terclim by ICS banner
IVES 9 IVES Conference Series 9 International Congress on Grapevine and Wine Sciences 9 2ICGWS-2023 9 Applicability of spectrofluorometry and voltammetry in combination with machine learning approaches for authentication of DOCa Rioja Tempranillo wines

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

Abstract

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).

Linear sweep voltammetry (using disposable carbon paste electrodes) and A-TEEM signals of 132 Tempranillo red wines were acquired. Data were analysed following non-supervised statistical strategies such as principal component analysis (PCA) to reduce the number of variables, and two-way ANOVA (origin and category) and supervised modeling strategies derived from machine learning algorithms.

The voltammogram in the region of 691-771 mV provided clear classification of the three ageing categories and Rioja Oriental and Rioja Alavesa/Alta could be separated, but Alavesa could not be differentiated from Alta based on voltammetric signals. Results showed that A-TEEM was more efficient in classifying subareas and ageing categories of Tempranillo Rioja wines, with an ML approach using extreme gradient boosting discriminant analysis (XGBDA) providing 100% correct class assignment for subregion and wine category. A-TEEM coupled with ML algorithms is presented as a powerful and rapid approach to classify Tempranillo Rioja wines according to their origin and style of ageing.

Acknowledgements: This project was funded by the Corporation DOCa Rioja and the José Castillejo program through the Ministerio de Universidades: Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i, Subprograma Estatal de Movilidad, del Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (CAS21/00221).

DOI:

Publication date: October 13, 2023

Issue: ICGWS 2023

Type: Poster

Authors

M. P. Sáenz-Navajas1*, S. Bastian2, D. W. Jeffery2

1Instituto de Ciencias de la Vid y del Vino (Universidad de La Rioja-Consejo Superior de Investigaciones Científicas-Gobierno de La Rioja). Departamento de Enología, Logroño, La Rioja, Spain
2School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia

Contact the author*

Keywords

designated origin, A-TEEM, extreme gradient boosting, classification, red wine, statistical modeling

Tags

2ICGWS | ICGWS | ICGWS 2023 | IVES Conference Series

Citation

Related articles…

Addition of glutathione-rich inactivated yeasts to white musts: effects on wine composition and sensory quality

Glutathione plays a key role in preventing some oxidative processes during winemaking. This molecule limits the must enzymatic oxidation, reacts with caffeic acid and generates a colourless compound that prevents subsequent browning. It also has a protective effect on wine aroma, preventing the oxidation of the volatile compounds with a high sensory impact.

Vineyard yield estimation using image analysis: assessing bunch occlusions and its dependency on fruiting zone canopy features

Performing accurate vineyard yield estimation is of upmost importance as it provides important benefits to the whole vine and wine industry. Recently, image-analysis approaches have been explored to address this issue however this approach has as main challenge the bunch occlusion, mostly by vegetation but also by neighboring bunches. The present work aims at assessing the magnitude of bunch occlusion by neighboring bunches and to evaluate its dependency on a selection of vegetative and reproductive vine parameters assessed at fruiting zone. Forty vine segments (1 m) of two vineyard plots of the white cultivars ‘Alvarinho’ and ‘Arinto’ were assessed for vegetative and reproductive features at fruiting zone and imaged with a 2D camera.

Accumulation of deleterious mutations in grapevine and its relationship with traits of interest for wine production and resilience

Deleterious mutations that severely reduce population fitness are rapidly removed from the gene pool by purifying selection. However, evolutionary drivers such as genetic drift brought about by demographic bottlenecks may comprise its efficacy by allowing deleterious mutations to accumulate, thereby limiting the adaptive potential of populations. Moreover, positive selection can hitchhike mildly deleterious mutations due to linkage caused by lack of recombination. Similarly, in the context of species domestication, artificial selection mimics these evolutionary processes, which can have undesirable consequences for production and resilience. In this study, we evaluated the extent of the accumulation of deleterious mutations and the magnitude of their effects (also known as genetic load) at the whole-genome scale for ca.

Oxidability of wines made from Spanish minority grape varieties

The phenolic profile of a wine plays an essential role in its oxidative capacity and in both white and red wines it defines its shelf life[1]. The study of minority varieties to produce wines with peculiar characteristics necessarily includes the phenolic and oxidative characterization of the wines produced. This paper presents the study of wines made from 24 minority and majority white and red grape varieties, focusing on phenolic characteristics (total phenols, slightly polymerized phenols, highly polymerized phenols, anthocyanins…), color, as well as parameters related to the oxidability of the wines and their capacity to consume oxygen [2].

Unveiling a hidden link: does time hold the key to altered spectral signatures of grapevines under drought?

Remote sensing technology captures spectral data beyond the visible range, making it useful for monitoring plant stress. Vis-NIR (Visible-Near Infrared) spectroscopy (400-1000 nm) is commonly used to indirectly assess plant status during drought. One example is the widespread use of normalized difference vegetation index (NDVI) that is strongly linked to green biomass. However, a knowledge gap exists regarding the applicability of this method to all the drought conditions and if it is a direct correlation to the water status of the plant.