Aroma compounds and characteristics of noble-rot wines of Chardonnay grapes artificially botrytized in the vineyard

Wang, Xing-Jie; Tao, Yong-Sheng; Wu, Yun; An, Rong-yan; Yue, Zhuo-ya

doi:10.1016/j.foodchem.2017.01.007

Cited by 76 publications

(55 citation statements)

References 32 publications

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“…These compounds could be divided in nine chemical classes with their characteristic aroma descriptors: Terpenoids (two compounds), norisoprenoids (three compounds), lactones (two compounds), aldehydes and ketones (six compounds), alcohols (six compounds), benzenoids (nine compounds), alkenes (one compound), acids (eight compounds) and esters (13 compounds). The identified components have been reported before in grape and grape-derived products (Weldegergis et al, 2011;Ferrandino et al, 2012;Galano et al, 2015;Ghaste et al, 2015;González-Barreiro et al, 2015;Vararu et al, 2015;Yuan and Qian, 2016;Wang et al, 2017).…”

Section: Statistical Analysessupporting

confidence: 71%

Aroma characterization of grape juice enriched with grapevine by-products using thermomaceration

Martínez

Valderrama

Bruijn

2017

Chilean J. Agric. Res.

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Section: Statistical Analysessupporting

confidence: 71%

Aroma characterization of grape juice enriched with grapevine by-products using thermomaceration

Martínez

Valderrama

Bruijn

2017

Chilean J. Agric. Res.

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“…For this purpose, we have chosen 6 published/previously used data sets for developing predictive in silico QSAR models towards various endpoints. The first data set represents toxicity of 306 ionic liquids towards Vibrio fischeri , the second set is on odor threshold of 86 wine components, the third set deals with sweetness potency of 240 organic molecules, the fourth set is on 224 cyclin‐dependent kinase 5/p25 (CDK5/p25) inhibitors, the fifth set represents acetylcholinesterase (AChE) inhibitory activity of 426 functionalized organic chemicals, and the final set is a data set comprising solubility of C 60 in 156 organic solvents . All 6 data sets were first rationally divided into respective training and test sets using 3 different techniques, namely, sorted response (Division 1), Kennard‐Stone algorithm (Division 2), and modified k ‐medoids clustering (Division 3) using 2 software tools, namely, Dataset Division version 1.2 (for sorted response and Kennard‐Stone algorithm‐based divisions) and Modified k‐Medoid version 1.2 developed by us .…”

Section: Methodsmentioning

confidence: 99%

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

Roy

Ambure

Kar

et al. 2018

Journal of Chemometrics

100

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Quantitative structure-activity/property/toxicity relationship (QSAR/QSPR/ QSTR) models are effectively employed to fill data gaps by predicting a given response from known structural features or physicochemical properties of new query compounds. The performance of a model should be assessed based on the quality of predictions checked through diverse validation metrics, which confirm the reliability of the developed QSAR models along with the acceptability of their prediction quality for untested compounds. There is an ongoing effort by QSAR modelers to improve the quality of predictions by lowering the predicted residuals for query compounds. In this endeavor, consensus models integrating all validated individual models were found to be more externally predictive than individual models in many previous studies. The objective of this work has been to explore whether the quality of predictions of external compounds can be enhanced through an "intelligent" selection of multiple models. The consensus predictions used in this study are not simple average of predictions from multiple models. It has been considered in the present study that a particular QSAR model may not be equally effective for prediction of all query compounds in the list. Our approach is different from the previous ones in that none of the previously reported methods considered selection of predictive models in a query compound specific way while at the same time using all or most of the valid models for the total set of query chemicals. We have implemented our approach in a software tool that is freely available via the web http://teqip.jdvu.ac.in/QSAR_ Tools/ and http://dtclab.webs.com/software-tools.QSAR models (based on OECD guidelines 4 ) can be used to "generate" data which can be used for regulatory decisions. The importance of QSAR has also greatly enhanced in recent years due to their potential application in challenging areas like modeling of responses for chemical mixtures, bioactive peptides, nanomaterials, and others. 5 The accuracy and reliability of predictions of QSAR models are very important in their application in regulatory decision support process. Validation is the method which checks reliability and precision of predictions of QSAR models. 6 Although, several techniques including cross-validation, Y-scrambling, and test set validation are commonly employed, in general, external validation is considered as the gold standard for checking predictive ability of QSAR models. 7 However, some group of scientists think cross-validation is better suited for checking predictive ability of QSAR models in order to avoid loss of information from splitting of the data set into training and test sets. 8 They have also argued that the test of predictive ability of QSAR models from a single training-test split is biased and insufficient. 8 Although a comparison of suitability of cross-validation vs external validation for judging predictive ability of QSAR models is a matter of debate, the importance of a test to check quality of predictions ...

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“…The effects of noble rot on wine quality were also investigated in Italian passito wines such as Amarone, Recioto, and Vin Santo, produced from off-vine withered grapes [3][4][5][6]. Recently, the chemical and sensory properties of Chardonnay botrytized wine from artificially infected grapes has been analyzed [7].…”

Section: Introductionmentioning

confidence: 99%

“…Potential key odorant molecules related to the aroma of botrytized wine have been identified in different types of wine such as sweet botrytized wines from the Bordeaux region [9], Tokaji Aszu' wine [10], sweet Fiano wine [11], and sweet Chardonnay wine [7]. In the case of Italian passito wine obtained from noble-rotten grapes, besides mushroom smelling compound 1-octen-3-ol, other molecules with floral attributes such as monoterpens (i.e., 4-terpineol) and with vinegar notes such as acetamides (i.e., N-(3-methylbutyl)acetamide) are recognized to be indicative of mold infection in withered grapes [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Correlating Noble Rot Infection of Garganega Withered Grapes with Key Molecules and Odorants of Botrytized Passito Wine

Simonato

Lorenzini

Cipriani

et al. 2019

Foods

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Experimental passito wines with different percentages of naturally noble-rotten grapes of the Garganega variety were analyzed to evaluate key molecules and odorants related to the typical aroma and sensory profile of botrytized passito wine. Remarkable changes in the concentration of 1-octen-3-ol, 4-terpineol, benzaldehyde, N-(3-methylbutyl)acetamide, and sherry lactone 1 and 2 were observed between sound and noble-rotten wines. Wines were perceived to be different for floral, honey, figs, apricot, and caramel scents. By partial least square regression these descriptors were well correlated to samples. An important positive contribution of sherry lactones, N-(3-methylbutyl)acetamide, vanillin, benzaldehyde, and γ-butyrolactone to honey, apricot, and caramel was observed. It is conceivable that oxidative effects of Botrytis cinerea infection play an important role in the genesis of these chemical and sensory aroma markers. This study provides a predictive tool for winemakers that use natural grape withering to produce wines whose aroma profile is not standardized due to the seasonal variation of noble rot incidence.

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Aroma compounds and characteristics of noble-rot wines of Chardonnay grapes artificially botrytized in the vineyard

Cited by 76 publications

References 32 publications

Aroma characterization of grape juice enriched with grapevine by-products using thermomaceration

Aroma characterization of grape juice enriched with grapevine by-products using thermomaceration

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

Correlating Noble Rot Infection of Garganega Withered Grapes with Key Molecules and Odorants of Botrytized Passito Wine

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