Evidence map›Paper›PMID 39511398›Full record

ArticleScientific reports2024

Regression prediction of tobacco chemical components during curing based on color quantification and machine learning.

Yang Meng, Qiang Xu, Guangqing Chen, Jianjun Liu, Shuoye Zhou, Yanling Zhang, Aiguo Wang, Jianwei Wang, Ding Yan, Xianjie Cai and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. 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

16 authors.

Yang Meng *Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China.
Qiang Xu *Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China.
Guangqing ChenHenan Provincial Tobacco Company, Zhengzhou, 450001, China.
Jianjun LiuHenan Provincial Tobacco Company, Zhengzhou, 450001, China.
Shuoye ZhouHenan Provincial Tobacco Company, Zhengzhou, 450001, China.
Yanling ZhangZhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China.
Aiguo WangZhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China.
Jianwei WangZhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China.
Ding YanShanghai Tobacco Company, 200000, Shanghai, China.
Xianjie CaiShanghai Tobacco Company, 200000, Shanghai, China.
Junying LiPingdingshan Branch of Henan Provincial Tobacco Company, Henan, 467000, China.
Xuchu ChenPingdingshan Branch of Henan Provincial Tobacco Company, Henan, 467000, China.
Qiuying LiNanping Branch of Fujian Provincial Tobacco Company, Nanping, 353000, China.
Qiang ZengNanping Branch of Fujian Provincial Tobacco Company, Nanping, 353000, China.
Weimin GuoZhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China. guoweimin1984@sina.com.
Yuanhui WangCollege of Food Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China. wangyuanhui2014@haut.edu.cn.

Funding

the major science and technology program of CNTC No. 110202101084 ( SJ-08 )
6 · The paper itself

Abstract

Color is one of the most important indicators to characteristic the quality of tobacco, which is strongly related to the variations of chemical components. In order to clarify the relationship between the changes of tobacco color and chemical components, here we established several prediction models of chemical components with the color values of tobacco based on machine learning algorithms. The results of correlation analysis showed that tobacco moisture content was highly significantly correlated with the parameters such as a

Indexed as

ColorMachine LearningNicotianaAlgorithmsPlant LeavesChemical componentsColor valuesMachine learning algorithmPrediction modelTobacco curing

Identifiers

PMID39511398
PMCPMC11543802

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.