Evidence map›Paper›PMID 37291144›Full record

ArticleNPJ science of food2023

Predicting the storage time of green tea by myricetin based on surface-enhanced Raman spectroscopy.

Mengxuan Xiao, Yingqi Chen, Fangling Zheng, Qi An, Mingji Xiao, Huiqiang Wang, Luqing Li, Qianying Dai

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ science of food, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
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

3 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
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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 at 1 institution in 1 country.

Mengxuan XiaoState Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Yingqi ChenState Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Fangling ZhengState Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Qi AnState Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Mingji XiaoState Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Huiqiang WangState Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Luqing LiState Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China.
Qianying DaiState Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, Anhui, China. daiqianying@ahau.edu.cn.ORCID http://orcid.org/0000-0003-3258-1287
Anhui Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quality of green tea changes rapidly due to the oxidation and degradation of polyphenols during storage. Herein, a simple and fast Surface-enhanced Raman spectroscopy (SERS) strategy was established to predict changes in green tea during storage. Raman spectra of green tea with different storage times (2020-2015) were acquired by SERS with silver nanoparticles. The PCA-SVM model was established based on SERS to quickly predict the storage time of green tea, and the accuracy of the prediction set was 97.22%. The Raman peak at 730 cm

Identifiers

PMID37291144
PMCPMC10250423
OpenAlexW4379967332

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.