Assessment of wine quality traits can be costly,time-consuming,and usually undertaken with complex chemical analysis and sensory evaluation in specialized laboratories.This study aimed to use novel digital technologie...Assessment of wine quality traits can be costly,time-consuming,and usually undertaken with complex chemical analysis and sensory evaluation in specialized laboratories.This study aimed to use novel digital technologies based on near-infrared(NIR)spectroscopy and a low-cost electronic nose(e-nose)integrated with machine learning modeling to assess wine quality traits and provenance in a vertical vintage of Shiraz wines.Results showed highly accurate machine learning(ML)models for the classification of wine vintages for Model 1(NIR;98.3%)and Model 2(e-nose;99.5%),and prediction of(i)intensity of sensory descriptors(NIR Model 3;R=0.97),(ii)peak area of volatile aromatic compounds(NIR Model 4;R=0.96),(iii)physicochemical parameters(NIR Model 5;R=0.94),(iv)intensity of sensory descriptors(e-nose Model 6;R=0.97),(v)peak area of volatile aromatic compounds(e-nose Model 7;R=0.96),and(vi)physicochemical parameters(e-nose Model 8;R=0.93).Winemakers may use these models to assess vintages and maintain high-quality wines associated with specific vineyards and regions or for a distinctive wine variety.Models could be developed further by including data from different vineyards,regions,wine variety,seasonality,other production,and winemaking techniques.展开更多
基金Natalie Harris is funded with a NorVicFoods Research Scholarship at The University of Melbourne with support from the Department of Education and Training,Victoria,Australia.
文摘Assessment of wine quality traits can be costly,time-consuming,and usually undertaken with complex chemical analysis and sensory evaluation in specialized laboratories.This study aimed to use novel digital technologies based on near-infrared(NIR)spectroscopy and a low-cost electronic nose(e-nose)integrated with machine learning modeling to assess wine quality traits and provenance in a vertical vintage of Shiraz wines.Results showed highly accurate machine learning(ML)models for the classification of wine vintages for Model 1(NIR;98.3%)and Model 2(e-nose;99.5%),and prediction of(i)intensity of sensory descriptors(NIR Model 3;R=0.97),(ii)peak area of volatile aromatic compounds(NIR Model 4;R=0.96),(iii)physicochemical parameters(NIR Model 5;R=0.94),(iv)intensity of sensory descriptors(e-nose Model 6;R=0.97),(v)peak area of volatile aromatic compounds(e-nose Model 7;R=0.96),and(vi)physicochemical parameters(e-nose Model 8;R=0.93).Winemakers may use these models to assess vintages and maintain high-quality wines associated with specific vineyards and regions or for a distinctive wine variety.Models could be developed further by including data from different vineyards,regions,wine variety,seasonality,other production,and winemaking techniques.
