Evidence map›Paper›PMID 41235060›Full record

ArticleFood chemistry: X2025

Rapid detection of synthetic pigments in black tea using hyperspectral imaging technology and machine learning.

Zhihan Wang, Weijie Peng, Zhengrui Tian, Lin Zhu, Mengqi Guo, Zhiwei Chen, Hewei Meng, Chunwang Dong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Food chemistry: X, 2025. 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. Review
  2. Review
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

8 authors.

Zhihan WangTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Weijie PengTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Zhengrui TianTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Lin ZhuTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Mengqi GuoTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Zhiwei ChenTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Hewei MengCollege of Mechanical and Electronic Engineering, Shihezi University, Shihezi 832000, China.
Chunwang DongTea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we propose a novel approach that integrates hyperspectral imaging with machine learning to address the challenge of rapid and non-destructive detection of artificial pigment adulteration in Wuyi Congou tea. Compared with traditional chemical methods, this method enables the identification of pigment types, quantification of adulteration rate, and visualization of pigment distribution. A hyperspectral dataset of black tea adulterated with three synthetic pigments (Tartrazine, Carmine, and Sunset Yellow) was constructed, supporting the establishment of qualitative identification and quantitative prediction models. The qualitative model achieved an accuracy greater than 98 %, while the quantitative model yielded a predictive correlation coefficient (R

Indexed as

ChemometricsHyperspectral imagingMachine learningQualitative analysisQuantitative analysisSynthetic pigments

Identifiers

PMID41235060
PMCPMC12607121

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.