Evidence map›Paper›PMID 40491977›Full record

ReviewFood science & nutrition2025

A Critical Review of Alcohol Reduction Methods for Red Wines From the Perspective of Phenolic Compositions.

Jingxian An, Zhipeng Zhang, Anwen Jin, Muqiu Tan, Shilong Jiang, Yilin Li

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 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

6 authors.

Jingxian AnChemical and Materials Engineering The University of Auckland New Zealand.
Zhipeng ZhangJiangxi Copper Technology Institute co., Ltd. Nanchang China.
Anwen JinHeilongjiang Feihe Dairy co., Ltd Beijing China.
Muqiu TanHeilongjiang Feihe Dairy co., Ltd Beijing China.
Shilong JiangHeilongjiang Feihe Dairy co., Ltd Beijing China.ORCID https://orcid.org/0000-0001-6975-0844
Yilin LiHeilongjiang Feihe Dairy co., Ltd Beijing China.ORCID https://orcid.org/0000-0003-4532-2567

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing demand for low-alcohol and nonalcoholic wines has sparked significant interest in alcohol reduction methods within the wine industry. This review examines various techniques for reducing alcohol content in red wines, focusing on their impact on phenolic compositions, which are crucial for wine quality and health benefits. Alcohol reduction methods have been analyzed across viticultural processes, fermentation techniques, and postfermentation treatments including membrane separation, vacuum distillation, and supercritical extraction. Main findings indicate that while all alcohol reduction methods can lead to some degree of phenolic loss, certain techniques like osmotic distillation show promise in preserving phenolic compositions. The review explores strategies to compensate for phenolic loss through mechanical methods, blending techniques, and food additive additions. Notably, the loss of certain phenolic compositions, particularly volatile phenols associated with smoke taints and small-molecular-weight phenolics, can sometimes positively influence sensory attributes. The paper concludes by highlighting the need for a comprehensive sensory attributes database for dealcoholized wines to assist in selecting optimal methods for balancing sensory attributes and wine quality. Further research is needed to explore how available methods can improve the sensory attributes of dealcoholized wines.

Indexed as

alcohol reductiondealcoholized winephenolic compositionsphenolic losssensory attributeswine quality

Identifiers

PMID40491977
PMCPMC12146494

What OpenQuestion holds

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LicenceCC BY
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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.