Evidence map›Paper›PMID 39944280›Full record

ArticleFood chemistry: X2025

Maximizing japonica rice quality by high-pressure steam: Insights into improvement.

Saadia Zainab, Xianqing Zhou, Yurong Zhang, Saira Tanweer, Tariq Mehmood

Abstract read
In one paragraph

Article in Food chemistry: X, 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. Article
  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.

Saadia ZainabSchool of Food and Strategic Reserves, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China.
Xianqing ZhouSchool of Food and Strategic Reserves, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China.
Yurong ZhangSchool of Food and Strategic Reserves, College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China.
Saira TanweerDepartment of Food Science and Technology, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Pakistan.
Tariq MehmoodInstitue of Food Science and Technology, Faculty of Natural and Applied Sciences, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The research was carried out to optimize the cooking process based on the quality parameters of rice. The steaming process with high pressure proved to have more acceptance among consumers. Based on the textural and gel properties of steamed rice, rice samples soaked for 15 min with a 1:1.3 ratio and steamed at 110 °C and 0.042 MPa were found to be a more effective treatment. Stress-relaxation study and pasting curve model were conducted to comprehensively explain the combinational effect of independent variables. Principal component analysis was carried out to investigate the association between textural and gel characteristics of cooked rice treatments. Characterization was done to check the impact of high-pressure steam on rice. Starch, protein, and amylose content of rice sample were recorded as 78.6, 6.6, and 18.7 % accordingly. High-pressure steam increases the solubility and swelling power while reducing the gelatinization temperature and causing a yellowish color. Pseudoplastic fluid behavior was recorded for both treatments while crystalline structure converted into V-type from A-type in the optimized sample. Optimized samples had lower values of To, Tp, and Tc while higher values of enthalpy and A

Indexed as

DepictionEscalationMathematical approachTextureViscoelasticity

Identifiers

PMID39944280
PMCPMC11814526

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.