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

Whole berry and skin texture analysis was applied to berries from 22 red wine grape cultivars and linked to the total flavonoid content. Three machine-learning techniques (regression tree, random forest and gradient boosting machine) were then applied. Models reached a high accuracy both in the external and internal validation. The R2 ranged from 0.75 to 0.85 for the external validation and from 0.65 to 0.75 for the internal validation, while RMSE (Root Mean Square Error) went from 0.95 mg g−1 to 0.7 mg g−1 in the external validation and from 1.3 mg g−1 to 1.1 mg g−1 in the internal validation.