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<p>Quercetin (QUR), a widely distributed plant-based flavonoid, has many important medicinal properties, which is why it is extensively used in various types of food supplements, beverages, and drugs, of course within an acceptable daily consumption limit. As a result, there is a need for the development of rapid and inexpensive mechanism that can detect the presence of Quercetin. In this paper, a simple, highly cost-effective, and sensitive carbon paste electrode (CPE) based electrochemical sensor is developed for detecting the active biomolecule, Quercetin. The developed sensor has been used to investigate the electrochemical nature of QUR by performing electroanalysis using an efficient three electrode system approach. The voltammetric response exhibits the satisfactory detection of QUR of various concentrations in the wide linear range of 5 µM to 200 µM with 0.1&#xa0;M phosphate buffered saline 5 (PBS 5) buffer solution and the limit of detection (LOD) was evaluated to be 0.206 µM. The appropriate buffer and pH of the medium was selected using suitable optimization study. The effect of scan rate variation was investigated in the range of 0.01 Vs<sup>-1</sup> to 0.25 Vs<sup>-1</sup>. The statistical analysis using unsupervised machine learning based clustering method principal component analysis (PCA) was also performed on the voltammogram data obtained from concentration variation study considering five different QUR concentrations with separability index (SI) value of 10.1695 for effective data clustering. The repeatability, reproducibility and long-term stability were measured with acceptable relative standard deviation (RSD) values of 5.99%, 1.00% and 10.56%, respectively. The predictive outcome of the developed sensor was recorded using supervised machine learning based partial least square regression (PLSR) model with acceptable average accuracy of 83.74% and minimal root mean square error of prediction (RMSEP) value of 3.08. The reliability of the sensor was also evaluated further by performing the real sample analysis of the developed CPE sensor in QUR enriched vegetables such as organic onion and tomato with satisfactory average recovery rates of 96.41% and 98.27% respectively.</p>

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Development and Performance Analysis of CPE Based Sensor for Voltammetric Detection of Quercetin

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • A. H.M. Toufique Ahmed,
  • Samhita Dasgupta,
  • Ipshita Bhattacharjee,
  • Shreya Firdousi,
  • Sumani Mukherjee,
  • Rajib Bandyopadhyay,
  • Bipan Tudu

摘要

Quercetin (QUR), a widely distributed plant-based flavonoid, has many important medicinal properties, which is why it is extensively used in various types of food supplements, beverages, and drugs, of course within an acceptable daily consumption limit. As a result, there is a need for the development of rapid and inexpensive mechanism that can detect the presence of Quercetin. In this paper, a simple, highly cost-effective, and sensitive carbon paste electrode (CPE) based electrochemical sensor is developed for detecting the active biomolecule, Quercetin. The developed sensor has been used to investigate the electrochemical nature of QUR by performing electroanalysis using an efficient three electrode system approach. The voltammetric response exhibits the satisfactory detection of QUR of various concentrations in the wide linear range of 5 µM to 200 µM with 0.1 M phosphate buffered saline 5 (PBS 5) buffer solution and the limit of detection (LOD) was evaluated to be 0.206 µM. The appropriate buffer and pH of the medium was selected using suitable optimization study. The effect of scan rate variation was investigated in the range of 0.01 Vs-1 to 0.25 Vs-1. The statistical analysis using unsupervised machine learning based clustering method principal component analysis (PCA) was also performed on the voltammogram data obtained from concentration variation study considering five different QUR concentrations with separability index (SI) value of 10.1695 for effective data clustering. The repeatability, reproducibility and long-term stability were measured with acceptable relative standard deviation (RSD) values of 5.99%, 1.00% and 10.56%, respectively. The predictive outcome of the developed sensor was recorded using supervised machine learning based partial least square regression (PLSR) model with acceptable average accuracy of 83.74% and minimal root mean square error of prediction (RMSEP) value of 3.08. The reliability of the sensor was also evaluated further by performing the real sample analysis of the developed CPE sensor in QUR enriched vegetables such as organic onion and tomato with satisfactory average recovery rates of 96.41% and 98.27% respectively.