Evidence map›Paper›PMID 39517308›Full record

ArticleFoods (Basel, Switzerland)2024

Diversity Analysis and Comprehensive Evaluation of 101 Soybean (

Xiaoyan Zhang, Gufeng Wu, Yuhe Wu, Ning Tang, Lu Huang, Dongqing Dai, Xingxing Yuan, Chenchen Xue, Xin Chen

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Soybean seed coat properties as determinants of natto and sprout quality.Journal of the science of food and agriculture · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. 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

9 authors.

Xiaoyan ZhangInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.
Gufeng WuInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.
Yuhe WuSchool of Food Science, Nanjing Xiaozhuang University, Nanjing 211171, China.
Ning TangSchool of Food Science, Nanjing Xiaozhuang University, Nanjing 211171, China.
Lu HuangInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.ORCID 0000-0003-0016-0111
Dongqing DaiInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.
Xingxing YuanInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.
Chenchen XueInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.ORCID 0000-0002-9703-2986
Xin ChenInstitute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.

Funding

the Core Technology Development for Breeding Program of Jiangsu Province JBGS-2021-014the Earmarked Fund for Jiangsu Agricultural Industry Technology System JATS[2024]025the National Natural Science Foundation of China 31902001Zhongshan Biological Breeding Laboratory ZSBBL
6 · The paper itself

Abstract

Soybean sprouts are a common culinary vegetable due to their high nutrition and tasty flavors. To select soybean varieties with excellent sprout quality, 101 soybean materials were collected from different regions of China, and eight sprout quality parameters were determined for overall evaluation. The results showed that eight sprout quality parameters achieved varying degrees of difference and correlation. Based on the principal component analysis (PCA), three principal components were extracted, with a cumulative contribution rate of 78.314%. Further, the comprehensive evaluation value (D) of soybean sprout quality was calculated by membership function analysis based on PCA, and the quality of soybean sprouts was ranked accordingly. Subsequently, a regression equation for the prediction of soybean sprout quality was established using a stepwise regression analysis, and the model showed a good prediction performance (correlation coefficient of prediction > 0.8; residual predictive deviation > 2.0). On these grounds, it was proposed that the quality of soybean sprouts could be comprehensively predicted by four parameters: hypocotyl length, edible rate, 100-seed weight, and total isoflavone content and saponin content. In conclusion, this study provides excellent varieties for soybean sprout production and new variety breeding, and it provides an important reference for the prediction of soybean sprout quality.

Indexed as

comprehensive evaluationparameter screeningprincipal component analysis (PCA)quality characteristicssoybean sprouts

Identifiers

PMID39517308
PMCPMC11545536

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.