Cargando…

Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy

The authenticity of tea has become more important to the industry while the supply chains become complex. The quality and price of tea produced in different regions varies greatly. Currently, a rapid analytical method for testing the geographical origin of tea is missing. XRF is emerging as a screen...

Descripción completa

Detalles Bibliográficos
Autores principales: Lim, Cia Min, Carey, Manus, Williams, Paul N., Koidis, Anastasios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898033/
https://www.ncbi.nlm.nih.gov/pubmed/33665618
http://dx.doi.org/10.1016/j.crfs.2021.02.002
_version_ 1783653789004201984
author Lim, Cia Min
Carey, Manus
Williams, Paul N.
Koidis, Anastasios
author_facet Lim, Cia Min
Carey, Manus
Williams, Paul N.
Koidis, Anastasios
author_sort Lim, Cia Min
collection PubMed
description The authenticity of tea has become more important to the industry while the supply chains become complex. The quality and price of tea produced in different regions varies greatly. Currently, a rapid analytical method for testing the geographical origin of tea is missing. XRF is emerging as a screening technique for mineral and elemental analysis with applications in the traceability of foodstuffs, including tea. This study aims to develop a reliable multivariate classification model using XRF spectroscopy to obtain the mineral content. A total of 75 tea samples from tea producing countries throughout the world were analysed. After variable shortlisting, 18 elements were used to construct the multivariate models. Tea origin was determined by classifying the tea into 5 major geographical regions producing most of the global tea. PCA showed initial clustering in some regions, although the types of teas included in the study (black, green, white, herbal) showed no discrete cluster membership. The prediction power of each classification model developed was determined by using two multivariate classifiers, SIMCA and PLS-DA, against an independent validation set. The average overall correct classification rates of PLS-DA models were between 54-85% while the results of SIMCA models were between 70-84% resolving the poor clustering initially shown by PCA. This study demonstrated the potential of geographical origin of tea prediction using elemental contents of tea. Naturally, the classification can be linked not only to origin but to the type of tea as well. PRACTICAL APPLICATION: Wholesalers and retailers need a rapid and robust screening tool to confirm the origin and type of tea they sell to consumers. X-Ray fluorescence spectroscopy proved a good technique for achieving this in commercial teas sourced worldwide. Building on multivariate models, broad classification was accomplished both in terms of origin (Asian vs non-Asian) and in tea type with zero sample preparation and low cost of analysis.
format Online
Article
Text
id pubmed-7898033
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-78980332021-03-03 Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy Lim, Cia Min Carey, Manus Williams, Paul N. Koidis, Anastasios Curr Res Food Sci Research Paper The authenticity of tea has become more important to the industry while the supply chains become complex. The quality and price of tea produced in different regions varies greatly. Currently, a rapid analytical method for testing the geographical origin of tea is missing. XRF is emerging as a screening technique for mineral and elemental analysis with applications in the traceability of foodstuffs, including tea. This study aims to develop a reliable multivariate classification model using XRF spectroscopy to obtain the mineral content. A total of 75 tea samples from tea producing countries throughout the world were analysed. After variable shortlisting, 18 elements were used to construct the multivariate models. Tea origin was determined by classifying the tea into 5 major geographical regions producing most of the global tea. PCA showed initial clustering in some regions, although the types of teas included in the study (black, green, white, herbal) showed no discrete cluster membership. The prediction power of each classification model developed was determined by using two multivariate classifiers, SIMCA and PLS-DA, against an independent validation set. The average overall correct classification rates of PLS-DA models were between 54-85% while the results of SIMCA models were between 70-84% resolving the poor clustering initially shown by PCA. This study demonstrated the potential of geographical origin of tea prediction using elemental contents of tea. Naturally, the classification can be linked not only to origin but to the type of tea as well. PRACTICAL APPLICATION: Wholesalers and retailers need a rapid and robust screening tool to confirm the origin and type of tea they sell to consumers. X-Ray fluorescence spectroscopy proved a good technique for achieving this in commercial teas sourced worldwide. Building on multivariate models, broad classification was accomplished both in terms of origin (Asian vs non-Asian) and in tea type with zero sample preparation and low cost of analysis. Elsevier 2021-02-09 /pmc/articles/PMC7898033/ /pubmed/33665618 http://dx.doi.org/10.1016/j.crfs.2021.02.002 Text en © 2021 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Paper
Lim, Cia Min
Carey, Manus
Williams, Paul N.
Koidis, Anastasios
Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy
title Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy
title_full Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy
title_fullStr Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy
title_full_unstemmed Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy
title_short Rapid classification of commercial teas according to their origin and type using elemental content with X-ray fluorescence (XRF) spectroscopy
title_sort rapid classification of commercial teas according to their origin and type using elemental content with x-ray fluorescence (xrf) spectroscopy
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898033/
https://www.ncbi.nlm.nih.gov/pubmed/33665618
http://dx.doi.org/10.1016/j.crfs.2021.02.002
work_keys_str_mv AT limciamin rapidclassificationofcommercialteasaccordingtotheiroriginandtypeusingelementalcontentwithxrayfluorescencexrfspectroscopy
AT careymanus rapidclassificationofcommercialteasaccordingtotheiroriginandtypeusingelementalcontentwithxrayfluorescencexrfspectroscopy
AT williamspauln rapidclassificationofcommercialteasaccordingtotheiroriginandtypeusingelementalcontentwithxrayfluorescencexrfspectroscopy
AT koidisanastasios rapidclassificationofcommercialteasaccordingtotheiroriginandtypeusingelementalcontentwithxrayfluorescencexrfspectroscopy