A deep learning-enabled smartphone platform for rapid and sensitive colorimetric detection of dimethoate pesticide.
Anal Bioanal Chem
; 415(29-30): 7127-7138, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-37770666
ABSTRACT
A novel deep learning-enabled smartphone platform is developed to assist a colorimetric aptamer biosensor for fast and highly sensitive detection of dimethoate. The colorimetric determination of dimethoate is based on the specific binding of dimethoate and aptamer, which leads to the aggregation of AuNPs in high-concentration NaCl solution, resulting in an obvious color change from red to blue. This color change provides sufficient data for self-learning enabled by a convolutional neural network (CNN) model, which is established to predict dimethoate concentration based on images acquired from a smartphone. To enhance user-friendliness for non-experts, the CNN model is then embedded into a smartphone app, enabling offline detection of dimethoate pesticide in real environments within just 15 min using a pre-configured colorimetric probe. The developed platform exhibits superior performance, achieving a regression coefficient of 0.9992 in the concentration range of 0-10 µM. Moreover, the app's performance is found to be consistent with the ELISA kit. These remarkable findings demonstrate the potential of combining colorimetric biosensors with smartphone-based deep learning methods for the development of portable and affordable tools for pesticide detection.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Praguicidas
/
Técnicas Biossensoriais
/
Aptâmeros de Nucleotídeos
/
Nanopartículas Metálicas
/
Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Anal Bioanal Chem
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China