Classification of 7 monofloral honey varieties by PTR-ToF-MS direct headspace analysis and chemometrics
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- 作者：Erna Schuhfrieda ; erna.schuhfried@uibk.ac.at" class="auth_mail" title="E-mail the corresponding author ; José ; Sá ; nchez del Pulgarb ; 1 ; jsapuri@hotmail.com" class="auth_mail" title="E-mail the corresponding author ; Marco Bobbac ; 1 ; marco_bobba@yahoo.it" class="auth_mail" title="E-mail the corresponding author ; Roberto Pirod ; RPiro@izsvenezie.it" class="auth_mail" title="E-mail the corresponding author ; Luca Cappelline ; luca.cappellin@fmach.it" class="auth_mail" title="E-mail the corresponding author ; Tilmann D. Mä ; rka ; tilmann.maerk@uibk.ac.at" class="auth_mail" title="E-mail the corresponding author ; Franco Biasiolie ; franco.biasioli@fmach.it" class="auth_mail" title="E-mail the corresponding author
- 关键词：PTR-ToF-MS ; Classification ; Neural networks ; Honey ; Floral origin
- 刊名：Talanta
- 出版年：2016
- 出版时间：15 January 2016
- 年：2016
- 卷：147
- 期：Complete
- 页码：213-219
- 全文大小：1759 K
文摘
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The headspace of 70 unifloral honeys (7 classes) were characterized by PTR-ToF-MS.
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Headspace analysis is rapid, non-invasive and completely automated.
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Supervised and unsupervised methods provide low classification errors.
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Classification success ranges from 81 to 99% in the internal validation sets.