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DNA-based verification that gelatin-containing foods and cosmetics do not contain pork products has always been a challenge due to DNA damage and destruction during gelatin production. 

In this study (open access) the authors report that – by careful optimisation of conditions – they could successfully apply a “traditional” PCR test to the problem.

They describe DNA extraction, post-isolation DNA analysis, annealing temperature and primer concentration optimization, specificity assay, amplification efficiency trial, sensitivity test, repeatability examination, and marketed sample analysis.

They report that the developed method demonstrated good specificity under optimized conditions. It achieved a good amplification efficiency of 101.2% with an R² of 0.994. The real-time PCR technique had a limit of detection of 1,316 pg in the sensitivity examination and a coefficient of variation of 0.81% in the repeatability testing.

They tested 10 retail samples (five facial mask cosmetics, food additive gelatin powder, two marshmallow products, and two gummy candy products), reporting that all of the samples displayed no amplification and were thus considered not to contain porcine DNA, consistent with the manufacturers’ labels.

The authors conclude that their real-time PCR method meets the validation criteria for qualitative analysis, including specificity, amplification efficiency, sensitivity, and repeatability.

An electronic nose (“e-nose”) is a sensor used to selectively measure volatile organic compounds.  Although e-noses have advantages in terms of cost and ease of use, they also have inherent limitations in terms of sensitivity to detect subtle variations in compound concentrations, leading to inconsistent results if not properly managed. The data generated by e noses generally require advanced processing techniques for interpretation of complex signal patterns. This is why e-nose food classification applications tend to use Deep Learning techniques such as Recurrent Neural Networks.

In this publication (open access) the authors used an array of 7 sensors to build a model to differentiate pork, bovine and fish gelatin.  The model was based on a commercial sample of each, dissolved in water as a 1% solution and warmed.  The model was then applied to different in-house mixtures of the gelatins at different time-points after preparation.  The authors do no report if it was validated with orthogonal samples of verified origin.  The sensors had selective sensitivity to a range of volatiles including ethanol, methane, propane, butane, ammonia and hydrogen sulfide.

The authors report that classification efficiency, as measured by the AUC (Area Under the ROC Curve), was variable when considering one sensor in isolation but was good when all 7 sensors were multiplexed.  The AUC increased with time from sample preparation, rising to over 98% at 2-hours from the samples being prepared.  The authors conclude that this makes the technique a promising candidate for constructing a routine instrument to check the species of commercial gelatin.

This paper (purchase required) reports a method to differentiate pork gelatin from beef gelatin (down to 0.01% cross-contamination levels) based on the LC-MSMS analysis of 13 peptide marker ions (8 for bovine, 5 for porcine).  The authors report that their method was validated at three concentration levels and accurately identified the gelatin species in pharmaceutical capsules and gels.

LC-MSMS analysis of peptides provides an alternative approach to DNA testing, which has known difficulties in application to highly processed products like gelatin due to the low amount of viable DNA or distinctive fragments.  LC-MSMS is the approach described in a recent Defra research report which is referenced on the FAN research pages (scroll down table to FA0177).