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A rapid dynamic headspace method for authentication of whiskies using artificial neural networks

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journal contribution
posted on 2023-09-05, 13:17 authored by James SwiftJames Swift, Matthew TurnerMatthew Turner, Jim ReynoldsJim Reynolds

A rapid headspace analysis method for the authenticity testing of whiskies of different brands and years was developed for a low cost, deployable atmospheric pressure ionisation mass spectrometer, which required minimal sample preparation. Principal component analysis was applied to the time-averaged mass spectra, the classification results for which were compared against artificial neural network methods. The artificial neural network was found to outperform PCA, achieving ≥95% accuracy for all sampling conditions, with only two misclassifications under the ideal conditions, while requiring less development time.

History

School

  • Science

Department

  • Chemistry

Published in

Food Chemistry Advances

Volume

3

Publisher

Elsevier

Version

  • VoR (Version of Record)

Publisher statement

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Acceptance date

2023-08-15

Publication date

2023-08-26

Copyright date

2023

ISSN

2772-753X

Language

  • en

Depositor

Dr Jim Reynolds. Deposit date: 1 September 2023

Article number

100417

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