Evidence map›Paper›PMID 41464968›Full record

ArticleFoods (Basel, Switzerland)2025

Spectral Multi-Scale Attention Fusion Network for Rapid Detection of Black Tea Adulteration Using a Handheld Spectrometer.

Jiawei Tang, Yongyan Chen, Qing Meng, Bo Zhao, Dongling Qiao, Guohua Zhao, Jia Chen

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Jiawei TangChinese-Hungarian Cooperative Research Centre for Food Science, College of Food Science, Southwest University, Chongqing 400715, China.
Yongyan ChenChinese-Hungarian Cooperative Research Centre for Food Science, College of Food Science, Southwest University, Chongqing 400715, China.
Qing MengIntegrative Science Center of Germplasm Creation in Western China (Chongqing) Science City, College of Food Science, Southwest University, Chongqing 400715, China.
Bo ZhaoKey Laboratory of Condiment Supervision Technology, State Administration for Market Regulation, Chongqing 400731, China.
Dongling QiaoChinese-Hungarian Cooperative Research Centre for Food Science, College of Food Science, Southwest University, Chongqing 400715, China.
Guohua ZhaoCollege of Life Sciences, Sichuan Normal University, Chengdu 610101, China.
Jia ChenChinese-Hungarian Cooperative Research Centre for Food Science, College of Food Science, Southwest University, Chongqing 400715, China.ORCID 0000-0002-2854-3563

Funding

Chongqing Key Project of Technological Innovation and Application Development CSTB2022TIAD-KPX0091National Key Research and Development Program of China 2024YFE0213900Open Fund of Yunnan Key Laboratory of Tea Germplasms Conservation and Utilization in the Lancang River Basin YNTGCU202509
6 · The paper itself

Abstract

Black tea is a widely consumed beverage whose high economic value has led some producers to illegally add artificial colorants such as Sunset Yellow, Tartrazine, and Ponceau 4R, posing health risks. Although near-infrared (NIR) spectroscopy offers a rapid, non-destructive detection method, its use in trace-level colorant detection is limited due to low adulterant concentrations and interference from natural tea pigments. Hence, we developed a rapid, non-destructive method for detecting trace adulteration (from 0.1 to 0.5 g·kg

Indexed as

adulterationartificial colorantsblack teahandheld spectrometerrapid detection

Identifiers

PMID41464968
PMCPMC12732540

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.