Genzardi Dario, Caruso Immacolata, Poeta Elisabetta, Sberveglieri Veronica, Núñez Carmona Estefanía
Institute of Bioscience and Bioresources (CNR-IBBR), National Research Council, Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy.
Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Via Pietro Vivarelli, 10, 41125 Modena, MO, Italy.
Sensors (Basel). 2025 May 7;25(9):2951. doi: 10.3390/s25092951.
We evaluated the efficacy of an innovative technique using an S3+ device equipped with two custom-made nanosensors (e-nose). These sensors are integrated into kitchen appliances, such as planetary mixers, to monitor and assess dough leavening from preparation to the fully risen stage. Since monitoring in domestic appliances is often subjective and non-reproducible, this approach aims to ensure safe, high-quality, and consistent results for consumers. Two sensor chips, each with three metal oxide semiconductor (MOS) elements, were used to assess doughs prepared with flours of varying strengths (W200, W250, W390). Analyses were conducted continuously (from the end of mixing to 1.5 h of leavening) and in two distinct phases: pre-leavening (PRE) and post-leavening (POST). The technique was validated through solid-phase micro-extraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), used to analyze volatile profiles in both phases. The S3+ device clearly discriminated between PRE and POST samples in 3D Linear Discriminant Analysis (LDA) plots, while 2D LDA confirmed flour-type discrimination during continuous leavening. These findings were supported by SPME-GC-MS results, highlighting differences in the volatile organic compound (VOC) profiles. The system achieved 100% classification accuracy between PRE and POST stages and effectively distinguished all flour types. Integrating this e-nose into kitchen equipment offers a concrete opportunity to optimize leavening by identifying the ideal endpoint, improving reproducibility, and reducing waste. In future applications, sensor data could support feedback control systems capable of adjusting fermentation parameters like time and temperature in real time.
我们评估了一种创新技术的功效，该技术使用配备两个定制纳米传感器（电子鼻）的S3+设备。这些传感器集成到厨房电器中，如行星式搅拌机，以监测和评估面团从准备到完全发酵阶段的发酵情况。由于在家用电器中的监测往往具有主观性且不可重复，这种方法旨在为消费者确保安全、高质量和一致的结果。使用两个各有三个金属氧化物半导体（MOS）元件的传感器芯片来评估用不同强度（W200、W250、W390）的面粉制作的面团。分析在两个不同阶段持续进行（从搅拌结束到发酵1.5小时）：发酵前（PRE）和发酵后（POST）。该技术通过固相微萃取结合气相色谱 - 质谱联用（SPME - GC - MS）进行验证，用于分析两个阶段的挥发性成分。在三维线性判别分析（LDA）图中，S3+设备能够清晰地区分PRE和POST样本，而二维LDA证实了在持续发酵过程中对面粉类型的区分。这些发现得到了SPME - GC - MS结果的支持，突出了挥发性有机化合物（VOC）谱的差异。该系统在PRE和POST阶段之间实现了100%的分类准确率，并有效地区分了所有面粉类型。将这种电子鼻集成到厨房设备中提供了一个切实的机会，通过确定理想的终点来优化发酵，提高可重复性并减少浪费。在未来的应用中，传感器数据可以支持能够实时调整发酵参数（如时间和温度）的反馈控制系统。
