Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue

Cynomorium songaricum Rupr. is a well-known and widespread plant in China. It has very high medicinal values in many aspects. The study aimed at discriminating and predicting C. songaricum from major growing areas in China. An electronic tongue was used to analyze C. songaricum based on flavor. Disc...

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Detalles Bibliográficos
Autores principales: Ding, Jiaji, Gu, Caimei, Huang, Linfang, Tan, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6282117/
https://www.ncbi.nlm.nih.gov/pubmed/30595938
http://dx.doi.org/10.1155/2018/5894082
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author Ding, Jiaji
Gu, Caimei
Huang, Linfang
Tan, Rui
author_facet Ding, Jiaji
Gu, Caimei
Huang, Linfang
Tan, Rui
author_sort Ding, Jiaji
collection PubMed
description Cynomorium songaricum Rupr. is a well-known and widespread plant in China. It has very high medicinal values in many aspects. The study aimed at discriminating and predicting C. songaricum from major growing areas in China. An electronic tongue was used to analyze C. songaricum based on flavor. Discrimination was achieved by principal component analysis and linear discriminant analysis. Moreover, a prediction model was established, and C. songaricum was classified by geographical origins with 100% degree of accuracy. Therefore, the identification method presented will be helpful for further study of C. songaricum.
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spelling pubmed-62821172018-12-30 Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue Ding, Jiaji Gu, Caimei Huang, Linfang Tan, Rui J Anal Methods Chem Research Article Cynomorium songaricum Rupr. is a well-known and widespread plant in China. It has very high medicinal values in many aspects. The study aimed at discriminating and predicting C. songaricum from major growing areas in China. An electronic tongue was used to analyze C. songaricum based on flavor. Discrimination was achieved by principal component analysis and linear discriminant analysis. Moreover, a prediction model was established, and C. songaricum was classified by geographical origins with 100% degree of accuracy. Therefore, the identification method presented will be helpful for further study of C. songaricum. Hindawi 2018-11-22 /pmc/articles/PMC6282117/ /pubmed/30595938 http://dx.doi.org/10.1155/2018/5894082 Text en Copyright © 2018 Jiaji Ding et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ding, Jiaji
Gu, Caimei
Huang, Linfang
Tan, Rui
Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue
title Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue
title_full Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue
title_fullStr Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue
title_full_unstemmed Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue
title_short Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue
title_sort discrimination and geographical origin prediction of cynomorium songaricum rupr. from different growing areas in china by an electronic tongue
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6282117/
https://www.ncbi.nlm.nih.gov/pubmed/30595938
http://dx.doi.org/10.1155/2018/5894082
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