Evidence map›Paper›PMID 42559071›Full record

ArticleFrontiers in nutrition2026

Three-dimensional characterization of fibrous microstructure in meat analogs using micro-computed X-ray tomography/μCT-based granulometry.

Xinyu Pan, Tiaan Friedrich, Michelle Müller, Magdalena Bock, Jan Engmann, Guilherme De Oliveira Reis, Mark Ambühl, Christoph Hartmann, Heiko Briesen

Abstract read
In one paragraph

Article in Frontiers in nutrition, 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

9 authors.

Xinyu PanProcess Systems Engineering, Technische Universität München, Freising, Germany.
Tiaan FriedrichProcess Systems Engineering, Technische Universität München, Freising, Germany.
Michelle MüllerProcess Systems Engineering, Technische Universität München, Freising, Germany.
Magdalena BockProcess Systems Engineering, Technische Universität München, Freising, Germany.
Jan EngmannSociété des Produits Nestlé S.A., Lausanne, Switzerland.
Guilherme De Oliveira ReisSociété des Produits Nestlé S.A., Lausanne, Switzerland.
Mark AmbühlSociété des Produits Nestlé S.A., Lausanne, Switzerland.
Christoph HartmannSociété des Produits Nestlé S.A., Lausanne, Switzerland.
Heiko BriesenProcess Systems Engineering, Technische Universität München, Freising, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fibrous microstructure of plant-based meat analogs governs their textural properties, yet quantitative 3D characterization remains limited. In this study, a granulometry-based approach was extended to 3D micro-computed X-ray tomography (μCT) datasets to characterize the fiber architecture of high-moisture extruded soy protein concentrate meat analogs. Five prototype samples, produced at extrusion temperatures from 90°C to 170 °C, and three commercial products were analyzed for characteristic fiber width and length, orientation distribution, anisotropy, and shape metrics. The characteristic fiber width increased progressively with extrusion temperature, whereas anisotropy first increased and then decreased, indicating an optimal temperature window for fiber alignment. The commercial samples exhibited coarser structures with lower anisotropy, attributed to more complex ingredient composition. The proposed approach provides a comprehensive quantitative framework for evaluating fibrous microstructures in extruded plant-based meat analogs.

Indexed as

3D microstructureanisotropycharacteristic sizefiber orientationgranulometrymeat analogmicro-computed X-ray tomography

Identifiers

PMID42559071
PMCPMC13440075

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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.