Evidence map›Paper›PMID 41658529›Full record

ArticleFrontiers in plant science2025

Geographic authentication of

Yinggang Zheng, Songping Lan, Haoran Hu, Xinwei Huang, Huan Wang, He Cao, Xiaoli Liu, Yaowen Yang, Shengguo Ji, Hui Xie

Abstract read
PubMed Full text at PMC Publisher
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

10 authors.

Yinggang Zheng *Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Songping Lan *School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, China.
Haoran Hu *State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China.
Xinwei HuangShanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Huan WangState Key Laboratory of Genetic Engineering, School of Life Sciences, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China.
He CaoSchool of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, China.
Xiaoli LiuCollege of Chinese Materia Medica and Yunnan Key Laboratory of Southern Medicinal Utilization, Yunnan University of Chinese Medicine, Kunming, China.
Yaowen YangCollege of Chinese Materia Medica and Yunnan Key Laboratory of Southern Medicinal Utilization, Yunnan University of Chinese Medicine, Kunming, China.
Shengguo JiSchool of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, China.
Hui XieState Key Laboratory of Genetic Engineering, School of Life Sciences, Zhangjiang Fudan International Innovation Center, Human Phenome Institute, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The dried ripe fruit or seed of Objective: This study aims to authenticate the geographical origins of Methods: Near-infrared spectroscopy (NIRS) combined with machine learning (ML) techniques was used to determine the specific geographical origins of Results: The results demonstrated that Fourier transform Near-infrared spectroscopy (FT-NIR) followed by a multi-layer perceptron (MLP) was the optimal strategy among all methods tested. This approach achieved a high accuracy of 96.97%. Additionally, feature dimensionality reduction analysis was applied using the Catboost model. This analysis identified certain spectral ranges that contained important features for the model. Conclusion: This study indicates that pretreatment of NIRS raw data and the use of ML are potential strategies for rapid and specific geographic authentication of plants.

Indexed as

Amomum tsaokofeature reduction analysisFT-NIRgeographical authenticationmachine learning

Identifiers

PMID41658529
PMCPMC12872912

What OpenQuestion holds

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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.