Rapid evaporative ionization mass spectrometry for high-throughput screening in food analysis: The case of boar taint.
Abstract
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.
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BibTeX
@article{Verplanken2016,
title = {Rapid evaporative ionization mass spectrometry for high-throughput screening in food analysis: The case of boar taint},
author = {Verplanken, Kaat and Stead, Sara and Jandova, Renata and Van Poucke, Christof and Claereboudt, Jan and Vanden Bussche, Julie and De Saeger, Sarah and Takats, Zoltan and Wauters, Jella and Vanhaecke, Lynn},
journal = {Talanta},
volume = {169},
pages = {30–36},
year = {2017},
publisher = {Elsevier},
abstract = {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.},
}