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Article

A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines’ Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy

Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
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Author to whom correspondence should be addressed.
Received: 27 April 2020 / Revised: 28 May 2020 / Accepted: 5 June 2020 / Published: 9 June 2020
(This article belongs to the Special Issue Improving Wine Quality and Safety)
Berry cell death (BCD) is linked to the development of flavors and aromas in berries and wine. The BCD pattern and rate within a growing season start at around 90–100 days after anthesis (DAA), and the rate until harvest depends on environmental factors. This study assessed the BCD effects on berry and wine composition from a boutique commercial vineyard in Victoria, Australia, using fluorescent imaging. Results showed differences in wine sensory profiles from the two blocks studied, mainly related to variations in BCD, due to differences in altitude between blocks. Furthermore, two machine learning (ML) models were constructed using near-infrared spectroscopy (NIR) measurements from full berries as inputs and living tissue (LT) and dead tissue (DT) from berries as targets (Model 1). Model 2 was developed using Brix, LT, DT from the east and west sides of canopies as inputs and using 19 sensory descriptors from wines as targets. High correlation and performances were achieved for both models without signs of overfitting (R = 0.94 and R = 0.80, respectively). These models could be used for decision-making purposes as an objective and comprehensive berry maturity assessment obtained in a non-destructive, accurate, and in a real-time fashion close to harvest, to secure specific wine styles. View Full-Text
Keywords: artificial intelligence; fluorescein diacetate; berry maturity; wine quality; machine learning artificial intelligence; fluorescein diacetate; berry maturity; wine quality; machine learning
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MDPI and ACS Style

Fuentes, S.; Tongson, E.; Chen, J.; Gonzalez Viejo, C. A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines’ Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy. Beverages 2020, 6, 39. https://0-doi-org.brum.beds.ac.uk/10.3390/beverages6020039

AMA Style

Fuentes S, Tongson E, Chen J, Gonzalez Viejo C. A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines’ Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy. Beverages. 2020; 6(2):39. https://0-doi-org.brum.beds.ac.uk/10.3390/beverages6020039

Chicago/Turabian Style

Fuentes, Sigfredo, Eden Tongson, Juesheng Chen, and Claudia Gonzalez Viejo. 2020. "A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines’ Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy" Beverages 6, no. 2: 39. https://0-doi-org.brum.beds.ac.uk/10.3390/beverages6020039

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