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…

From a local to an international scale: sensory benchmarking of PDO wines. Quincy and Reuilly PDO wines (Sauvignon blanc) as a case study (France)

In a collective marketing strategy, the Protected Designation of Origin (PDO) can be used as a quality indicator. To highlight terroir specificities, it is useful to know how the wines are positioned on the local, national or international market from a sensory point of view. This is especially true for a comparison of varietal wines (e.g. Sauvignon blanc). We focus on the case of two closed Loire Valley PDO (France): Quincy and Reuilly. Three distinct tastings were organized. Firstly, at the local level comparing the 2 PDO (11 and 9 wines, 17 professional assessors); secondly at a regional level adding 3 closed PDO: Menetou-Salon, Sancerre and Pouilly-Fumé (3 wines per PDO, 16 assessors) and thirdly at an international level comparing these 5 PDO with Sauvignon Blanc wines coming from South Africa, New Zealand and Chile (1 to 3 wines per PDO, 19 assessors). All the wines were from the 2019 vintage and were considered to have a traditional elaboration process without contact with oak. A sensory descriptive analysis was performed using an aroma wheel allowing to combine a Check-All-That-Apply methodology, often used in sensory benchmarking, with a hierarchical structuration of the attributes. The aim is to facilitate data acquisition in a professional context without common training, to consider the hierarchical relationships among the attributes during the data analysis and to be able to characterize wines with a large range of sensorial variability. We use univariate, multivariate and clustering analyses. Similarities and differences between Quincy and Reuilly PDO wines and other Sauvignon blanc wines were identified. Specific attributes can distinguish the two PDO and different proximities exist with other local PDO, while clear differences were observed compared to international wines. Our study contributes to propose and discuss a method to do a wine sensory benchmarking highlighting sensory specificities linked to origin.

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 multi-level and multi-scale spectral data source on vineyard state assessment

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Elevational range shifts of mountain vineyards: Recent dynamics in response to a warming climate

Increasing temperatures worldwide are expected to cause a change in spatial distribution of plant species along elevational gradients and there are already observable shifts to higher elevations as a consequence of climate change for many species. Not only naturally growing plants, but also agricultural cultivations are subject to the effects of climate change, as the type of cultivation and the economic viability depends largely on the prevailing climatic conditions. A shift to higher elevations therefore represents a viable adaptation strategy to climate change, as higher elevations are characterized by lower temperatures. This is especially important in the case of viticulture because a certain wine-style can only be achieved under very specific climatic conditions. Although there are several studies investigating climatic suitability within winegrowing regions or longitudinal shifts of winegrowing areas, little is known about how fast vineyards move to higher elevations, which may represent a viable strategy for winegrowers to maintain growing conditions and thus wine-style, despite the effects of climate change. We therefore investigated the change in the spatial distribution of vineyards along an elevational gradient over the past 20 years in the mountainous wine-growing region of Alto Adige (Italy). A dataset containing information about location and planting year of more than 26000 vineyard parcels and 30 varieties was used to perform this analysis. Preliminary results suggest that there has been a shift to higher elevations for vineyards in general (from formerly 700m to currently 850 m a.s.l., with extreme sites reaching 1200 m a.s.l.), but also that this development has not been uniform across different varieties and products (i.e. vitis vinifera vs hybrid varieties and still vssparkling wines). This is important for climate change adaptation as well as for rural development. Mountain areas, especially at mid to high elevations, are often characterized by severe land abandonment which can be avoided to some degree if economically viable and sustainable land management strategies are available.

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: