Learning Compact Multispectral Signatures for Geographical-Origin Authentication of

Zhihui Fan, Shaowen Jing, Chao Ma, Sen Wang, Zhenzhen Chen, Jiayu Huang, Mingkun Zhang

Abstract read

In one paragraph

Article in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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5 · Who and what money

Authors and funding

7 authors.

Zhihui FanCollege of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.ORCID 0009-0005-2805-4750

Shaowen JingCollege of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Chao MaCollege of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Sen WangArtificial Intelligence Department, Yuxin Electronic Technology Group Co., Ltd., Zhengzhou 450000, China.

Zhenzhen ChenArtificial Intelligence Department, Yuxin Electronic Technology Group Co., Ltd., Zhengzhou 450000, China.

Jiayu HuangCollege of Electronics and Information Engineering, South China University of Technology, Guangzhou 510641, China.

Mingkun ZhangCollege of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.ORCID 0000-0003-2797-8781

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Geographical authentication of medicinal plant materials remains challenging because multispectral variables are often highly collinear and sample grouping can complicate reliable model validation. Existing correlation-based feature-selection strategies also require careful adaptation to multiclass problems to avoid artificial ordering of class labels and information leakage during model development. Therefore, this study aimed to develop a compact and leakage-controlled multispectral learning framework for geographical-origin discrimination. This study analyzed 800 physical

Indexed as

Deep LearningPinelliaGeographyMultilayer PerceptronsPlants, Medicinalfeature selectiongeographical originmedicinal plant authenticationmultilayer perceptronmultispectral imagingnested group validationPearson correlationPinellia ternata

Identifiers

PMID42738797

DOI10.3390/molecules31173138

PMCPMC13567037

What OpenQuestion holds

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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.