Early Prediction of Shiraz Wine Quality Based on Small Volatile Compounds in Grapes
Wine producers perform early wine quality prediction based on berry morphology, the taste of the berry and the measurement of basic chemical parameters. Incorporating analysis on grape and wine volatiles could potentially achieve a more accurate prediction of wine quality, but forming these models r...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2023-01-01
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| Series: | Australian Journal of Grape and Wine Research |
| Online Access: | http://dx.doi.org/10.1155/2023/2990963 |
| _version_ | 1797676439486595072 |
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| author | Jiaqiang Luo Jamie Selby-Pham Kimber Wise Yinhao Wu Jiacan Sun Yameng Qu Tian Cao Pangzhen Zhang Philip J. Marriott Kate Howell |
| author_facet | Jiaqiang Luo Jamie Selby-Pham Kimber Wise Yinhao Wu Jiacan Sun Yameng Qu Tian Cao Pangzhen Zhang Philip J. Marriott Kate Howell |
| author_sort | Jiaqiang Luo |
| collection | DOAJ |
| description | Wine producers perform early wine quality prediction based on berry morphology, the taste of the berry and the measurement of basic chemical parameters. Incorporating analysis on grape and wine volatiles could potentially achieve a more accurate prediction of wine quality, but forming these models requires careful selection of grapes, controlled fermentations, and standardised quality assessment. Here, we present 3 models for the prediction of quality in Shiraz wine. Modelling was performed by general regression analysis with 4-fold cross-validation: Model 1 (R2 = 99.97% and 4-foldR2 = 97.61%) for prediction of wine quality from wine volatiles, Model 2 (R2 = 99.89% and 4-foldR2 = 98.42%) for early prediction of wine quality from free-bound and glycosidically bound grape volatiles, and Model 3 (R2 = 91.62% and 4-foldR2 = 80.21%) for the prediction of wine quality from free grape volatiles only. The accuracy of these models presents an advancement in the early prediction of wine quality and provides a valuable tool to assist grape growers and winemakers to support the understanding of quality in the vineyard to better direct scarce resources. |
| first_indexed | 2024-03-11T22:28:09Z |
| format | Article |
| id | doaj.art-b126d7307c7843518aa9de1373233a1e |
| institution | Directory Open Access Journal |
| issn | 1755-0238 |
| language | English |
| last_indexed | 2024-03-11T22:28:09Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Australian Journal of Grape and Wine Research |
| spelling | doaj.art-b126d7307c7843518aa9de1373233a1e2023-09-24T00:00:09ZengHindawi-WileyAustralian Journal of Grape and Wine Research1755-02382023-01-01202310.1155/2023/2990963Early Prediction of Shiraz Wine Quality Based on Small Volatile Compounds in GrapesJiaqiang Luo0Jamie Selby-Pham1Kimber Wise2Yinhao Wu3Jiacan Sun4Yameng Qu5Tian Cao6Pangzhen Zhang7Philip J. Marriott8Kate Howell9School of Agriculture and FoodCannabis and Biostimulants Research GroupCannabis and Biostimulants Research GroupSchool of Agriculture and FoodSchool of Natural SciencesSchool of Agriculture and FoodCollege of Food Science and TechnologySchool of Agriculture and FoodSchool of ChemistrySchool of Agriculture and FoodWine producers perform early wine quality prediction based on berry morphology, the taste of the berry and the measurement of basic chemical parameters. Incorporating analysis on grape and wine volatiles could potentially achieve a more accurate prediction of wine quality, but forming these models requires careful selection of grapes, controlled fermentations, and standardised quality assessment. Here, we present 3 models for the prediction of quality in Shiraz wine. Modelling was performed by general regression analysis with 4-fold cross-validation: Model 1 (R2 = 99.97% and 4-foldR2 = 97.61%) for prediction of wine quality from wine volatiles, Model 2 (R2 = 99.89% and 4-foldR2 = 98.42%) for early prediction of wine quality from free-bound and glycosidically bound grape volatiles, and Model 3 (R2 = 91.62% and 4-foldR2 = 80.21%) for the prediction of wine quality from free grape volatiles only. The accuracy of these models presents an advancement in the early prediction of wine quality and provides a valuable tool to assist grape growers and winemakers to support the understanding of quality in the vineyard to better direct scarce resources.http://dx.doi.org/10.1155/2023/2990963 |
| spellingShingle | Jiaqiang Luo Jamie Selby-Pham Kimber Wise Yinhao Wu Jiacan Sun Yameng Qu Tian Cao Pangzhen Zhang Philip J. Marriott Kate Howell Early Prediction of Shiraz Wine Quality Based on Small Volatile Compounds in Grapes Australian Journal of Grape and Wine Research |
| title | Early Prediction of Shiraz Wine Quality Based on Small Volatile Compounds in Grapes |
| title_full | Early Prediction of Shiraz Wine Quality Based on Small Volatile Compounds in Grapes |
| title_fullStr | Early Prediction of Shiraz Wine Quality Based on Small Volatile Compounds in Grapes |
| title_full_unstemmed | Early Prediction of Shiraz Wine Quality Based on Small Volatile Compounds in Grapes |
| title_short | Early Prediction of Shiraz Wine Quality Based on Small Volatile Compounds in Grapes |
| title_sort | early prediction of shiraz wine quality based on small volatile compounds in grapes |
| url | http://dx.doi.org/10.1155/2023/2990963 |
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