Evidence map›Paper›PMID 41626331›Full record

ArticleFrontiers in plant science2025

Quantitative analysis of tobacco blending proportions based on hyperspectral imaging and data fusion.

Yifan Jiang, Qinlin Xiao, Xudong Huang, Ruifang Gu, Jing Wen, Xixiang Zhang, Yang Liu, Li Li, Xiaojing Chen, Juan Yang and 1 more

Abstract read
In one paragraph

Article in Frontiers in plant science, 2025. 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

11 authors.

Yifan Jiang *College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Qinlin Xiao *College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Xudong HuangSchool of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun, China.
Ruifang GuTechnology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu, China.
Jing WenTechnology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu, China.
Xixiang ZhangTechnology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu, China.
Yang LiuTechnology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu, China.
Li LiTechnology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu, China.
Xiaojing ChenCollege of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China.
Juan YangTechnology Center, China Tobacco Sichuan Industrial Co., Ltd., Chengdu, China.
Yong HeCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid and accurate detection of tobacco blending proportions is essential for quality control in the tobacco industry. This study proposes a method for the quantitative analysis of tobacco components based on multispectral fusion, integrating visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectral data. The method employs the minimum covariance determinant (MCD) for anomaly detection and constructs a quantitative model using partial least squares regression (PLSR). The experimental data comprise two matrices of dimensions 400 × 90 and 220 × 90, each containing 90 samples. Experimental results demonstrate that multispectral fusion significantly improves the model's quantitative analysis performance compared to using a single spectrum. The adopted preprocessing strategy effectively reduces noise interference and enhances feature extraction capability. When predicting tobacco silk content, the fused spectral model achieved the highest prediction accuracy with R

Indexed as

blending proportionshyperspectral imagingmultispectral fusionquantitative analysistobacco

Identifiers

PMID41626331
PMCPMC12852377

What OpenQuestion holds

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LicenceCC BY
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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.