Evidence map›Paper›PMID 38779497›Full record

ArticleFood chemistry: X2024

The utilization of a data fusion approach to investigate fingerprint profiles of dark tea from China's different altitudes.

Zhenhong Wang, Yuanxi Han, Liyou Zhang, Yongxiang Ye, Liping Wei, Liang Li

Abstract read
In one paragraph

Article in Food chemistry: X, 2024. 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

6 authors.

Zhenhong WangResources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China.
Yuanxi HanFood Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China.
Liyou ZhangResources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China.
Yongxiang YeFood Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China.
Liping WeiResources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China.
Liang LiFood Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dark tea refers to a kind of post-fermented product, and its quality and price vary owing to the distinct altitudes at which it grows. In this study, a novel method based on high performance liquid chromatography with a diode-array detector (HPLC-DAD) and an evaporative light scattering detector (HPLC-ELSD) was proposed for the classification of dark teas from distinct altitudes in China. Through implementing a strategy fusing feature-level data to construct a combined dataset, the classification performance of dark teas from distinct altitudes in China was evaluated after preprocessing. The results suggested that, through the feature fusion strategy, the identification accuracy rate increased from <70% of a single detector to 76.923%. After the implementation of preprocessing, the identification accuracy rate was further improved. Typically, the model identification accuracy rate after short-time Fourier Transform (STFT) treatment reached 92.85%, and the AUROC value was higher than 0.84, exhibiting a favorable generalization ability. This study provides a new thinking for the identification technology of dark teas from different altitudes in China.

Indexed as

ClassificationDark teaData fusionHigh-performance liquid chromatography

Identifiers

PMID38779497
PMCPMC11108843

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.