Food fraud prevention and detection priorities can be different in different countries. In Iran, as in many countries, pork meat is an unlikely adulterant in beef or chicken sausages as there is virtually no pork production; it is legally and culturally proscribed. Donkey and horse, however, is not food grade but is cheap and readily available as an adulterant. Laboratories with PCR are scarce and the need is for rapid, portable verification tests.
The researchers in this study (open access) sought to address this need by developing a classification model using non-destructive FTIR. They deliberately omitted any extraction or defatting step so that the test could be applied directly to a 3mm slice of the intact sample. They trained the model using sausages prepared in-house that mimicked – as far as possible – the typical recipes used in Iran (40 – 60% meat content, along with soy flour, egg, herbs and spices). They prepared 20-each of beef, chicken, horse and donkey sausages using meat sourced directly from veterinary schools. For the training set, triplicate sub-samples were measured from each sausage and then the triplicates averaged. Some pre-processing was applied to the data before dimension reductions using supervised machine learning. 30% of the samples were reserved as a validation set and kept independent of the training set. Additionally, within the training set, a 5-fold cross-validation procedure was used to iterate an internal check against over-fitting.
The researchers were able to separate the four species into distinct clusters using Principle Component Analysis. They also postulate a chemical rationale as to why these identified signals should differ between species. They conclude that their approach could form the basis of a rapid non-destructive test with practical application.
Image – from paper
