Kontogiannis Sotirios, Tsoumani Meropi, Kokkonis George, Pikridas Christos, Kotseridis Yorgos
Laboratory Team of Distributed Microcomputer Systems, Department of Mathematics, University of Ioannina, 45110 Ioannina, Greece.
Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, Greece.
Sensors (Basel). 2025 Jun 21;25(13):3877. doi: 10.3390/s25133877.
This paper introduces SmartBarrel, an innovative IoT-based sensory system that monitors and forecasts wine fermentation processes. At the core of SmartBarrel are two compact, attachable devices-the probing nose (E-nose) and the probing tongue (E-tongue), which mount directly onto stainless steel wine tanks. These devices periodically measure key fermentation parameters: the nose monitors gas emissions, while the tongue captures acidity, residual sugar, and color changes. Both utilize low-cost, low-power sensors validated through small-scale fermentation experiments. Beyond the sensory hardware, SmartBarrel includes a robust cloud infrastructure built on open-source Industry 4.0 tools. The system leverages the ThingsBoard platform, supported by a NoSQL Cassandra database, to provide real-time data storage, visualization, and mobile application access. The system also supports adaptive breakpoint alerts and real-time adjustment to the nonlinear dynamics of wine fermentation. The authors developed a novel deep learning model called V-LSTM (Variable-length Long Short-Term Memory) to introduce intelligence to enable predictive analytics. This auto-calibrating architecture supports variable layer depths and cell configurations, enabling accurate forecasting of fermentation metrics. Moreover, the system includes two fuzzy logic modules: a device-level fuzzy controller to estimate alcohol content based on sensor data and a fuzzy encoder that synthetically generates fermentation profiles using a limited set of experimental curves. SmartBarrel experimental results validate the SmartBarrel's ability to monitor fermentation parameters. Additionally, the implemented models show that the V-LSTM model outperforms existing neural network classifiers and regression models, reducing RMSE loss by at least 45%. Furthermore, the fuzzy alcohol predictor achieved a coefficient of determination (R2) of 0.87, enabling reliable alcohol content estimation without direct alcohol sensing.
本文介绍了智能酒桶（SmartBarrel），这是一种基于物联网的创新型传感系统，用于监测和预测葡萄酒发酵过程。智能酒桶的核心是两个紧凑的、可附着的设备——探测鼻（电子鼻）和探测舌（电子舌），它们可直接安装在不锈钢葡萄酒罐上。这些设备定期测量关键发酵参数：探测鼻监测气体排放，而探测舌捕捉酸度、残余糖分和颜色变化。两者都使用通过小规模发酵实验验证的低成本、低功耗传感器。除了传感硬件，智能酒桶还包括一个基于开源工业4.0工具构建的强大云基础设施。该系统利用由NoSQL Cassandra数据库支持的ThingsBoard平台，提供实时数据存储、可视化和移动应用访问。该系统还支持自适应断点警报以及对葡萄酒发酵非线性动态的实时调整。作者开发了一种名为V-LSTM（可变长度长短期记忆）的新型深度学习模型，以引入智能实现预测分析。这种自动校准架构支持可变的层深度和单元配置，能够准确预测发酵指标。此外，该系统包括两个模糊逻辑模块：一个设备级模糊控制器，用于根据传感器数据估计酒精含量；一个模糊编码器，使用一组有限的实验曲线综合生成发酵曲线。智能酒桶的实验结果验证了其监测发酵参数的能力。此外，所实施的模型表明，V-LSTM模型优于现有的神经网络分类器和回归模型，将均方根误差损失至少降低了45%。此外，模糊酒精预测器的决定系数（R2）达到了0.87，能够在不直接检测酒精的情况下可靠地估计酒精含量。
