Evidence map›Paper›PMID 39599042›Full record

ArticleSensors (Basel, Switzerland)2024

Strategy for Accurate Detection of Six Tropane Alkaloids in Honey Using Lateral Flow Immunosensors.

Boyan Sun, Chuanlei Wang, Zile Wang, Jiayi Liang, Ke Han, Shuai Zhang, Chunchao Yin, Xiaomei Wang, Chujun Liu, Zhiyue Feng and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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

12 authors.

Boyan SunNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Chuanlei WangNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Zile WangNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Jiayi LiangDepartment of Chemistry, Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Ke HanNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Shuai ZhangNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Chunchao YinNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Xiaomei WangNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Chujun LiuNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Zhiyue FengNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
Sihan WangDepartment of Chemistry, Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Haiyang JiangNational Key Laboratory of Veterinary Public Health Security, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Department of Veterinary Pharmacology and Toxicology, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.ORCID 0000-0002-0529-0719

Funding

National Natural Science Foundation of China 32373096
6 · The paper itself

Abstract

Honey, a widely consumed food, is susceptible to contamination by various toxic substances during production. Tropane alkaloids, with their potent neurotoxicity, are frequently found in honey. Hence, there is an acute need for rapid and effective detection methods to monitor these alkaloids. Lateral flow immunoassay (LFIA), known for its simple operation, low cost, and reliable results, holds great promise. In this study, we developed an efficient and user-friendly analytical method for the simultaneous detection of six tropane alkaloids (atropine, L-hyoscyamine, scopolamine, anisodamine, homatropine, and apoatropine) in honey based on an AuNPs lateral flow immunoassay (AuNPs-LFIA) with broad-spectrum antibodies. Under optimal conditions, the calculated detection limits were 0.22, 0.29, 0.51, 6.34, 0.30, and 0.94 ng/mL, respectively. By diluting the honey sample five times, the contaminants can be readily detected using LFIA. Semi-quantitative and quantitative analyses can be completed within 17 min. This innovative method fills the void in LFIA for detecting tropane alkaloids and serves as a valuable reference for LFIA detection of honey samples, providing a crucial strategy for the accurate detection of these important compounds.

Indexed as

GoldHoneyTropanesAlkaloidsBiosensing TechniquesImmunoassayLimit of DetectionMetal NanoparticlesAlkaloidsGoldTropanesAuNP-LFIAhoneylateral flow immunoassaymulti-targettropane alkaloids

Identifiers

PMID39599042
PMCPMC11598261

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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.