Evidence map›Paper›PMID 41737706›Full record

ReviewJournal of food science and technology2026

Automated monitoring of alcoholic fermentation: trends and challenges.

Tomáš Horváth, Szilárd Kun, László Sipos, Duc Trung Pham

Abstract readReview
In one paragraph

Review in Journal of food science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Tomáš HorváthSchool of Computing, Engineering and the Built Environment, Edinburgh Napier University, Sighthill Court, Edinburgh, EH11 4BN Scotland, UK.ORCID 0000-0002-9438-840X
Szilárd KunInstitute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Villányi út 29-43, Budapest, 1118 Hungary.
László SiposInstitute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, Villányi út 29-43, Budapest, 1118 Hungary.
Duc Trung PhamInstitute of Academia-Industry Cooperation, Faculty of Informatics, Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, 1117 Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The progress of fermentation, an important step in spirit production, needs to be monitored regularly to detect possible faults. Automated monitoring of fermentation, however, is often limited to only a few parameters of the mash such as, and mainly, its temperature. With the advance of sensor technology and data analytics, various solutions to automated fermentation monitoring emerged, mainly for the beer and wine industry, however, these are not yet critically evaluated and compared. Thus, scientific articles on automated monitoring of alcoholic fermentation are reviewed and evaluated here according to the type of sensors used, the type of fermented material, and the reproducibility and feasibility of the presented solutions. Possible data analytics methods to utilize are introduced and their pros and cons are discussed. A critical evaluation from scientific and industrial perspectives is provided with prospects for the distilling industry where mashes of various states of matter, inhomogeneity and viscosity can appear. Key findings and conclusions of this review are: Electronic nose and electronic tongue biosensors are a promising direction in the area. A publicly available database on recorded data from e-nose and e-tongue as well as other sensors on fermentation monitoring is needed but still missing. Current solutions on automated fermentation monitoring are rather isolated studies, conducted in laboratories, yet to be evaluated and tested in industrial environments. The use of machine learning techniques in these studies, in general, does not comply with the well-established standards in data science and artificial intelligence.

Indexed as

Alcoholic fermentationArtificial intelligenceData analyticsElectronic noseElectronic tongueSensors

Identifiers

PMID41737706
PMCPMC12926280

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.