Ensuring Market Authenticity of Labaneh: Machine Learning-Driven Analysis of Fatty Acid Profiles for Detecting Non-Milk Fat Adulterants in Jordanian Products
Abstract
Labaneh, a traditional Middle Eastern dairy product, has gained popularity not only domestically but also internationally. However, concerns regarding the authenticity of Labaneh due to potential adulteration with non-milk fats have emerged. Such adulteration not only compromises the quality and nutritional value of Labaneh but also poses health risks to consumers. Therefore, there is an urgent need for reliable and efficient methods to detect non-milk fat adulterants in Labaneh products. In this study, we propose a novel approach utilizing machine learning-driven analysis of fatty acid profiles to detect non-milk fat adulterants in Labaneh. Fatty acid profiles serve as unique chemical fingerprints that can provide valuable information about the composition of dairy products. By employing machine learning algorithms, such as support vector machines (SVM) and artificial neural networks (ANN), we aim to develop predictive models capable of accurately identifying adulterated Labaneh samples. The study focuses on Labaneh products from Jordan, a region renowned for its rich culinary heritage. Samples of authentic Labaneh and potentially adulterated Labaneh containing various concentrations of non-milk fats are collected from local markets across Jordan. Fatty acid methyl esters (FAMEs) are extracted from the samples and analyzed using gas chromatography-mass spectrometry (GC-MS) to obtain their fatty acid profiles.
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