Evidence map›Paper›PMID 42676819›Full record

ArticleFrontiers in nutrition2026

Physicochemical characteristics and metabolomic profiling of representative rice cultivars distinct in eating quality.

Dongping Yao, Siyu Peng, Xiaomei Li, Xinjie Huang, Chaoming Xing, Jiangwei Yin, Bin Bai

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

7 authors.

Dongping Yao *College of Plant Science and Technology, Hunan Biological and Electromechanical Polytechnic, Changsha, Hunan, China.
Siyu Peng *College of Agronomy, Hunan Agricultural University, Changsha, Hunan, China.
Xiaomei LiCollege of Agronomy, Hunan Agricultural University, Changsha, Hunan, China.
Xinjie HuangCollege of Plant Science and Technology, Hunan Biological and Electromechanical Polytechnic, Changsha, Hunan, China.
Chaoming XingCollege of Agronomy, Hunan Agricultural University, Changsha, Hunan, China.
Jiangwei YinCollege of Agronomy, Hunan Agricultural University, Changsha, Hunan, China.
Bin BaiState Key Laboratory of Hybrid Rice, Hunan Hybrid Rice Research Center, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to identify shared physicochemical disparities between high eating quality rice and conventional high-yield cultivars across indica and japonica subspecies, and screen core taste-related trait indicators, while characterizing intrasubtype metabolic variation in indica rice. Four representative rice cultivars (two premium fragrant rice: Yuzhenxiang, Daohuaxiang No. 2; two high-yield rice: Huanghuazhan, Kendao 2066) were comprehensively evaluated via sensory, texture, RVA and multi-component quantitative detection; Untargeted metabolomics was further performed on the two indica cultivars Yuzhenxiang and Huanghuazhan. Rice with superior eating quality featured low protein, amino acids and citric acid, moderate soluble sugars, abundant 2-AP, elevated amylopectin long/short chain ratio and low gelatinization temperature. A total of 1,318 differential metabolites were identified, with arginine biosynthesis and linoleic acid metabolism as the most significantly enriched pathways. A three-indicator multiple linear regression model with

Indexed as

2-acetyl-1-pyrrolineamylopectin long-to-short chain ratioeating qualitymetabolomephysicochemical propertiesrice

Identifiers

PMID42676819
PMCPMC13527028

What OpenQuestion holds

Textmetadata
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.