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Data publikacji: 2025-09-08

Investigation of Halloumi Cheese Adulteration Due to the Addition of Milk Powder Using BET and FTIR Measurements

M. Tarapoulouzi, Malgorzata Ruggiero-Mikolajczyk, I. Pashalidis, C. Theocharis

Analytica 6 (2025-09-08) 34

Abstrakt

Halloumi cheese, a traditional Cypriot dairy product with Protected Designation of Origin (PDO) status, is renowned for its unique texture and high melting point. PDO certification is crucial for Halloumi cheese as it ensures the product’s authenticity, protects its traditional production methods and geographical origin, and safeguards consumers and producers against fraud and mislabeling. However, concerns over adulteration, particularly through the addition of skim milk powder, pose challenges to its authenticity and quality control. This study is the first to analyze Halloumi cheese using Brunauer–Emmett–Teller (BET) analysis and Fourier Transform Infrared (FTIR) spectroscopy, providing a novel approach to assessing its composition and authenticity. Furthermore, it marks the first time Halloumi samples have been examined in the context of PDO certification. Alongside PDO-certified Halloumi, two additional sample sets were produced following PDO specifications for moisture, fat, and salt content, with the controlled incorporation of skim milk powder as an adulterant at concentrations of 1% and 5%. Principal component analysis (PCA) was employed to visualize and interpret the spectral data, revealing promising results. Chemometric analysis showed that the specific surface area from BET measurements and the FTIR spectral subregion between 1650 and 1100 cm−1 were key factors, and they were retained for model construction. These findings could play a crucial role in establishing official food fraud detection methodologies, particularly for the Cyprus and EU markets. While this study serves as an initial investigation, additional samples will be tested in future studies to validate these preliminary results and to assess the potential of applying these techniques in real-world food fraud detection scenarios.

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