| Title: | Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling |
| Author(s): | Summerson V; Gonzalez Viejo C; Pang A; Torrico DD; Fuentes S; |
| Address: | "Digital Agriculture, Food and Wine Group, Faculty of Veterinary and Agricultural Sciences, School of Agriculture and Food, The University of Melbourne, Building 142, Parkville, VIC 3010, Australia. Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, Canterbury, New Zealand" |
| DOI: | 10.3390/molecules26165108 |
| ISSN/ISBN: | 1420-3049 (Electronic) 1420-3049 (Linking) |
| 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 (p < 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = -0.93), decanoic acid, ethyl ester (r = -0.94), and octanoic acid, 3-methylbutyl ester (r = -0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making" |
| Keywords: | "*Electronic Nose Gas Chromatography-Mass Spectrometry *Machine Learning Multivariate Analysis Neural Networks, Computer Odorants/analysis Principal Component Analysis *Smoke Vitis/*chemistry Volatile Organic Compounds/*analysis Wine/*analysis artificial n;" |
| Notes: | "MedlineSummerson, Vasiliki Gonzalez Viejo, Claudia Pang, Alexis Torrico, Damir D Fuentes, Sigfredo eng Switzerland 2021/08/28 Molecules. 2021 Aug 23; 26(16):5108. doi: 10.3390/molecules26165108" |
