Investigating the use of gradient boosting machine, random forest and their ensemble to predict skin flavonoid content from berry physical-mechanical characteristics in wine grapes
暂无分享,去创建一个
Simone Giacosa | Luca Rolle | Luca Brillante | Federica Gaiotti | Lorenzo Lovat | Simone Vincenzi | Fabrizio Torchio | Susana Río Segade | Diego Tomasi | D. Tomasi | L. Rolle | S. R. Segade | F. Gaiotti | S. Vincenzi | Luca Brillante | S. Giacosa | F. Torchio | L. Lovat
[1] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[2] Simone Giacosa,et al. Possible use of texture characteristics of winegrapes as markers for zoning and their relationship with anthocyanin extractability index , 2011 .
[3] Greg Ridgeway,et al. Generalized Boosted Models: A guide to the gbm package , 2006 .
[4] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[5] L. Rolle,et al. Mechanical Behavior of Winegrapes under Compression Tests , 2008, American Journal of Enology and Viticulture.
[6] Achim Zeileis,et al. Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.
[7] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[8] Simone Giacosa,et al. Influence of grape density and harvest date on changes in phenolic composition, phenol extractability indices, and instrumental texture properties during ripening. , 2011, Journal of agricultural and food chemistry.
[9] Rich Caruana,et al. Getting the Most Out of Ensemble Selection , 2006, Sixth International Conference on Data Mining (ICDM'06).
[10] Stefanos Koundouras,et al. Influence of vineyard location and vine water status on fruit maturation of nonirrigated cv. Agiorgitiko (Vitis vinifera L.). Effects on wine phenolic and aroma components. , 2006, Journal of agricultural and food chemistry.
[11] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[12] Shao-Hua Li,et al. Berry ripening: recently heard through the grapevine. , 2013, Journal of experimental botany.
[13] A. Vianello,et al. Transport and accumulation of flavonoids in grapevine (Vitis vinifera L.) , 2008, Plant signaling & behavior.
[14] Simone Giacosa,et al. Berry skin thickness as main texture parameter to predict anthocyanin extractability in winegrapes , 2011 .
[15] B. Ripley,et al. Recursive Partitioning and Regression Trees , 2015 .
[16] F. Tomás-Barberán,et al. Flavonoids in Food and Their Health Benefits , 2004, Plant foods for human nutrition.
[17] Beńed́icte Lorrain,et al. Evolution of Analysis of Polyhenols from Grapes, Wines, and Extracts , 2013, Molecules.
[18] Matthias Schmid,et al. Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages , 2012 .
[19] Luca Rolle,et al. Volatile fingerprint and physico-mechanical properties of ‘Muscat blanc’ grapes grown in mountain area: a first evidence of the influence of water regimes. , 2013 .
[20] Simone Giacosa,et al. Instrumental Texture Analysis Parameters as Winegrapes Varietal Markers and Ripeness Predictors , 2011 .
[21] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[22] Jason Weston,et al. Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.
[23] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[24] Alain Carbonneau,et al. Phenolic potential of Tannat, Cabernet-Sauvignon and Merlot grapes and their correspondence with wine composition , 2004 .
[25] Max Kuhn,et al. caret: Classification and Regression Training , 2015 .
[26] I. Razmilic,et al. Apple Peel Supplemented Diet Reduces Parameters of Metabolic Syndrome and Atherogenic Progression in ApoE−/− Mice , 2015, Evidence-based complementary and alternative medicine : eCAM.
[27] Simone Giacosa,et al. Winegrape berry skin thickness determination: comparison between histological observation and texture analysis determination , 2015 .
[28] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[29] Seema Bhagwat,et al. Flavonoid content of U.S. fruits, vegetables, and nuts. , 2006, Journal of agricultural and food chemistry.
[30] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[31] Klaus Nordhausen,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .
[32] Leo Breiman,et al. Bias, Variance , And Arcing Classifiers , 1996 .
[33] Simone Giacosa,et al. Use of instrumental acoustic parameters of winegrape seeds as possible predictors of extractable phenolic compounds. , 2013, Journal of agricultural and food chemistry.
[34] Silvia Guidoni,et al. Berry Size and Qualitative Characteristics of Vitis vinifera L. cv. Syrah , 2016 .
[35] Leigh Francis,et al. Flavonoids and C13-norisoprenoids in Vitis vinifera L. cv. Shiraz: relationships between grape and wine composition, wine colour and wine sensory properties. , 2010 .
[36] Simone Giacosa,et al. Rapid methods for the evaluation of total phenol content and extractability in intact grape seeds of Cabernet-Sauvignon: Instrumental mechanical properties and FT-NIR spectrum , 2012 .
[37] R. Tibshirani,et al. Improvements on Cross-Validation: The 632+ Bootstrap Method , 1997 .
[38] João Laranjinha,et al. LDL isolated from plasma-loaded red wine procyanidins resist lipid oxidation and tocopherol depletion. , 2008, Journal of agricultural and food chemistry.
[39] Maria Liakata,et al. Merits of random forests emerge in evaluation of chemometric classifiers by external validation. , 2013, Analytica chimica acta.
[40] Irina Volf,et al. A critical review of methods for characterisation of polyphenolic compounds in fruits and vegetables. , 2011, Food chemistry.
[41] Geoffrey J McLachlan,et al. Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[42] José Miguel Hernández-Hierro,et al. Determination of phenolic compounds of grape skins during ripening by NIR spectroscopy , 2011 .
[43] Daniel Cozzolino,et al. The role of visible and infrared spectroscopy combined with chemometrics to measure phenolic compounds in grape and wine samples. , 2015, Molecules.
[44] J. Delabar,et al. Effects of red wine polyphenolic compounds on paraoxonase-1 and lectin-like oxidized low-density lipoprotein receptor-1 in hyperhomocysteinemic mice. , 2009, The Journal of nutritional biochemistry.
[45] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[46] Luca Rolle,et al. Influence of Wine-Grape Skin Hardness on the Kinetics of Anthocyanin Extraction , 2012 .
[47] Rich Caruana,et al. Ensemble selection from libraries of models , 2004, ICML.
[48] Luca Rolle,et al. Impact of grapes heterogeneity according to sugar level on both physical and mechanical berries properties and their anthocyanins extractability at harvest. , 2013 .
[49] Luca Rolle,et al. Phenolic ripeness assessment of grape skin by texture analysis , 2008 .