Validation of an Optical Technology for the Determination of pH in Milk during Yogurt Manufacture

Current systems that allow inline pH control in the fermented dairy industry have drawbacks, such as protein adhesion on the non-glass pH probes, measurement distortion, frequent recalibration needs, and sensitivity to extreme pH conditions encountered during clean-in-place operations. Therefore, the objective of this study was to validate the feasibility of estimating the pH of milk during the yogurt making process by using a NIR light backscatter sensor measuring under different fermentation temperatures and milk protein concentrations using a mathematical model that correlates the light scatter signal with pH. Three replications of the experiment with two protein concentrations (3.5 and 4.0%) and two fermentation temperatures (43 and 46 °C) were used to validate this inline pH prediction model. Continuous and discontinuous measurements of pH were collected as a reference during fermentation, simultaneously with the light backscatter data acquisition. Also, the effect of adjusting the initial voltage gain of the light scatter device on the accuracy of the pH prediction model was evaluated. Temperature and initial voltage were the main factors affecting the fitting accuracy of the model. The adjustment of the initial voltage gain improved the pH prediction model fit. The model has been successfully validated for both continuous and discontinuous measurements of pH, with SEP values < 0.09 pH units and CV < 1.78%. The proposed optical inline and non-destructive method was feasible for inline pH monitoring of milk fermentation, avoiding traditional manual pH measurement.

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Publication Details

Journal
Foods
Published
2024-08-30
DOI
https://doi.org/10.3390/foods13172766
Citations
13
Primary Topic
Meat and Animal Product Quality
Type
article
Field-Weighted Citation Impact
4.43
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article

Validation of an Optical Technology for the Determination of pH in Milk during Yogurt Manufacture

Óscar Montaño Arango, Manuel Castillo, Jhunior Marcía, Ricardo S. Alemán et al.
13 citations
Foods
Meat and Animal Product Quality
4.43
article

Validation of an Optical Technology for the Determination of pH in Milk during Yogurt Manufacture

Óscar Montaño Arango, Manuel Castillo, Jhunior Marcía, Ricardo S. Alemán, Siqi Liu, Fanny Contreras
article en
13 citations

Abstract

Current systems that allow inline pH control in the fermented dairy industry have drawbacks, such as protein adhesion on the non-glass pH probes, measurement distortion, frequent recalibration needs, and sensitivity to extreme pH conditions encountered during clean-in-place operations. Therefore, the objective of this study was to validate the feasibility of estimating the pH of milk during the yogurt making process by using a NIR light backscatter sensor measuring under different fermentation temperatures and milk protein concentrations using a mathematical model that correlates the light scatter signal with pH. Three replications of the experiment with two protein concentrations (3.5 and 4.0%) and two fermentation temperatures (43 and 46 °C) were used to validate this inline pH prediction model. Continuous and discontinuous measurements of pH were collected as a reference during fermentation, simultaneously with the light backscatter data acquisition. Also, the effect of adjusting the initial voltage gain of the light scatter device on the accuracy of the pH prediction model was evaluated. Temperature and initial voltage were the main factors affecting the fitting accuracy of the model. The adjustment of the initial voltage gain improved the pH prediction model fit. The model has been successfully validated for both continuous and discontinuous measurements of pH, with SEP values < 0.09 pH units and CV < 1.78%. The proposed optical inline and non-destructive method was feasible for inline pH monitoring of milk fermentation, avoiding traditional manual pH measurement.

FoodsVol. 13(17)
Universitat Autònoma de Barcelona (ES), Louisiana State University Agricultural Center (US), University of Nariño (CO), Universidad Nacional de Agricultura (HN), Auburn University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Meat and Animal Product Quality
4.43
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