Evidence map›Paper›PMID 39727855›Full record

ArticleBiosensors2024

A New Caffeine Detection Method Using a Highly Multiplexed Smartphone-Based Spectrometer.

Erhuan Zhuo, Huanxin Xia, Huan Hu, Yu Lin

Abstract read
In one paragraph

Article in Biosensors, 2024. 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. Tailoring NaSensors (Basel, Switzerland) · 2026
    Article
  2. 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

4 authors.

Erhuan ZhuoZhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China.ORCID 0009-0006-5987-9998
Huanxin XiaZhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China.
Huan HuZhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China.ORCID 0000-0002-1317-5470
Yu LinZhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Haining 314400, China.ORCID 0000-0001-5109-0972

Funding

the Natural Science Foundation of Zhejiang Province LQ21F050012
6 · The paper itself

Abstract

Smartphones equipped with highly integrated sensors are increasingly being recognized as powerful tools for rapid on-site testing. Here, we propose a low-cost, portable, and highly multiplexed smartphone-based spectrometer capable of collecting three types of spectra-transmission, reflection, and fluorescence-by simply replacing the optical fiber attached to the housing. Spectral analysis is performed directly on the smartphone using a custom-developed app. Furthermore, we introduce a high signal-to-noise ratio (SNR) caffeine detection scheme that leverages aspirin and salicylic acid as fluorescent probes, allowing for the rapid and straightforward detection of caffeine in various samples. The fluorescence quenching of the probes was found to be linearly related to the caffeine concentration (0-200 μM), and the recoveries of the commercially available caffeine-containing samples were in the range of 98.0333-105.6000%, with a limit of detection (LOD) of 2.58 μM. The reliability and stability of the on-site assay using the smartphone spectrometer were verified. More importantly, this spectrometer demonstrates great potential as a versatile device for use outside of laboratory settings by enabling different operating modes tailored to various scenarios.

Indexed as

CaffeineLimit of DetectionSmartphoneBiosensing TechniquesReproducibility of ResultsSignal-To-Noise RatioSpectrometry, FluorescenceCaffeineaspirincaffeineon-site detectionpoint-of-care testingsmartphone-based spectroscopy

Identifiers

PMID39727855
PMCPMC11674217

What OpenQuestion holds

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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.