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Mixed-milk cheeses (cheese made with a mix of milk from different species) are common on the European market, particularly in Spain.  They present a motive to fraudulently increasing the proportion of the cheapest milk species above its maximum legal specification.  For example, Iberico cheese must contain a maximum of 50% cow's milk and a minimum of 15% each of goat's and sheep's milk. Verifying the proportions of milk in the final product by quantitative analytical testing is a challenge.

In this study (open access) the authors developed and validated a quantitative LC-MSMS method based on protein markers for each species.  They selected their markers using shotgun proteomics of 6 cheeses of known proportions that had been specially made in a pilot plant following the industrial process for manufacturing Iberico-type cheese.

They optimised a quantitative low-resolution LC-MSMS method for these markers and then validated it following AOAC guidelines.  They report that the method demonstrated linearity with detection limits less than 1% for all 3 species and showed good repeatability (CV = 8%), reproducibility (CV = 10%) and accuracy (99.6%),.

They applied the method commercial cheeses with diverse compositions and ripening times. They report that measurements were unaffected by either ripening or production process.

Photo by Sam Carter on Unsplash

In this study (open access) researchers developed and piloted a single in-line sensor to classify yoghurt as either sheep, goat or milk origin and simultaneously check viscosity and pH Quality Attribute Specifications.  Their goal is a rapid in-line sensor that incorporates automated decision making, for routine use in the dairy industry.

Their reference dataset was sourced directly from two reputable Spanish companies and included both pasteurised and UHT yoghurts.

They found that the animal origin of milk could be predicted by building models based on the spectral data between 400 and 600 nm whilst viscosity and pH could be predicted by building models based on the spectral data between 800 and 1800 nm. To identify the animal origin of milk, they used Partial Least Squares-Discriminant Analysis (PLS-DA), achieving 100 % accuracy (95 % confidence interval). 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; R²=0.94–0.96; MAE=0.16–0.17).

They conclude that their study demonstrates that the proposed spectroscopic method offers 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.

[picture – from the publication]

Halloumi produced in Cyprus has a transitional exception until 2029 from the EU PDO regulations which stipulate that >50% of the milk content must be from sheep or goats.  This is because of the relatively low sheep/goat milk production on the island.  However, national Cypriot law still stipulates that the sheep/goat milk content must be >19% during this transition.  Major dairy companies on Cyprus have lobbied against this transitional law, arguing that it is unachievable without large scale import of sheep/goat milk powder.

It has been reported that a 2024 survey of one of the largest halloumi brands on sale in Cyprus found sheep/goat milk content at only 5%.  The same newspaper also reports that the regulators are working with Bureau Veritas on building a reference database of compositional parameters, to address longstanding analytical challenges in verifying the sheep/goat milk content of imported milk powder.

Photo by Ambitious Studio* | Rick Barrett on Unsplash