Texture Independently Drives Liking in AI-Generated Alternative Protein Burgers.

Vahidullah Tac, Aeneas O Koosis, Ellen Kuhl

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 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

3 authors.

Vahidullah TacDepartment of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-3027-6687

Aeneas O KoosisDepartment of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-8513-8089

Ellen KuhlDepartment of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-6283-935X

Funding

European Research Council Advanced Grant 101141626Schmidt Sciences FellowshipStanford Bio-X Snack Grant 2025Stanford Doerr School of Sustainability Accelerator Grant 2025U.S. National Science Foundation CMMI Award 2320933

6 · The paper itself

Abstract

Texture shapes how we perceive and like food, yet clear links between mechanical measurements and sensory perception of texture remain elusive. Here we combine sensory data from a blind tasting involving 101 participants with mechanical texture profile analysis across six burgers to identify the textural features that drive consumer perception and liking. We compare five burgers-generated via artificial intelligence-with animal-based, plant-based, mushroom-based, and hybrid animal-mushroom patties, and the classical Big Mac

Indexed as

artificial intelligencegenerative AImachine learningplant-based meatsensory surveytexture profile analysis

Identifiers

PMID42279812

DOI10.3390/foods15112026

PMCPMC13256903

What Socratic holds

Textmetadata

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 Socratic graph.