Cluster removal during the growing season is a widely utilized vineyard management practice aiming to balance crop load to the capacity of the vine to ripen the fruit. Research was undertaken over two growing seasons ...Cluster removal during the growing season is a widely utilized vineyard management practice aiming to balance crop load to the capacity of the vine to ripen the fruit. Research was undertaken over two growing seasons (2008-2009) in Hawke’s Bay, a cool climate region of New Zealand, to establish the influence of different times of crop removal on Merlot vine growth and fruit and wine composition. The test vineyard was high-yielding, to 23 t/ha, and vigorous. A commercial standard of apical cluster thinning to remove 20 clusters per vine from vines with ca 44 clusters in 2008 and ca 47 in 2009, was carried out on uniform vigour, 7yo grafted Merlot vines at nine times between prebloom and six weeks post veraison. The 2009 season was naturally higher yielding. Timing of crop removal had no significant effect on vine vegetative growth in terms of enhanced shoot growth as measured by cane weights at pruning, or canopy leaf density. Time of thinning also had no effect on overall grape yield, cluster weight, and berry weight. There were limited effects on fruit ripeness in one season (2009) at the veraison time of thinning only, with increased Brix and lower TA levels. Berry anthocyanin concentrations were enhanced by cluster thinning in 2008, and more so when undertaken at or soon after veraison. There was however no influence of removal timing on anthocyanin levels and total phenolics in the wines. Grape ripeness, must and wine composition tended to respond more from crop removal at veraison than the other times evaluated. Data suggest that vine response was modified by excessive leafiness and shading.展开更多
Wines with a clear geographical origin are an issue of interest for consumers and food industries.This paper presents a data mining study of Merlot wines from South America to identify the fingerprint of their geograp...Wines with a clear geographical origin are an issue of interest for consumers and food industries.This paper presents a data mining study of Merlot wines from South America to identify the fingerprint of their geographical origin.A group of samples from Argentina(n=17),Brazil(n=12),Chile(n=48),and Uruguay(n=6)was analyzed.Twenty chemical compounds were determined by high-performance liquid chromatography(HPLC).These compounds include antioxidant activity,total polyphenols,total anthocyanins,individual anthocyanins and color.Four binary classification problems were performed(Brazil versus non-Brazil,Argentina versus non-Argentina,Chile versus non-Chile,and Uruguay versus non-Uruguay)to investigate the geographic characteristics of each country.Through the evaluation of binary classifications in our dataset it was possible to identify the main variables(chemical compounds)that discriminate between the countries.We used the following algorithms:Synthetic Minority over-sample Technique and under-sampling to balance the dataset of each classification approach,the Relief algorithm to obtain a variable importance ranking and the classifiers Support Vector Machines,Multilayer Perceptron and Radial Basis Function Network with dynamic decay adjustment.SVM model obtained the highest performance measures among the classifiers for each dataset(93.73%of accuracy for the Brazil versus non-Brazil,91.18%for the Argentina versus non-Argentina,79.16%for the Chile versus non-Chile,and 91.67%for the Uruguay versus non-Uruguay classification).These accuracies were achieved by the search of the possible variable subsets according to Relief for each classification approach.We found that some variables,such as DPPH,wine color and individual anthocyanins,are among the most important variables in the characterization of Merlot wines.展开更多
文摘Cluster removal during the growing season is a widely utilized vineyard management practice aiming to balance crop load to the capacity of the vine to ripen the fruit. Research was undertaken over two growing seasons (2008-2009) in Hawke’s Bay, a cool climate region of New Zealand, to establish the influence of different times of crop removal on Merlot vine growth and fruit and wine composition. The test vineyard was high-yielding, to 23 t/ha, and vigorous. A commercial standard of apical cluster thinning to remove 20 clusters per vine from vines with ca 44 clusters in 2008 and ca 47 in 2009, was carried out on uniform vigour, 7yo grafted Merlot vines at nine times between prebloom and six weeks post veraison. The 2009 season was naturally higher yielding. Timing of crop removal had no significant effect on vine vegetative growth in terms of enhanced shoot growth as measured by cane weights at pruning, or canopy leaf density. Time of thinning also had no effect on overall grape yield, cluster weight, and berry weight. There were limited effects on fruit ripeness in one season (2009) at the veraison time of thinning only, with increased Brix and lower TA levels. Berry anthocyanin concentrations were enhanced by cluster thinning in 2008, and more so when undertaken at or soon after veraison. There was however no influence of removal timing on anthocyanin levels and total phenolics in the wines. Grape ripeness, must and wine composition tended to respond more from crop removal at veraison than the other times evaluated. Data suggest that vine response was modified by excessive leafiness and shading.
基金Authors are grateful to Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico(CNPq)for financial support.
文摘Wines with a clear geographical origin are an issue of interest for consumers and food industries.This paper presents a data mining study of Merlot wines from South America to identify the fingerprint of their geographical origin.A group of samples from Argentina(n=17),Brazil(n=12),Chile(n=48),and Uruguay(n=6)was analyzed.Twenty chemical compounds were determined by high-performance liquid chromatography(HPLC).These compounds include antioxidant activity,total polyphenols,total anthocyanins,individual anthocyanins and color.Four binary classification problems were performed(Brazil versus non-Brazil,Argentina versus non-Argentina,Chile versus non-Chile,and Uruguay versus non-Uruguay)to investigate the geographic characteristics of each country.Through the evaluation of binary classifications in our dataset it was possible to identify the main variables(chemical compounds)that discriminate between the countries.We used the following algorithms:Synthetic Minority over-sample Technique and under-sampling to balance the dataset of each classification approach,the Relief algorithm to obtain a variable importance ranking and the classifiers Support Vector Machines,Multilayer Perceptron and Radial Basis Function Network with dynamic decay adjustment.SVM model obtained the highest performance measures among the classifiers for each dataset(93.73%of accuracy for the Brazil versus non-Brazil,91.18%for the Argentina versus non-Argentina,79.16%for the Chile versus non-Chile,and 91.67%for the Uruguay versus non-Uruguay classification).These accuracies were achieved by the search of the possible variable subsets according to Relief for each classification approach.We found that some variables,such as DPPH,wine color and individual anthocyanins,are among the most important variables in the characterization of Merlot wines.
