 IVES 9 IVES Conference Series 9 Macrowine 9 Macrowine 2021 9 Grapevine diversity and viticultural practices for sustainable grape growing 9 A microwave digestion ICP-MS method for grapevine bark elemental profiling

A microwave digestion ICP-MS method for grapevine bark elemental profiling

Abstract

Aim: A rapid and reproducible microwave (MW)-assisted acid digestion protocol was developed to determine the elemental composition of grapevine bark samples using ICP-MS. A representative grapevine bark sample and a similar matrix Certified Reference Material (CRM) were used for method optimisation. The method was subsequently applied to a set of New Zealand vineyard grapevine bark samples consisting of seven different grape varieties.

Methods: A homogenous bark sample and a CRM (ERMCD281) were treated with 16 different acid combinations and microwave digestion settings prior to ICP-MS analysis. 54 chemical elements were measured in the samples. Calibration standards were prepared in matrix matched solutions from single elements standards (Inorganic Ventures, USA).

Results: The acid digestion combination of HNO3, H2O2, and HCl with a MW digestion of 15 minutes was shown to give optimal results. 48 elements could be measured in a representative grapevine bark sample using this procedure and 27 elements in a reference CRM sample. Ca was the most abundant element present in all grape variety bark samples.

Conclusions

A method was developed and validated for an MW digestion of grapevine bark samples using ICP-MS. The application of this new method showed that bark from different grape varieties varies in elemental composition within a vineyard site.

Acknowledgments

The authors wish to thank the Bragato Research Institute, New Zealand Winegrowers, and the Ministry of Business, Industry, and Employment (MBIE), for funding this work.

DOI:

Publication date: September 2, 2021

Issue: Macrowine 2021

Type: Article

Authors

Alexandra Lowrey

University of Auckland, New Zealand,Bruno FEDRIZZI, University of Auckland Rebecca JELLEY, University of Auckland Stuart MORROW, University of Auckland

Contact the author

Keywords

icp-ms, grapevine bark, trace elements, microwave digestion

Citation

Using NIR/SWIR hyperspectral camera mounted on a UAV to assess grapevine water status in a variably irrigated vineyard

Vineyards face climate change, increasing temperatures, and drought affecting vine water status. Water deficit affects plant physiology and can ultimately decrease yield and grape quality when it is not well managed. Monitoring vine water status and irrigation can help growers better manage their vineyards.

VOLATILE AND GLYCOSYLATED MARKERS OF SMOKE IMPACT: EVOLUTION IN BOTTLED WINE

Smoke impact in wines is caused by a wide range of volatile phenols found in wildfire smoke. These compounds are absorbed and accumulate in berries, where they may also become glycosylated. Both volatile and glycosylated forms eventually end up in wine where they can cause off-flavors. The impact on wine aroma is mainly attributed to volatile phenols, while in-mouth hydrolysis of glycosylated forms may be responsible for long-lasting “ashy” aftertastes (1).

On quality assurance of winemaking components

This report examines product quality assurance issues arising when technological aids and food additives are utilized in winemaking.

Does wine expertise influence semantic categorization of wine odors?

Aromatic characterization is a key issue to enhance wines knowledge. While several studies argue the importance of wine expertise in the ability of performing odor-related sensory tasks, there is still little attention paid to the influence of expertise on the semantic representation of wine odors.

Applicability of spectrofluorometry and voltammetry in combination with machine learning approaches for authentication of DOCa Rioja Tempranillo wines

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).