Issue 5, 2025

Molecularly imprinted electrochemical sensor to sensitively detect tetramethylpyrazine in Baijiu

Abstract

Tetramethylpyrazine (TMP) is a compound known for its natural health benefits, but current detection methods for TMP are overly expensive and time-consuming. In this study, we developed functional materials with TMP molecular recognition properties using molecularly imprinted technology. As TMP does not produce electrochemical signals in the detection potential range, hexacyanoferrate was selected as a redox probe, combined with the highly conductive polymer PEDOT:PSS to enhance electrode conductivity. When coupled with the TMP-specific functional materials prepared through molecular imprinting, an electrochemical sensor specifically recognizing TMP was successfully developed, and this was confirmed through characterization techniques such as ultraviolet spectroscopy and scanning electron microscopy. Additionally, the crucial experimental parameters were optimized for improved performance. Under optimal conditions, the use of differential pulse voltammetry (DPV) to measure the peak currents of hexacyanoferrate showed a linear relationship with TMP concentrations from 0.50 × 10−6 to 5.00 × 10−3 M, achieving a detection limit of 2.1 × 10−7 M. This method proved effective for quantifying TMP in Baijiu samples, demonstrating good precision with relative standard deviations (RSD) ranging from 2.71% to 3.28%, and recovery percentages between 95.77% and 101.88%. These results indicate the potential of the molecularly imprinted polymer (MIP) sensor for accurately measuring TMP in actual samples.

Graphical abstract: Molecularly imprinted electrochemical sensor to sensitively detect tetramethylpyrazine in Baijiu

Article information

Article type
Paper
Submitted
21 Dec 2024
Accepted
19 Jan 2025
First published
20 Jan 2025
This article is Open Access
Creative Commons BY license

Analyst, 2025,150, 887-893

Molecularly imprinted electrochemical sensor to sensitively detect tetramethylpyrazine in Baijiu

Y. Rui, J. Wu, Q. Tang, J. Pu, W. Wang and S. Ding, Analyst, 2025, 150, 887 DOI: 10.1039/D4AN01559B

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