Evidence map›Paper›PMID 41717021›Full record

ArticleFrontiers in nutrition2026

Co-modification of corn flour by

Xingda Xu, Yifan Guo, Hongdi Sun, Yutong Zhang, Wanting Yang, Hua Zheng, Lei Xu, Yan Wang

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
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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

8 authors.

Xingda Xu *College of Food and Biological Engineering, Qiqihar University, Qiqihar, China.
Yifan Guo *Crop Resources Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China.
Hongdi SunCollege of Food and Biological Engineering, Qiqihar University, Qiqihar, China.
Yutong ZhangCollege of Food and Biological Engineering, Qiqihar University, Qiqihar, China.
Wanting YangCollege of Food and Biological Engineering, Qiqihar University, Qiqihar, China.
Hua ZhengTonghua Central Hospital, Tonghua, China.
Lei XuCollege of Food and Biological Engineering, Qiqihar University, Qiqihar, China.
Yan WangCollege of Food and Biological Engineering, Qiqihar University, Qiqihar, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The complex protein structure, high glycemic index (GI), coarse taste and poor formability of corn significantly hinder its deep processing performance and represent key technical bottlenecks in the industrialization of corn-based food products. Methods: In this study, co-modification with optimized lactic acid bacteria (Lactobacillus HR) and transglutaminase (TGase) effectively converted corn flour into a low-glycemic index (GI) product, primarily due to flour's high starch content. Results: The results revealed that fermentation under the optimum circumstances (36.83 °C, fermentation time of 49.25 h, inoculum level of 1.03 × 108 CFU/mL, and enzyme dosage of 2.58‰) produced flour with significant nutritional improvements. Co-modification yielded the lowest GI (48.87) and enhanced the nutritional profile, with increased soluble protein content (5.96  mg/g) and resistant starch levels (51.19%). FTIR analysis revealed an increase in the (1047/1022) cm Conclusion: The co- modification of Lactobacillus HR and TGase caused a more compact internal structure, which further reduced the hydrolyzability of corn starch. Based on the above results, this study indicates that the integrated biological treatment is a feasible method for improving the nutritional quality and functional properties of cereal flours.

Indexed as

bacteria-enzyme co-midificationcereal processingcorn flourlow glycemic indexoptimization

Identifiers

PMID41717021
PMCPMC12913172

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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.