Evidence map›Paper›PMID 36952027›Full record

ArticleMikrochimica acta2023

A device-independent method for the colorimetric quantification on microfluidic sensors using a color adaptation algorithm.

Junjie Feng, Huiyun Jiang, Yan Jin, Shenghui Rong, Shiqiang Wang, Haozhi Wang, Lin Wang, Wei Xu, Bing Sun

Abstract read
PubMed Publisher
In one paragraph

Article in Mikrochimica acta, 2023. 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

9 authors.

Junjie FengSINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China. feng-jj@hotmail.com.ORCID http://orcid.org/0000-0001-8900-2691
Huiyun JiangSINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China.
Yan JinSINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China.
Shenghui RongOcean University of China, School of Electronic Engineering, 238 Songling Road, Qingdao, 266100, China.
Shiqiang WangSINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China.
Haozhi WangSINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China.
Lin WangSINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China.
Wei XuSINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China.
Bing SunSINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China. sunb.qday@sinopec.com.

Funding

National Natural Science Foundation of China 21908249
6 · The paper itself

Abstract

A general and adaptable method is proposed to reliably extract quantitative information from smartphone images of microfluidic sensors. By analyzing and processing the color information of selected standard substances, the influence of light conditions, device differences, and human factors could be significantly reduced. Machine learning and multivariate fitting methods were proved to be effective for chroma correction, and a key element was the training of sample size and the fitting form, respectively. A custom APP was developed and validated using a high-sensitivity chromium ion quantification paper chip. The average chroma deviations under different conditions were reduced by more than 75% in RGB color space, and the concentration test error was reduced by more than half compared with the commonly used method. The proposed approach could be a beneficial supplement to existing and potential colorimetry-based detection methods.

Indexed as

ColorimetryImage processingMicrofluidic sensorsPaper-based analytical devicesSmartphone

Identifiers

PMID36952027

What OpenQuestion holds

Textmetadata
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.