Evidence map›Paper›PMID 40363388›Full record

ArticleSensors (Basel, Switzerland)2025

Nano-Tailored Triple Gas Sensor for Real-Time Monitoring of Dough Preparation in Kitchen Machines.

Dario Genzardi, Immacolata Caruso, Elisabetta Poeta, Veronica Sberveglieri, Estefanía Núñez Carmona

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Dario GenzardiInstitute of Bioscience and Bioresources (CNR-IBBR), National Research Council, Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy.ORCID 0000-0001-7162-8258
Immacolata CarusoInstitute of Bioscience and Bioresources (CNR-IBBR), National Research Council, Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy.
Elisabetta PoetaDepartment of Life Sciences, University of Modena and Reggio Emilia, Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy.
Veronica SberveglieriInstitute of Bioscience and Bioresources (CNR-IBBR), National Research Council, Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy.ORCID 0000-0002-5991-9391
Estefanía Núñez CarmonaInstitute of Bioscience and Bioresources (CNR-IBBR), National Research Council, Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, RE, Italy.ORCID 0000-0002-4621-0658

Funding

Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 - Call for tender No. 341 of 15 March 2022 of Italian Ministry of University and Research funded by the European Union - NextGenerationEU; Project Project code PE00000003
6 · The paper itself

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.

Indexed as

fermentationIoT systemleaveningMOS gas sensorsvolatile organic compounds (VOCs) analysis

Identifiers

PMID40363388
PMCPMC12074181

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Registered trials

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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.