Application of a novel S3 nanowire gas sensor device in parallel with GC-MS for the identification of Parmigiano Reggiano from US and European competitors

Application of a novel S3 nanowire gas sensor device in parallel with GC-MS for the... Parmigiano Reggiano is a typical Italian product known all over the world. Appreciated for its qualities, it is also one of the most counterfeit foods. To avoid this, two different techniques has been used: GC-MS with SPME and S3 gas sensor. With the first one, characteristic VOCs of different cheeses has been found. The most present classes are alcohols, aldehydes, hydrocarbons, esters and ketones, for a total of 58 compounds. Statistical analysis has shown that only 6 of them could be used to distinguish between Parmigiano Reggiano and its competitors. Instead, S3 is a new, rapid, economic and user-friendly approach. Assessing the variation of sensors resistances, it has been possible to discriminate between different kind of cheeses with an accuracy higher than 80%. Data analysis was performed considering PLS to have a visual representation of samples, the SFS algorithm for features selection and PLS-DA as classifier. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Food Engineering Elsevier

Application of a novel S3 nanowire gas sensor device in parallel with GC-MS for the identification of Parmigiano Reggiano from US and European competitors

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0260-8774
D.O.I.
10.1016/j.jfoodeng.2018.05.009
Publisher site
See Article on Publisher Site

Abstract

Parmigiano Reggiano is a typical Italian product known all over the world. Appreciated for its qualities, it is also one of the most counterfeit foods. To avoid this, two different techniques has been used: GC-MS with SPME and S3 gas sensor. With the first one, characteristic VOCs of different cheeses has been found. The most present classes are alcohols, aldehydes, hydrocarbons, esters and ketones, for a total of 58 compounds. Statistical analysis has shown that only 6 of them could be used to distinguish between Parmigiano Reggiano and its competitors. Instead, S3 is a new, rapid, economic and user-friendly approach. Assessing the variation of sensors resistances, it has been possible to discriminate between different kind of cheeses with an accuracy higher than 80%. Data analysis was performed considering PLS to have a visual representation of samples, the SFS algorithm for features selection and PLS-DA as classifier.

Journal

Journal of Food EngineeringElsevier

Published: Nov 1, 2018

References

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