Evidence map›Paper›PMID 39852091›Full record

ArticleBiosensors2025

MicroVi: A Cost-Effective Microscopy Solution for Yeast Cell Detection and Count in Wine Value Chain.

Ismael Benito-Altamirano, Sergio Moreno, David M Vaz-Romero, Anna Puig-Pujol, Gemma Roca-Domènech, Joan Canals, Anna Vilà, Joan Daniel Prades, Ángel Diéguez

Abstract read
In one paragraph

Article in Biosensors, 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

9 authors.

Ismael Benito-AltamiranoDepartment of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.ORCID 0000-0002-2504-6123
Sergio MorenoDepartment of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.ORCID 0000-0002-4471-1555
David M Vaz-RomeroDepartment of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.ORCID 0009-0007-9906-0730
Anna Puig-PujolINCAVI-IRTA, Catalan Institute of Vine and Wine-Institute of Agrifood Research and Technology, Plaça Àgora, 2, Vilafranca del Penedès, 08720 Barcelona, Spain.ORCID 0000-0001-5041-6432
Gemma Roca-DomènechINCAVI-IRTA, Catalan Institute of Vine and Wine-Institute of Agrifood Research and Technology, Plaça Àgora, 2, Vilafranca del Penedès, 08720 Barcelona, Spain.
Joan CanalsDepartment of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.ORCID 0000-0002-5431-8554
Anna VilàDepartment of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.ORCID 0000-0003-3088-0590
Joan Daniel PradesDepartment of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.
Ángel DiéguezDepartment of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain.ORCID 0000-0001-6721-7245

Funding

European Regional Development Fund PID2019-105714RB-I00European Regional Development Fund PID2022-136833OB-C21European Union 737089FEADER and Departament d'Acció Climàtica, Alimentació i Agenda Rural (DACC) of the Generalitat de Catalunya 56 30123 2021 2A
6 · The paper itself

Abstract

In recent years, the wine industry has been researching how to improve wine quality along the production value chain. In this scenario, we present here a new tool, MicroVi, a cost-effective chip-sized microscopy solution to detect and count yeast cells in wine samples. We demonstrate that this novel microscopy setup is able to measure the same type of samples as an optical microscopy system, but with smaller size equipment and with automated cell count configuration. The technology relies on the top of state-of-the-art computer vision pipelines to post-process the images and count the cells. A typical pipeline consists of normalization, feature extraction (i.e., SIFT), image composition (to increase both resolution and scanning area), holographic reconstruction and particle count (i.e., Hough transform). MicroVi achieved a 2.19 µm resolution by properly resolving the G7.6 features from the USAF Resolving Power Test Target 1951. Additionally, we aimed for a successful calibration of cell counts for

Indexed as

MicroscopySaccharomyces cerevisiaeWineCell CountCost-Benefit Analysisab-on-a-chipchip-sized microscopyholographywine qualityyeast cell count

Identifiers

PMID39852091
PMCPMC11763666

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.