Support Vector Machine (SVM) was explored as a tool for the early detection of abnormal fermentations, which are common in the wine industry. A database of about 18,000 data from 38 fermentations and 45 variables was used. Two cases were studied: (I) measurements of five groups (fermentation control variables, organic acids, amino acids, saturated and unsaturated fatty acids); and (II) four variables (density, YAN, brix and acidity). In addition, different kernels, training/testing configurations, and cut-off time were evaluated. Main results indicated that 80% of wine fermentations were well predicted using information of amino acids. In addition, density and YAN were the best individual chemical markers for prediction, with over 90% of accuracy at first 48 hours of the process. Therefore, SVM can be used as a decision support tool for wine fermentation monitoring. Using data from the first 72 hours, it is possible classify abnormal fermentations with high precision.

支持向量机（SVM）被开发为早期检测异常发酵的工具，这在葡萄酒行业中很常见。使用了来自38个发酵和45个变量的大约18,000个数据的数据库。研究了两个案例：（I）测量五组（发酵控制变量，有机酸，氨基酸，饱和和不饱和脂肪酸）；（II）四个变量（密度，YAN，白利糖度和酸度）。此外，还评估了不同的内核，训练/测试配置和截止时间。主要结果表明，使用氨基酸信息可以很好地预测80％的葡萄酒发酵。此外，密度和YAN是预测的最佳个体化学标记，在该过程的前48小时内，准确率超过90％。因此，SVM可以用作葡萄酒发酵监控的决策支持工具。使用前72小时的数据，可以对异常发酵进行高精度分类。