Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics

M.Sc. The South African wine industry is well respected internationally for producing high quality wines. The possible adulteration of these wines can lead to loss of reputation and a loss of sales and could also be dangerous to consumer’s health. Multi-element analysis of wines is one way of implem...

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Main Author: Van der Linde, Gert
Format: Thesis
Language:unknown
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10210/3172
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spelling ftunivjohannesbu:uj:6766 2023-05-15T18:43:00+02:00 Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics Van der Linde, Gert 2010-04-08T08:45:39Z http://hdl.handle.net/10210/3172 unknown uj:6766 http://hdl.handle.net/10210/3172 Wine and wine making Inductively coupled plasma mass spectrometry Provenance trials Trace elements Thesis 2010 ftunivjohannesbu 2020-07-21T06:47:32Z M.Sc. The South African wine industry is well respected internationally for producing high quality wines. The possible adulteration of these wines can lead to loss of reputation and a loss of sales and could also be dangerous to consumer’s health. Multi-element analysis of wines is one way of implementing quality control and the same multi-element data can also be used to prove the point of origin. The metal content of the fruit (grapes) should represent the metal content of the soil in which the plants (vineyards) were grown. An Inductively Coupled Plasma Mass Spectrometer (ICP-MS) was used with correct internal standard and interference correction to obtain reliable concentrations for 27 elements (Li, B, Al, Sc, V, Cr, Mn, Co, Ni, Cu, Zn, Se, Rb, Sr, Zr, Nb, Mo, Cd, Sn, Sb, Ba, Ce, Nd, W, Tl, Pb and U) of 1:1 diluted wines and microwave digested vineyard soil from four South-African wine-producing regions: Stellenbosch, Swartland, Robertson and Walker Bay. This multi-element data was then interpreted using multivariate statistical analysis in order to determine which elements have the ability to discriminate between the four regions. Li, B, Sc, Ni, Mn, Co, Cu, and Rb were the elements that were identified to have discrimination ability. 96% of wines and 100% of vineyard soils were correctly classified. Indirectly it has been proven that the metal content of the soil can be correlated to the metal content of the wine. This methodology can be reliably used in industry for quality control and routine provenance determination Thesis Walker Bay The University of Johannesburg: UJContent Walker Bay ENVELOPE(-60.700,-60.700,-62.633,-62.633)
institution Open Polar
collection The University of Johannesburg: UJContent
op_collection_id ftunivjohannesbu
language unknown
topic Wine and wine making
Inductively coupled plasma mass spectrometry
Provenance trials
Trace elements
spellingShingle Wine and wine making
Inductively coupled plasma mass spectrometry
Provenance trials
Trace elements
Van der Linde, Gert
Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics
topic_facet Wine and wine making
Inductively coupled plasma mass spectrometry
Provenance trials
Trace elements
description M.Sc. The South African wine industry is well respected internationally for producing high quality wines. The possible adulteration of these wines can lead to loss of reputation and a loss of sales and could also be dangerous to consumer’s health. Multi-element analysis of wines is one way of implementing quality control and the same multi-element data can also be used to prove the point of origin. The metal content of the fruit (grapes) should represent the metal content of the soil in which the plants (vineyards) were grown. An Inductively Coupled Plasma Mass Spectrometer (ICP-MS) was used with correct internal standard and interference correction to obtain reliable concentrations for 27 elements (Li, B, Al, Sc, V, Cr, Mn, Co, Ni, Cu, Zn, Se, Rb, Sr, Zr, Nb, Mo, Cd, Sn, Sb, Ba, Ce, Nd, W, Tl, Pb and U) of 1:1 diluted wines and microwave digested vineyard soil from four South-African wine-producing regions: Stellenbosch, Swartland, Robertson and Walker Bay. This multi-element data was then interpreted using multivariate statistical analysis in order to determine which elements have the ability to discriminate between the four regions. Li, B, Sc, Ni, Mn, Co, Cu, and Rb were the elements that were identified to have discrimination ability. 96% of wines and 100% of vineyard soils were correctly classified. Indirectly it has been proven that the metal content of the soil can be correlated to the metal content of the wine. This methodology can be reliably used in industry for quality control and routine provenance determination
format Thesis
author Van der Linde, Gert
author_facet Van der Linde, Gert
author_sort Van der Linde, Gert
title Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics
title_short Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics
title_full Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics
title_fullStr Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics
title_full_unstemmed Multi-element analysis of South African wines and their provenance soils by ICP-MS and their classification according to geographical origin using multivariate statistics
title_sort multi-element analysis of south african wines and their provenance soils by icp-ms and their classification according to geographical origin using multivariate statistics
publishDate 2010
url http://hdl.handle.net/10210/3172
long_lat ENVELOPE(-60.700,-60.700,-62.633,-62.633)
geographic Walker Bay
geographic_facet Walker Bay
genre Walker Bay
genre_facet Walker Bay
op_relation uj:6766
http://hdl.handle.net/10210/3172
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