Evidence map›Paper›PMID 40724283›Full record

ArticleFoods (Basel, Switzerland)2025

Development of Texture-Modified Meat and Thickened Soup Combination for Oral Dysphagia Patients with Uniform Firmness and Solid Appearance.

Sergio Hernández, Samuel Verdú, Pau Talens, Raúl Grau

Abstract read
In one paragraph

Article in Foods (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

4 authors.

Sergio HernándezInstituto de Ingeniería de Alimentos-FoodUPV, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.ORCID 0000-0002-5539-2166
Samuel VerdúInstituto de Ingeniería de Alimentos-FoodUPV, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.ORCID 0000-0002-3910-4582
Pau TalensInstituto de Ingeniería de Alimentos-FoodUPV, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.ORCID 0000-0001-7318-3336
Raúl GrauInstituto de Ingeniería de Alimentos-FoodUPV, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.ORCID 0000-0002-0540-5539

Funding

Ministerio de España de Ciencia, Innovación y Universidades RTI2018-098842-B-I00Universitat Politècnica de València Funding for open access charge
6 · The paper itself

Abstract

This study aimed to improve the visual appeal of texture-modified (TM) dishes for individuals with dysphagia by developing a method to unify the texture of solid and liquid components through innovative food processing techniques. It investigated various meat-softening methods while preserving its solid appearance and ensuring a uniform texture when combined with a thickened soup. A grinding and reconstitution approach enabled the incorporation of pea protein (0% and 1%), olive oil (0%, 5%, or 10%), and papain (0% and 0.2%) to enhance the nutritional and sensory properties. This method successfully matched the firmness of TM meat with that of the thickened soup. Papain significantly reduced the firmness, and olive oil decreased the cohesiveness. After categorizing the TM meat and thickened soup as IDDSI level 4, four dishes at three firmness levels were developed. This study highlighted the potential of this approach to integrate solid and liquid food matrices, contributing to the advancement of TM food engineering and to the challenge of improving visual sensory acceptance and personalizing TM diets for individuals with dysphagia.

Indexed as

dysphagiaguar gumIDDSI level 4papaintexture-modified foodstransglutaminase

Identifiers

PMID40724283
PMCPMC12294358

What OpenQuestion holds

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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.