ArticleSensors (Basel, Switzerland)2025
Nano-Tailored Triple Gas Sensor for Real-Time Monitoring of Dough Preparation in Kitchen Machines.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Influence of CuO on the performance of conducting polymer matrix for screen-printed humidity sensing applications.Mikrochimica acta · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.