1f845d585c9c03325e173293aec42558 dairy-06-00041-v2.pdf 562a3431e049273cc91fb1f6dae93385ef72a3ca dairy-06-00041-v2.pdf 0b9d4cc09a598369cc5a3297b90961e361ae5f06bf088955d64f11db5ad8e799 dairy-06-00041-v2.pdf Title: Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation Subject: 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. Keywords: fermentation; pH; acidity; CIELAB colour space; artificial neural networks Author: Ulises Alvarado, Jhon Tacuri, Alejandro Coloma, Edgar Gallegos Rojas, Herbert Callo, Cristina Valencia-Sullca, Nancy Curasi Rafael and Manuel Castillo Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25; modified using OpenPDF 1.4.2 CreationDate: Tue Aug 19 05:27:42 2025 CEST ModDate: Tue Aug 19 05:32:09 2025 CEST Custom Metadata: yes Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 15 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 766601 bytes Optimized: no PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- YIVWYZ+CMSY10 Type 1 Builtin yes yes yes 126 0 RQCKFA+URWPalladioL-Bold Type 1 Custom yes yes yes 127 0 HTUICQ+URWPalladioL-Roma Type 1 Custom yes yes yes 128 0 NAZUMY+URWPalladioL-Ital Type 1 Custom yes yes yes 129 0 QKZKVH+VnURWPalladioL Type 1 Custom yes yes yes 130 0 CZKGAS+CMR10 Type 1 Builtin yes yes yes 219 0 COFPFM+PalatinoLinotype TrueType WinAnsi yes yes no 240 0 COFPLO+TimesNewRoman TrueType WinAnsi yes yes no 241 0 COFPFM+PalatinoLinotype TrueType WinAnsi yes yes no 288 0 COFPLO+TimesNewRoman TrueType WinAnsi yes yes no 289 0 AHGELJ+PalatinoLinotype TrueType WinAnsi yes yes no 324 0 AHGFBL+TimesNewRoman TrueType WinAnsi yes yes no 325 0 AHGHKC+TimesNewRoman,Bold TrueType WinAnsi yes yes no 326 0 COFPFM+PalatinoLinotype TrueType WinAnsi yes yes no 332 0 COFPLO+TimesNewRoman TrueType WinAnsi yes yes no 333 0 COGDBB+TimesNewRoman,Bold TrueType WinAnsi yes yes no 334 0 CLGHIK+PalatinoLinotype TrueType WinAnsi yes yes no 387 0 CLGHKN+TimesNewRoman,Bold TrueType WinAnsi yes yes no 388 0 CLGHJM+TimesNewRoman TrueType WinAnsi yes yes no 389 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-09-10 03:48:51 CEST RepresentationInformation: dairy-06-00041-v2.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-09-09 11:40:56 CEST Size: 766601 Format: PDF Version: 1.5 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 513 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Item: Title: Materials and Methods Children: Item: Title: Experimental Design Item: Title: Sample Preparation Item: Title: Inoculum Preparation Item: Title: Yogurt Fermentation Process Item: Title: Physicochemical Analysis of Yogurt During Fermentation Children: Item: Title: pH Measurement Item: Title: Lactic Acid Concentration Measurement Item: Title: Colour Measurement in the CIELAB System Item: Title: Model Design, Training, and Implementation Item: Title: Results and Discussion Children: Item: Title: Monitoring of pH During Yogurt Fermentation Item: Title: Monitoring of Acidity During Yogurt Fermentation Item: Title: Colour Behaviour During Fermentation Item: Title: Development of ANN Models for the Prediction of pH and Acidity Item: Title: Conclusions Item: Title: References Info: Title: Development of a Hybrid System Based on the CIELAB Colour Space and Artificial Neural Networks for Monitoring pH and Acidity During Yogurt Fermentation Author: Ulises Alvarado, Jhon Tacuri, Alejandro Coloma, Edgar Gallegos Rojas, Herbert Callo, Cristina Valencia-Sullca, Nancy Curasi Rafael and Manuel Castillo Subject: 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. 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