Development and Detailed Performance Study of a Carbon Paste Electrode for the Electrochemical Detection of Malachite Green

Author:

Dasgupta Samhita1,Nag Shreya12,Roy Runu Banerjee1,Bandyopadhyay Rajib1,Pramanik Panchanan3,Das Deepak Kumar3,Tudu Bipan1

Affiliation:

1. Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata 700106, West Bengal, India

2. University of Engineering and Management, Kolkata, West Bengal, India

3. Department of Chemistry and Nanoscience, GLA University, Mathura, Uttar Pradesh, India

Abstract

The present investigation aims to lighten a quick electrochemical detection technique of malachite green (MG) content using an easy and affordable carbon paste electrode (CPE). MG finds a wide area of application for controlling external fungi plus parasites on the fish egg for its antifungal and antiparasitic features. However, MG is noticed to be dreadfully poisonous and unsafe. Hence, the determination of MG in aquaculture is very much necessary. In this current report, the electrochemical behavior of CPE has been studied using a three-electrode system containing a silver-silver chloride (Ag/AgCl) reference electrode and a platinum counter electrode to record the corresponding cyclic voltammetry (CV) and differential pulse voltammetry (DPV) responses. CV plots in the applied potential range from 0.3 V to 1.5 V described a strong indication of MG existence, while DPV results successfully illustrated the quantification of several MG concentrations. The linear range of operation was from 10[Formula: see text][Formula: see text]M to 1000[Formula: see text][Formula: see text]M with the lowest limit of detection (LOD) as 0.78 [Formula: see text]M. Various concentrations like 50, 300, 600, 800, and 1000[Formula: see text][Formula: see text]M were assessed via principal component analysis (PCA) with effective data clustering (separability index (SI) found as 131.08). The prediction estimations of MG content using PLSR (partial least square regression) along with PCR (principal component regression) algorithms were also carried out, resulting in 95.39% and 93.02% prediction accuracies, respectively. Moreover, applying this CPE over natural aqueous sample extracts exhibited a reasonable recovery rate of 94.85–97.3%.

Publisher

World Scientific Pub Co Pte Ltd

Subject

General Medicine

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