abstract": "Monitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labour-intensive. 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Ulises Alvarado"
    },
    {
      "name": "Jhon Tacuri"
    },
    {
      "name": "Alejandro Coloma"
    },
    {
      "name": "Edgar Gallegos Rojas"
    },
    {
      "name": "Herbert Callo"
    },
    {
      "name": "Cristina Valencia-Sullca"
    },
    {
      "name": "Nancy Curasi Rafael"
    },
    {
      "name": "Manuel Castillo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cxqbbkhpp21d2.bib",
  "cited_by": 11,
  "doi": "10.3390/dairy6040041",
  "fulltext_url": null,
  "id": "cxqbbkhpp21d2",
  "issns": [
    "2624-5647"
  ],
  "landing_url": "https://hal.science/hal-05842746v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
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      "0.info"
    ],
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      "hal",
      "0.sdv"
    ],
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      "hal",
      "1.info.info-mo"
    ],
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    [
      "openalex",
      "1602|Spectroscopy and Chemometric Analyses"
    ]
  ],
  "openalex": "W4412940884",
  "pdf_url": "https://hal.science/hal-05842746/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "MDPI",
  "ris": "https://research.draconitic.com/paper/cxqbbkhpp21d2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "hal-05842746",
      "source": "hal",
      "url": "https://hal.science/hal-05842746v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.bio",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation",
  "title_text": "Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/cxqbbkhpp21d2",
  "venue": "Dairy",
  "versions": []
}