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...
Main Author: | |
---|---|
Format: | Thesis |
Language: | unknown |
Published: |
2010
|
Subjects: | |
Online Access: | http://hdl.handle.net/10210/3172 |
id |
ftunivjohannesbu:uj:6766 |
---|---|
record_format |
openpolar |
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 |
_version_ |
1766232790627516416 |