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Fermentation monitoring by Bayesian states estimators. Application to red wines elaboration
Control Engineering Practice ( IF 4.9 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.conengprac.2020.104608
Cecilia Fernández , Nadia Pantano , Francisco Rossomando , Adriana Amicarelli , Gustavo Scaglia

Abstract Winemakers must understand all chemical aspects involved and make the right decisions to obtain a high quality product. In a winemaking process, the tracking and control of certain variables are keys to achieve a proper fermentation. This paper presents state estimators design based on Gaussian processes, for on-line alcoholic fermentation monitoring in red wines. For this study, 18 fermentations of three different varietals, Cabernet Sauvignon, Malvec and Tannat, were analyzed to train and validate the estimators. Samples were taken from Merced del Estero, a San Juan industrial winery. Then, cell concentration was determined by neubauer chamber count, while ethanol and total sugars concentrations by infrared absorption spectroscopy. Results show a suitable prediction of cell and ethanol content when only substrate measurement is available. Furthermore, the proposed estimator is compared with a competitive approach (neural network) to highlight the suitability of Bayesian theory for this type of application. This paper provides a reliable monitoring tool, with low computational and economic cost to facilitate the work of winemakers.

中文翻译:

贝叶斯状态估计器的发酵监测。应用于红酒精制

摘要 酿酒师必须了解所涉及的所有化学方面,并做出正确的决定以获得高质量的产品。在酿酒过程中,对某些变量的跟踪和控制是实现适当发酵的关键。本文介绍了基于高斯过程的状态估计器设计,用于红葡萄酒中的在线酒精发酵监测。在本研究中,对赤霞珠、马尔维克和丹娜这三种不同品种的 18 次发酵进行了分析,以训练和验证估算器。样品取自圣胡安工业酿酒厂 Merced del Estero。然后,通过纽鲍尔室计数确定细胞浓度,而通过红外吸收光谱确定乙醇和总糖浓度。结果显示当只有底物测量可用时,细胞和乙醇含量的合适预测。此外,将提议的估计器与竞争方法(神经网络)进行比较,以突出贝叶斯理论对此类应用的适用性。本文提供了一种可靠的监测工具,以较低的计算和经济成本来促进酿酒师的工作。
更新日期:2020-10-01
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