 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

The potential of multispectral/hyperspectral technologies for early detection of “flavescence dorée” in a Portuguese vineyard

“Flavescence dorée” (FD) is a grapevine quarantine disease associated with phytoplasmas and transmitted to healthy plants by insect vectors, mainly Scaphoideus titanus. Infected plants usually develop symptoms of stunted growth, unripe cane wood, leaf rolling, leaf yellowing or reddening, and shrivelled berries. Since plants can remain symptomless up to four years, they may act as reservoirs of FD contributing to the spread of the disease. So far, conventional management strategies rely mainly on the insecticide treatments, uprooting of infected plants and use of phytoplasma-free propagation material. However, these strategies are costly and could have undesirable environmental impacts. Thus, the development of sustainable and noninvasive approaches for early detection of FD and its management are of great importance to reduce disease spread and select the best cultural practices and treatments. The present study aimed to evaluate if multispectral/hyperspectral technologies can be used to detect FD before the appearance of the first symptoms and if infected grapevines display a spectral imaging fingerprint. To that end, physiological parameters (leaf area, chlorophyll content and photosynthetic rate) were collected in concomitance to the measurements of plant reflectance (using both a portable apparatus and a remote sensing drone). Measurements were performed in two leaves of 8 healthy and 8 FD-infected grapevines, at four timepoints: before the development of disease symptoms (21st June); and after symptoms appearance (ii) at veraison (2nd August); at post-veraison (11th September); and at harvest (25th September). At all timepoints, FD infected plants revealed a significant decrease in the studied physiological parameters, with a positive correlation with drone imaging data and portable apparatus analyses. Moreover, spectra of either drone imaging and portable apparatus showed clear differences between healthy and FD-infected grapevines, validating multispectral/ hyperspectral technology as a potential tool for the early detection of FD or other grapevine-associated diseases.

Local adaptation tools to ensure the viticultural sustainability in a changing climate

[lwp_divi_breadcrumbs home_text="IVES" use_before_icon="on" before_icon="||divi||400" module_id="publication-ariane" _builder_version="4.19.4" _module_preset="default" module_text_align="center" module_font_size="16px" text_orientation="center"...

Extreme canopy management for vineyard adaptation to climate change: is it a good idea?

Climate change constitutes an enormous challenge for humankind and for all human activities, viticulture not being an exception. Long-term strategic changes are probably needed the most, but growers also need to deal with short-term changes: summers that are getting progressively warmer, earlier harvest dates and higher pH in musts and wines. In the last 10-15 years, a relevant corpus of research is being developed worldwide in order to evaluate to which extent extreme canopy management operations, aimed at reducing leaf area and, thus, limiting the source to sink ratio, could be useful to delay ripening. Although extreme canopy management can result in relevant delays in harvest dates, longer term studies, as well as detailed analysis of their implications on carbohydrate reserves, bud fertility and future yield are desirable before these practices can be recommended.

Short-term relationships between climate and grapevine trunk diseases in southern French vineyards

Climate change projections to support the transition to climate-smart viticulture

The Earth’s system is undergoing major changes through a wide range of spatial and temporal scales as a response to growing anthropogenic radiative forcing, which is pushing the whole system far beyond its natural variability. Sources of greenhouse gases largely exceed their sinks, thus leading to a strengthened greenhouse effect. More energy is thereby being supplied to the system, with inevitable shifts in climatic patterns and weather regimes. Over the last decades, these modifications have been manifested in the full statistical distributions of the atmospheric variables, with dramatic changes in the frequency and intensity of extremes. Natural hazards, such as severe droughts, floods, forest fires, or heatwaves, are being triggered by extreme atmospheric events worldwide, thus threatening human activities. Viticultculture is not only exposed to changing climates but is also highly vulnerable, as grapevine phenology and physiological development are strongly controlled by atmospheric conditions. Therefore, the assessment of climate change projections for a given region is critical for climate change adaptation and risk reduction in viticulture. By adopting timely and suitable measures, the future sustainability and resiliency of the sector can be fostered. Climate-grapevine chain modelling is an essential tool for better planning and management. However, the accuracy of the resulting projections is limited by many uncertainties that must be duly taken into account when transferring knowledge to stakeholders and decision-makers. Climate-smart viticulture will comprise ensembles of locally tuned strategies, envisioning both adaptation and mitigation, assisted by emerging technologies and decision-support systems.