As a subfield of Artificial Intelligence (AI), Machine Learning (ML) aims to understand the structure of the data and fit it into models, which later can be used in unseen data to achieve the desired task. ML has been...As a subfield of Artificial Intelligence (AI), Machine Learning (ML) aims to understand the structure of the data and fit it into models, which later can be used in unseen data to achieve the desired task. ML has been widely used in various sectors such as in Businesses, Medicine, Astrophysics, and many other scientific problems. Inspired by the success of ML in different sectors, here, we use it to predict the wine quality based on the various parameters. Among various ML models, we compare the performance of Ridge Regression (RR), Support Vector Machine (SVM), Gradient Boosting Regressor (GBR), and multi-layer Artificial Neural Network (ANN) to predict the wine quality. Multiple parameters that determine the wine quality </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">are</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> analyzed. Our analysis shows that GBR surpass</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">es</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> all other models’ performance with MSE, R, and MAPE of 0.3741, 0.6057, and 0.0873 respectively. This work demonstrate</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> how statistical analysis can be used to identify the components that mainly control the wine quality prior to the production. This will help wine manufacturer to control the quality prior to the wine production</span></span></span><span style="font-family:Verdana;">.展开更多
In response to increasing concerns over climate change,soil health and wine quality,grape growers are seeking new practices(e.g.,biochar application)to minimize their environmental footprint while increasing productiv...In response to increasing concerns over climate change,soil health and wine quality,grape growers are seeking new practices(e.g.,biochar application)to minimize their environmental footprint while increasing productivity and the quality of their products.To explore the potential of biochar-based amendments to achieve these goals in wine grape production,vineyard trials were established in the fall of 2018.Two Oregon sites were chosen with distinct soil types and climates(Willamette Valley and Rogue Valley)but planted with the same grapevine scion/rootstock Pinot noir combination.Four treatments were applied under vines at each location:no biochar-no tillage(NT);no biochar+tillage(B0);18 tons ha^(−1)biochar+tillage(B18);35 tons ha^(−1)biochar+tillage(B35).In 2019,a suite of soil health,plant,and crop variables were measured,and wines were produced after harvest.The incorporation of biochar modified the chemical and physical composition of soils at the two studied locations,increasing the bioavailability of carbon and nitrogen,their gravimetric water content and the concentration of plant available micro and macro nutrients.No responses of plant physiology parameters or productivity at either site were found after biochar incorporation when compared with controls.Conversely,a significant and gradual decrease in the amount of wine tannins was found as a result of biochar application in wines produced from grapes from the Woodhall location.Long-term field experiments are required to assess the effects of biochar on soil properties,vine physiol-ogy,productivity,and grape and wine quality several years after incorporation.展开更多
The geology and geomorphology of the territory as well as microclimate are local geographical features that serve as natural ecological resources. These factors influence the biosynthetic activities of plants and thei...The geology and geomorphology of the territory as well as microclimate are local geographical features that serve as natural ecological resources. These factors influence the biosynthetic activities of plants and their phenology, promoting biodiversity and the qualitative predispositions of grapes and wine. South Tyrol is one of the smallest wine-growing regions in Italy, but owing to its position amid the Alps, it is also one of the most multifaceted, a region of wide geographical diversity and remarkable ecological range, hosting a concentration of many different vine varieties and high quality wines. This applied territorial research investigates the particular environmental circumstances that favour this case. A data set describing approximately 26,000 vineyards and 5450 hectares has been employed to evaluate 18 subzones of wines and vines selected from 86 new geographical units defined within the DOC wine region. A new environmental mapping scheme called VHTG is proposed, based on the ecological indicators of grape variety, altitude, topoclimate and the geopedology of the vineyards. Using the VHTG method analyses, the comparisons between the territories of origin and their vine varieties can be rendered simpler and more direct, and it can distinguish the most suitable ecological conditions of wine production zones. It is now possible to examine more in detail the land suitability of the different cultivars, defined by the use of the ecological indicators summarized in the VHTG method. White grape varieties such as Sylvaner and Veltliner prefer high altitudes between 600 m and 900 m, a very high solar radiation SRI index from 80 to 95, and acidic sandy soils of silicate minerals. The most complete and intense tannic structure of regional Pinot Noir correlates to quite clayey soils with dolomite mineral, slightly alkaline, on vineyards at altitudes between 350 m and 410 m, with rather low SRI index from 60 to 75. Similar geopedological conditions favour Gewürztraminer, which, however, requests SRI from 75 to 85. Merlot and Cabernet vines are best expressed in the hottest regional sub-zones, on moderately clayey subalkaline soils at 250 - 350 m of altitudes and SRI around 80. The indigenous red grape variety Lagrein is mostly localized on alluvial cone at altitudes under 350 m, on soft and ventilated acid sands with volcanic silicate minerals.展开更多
Leaf removal and cluster thinning were carried out prior to veraison to evaluate the effects on berry quality of two Vitis vinifera cultivars（Cabernet Sauvignon and Ugni Blanc） in the Weibei Dryland of China in 2013...Leaf removal and cluster thinning were carried out prior to veraison to evaluate the effects on berry quality of two Vitis vinifera cultivars（Cabernet Sauvignon and Ugni Blanc） in the Weibei Dryland of China in 2013 and 2014, and comprehensive analysis of the chemical and volatile composition in berries was performed. The results showed that content of reducing sugar in both varieties was not affected while total acid was generally decreased by leaf removal and cluster thinning. The pH of berry juice was correspondingly higher in most treatment groups. Meanwhile, promoting effects on accumulation of total phenols, tannin in both varieties and total anthocyanins in Cabernet Sauvignon were found. As for monomeric anthocyanins, percentage of malvidin and its derivatives was decreased by leaf removal and cluster thinning. Besides, cinnamylated anthocyanins decreased with the intensity of cluster thinning. The accumulation of non-anthocyanin phenolics was similarly affected in the two varieties. Notably, cluster thinning was more effective on enhancing the phenolics content than leaf removal. The combination of middle level of leaf removal and cluster thinning was the most favor to the accumulation of phenolic acids. Furthermore, cluster thinning could also significantly enhance the synthesis of flavanols and stilbenes. Lastly, content and variety of aroma compounds in both grape varieties were also significantly affected by the treatments. The results provided a theoretical basis for a combination of leaf removal and cluster thinning to improve quality of grapes and wines.展开更多
The presence of multiple ecosystem functions and services(i.e.,ecosystem multifunctionality)has been proven to be maintained by biodiversity in natural terrestrial ecosystems.However,the mechanisms by which microbial ...The presence of multiple ecosystem functions and services(i.e.,ecosystem multifunctionality)has been proven to be maintained by biodiversity in natural terrestrial ecosystems.However,the mechanisms by which microbial diversity drives ecosystem functions in vineyards and the effects of ecosystem functions on wine quality remain unknown.Here,fifteen vineyards from five wine sub-regions(Shizuishan,Yinchuan,Yuquanying,Qingtongxia,and Hongsipu)in Ningxia were selected to assess the microbial community structure,ecosystem multifunctionality,and wine quality.Overall,each index differed among the vineyards from these five wine sub-regions in Ningxia.High-throughput sequencing revealed that bacterial and fungal communities varied among these vineyards.Bacterial communities were dominated by Actinobacteria,Proteobacteria,Chloroflexi,and Acidobacteria.Ascomycota was the dominant fungal phylum,followed by Basidiomycota and Mortierellomycota.In addition,fungal Shannon diversity rather than bacterial Shannon diversity showed a positive relationship with ecosystem multifunctionality.Correlation analysis revealed that ecosystem multifunctionality was positively correlated with wine acidity and negatively correlated with pH value and residual sugar content of wine.Soil chemical functions exhibited relationships with wine quality being similar to those of ecosystem multifunctionality;i.e.,positively related to wine acidity but negatively related to wine pH and residual sugar content.However,soil physical functions were negatively correlated with the alcohol and anthocyanin content of wine.The research results show that the ecosystem functions maintained by fungal diversity could be attributed to wine quality of vineyards.展开更多
文摘As a subfield of Artificial Intelligence (AI), Machine Learning (ML) aims to understand the structure of the data and fit it into models, which later can be used in unseen data to achieve the desired task. ML has been widely used in various sectors such as in Businesses, Medicine, Astrophysics, and many other scientific problems. Inspired by the success of ML in different sectors, here, we use it to predict the wine quality based on the various parameters. Among various ML models, we compare the performance of Ridge Regression (RR), Support Vector Machine (SVM), Gradient Boosting Regressor (GBR), and multi-layer Artificial Neural Network (ANN) to predict the wine quality. Multiple parameters that determine the wine quality </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">are</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> analyzed. Our analysis shows that GBR surpass</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">es</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> all other models’ performance with MSE, R, and MAPE of 0.3741, 0.6057, and 0.0873 respectively. This work demonstrate</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">s,</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> how statistical analysis can be used to identify the components that mainly control the wine quality prior to the production. This will help wine manufacturer to control the quality prior to the wine production</span></span></span><span style="font-family:Verdana;">.
基金USDA National Institute of Food and Agriculture[2018-67012-28080]U.S.Department of Agriculture,Agricultural Research Service(2072-12620-001)Oregon Wine Research Institute.
文摘In response to increasing concerns over climate change,soil health and wine quality,grape growers are seeking new practices(e.g.,biochar application)to minimize their environmental footprint while increasing productivity and the quality of their products.To explore the potential of biochar-based amendments to achieve these goals in wine grape production,vineyard trials were established in the fall of 2018.Two Oregon sites were chosen with distinct soil types and climates(Willamette Valley and Rogue Valley)but planted with the same grapevine scion/rootstock Pinot noir combination.Four treatments were applied under vines at each location:no biochar-no tillage(NT);no biochar+tillage(B0);18 tons ha^(−1)biochar+tillage(B18);35 tons ha^(−1)biochar+tillage(B35).In 2019,a suite of soil health,plant,and crop variables were measured,and wines were produced after harvest.The incorporation of biochar modified the chemical and physical composition of soils at the two studied locations,increasing the bioavailability of carbon and nitrogen,their gravimetric water content and the concentration of plant available micro and macro nutrients.No responses of plant physiology parameters or productivity at either site were found after biochar incorporation when compared with controls.Conversely,a significant and gradual decrease in the amount of wine tannins was found as a result of biochar application in wines produced from grapes from the Woodhall location.Long-term field experiments are required to assess the effects of biochar on soil properties,vine physiol-ogy,productivity,and grape and wine quality several years after incorporation.
文摘The geology and geomorphology of the territory as well as microclimate are local geographical features that serve as natural ecological resources. These factors influence the biosynthetic activities of plants and their phenology, promoting biodiversity and the qualitative predispositions of grapes and wine. South Tyrol is one of the smallest wine-growing regions in Italy, but owing to its position amid the Alps, it is also one of the most multifaceted, a region of wide geographical diversity and remarkable ecological range, hosting a concentration of many different vine varieties and high quality wines. This applied territorial research investigates the particular environmental circumstances that favour this case. A data set describing approximately 26,000 vineyards and 5450 hectares has been employed to evaluate 18 subzones of wines and vines selected from 86 new geographical units defined within the DOC wine region. A new environmental mapping scheme called VHTG is proposed, based on the ecological indicators of grape variety, altitude, topoclimate and the geopedology of the vineyards. Using the VHTG method analyses, the comparisons between the territories of origin and their vine varieties can be rendered simpler and more direct, and it can distinguish the most suitable ecological conditions of wine production zones. It is now possible to examine more in detail the land suitability of the different cultivars, defined by the use of the ecological indicators summarized in the VHTG method. White grape varieties such as Sylvaner and Veltliner prefer high altitudes between 600 m and 900 m, a very high solar radiation SRI index from 80 to 95, and acidic sandy soils of silicate minerals. The most complete and intense tannic structure of regional Pinot Noir correlates to quite clayey soils with dolomite mineral, slightly alkaline, on vineyards at altitudes between 350 m and 410 m, with rather low SRI index from 60 to 75. Similar geopedological conditions favour Gewürztraminer, which, however, requests SRI from 75 to 85. Merlot and Cabernet vines are best expressed in the hottest regional sub-zones, on moderately clayey subalkaline soils at 250 - 350 m of altitudes and SRI around 80. The indigenous red grape variety Lagrein is mostly localized on alluvial cone at altitudes under 350 m, on soft and ventilated acid sands with volcanic silicate minerals.
基金supported by the China Agriculture Research System for Grape Industry(CARS-29-zp-06)
文摘Leaf removal and cluster thinning were carried out prior to veraison to evaluate the effects on berry quality of two Vitis vinifera cultivars（Cabernet Sauvignon and Ugni Blanc） in the Weibei Dryland of China in 2013 and 2014, and comprehensive analysis of the chemical and volatile composition in berries was performed. The results showed that content of reducing sugar in both varieties was not affected while total acid was generally decreased by leaf removal and cluster thinning. The pH of berry juice was correspondingly higher in most treatment groups. Meanwhile, promoting effects on accumulation of total phenols, tannin in both varieties and total anthocyanins in Cabernet Sauvignon were found. As for monomeric anthocyanins, percentage of malvidin and its derivatives was decreased by leaf removal and cluster thinning. Besides, cinnamylated anthocyanins decreased with the intensity of cluster thinning. The accumulation of non-anthocyanin phenolics was similarly affected in the two varieties. Notably, cluster thinning was more effective on enhancing the phenolics content than leaf removal. The combination of middle level of leaf removal and cluster thinning was the most favor to the accumulation of phenolic acids. Furthermore, cluster thinning could also significantly enhance the synthesis of flavanols and stilbenes. Lastly, content and variety of aroma compounds in both grape varieties were also significantly affected by the treatments. The results provided a theoretical basis for a combination of leaf removal and cluster thinning to improve quality of grapes and wines.
基金supported by the National Key Research and Development of China(2017YFC1502806)Key Research and Development of Ningxia Hui Autonomous Region(2021BEF02017)+3 种基金Key Research and Development of Shaanxi province(2020ZDLNY07-08),Key Research and Development of Sichuan province(2020YFN0149)Fundamental Research Funds for the Central Universities(2452017148)Shaanxi Agricultural Collaborative Innovation and Extension Alliance(LMZD202105)Scientific and Technological Innovation of Experimental Demonstration Station of Northwest A&F University(SFZ202105).
文摘The presence of multiple ecosystem functions and services(i.e.,ecosystem multifunctionality)has been proven to be maintained by biodiversity in natural terrestrial ecosystems.However,the mechanisms by which microbial diversity drives ecosystem functions in vineyards and the effects of ecosystem functions on wine quality remain unknown.Here,fifteen vineyards from five wine sub-regions(Shizuishan,Yinchuan,Yuquanying,Qingtongxia,and Hongsipu)in Ningxia were selected to assess the microbial community structure,ecosystem multifunctionality,and wine quality.Overall,each index differed among the vineyards from these five wine sub-regions in Ningxia.High-throughput sequencing revealed that bacterial and fungal communities varied among these vineyards.Bacterial communities were dominated by Actinobacteria,Proteobacteria,Chloroflexi,and Acidobacteria.Ascomycota was the dominant fungal phylum,followed by Basidiomycota and Mortierellomycota.In addition,fungal Shannon diversity rather than bacterial Shannon diversity showed a positive relationship with ecosystem multifunctionality.Correlation analysis revealed that ecosystem multifunctionality was positively correlated with wine acidity and negatively correlated with pH value and residual sugar content of wine.Soil chemical functions exhibited relationships with wine quality being similar to those of ecosystem multifunctionality;i.e.,positively related to wine acidity but negatively related to wine pH and residual sugar content.However,soil physical functions were negatively correlated with the alcohol and anthocyanin content of wine.The research results show that the ecosystem functions maintained by fungal diversity could be attributed to wine quality of vineyards.
