Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Rua Diogo Botelho 1327, Porto 4169-005, Portugal.
Food Quality & Design Group, Wageningen University, Wageningen, The Netherlands.
Food Chem. 2021 Aug 1;352:129288. doi: 10.1016/j.foodchem.2021.129288. Epub 2021 Feb 16.
The complexity of the chemical reactions occurring during white wine storage, such as oxidation turns the capacity of prediction and consequently the capacity to avoid it extremely difficult. This study proposes an untarget methodology based on machine learning algorithms capable to classify wines according to their "oxidative-status". Instead of the most common approach in statistics using one class for classification, in this work eight classes were selected based on target oxidation markers for the extraction of relevant compounds. VIPS from OPLS-DA and mean decrease accuracy from random forest were used as feature selection parameters. Fifty-one molecules correlated with 5 classes, from which 23 were selected has having higher sensitivities (AUC > 0.85). For the first time to our knowledge hydroxy esters ethyl-2-hydroxy-3-methylbutanal and ethyl-2-hydroxy-4-methylpentanal were found to be correlated with oxidation markers and consequently to be discriminant of the wine oxidative status.
在白葡萄酒储存过程中发生的化学反应十分复杂，例如氧化作用，这使得预测变得极其困难，从而也难以避免氧化。本研究提出了一种基于机器学习算法的非靶向方法，该方法能够根据葡萄酒的“氧化状态”对其进行分类。在这项工作中，选择了八个类别，而不是统计学中常用的一种分类方法，而是基于目标氧化标志物来提取相关化合物。VIPS 来自 OPLS-DA 和随机森林的平均精度降低被用作特征选择参数。与 5 个类别相关的 51 个分子，其中 23 个被选择为具有更高的敏感性（AUC>0.85）。据我们所知，羟基酯乙基-2-羟基-3-甲基丁醛和乙基-2-羟基-4-甲基戊醛首次被发现与氧化标志物相关，因此可以区分葡萄酒的氧化状态。
