A method for detecting pefloxacin with the aid of a web-based concentration prediction system

By using BSA-AuNCs-GTP-Tb ratiometric fluorescent nanoprobes and a web-based concentration prediction system, the problem of requiring specialized equipment and manual calculations for pefloxacin detection has been solved, enabling rapid, accurate, and batch detection of pefloxacin.

CN117517277BActive Publication Date: 2026-08-11HENAN POLYTECHNIC UNIV
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Patent Information

Application Number
CN202311481504.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-11-09
Publication Date
2026-08-11
Estimated Expiration
2043-11-09

AI Technical Summary

Technical Problem

Existing methods for detecting pefloxacin require expensive specialized instruments and skilled personnel, limiting their application and the locations where they can be used. Furthermore, manual calculations are complex.

Method used

A BSA-AuNCs-GTP-Tb ratiometric fluorescent nanoprobe combined with a web-based concentration prediction system was used. Fluorescence images were captured by a smart device and uploaded to the web system. A deep learning algorithm was then used for the quantitative detection of pefloxacin, simplifying the operation process.

Benefits of technology

It enables rapid, accurate, and batch testing without the need for specialized equipment or manual calculations. It is easy to operate, applicable to a wide range of locations, reduces environmental interference, and improves testing efficiency and accuracy.

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Abstract

This invention belongs to the fields of nanocomposite material preparation and machine learning technology. Specifically, it is a web-based concentration prediction system-assisted method for detecting pefloxacin. Based on the interaction between a ratiometric fluorescent probe (BSA-AuNCs-GTP-Tb) and pefloxacin, it exhibits a variety of visible fluorescent colors under ultraviolet light. The concentration prediction system detects the target object, extracts the fluorescent colors, and identifies the relationship between pefloxacin concentration and color data. The ratiometric fluorescent probe itself has good stability and, compared to single-signal fluorescent probes, is less affected by external factors, exhibiting higher anti-interference capabilities. The concentration prediction system, embedded with deep learning and machine learning, offers high efficiency, accuracy, and stable operation, along with advantages such as simple operation, ease of use, and wide applicability.
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