Evidence map›Paper›PMID 39803225›Full record

ReviewFood science & nutrition2025

Comprehensive Sensory Evaluation in Low-Fat Emulsions: A Systematic Review of Diverse Food Applications.

Clara Talens, Saioa Alvarez-Sabatel, Esther Sanmartín, Laura Garcia-Fontanals, Pau Talens

Abstract readReview
In one paragraph

Review in Food science & nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

5 authors.

Clara TalensAZTI, Food Research, Basque Research and Technology Alliance (BRTA) Parque Tecnológico de Bizkaia, Astondo Bidea Derio Bizkaia Spain.ORCID https://orcid.org/0000-0003-3904-9007
Saioa Alvarez-SabatelAZTI, Food Research, Basque Research and Technology Alliance (BRTA) Parque Tecnológico de Bizkaia, Astondo Bidea Derio Bizkaia Spain.ORCID https://orcid.org/0000-0001-9864-9948
Esther SanmartínAZTI, Food Research, Basque Research and Technology Alliance (BRTA) Parque Tecnológico de Bizkaia, Astondo Bidea Derio Bizkaia Spain.ORCID https://orcid.org/0000-0002-7284-5518
Laura Garcia-FontanalsAZTI, Food Research, Basque Research and Technology Alliance (BRTA) Parque Tecnológico de Bizkaia, Astondo Bidea Derio Bizkaia Spain.ORCID https://orcid.org/0000-0002-1742-8113
Pau TalensInstituto Universitario de Ingeniería de Alimentos-FoodUPV Universitat Politècnica de València Valencia Spain.ORCID https://orcid.org/0000-0001-7318-3336

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prevalence of diet-related health issues has driven the demand for healthier food options, particularly those with reduced fat content. This systematic review evaluates the integration of sensory analysis in low-fat emulsion research, highlighting a significant gap in current practices. From an initial pool of 400 articles, 227 unique studies were screened, but only 15 (6.6%) included sensory analysis, underscoring a major shortfall in evaluating consumer acceptance. The reviewed studies investigated various emulsion types, including simple emulsions, emulsion gels, and Pickering emulsions, utilizing a diverse range of fat replacers, such as plant-based oils, proteins, and modified starches. These fat replacers included natural and modified ingredients such as banana peel flour, lard-based diacylglycerols, cedar oil cake, microparticulated egg white proteins,

Indexed as

consumer acceptanceemulsifying technologiesfat replacerslow‐fat emulsionssensory analysis

Identifiers

PMID39803225
PMCPMC11717037

What OpenQuestion 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 OpenQuestion graph.