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This study (open access) proposes a strategy to verify the authenticity of Mozzarella di Bufala Campana (MdBC).  MdBC is, a Protected Designation of Origin (PDO) cheese, Buffalo breeds are highly similar genetically, so detecting foreign buffalo milk in commercial cheese is more complicated than identifying cow, goat, or sheep milk. Fraud involving cow milk is particularly concerning because it is cheaper and more widely available, especially during peak MdBC production seasons

The researchers used a reference set of sixty-four anonymized PDO MdBC and foreign mozzarella samples provided by the Italian Central Inspectorate for Fraud Repression and Quality Protection of the Agrifood Products and Food, Ministry of Agricultural and Forestry Policies (Rome, Italy).  They used a sequential approach to verifying foreign milk species in buffalo mozzarella.  As a first screen, the casein was separated on a polyacrylamide gel.  This was generally sufficient to identify extraneous cows’ milk proteins.  In a second stage, the isolate casein was then digested with trypsin and the peptides analysed by MALDI-ToF-MS.

In cases requiring confirmation, nano-liquid chromatography coupled to electrospray tandem mass spectrometry (nano-LC-ESI-MS/MS) is used in central state laboratories for the highly sensitive detection of extraneous milk proteins in PDO buffalo MdBC cheese. The researchers report that analysis of the pH 4.6 soluble fraction from buffalo blue cheese identified 2828 buffalo-derived peptides and several bovine specific peptides, confirming milk adulteration.

They conclude that, despite a lower detection extent in the pH 4.6 insoluble fraction following tryptic hydrolysis, the presence of bovine peptides was still sufficient to verify fraud. This integrated proteomic approach, which combines electrophoresis and mass spectrometry technologies, significantly improves milk adulteration detection.

Photo by Audric Wonkam on Unsplash

There is no single definitive test for dilution of honey with foreign sugar syrups.  An untargeted test, often used to contribute to an analytical weight of evidence, is proton NMR followed by chemometric pattern recognition based on variations in the sugars profile.  One disadvantage of this technique is a lack of sensitivity. 

LCMS is a more sensitive technique and could – in principle – be used in a similar untargeted manner to drive pattern recognition statistics based on the sugar profiles of a database of reference honeys.  The limiting factor has been the computing power that would be needed to “re-set” the database each time a new chromatographic peak is measured or data from different chromatographic systems are combined. (this is why untargeted LCMS is often used in authenticity testing as a 1-off development tool to identify marker compounds, which are then used as the basis for a more routine targeted test, rather than being used as a routine untargeted test).

In this paper (open access), the authors resolved the computing power limitation by using their Bucketing of Untargeted LC-MS Spectra (BOULS) data processing approach which they have previously published.  They demonstrated that untargeted LCMS testing (combining data from different systems, HILIC column with MS in both positive and negative ionisation mode) could discriminate a range of adulterated honeys (rice, beet and high-fructose corn syrups added at 5% to a reference set of 34 North German honeys) from their unadulterated counterparts.

As is the case with all untargeted analytical techniques, the key to using this method routinely would be building a robust reference database of verified authentic honeys that is fully representative of all types and origins on the market.

Photo by Roberta Sorge on Unsplash