Qualitative analysis of yogurt using VIS-NIR spectroscopy simultaneously allows prediction of the animal origin of milk as well as monitoring of pH and viscosity during yogurt production
Year: 2025
Type: article
Abstract: Effective quality control during food manufacturing is essential to guarantee consistency, which is particularly challenging in fermented products. Industrial yogurt production requires careful monitoring of changes in pH and texture to ensure that the end product meets consumers’ demands. Additionally, the increasing market for products made with non-bovine milk requires confirming the origin of milk to avoid food fraud. This study demonstrates the potential of spectroscopy in the visible and near infrared range (Vis-NIR) for the simultaneous, rapid and accurate prediction of pH, viscosity and animal origin of milk (cow, goat or sheep) in yogurt by using a single device that acquires the spectral signature between 400 and 2500 nm. The optimal ranges to build predictive models were 400–600 nm for identification of the milk-producing species, and 800–1800 nm to estimate pH/viscosity. To identify the animal origin of milk, we used Partial Least Squares-Discriminant Analysis (PLS-DA), achieving 100 % accuracy (95 % confidence interval: 0.9075–1). The model used to predict pH and viscosity was built with Partial Least Squares Regression (PLSR). The predictive power was generally very good (MSE=0.04–0.06; R 2 =0.94–0.96; MAE=0.16–0.17). Overall, these results demonstrate that the proposed spectroscopic method significantly enhances food safety by offering a more efficient approach for the simultaneous prediction of pH, viscosity, and milk origin in yogurt compared to existing methods, that require separate and slower analyses. Further work still needs to be carried out to optimize the model and achieve real-time monitoring that enables automated decision-making and, therefore, proper implementation in the dairy industry.
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $3,450
Award: G827827252
OpenAlex ID: W4409839033