Smoke taint in wine has become a critical issue in the wine industry due to its significant negative impact on wine quality. Data-driven approaches including univariate analysis and predictive modeling are applied to a data set containing concentrations of 20 VOCs in 48 grape samples and 56 corresponding wine samples with a taster-evaluated smoke taint index. The resulting models for predicting the smoke taint index of wines are highly predictive when using as inputs VOC concentrations after log conversion in both grapes and wines (Pearson Correlation Coefficient PCC = 0.82; R² = 0.68) and less so when only grape VOCs are used (Pearson Correlation Coefficient PCC = 0.76; R² = 0.56), and the classification models also show the capacity for detecting smoke-tainted wines using both wine and grape VOC concentrations (Recall = 0.76; Precision = 0.92; F1 = 0.82) or using only grape VOC concentrations (Recall = 0.74; Precision = 0.92; F1 = 0.80). The performance of the predictive model shows the possibility of predicting the smoke taint index of the wine and grape samples before fermentation. The corresponding code of data analysis and predictive modeling of smoke taint in wine is available in the Github repository (https://github.com/IBPA/smoke_taint_prediction).

中文翻译：

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基于挥发性有机化合物的葡萄酒烟味预测模型

葡萄酒中的烟味污染已成为葡萄酒行业的一个关键问题，因为它对葡萄酒质量产生重大负面影响。包括单变量分析和预测建模在内的数据驱动方法应用于包含 48 个葡萄样品和 56 个相应葡萄酒样品中 20 种 VOC 浓度的数据集，并具有品酒师评估的烟雾污染指数。当使用葡萄和葡萄酒中对数转换后的 VOC 浓度作为输入时，用于预测葡萄酒烟雾污染指数的模型具有高度预测性（皮尔逊相关系数 PCC = 0.82；R ² = 0.68），而当仅使用葡萄 VOC 时预测性较差（皮尔逊相关系数 PCC = 0.76；R ² = 0.56），分类模型还显示了使用葡萄酒和葡萄 VOC 浓度（召回率 = 0.76；精度 = 0.92；F1 = 0.82）或使用仅葡萄 VOC 浓度（召回率 = 0.74；精度 = 0.92；F1 = 0.80）。预测模型的性能表明预测发酵前葡萄酒和葡萄样品烟熏污染指数的可能性。葡萄酒烟雾污染的数据分析和预测建模的相应代码可在 Github 存储库中找到（https://github.com/IBPA/smoke_taint_prediction）。