Macrowine 2021
IVES 9 IVES Conference Series 9 Determination of quality related polyphenols in chilean wines by absorbance-transmission and fluorescence excitation emission matrix (a-teem) analyses

Determination of quality related polyphenols in chilean wines by absorbance-transmission and fluorescence excitation emission matrix (a-teem) analyses

Abstract

Phenolic composition is essential to wine quality (Cleary et al., 2015; Bindon et al., 2020; Niimi et al., 2020) and its assessment is a strong industrial need to quality management. Objective of this work was to develop a rapid analysis method using the Absorbance-Transmission and fluorescence Excitation-Emission Matrix (A-TEEM) technique. Polyphenols exhibit characteristic and high fluorescence quantum yields, which makes them highly suitable for this technique. The method’s automatic real-time Inner Filter Effect (IFE) correction allows the quantification of minor compounds (Gilmore et al., 2016). IFE-corrected fluorescence EEM data and the absorbance data were combined, and the spectral data were regressed against the concentrations of 34 anthocyanins, flavan-3-ols, tannins, polymeric pigments, flavonols and hydroxycinnamic acids measured independently by HPLC-DAD and UV-vis. The study focused on comparing Partial Least Squares Regression (PLSR) and Extreme Gradient Boost Regression (XGBR) for the single- (fluorescence EEM or absorbance) and multi- (combined) block data. The calibration set comprised 1133 files acquired from 126 diverse experimental and commercial wines. Validation was carried out on two data sets, first by a 14% randomized sample split from the calibration data keeping instrument replicates together, and thereafter by another independent set of 96 files from 16 wines. As a general trend, validation of the multi-block data models with independent data using XGBR, compared to PLSR, yielded higher prediction correlation coefficients (R2P) and lower Root Mean Square Errors for Prediction (RMSEP). Considering all 34 compound fits, mean R2P of 0.947 with XGBR and of 0.899 with PLSR were obtained. The highest fits were obtained for compounds of the anthocyanin family with mean R2P of 0.974 (XGBR) and 0.954 (PLSR), respectively, while lower fits were found for flavan-3-oles with R2P of 0.878 (XGBR) and 0.771 (PLSR), indicating compound effects due to extraction and chromatographic and spectral analysis methods affecting repeatability and quantification limits. In general, precise model fits were found for compounds > 10 mg/L with R2P between 0.929 and 0.992 (XGBR) and between 0.875 and 0.992 (PLSR). Supplementary, all individual compounds could be identified according to their family by spectral fingerprints. However, these multi-block data sets were also associated with significantly higher R2P (and lower RMSEP) compared to a single block evaluation of the fluorescence EEM or absorbance data only. By using mean-centering and an Extended Mixture Model filter the multi-block data sets fit robustly using both XGBR and PLSR without the need to apply secondary variable selection algorithms. We conclude that analyzing the A-TEEM data using the multi-block organization and the XGBR algorithm facilitates a robust prediction of the key phenolic compound concentrations that strongly influence the Chilean wine quality.

DOI:

Publication date: September 7, 2021

Issue: Macrowine 2021

Type: Article

Authors

Doreen Schober

Center for Research and Innovation, Viña Concha y Toro, Ruta k-650 km 10, Pencahue, Región de Maule, Chile,Adam Gilmore, HORIBA Instruments Inc. 20 Knightsbridge Rd., Piscataway, NJ 08854, USA Jorge Zincker, Center for Research and Innovation, Viña Concha y Toro, Ruta k-650 km 10, Pencahue, Región de Maule, Chile Alvaro Gonzalez, Center for Research and Innovation, Viña Concha y Toro, Ruta k-650 km 10, Pencahue, Región de Maule, Chile

Contact the author

Keywords

quality, polyphenols, spectroscopy, a-teem, wine, machine learning

Citation

Related articles…

Downscaling of remote sensing time series: thermal zone classification approach in Gironde region

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

Frost risk projections in a changing climate are highly sensitive in time and space to frost modelling approaches

Late spring frost is a major challenge for various winegrowing regions across the world, its occurrence often leading to important yield losses and/or plant failure. Despite a significant increase in minimum temperatures worldwide, the spatial and temporal evolution of spring frost risk under a warmer climate remains largely uncertain. Recent projections of spring frost risk for viticulture in Europe throughout the 21st century show that its evolution strongly depends on the model approach used to simulate budburst. Furthermore, the frost damage modelling methods used in these projections are usually not assessed through comparison to field observations and/or frost damage reports.
The present study aims at comparing frost risk projections simulated using six spring frost models based on two approaches: a) models considering a fixed damage threshold after the predicted budburst date (e.g BRIN, Smoothed-Utah, Growing Degree Days, Fenovitis) and b) models considering a dynamic frost sensitivity threshold based on the predicted grapevine winter/spring dehardening process (e.g. Ferguson model). The capability of each model to simulate an actual frost event for the Vitis vinifera cv. Chadonnay B was previously assessed by comparing simulated cold thermal stress to reports of events with frost damage in Chablis, the northernmost winegrowing region of Burgundy. Models exhibited scores of κ > 0.65 when reproducing the frost/non-frost damage years and an accuracy ranging from 0.82 to 0.90.
Spring frost risk projections throughout the 21st century were performed for all winegrowing subregions of Bourgogne-Franche-Comté under two CMIP5 concentration pathways (4.5 and 8.5) using statistically downscaled 8×8 km daily air temperature and humidity of 13 climate models. Contrasting results with region-specific spring frost risk trends were observed. Three out of five models show a decrease in the frequency of frost years across the whole study area while the other two show an increase that is more or less pronounced depending on winegrowing subregion. Our findings indicate that the lack of accuracy in grapevine budburst and dehardening models makes climate projections of spring frost risk highly uncertain for grapevine cultivation regions.

Late frost protection in Champagne

Probably one of the most counterintuitive impacts of climate change on vine is the increased frequency of late frost. Champagne, due to its septentrional position is historically and regularly affected by this meteorological hazard. Champagne has therefore developed a strong experience in frost protection with first experiments dating from the end of 19th century. Frost protection can be divided in two parts: passive and active. Passive protection includes all the methods that do not seek to modify the vine’s environment or resistance at the time of frost. The most iconic passive protection in Champagne is the establishment of the individual reserve. This reserve allows to stock a certain quantity of clear wine during a surplus year to compensate a meteorological hazard like frost during the following years. Other common passive methods are the control of planting area (walls, bushes, topography), the choice of grape variety, late pruning, or the impact of grass cover and tillage. Active frost protection is also divided in two parts. Most of the existing techniques tend to modify vine’s environment. Most of the time they provide warmth (candles, heaters, windmills, heating cables…), or stabilise bud’s temperature above a lethal threshold (water sprinkling). The other way to actively fight is to enhance the resistance of buds to frost (elicitors). The Comité Champagne evaluates frost protection methods following three main axes: the efficiency, the profitability, and the environmental impact through a lifecycle assessment. This study will present the results on both passive and active protection following these three axes.

Effect of vigour and number of clusters on eonological parameters and metabolic profile of Cabernet Sauvignon red wines

Vegetative growth and yield are reported to affect grape and wine quality. They can be controlled through different techniques linked to vine management. The objective of this research was to determine the effect of vine vigour and number of clusters per vine on physicochemical composition and phenolic profile of red wines. The experiment was carried out during two vegetative cycles, with cv. Cabernet Sauvignon grafted onto Paulsen 1103. Three vine vigour were defined, according to shoot weight at previous harvests, being low, medium and high. Five treatments of number of clusters were used for each vigour, with 15, 22, 29, 36, and 45 clusters per vine. Grapes from all treatments were harvested in the same day from Brix and total acidity criteria. Thirty days after bottling, classical analyzes and phenolic compounds were performed. As results, different responses were obtained from each vintage. In 2020, a dry season from veraison to harvest, grapes and wines obtained from low vigour treatment and 45 clusters per vine was the highest in sugar and alcohol content respectively, while grapes and wines from high vigour and 15 clusters presented the lowest sugar and alcohol content. Total anthocyanins were higher in treatment with low vigour and 15 clusters, while the lowest amounts were found in low vigour with 45 clusters, as well as medium and high vigour with 36 clusters per vine. Total tannins were higher in high vigour with 22 clusters and medium vigour with 29 clusters, while were lower in low vigour with 36 clusters. In 2021, a wet season at harvest, responses were different, and great variations were observed between treatments. As conclusions, yield and vine vigour had strong influence on grape and wine quality, promoting different enological potentials on which can be indicated/used for aging strategies of red and even rosé wines.

1H-NMR-based Metabolomics to assess the impact of soil type on the chemical composition of Mediterranean red wines

The aim of this study was to evaluate the effects of different soil types on the chemical composition of Mediterranean red wines, through untargeted and targeted 1H-NMR metabolomics. One milliliter of raw wine was analyzed by means of a Bruker Avance II 400 spectrometer operating at 400.15 MHz. The spectra were recorded by applying the NOESYGPPS1D pulse sequency, to achieve water and ethanol signals suppression. No modification of the pH was performed to avoid any chemical alteration of the matrix. The generation of input variables for untargeted analysis was done via bucketing the spectra. The resulting dataset was preprocessed prior to perform unsupervised PCA, by means of MetaboAnalyst web-based tool suite. The identification of compounds for the targeted analysis was performed by comparison to pure compounds spectra by means of SMA plug-in of MNova 14.2.3 software. The dataset containing the concentrations (%) of identified compounds was subjected to one-way analysis of variance (ANOVA) to highlight significant differences among the wines. The untargeted analysis, carried out through the PCA, revealed a clear differentiation among the wines. The fragments of the spectra contributing mostly to the separation were attributed to flavonoids, aroma compounds and amino acids. The targeted analysis leaded to the identification of 68 compounds, whose concentrations were significant different among the wines. The results were related to soils physical-chemical analysis and showed that: 1) high concentrations of flavan-3-ols and flavonols are correlated with high clay content in soils; 2) high concentrations of anthocyanins, amino acids, and aroma compounds are correlated with neutral and moderately alkaline soil pH; 3) low concentrations of flavonoids and aroma compounds are correlated with high soil organic matter content and acidic pH. The 1H-NMR metabolomic analysis proved to be an excellent tool to discriminate between wines originating from grapes grown on different soil types and revealed that soils in the Mediterranean area exert a strong impact on the chemical composition of the wines.