Rapid evaporative ionisation mass spectrometry and chemometrics for high-throughput screening of growth promoters in meat producing animals.

In a proof of concept perspective, Rapid Evaporative Ionisation Mass Spectrometry (REIMS) was explored for the direct analysis of meat samples from ß-agonist treated livestock. In this context, the combination of REIMS with untargeted metabolomics was investigated to identify carcasses from treated animals on the basis of a modification of indirect metabolites profile. The REIMS analysis generated specific lipid profiles which enabled the differentiation of meat samples collected from pigs treated with ractopamine via their feeding regime. Furthermore, the strategy was found successful when tested on different muscle types (loin, shoulder and thigh), which further expands its applicability. Classification performances were greater than 95% accurate which fully answers requirements of a screening strategy. This research indicates that REIMS implemented in an untargeted-metabolomics workflow can be considered as a high-throughput and accurate strategy for real-time meat classification in relation to ractopamine (and wider ß-agonists) treatment in pig production. This approach may subsequently be implemented as a rapid screening test, at the slaughterhouse or at border inspection points, to detect such practice.

Rapid evaporative ionization mass spectrometry for high-throughput screening in food analysis: The case of boar taint.

Boar taint is a contemporary off-odor present in meat of uncastrated male pigs. As European Member States intend to abandon surgical castration of pigs by 2018, this off-odor has gained a lot of research interest. In this study, rapid evaporative ionization mass spectrometry (REIMS) was explored for the rapid detection of boar taint in neck fat. Untargeted screening of samples (n=150) enabled discrimination between sow, tainted and untainted boars. The obtained OPLS-DA models showed excellent classification accuracy, i.e. 99% and 100% for sow and boar samples or solely boar samples, respectively. Furthermore, the obtained models demonstrated excellent validation characteristics (R2(Y)=0.872-0.969; Q2(Y)=0.756-0.917), which were confirmed by CV-ANOVA (p<0.001) and permutation testing. In conclusion, in this work for the first time highly accurate and high-throughput (<10s) classification of tainted and untainted boar samples was achieved, rendering REIMS a promising technique for predictive modelling in food safety and quality applications.