Evidence map›Paper›PMID 39164410›Full record

ArticleScientific reports2024

Quality characterization of tobacco flavor and tobacco leaf position identification based on homemade electronic nose.

Hao Li, Qiuling Wang, Lu Han, Zhifei Chen, Genfa Wang, Qingfu Wang, Shengtao Ma, Bin Ai, Gaolei Xi

Abstract read
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 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
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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.

Hao LiTechnology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, 450016, China.
Qiuling WangTechnology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, 450016, China.
Lu HanTechnology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, 450016, China.
Zhifei ChenTechnology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, 450016, China.
Genfa WangTechnology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, 450016, China.
Qingfu WangTechnology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, 450016, China.
Shengtao MaTechnology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, 450016, China. taotaode12345@126.com.
Bin AiSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China. binai@cqu.edu.cn.
Gaolei XiTechnology Center, China Tobacco Henan Industrial Co., Ltd., Zhengzhou, 450016, China. xigaoleijida@126.com.

Funding

Henan Tobacco Industry Co., Ltd. Science and Technology Project No. AW202107Open Project of State Key Laboratory for Supramolecular Structure and Materials SKLSSM 202301
6 · The paper itself

Abstract

A set of nine unique tobacco extract samples was analyzed using a self-developed electronic nose (E-nose) system, a commercial E-nose, and gas chromatography-mass spectrometry (GC-MS). The evaluation employed principal component analysis, statistical quality control, and soft independent modeling of class analogies (SIMCA). These multifaceted statistical methods scrutinized the collected data. Subsequently, a quality control model was devised to assess the stability of the sample quality. The results showed that the custom E-nose system could successfully distinguish between tobacco extracts with similar odors. After further training and the development of a quality control model for accepted tobacco extracts, it was possible to identify samples with normal and abnormal quality. To further validate our E-nose and extend its use within the tobacco industry, we collected and accurately classified the flavors of different tobacco leaf positions, with a remarkable accuracy rate of 0.9744. This finding facilitates the practical application of our E-nose system for the efficient identification of tobacco leaf positions.

Indexed as

Electronic NoseGas Chromatography-Mass SpectrometryNicotianaPlant LeavesFlavoring AgentsOdorantsPrincipal Component AnalysisQuality ControlFlavoring AgentsElectronic noseMachine learningSensorTobacco extractTobacco leaf

Identifiers

PMID39164410
PMCPMC11336110

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.