A non-canonical phosphorylation site in β-casein A from non-Mediterranean water buffalo makes quantifiable the adulteration of Italian milk with foreign material by combined isoelectrofocusing-immunoblotting procedures

Assessment of cheese frauds, and relevant detection methods: A systematic review

Dairy products are widely consumed in the world due to their nutritional and functional characteristics. This group of food products are consumed by all age groups due to their health-giving properties. One of these products is cheese which has a high price compared to other dairy products. Because of this, it can be prone to fraud all over the world. Fraud in food products threatens the world's food safety and can cause serious damage to human health. There are many concerns among food authorities in the world about the fraud of food products. FDA, WHO, and the European Commission provide different legislations and definitions for fraud. The purpose of this review is to identify the most susceptible cheese type for fraud and effective methods for evaluating fraud in all types of cheeses. For this, we examined the Web of Science, Scopus, PubMed, and ScienceDirect databases. Mozzarella cheese had the largest share among all cheeses in terms of adulteration due to its many uses. Also, the methods used to evaluate different types of cheese frauds were PCR, Spectrometry, stable isotope, image analysis, electrophoretic, ELISA, sensors, sensory analysis, near-infrared and NMR. The methods that were most used in detecting fraud were PCR and spectrometry methods. Also, the least used method was sensory evaluation.

A Genotyping Method for Detecting Foreign Buffalo Material in Mozzarella di Bufala Campana Cheese Using Allele-Specific- and Single-Tube Heminested-Polymerase Chain Reaction

Mozzarella di Bufala Campana (MdBC) cheese is a Protected Designation of Origin (PDO) product that is important for the economy and cultural heritage of the Campania region. Food fraud can undermine consumers' trust in this dairy product and harm the livelihood of local producers. The current methods for detecting adulteration in MdBC cheese due to the use of buffalo material from foreign countries could exhibit limitations associated with the required use of expensive equipment, time-consuming procedures, and specialized personnel. To address these limits here, we propose a rapid, reliable, and cost-effective genotyping method that can detect foreign buffalo milk in a counterpart from the PDO area and in MdBC cheese, ensuring the quality and authenticity of the latter dairy product. This method is based on dedicated allele-specific and single-tube heminested polymerase chain reaction procedures. By using allele-specific primers that are designed to detect the nucleotide g.472G>C mutation of the CSN1S1B^bt allele, we distinguished an amplicon of 330 bp in the amplification product of DNA when extracted from milk and cheese, which is specific to the material originating from foreign countries. By spiking foreign milk samples with known amounts of the counterpart from the PDO area, the sensitivity of this assay was determined to be 0.01% v/v foreign to PDO milk. Based on a rough estimate of its simplicity, reliability, and cost, this method could be a valuable tool for identifying adulterated buffalo PDO dairy products.

Spotting Frozen Curd in PDO Buffalo Mozzarella Cheese Through Insights on Its Supramolecular Structure Acquired by 1H TD-NMR Relaxation Experiments

The addition of frozen curd (FC) during the production process of “Mozzarella di Bufala Campana”, an Italian cheese with Protected Designation of Origin (PDO), is a common fraud not involving modifications of the chemical composition in the final product. Its detection cannot thus be easily obtained by common analytical methods, which are targeted at changes in concentrations of diagnostic chemical species. In this work, the possibility of spotting this fraud by focusing on the modifications of the supramolecular structure of the food matrix, detected by time domain nuclear magnetic resonance (TD-NMR) experiments, was investigated. Cheese samples were manufactured in triplicate, according to the PDO disciplinary of production, except for using variable amounts of FC (i.e., 0, 15, 30, and 50% w/w). Relaxation data were analysed through different approaches: (i) Discrete multi-exponential fitting, (ii) continuous Laplace inverse fitting, and (iii) chemometrics approach. The strategy that lead to best detection results was the chemometrics analysis of raw Carr-Purcell-Meiboom-Gill (CPMG) decays, allowing to discriminate between compliant and adulterated samples, with as low as 15% of FC addition. The strategy is based on the use of machine learning for projection on latent structures of raw CPMG data and classification tasks for fraud detection, using quadratic discriminant analysis. By coupling TD-NMR raw decays with machine learning, this work opens the way to set up a system for detecting common food frauds modifying the matrix structure, for which no official authentication methods are yet available.