ArticleMolecules (Basel, Switzerland)2021
Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 31 citations in OpenAlex.
- Proposed SmartBarrel System for Monitoring and Assessment of Wine Fermentation Processes Using IoT Nose and Tongue Devices.Sensors (Basel, Switzerland) · 2025Article
- Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry.Sensors (Basel, Switzerland) · 2024Review
- Influence of saliva on the sensory properties of US commercial smoke affected wines: Preliminary findings.Food science & nutrition · 2024Article
- Nanotechnology and E-Sensing for Food Chain Quality and Safety.Sensors (Basel, Switzerland) · 2023Review
- Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies.Foods (Basel, Switzerland) · 2022Article
- Natural Product Phenolic Diglycosides Created from Wildfires, Defining Their Impact on California and Oregon Grapes and Wines.Journal of natural products · 2022Article
- Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling.Sensors (Basel, Switzerland) · 2022Article
- Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC.Frontiers in chemistry · 2022Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (
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Registered trials
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