Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak

Maximizing Electrochemical Information: A Perspective on Background-Inclusive Fast Voltammetry

This perspective encompasses a focused review of the literature leading to a tipping point in electroanalytical chemistry. We tie together the threads of a "revolution" quietly in the making for years through the work of many authors. Long-held misconceptions about the use of background subtraction in fast voltammetry are addressed. We lay out future advantages that accompany background-inclusive voltammetry, particularly when paired with modern machine-learning algorithms for data analysis.

Revealing the solid-solution interface interference behaviors between Cu2+ and As(III) via partial peak area analysis of simulations and experiments

The mutual interference in the sensing detection of heavy metal ions (HMIs) is considerably serious and complex. Besides, the co-existed ions may change the stripping peak intensity, shape and position of the target ion, which partly makes peak current analysis inaccurate. Herein, a promising approach of partial peak area analysis was proposed firstly to research the mutual interference. The interference between two species on their electrodeposition processes was investigated by simulating different kinetics parameters, including surface coverage, electro-adsorption, -desorption rate constant, etc. It was proved that the partial peak area is sensitive and regular to these interference kinetics parameters, which is favorable for distinctly identifying different interferences. Moreover, the applicability of the partial peak area analysis was verified on the experiments of Cu2+, As(III) interference at four sensing interfaces: glassy carbon electrode, gold electrode, Co3O4, and Fe2O3 nanoparticles modified electrodes. The interference behaviors between Cu2+ and As(III) relying on solid-solution interfaces were revealed and confirmed by physicochemical characterizations and kinetics simulations. This work proposes a new descriptor (partial peak area) to recognize the interference mechanism and provides a meaningful guidance for accurate detection of HMIs in actual water environment.

Determination of copper(II) and lead(II) ions in dairy products by an efficient and green method of heat-induced homogeneous liquid-liquid microextraction based on a deep eutectic solvent

In this study, a new homogeneous liquid-liquid microextraction method using a deep eutectic solvent has been developed for the extraction of Cu(II) and Pb(II) ions in dairy products. Initially, the deep eutectic solvent was synthesized using choline chloride and p-chlorophenol and used as the extraction solvent. The synthesized solvent was soluble in milk at 70 °C and its separation from the sample was performed by decreasing the temperature. By cooling, a cloudy solution was formed due to the low solubility of the solvent at low temperatures. On centrifugation, the fine droplets of the solvent containing the analytes settled at the bottom of the tube by sedimentation. The enriched analytes were determined by flame atomic absorption spectrometry. The effect of some important parameters such as the amount of protein precipitating agent , complexing agent amount, extraction solvent volume, salt addition, pH, and temperature on the extraction efficiency of the method was studied and optimized. Under the optimal conditions, the linear ranges of the method for Cu(II) and Pb(II) ions were obtained in the ranges of 0.10-50 and 0.50-50 μg L-1 with detection limits of 0.04 and 0.18 μg L-1, respectively. The repeatability of the developed method, expressed as relative standard deviation, was determined to be 3.2 and 3.9% for Cu(II) and Pb(II) ions, respectively. Finally, by determining the concentration of Cu(II) and Pb(II) ions in milk, doogh, and cheese samples, the feasibility of the method was successfully confirmed with the extraction recoveries of 95.9 and 92.1% for Cu(II) and Pb(II) ions, respectively.

Application and Progress of Chemometrics in Voltammetric Biosensing

The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.

Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms

The use of deep neural networks (DNNs) for the classification of electrochemical mechanisms using simulated voltammograms with one cycle of potential for training has previously been reported. In this paper, it is shown how valuable additional patterns for mechanism distinction become available when a new DNN is trained simultaneously on images obtained from three cycles of potential using tensor inputs. Significant improvements, relative to the single cycle training, in achieving the correct classification of E, EC_1^st and EC_2^nd mechanisms (E = electron transfer step and C_1^st and C_2^nd are first and second order follow up chemical reactions, respectively) are demonstrated with noisy simulated data for conditions where all mechanisms are close to chemically reversible and hence difficult to distinguish, even by an experienced electrochemist. Challenges anticipated in applying the new DNN to the classification of experimental data are highlighted. Directions for future development are also discussed.

Simultaneous serotonin and dopamine monitoring across timescales by rapid pulse voltammetry with partial least squares regression

Many voltammetry methods have been developed to monitor brain extracellular dopamine levels. Fewer approaches have been successful in detecting serotonin in vivo. No voltammetric techniques are currently available to monitor both neurotransmitters simultaneously across timescales, even though they play integrated roles in modulating behavior. We provide proof-of-concept for rapid pulse voltammetry coupled with partial least squares regression (RPV-PLSR), an approach adapted from multi-electrode systems (i.e., electronic tongues) used to identify multiple components in complex environments. We exploited small differences in analyte redox profiles to select pulse steps for RPV waveforms. Using an intentionally designed pulse strategy combined with custom instrumentation and analysis software, we monitored basal and stimulated levels of dopamine and serotonin. In addition to faradaic currents, capacitive currents were important factors in analyte identification arguing against background subtraction. Compared to fast-scan cyclic voltammetry-principal components regression (FSCV-PCR), RPV-PLSR better differentiated and quantified basal and stimulated dopamine and serotonin associated with striatal recording electrode position, optical stimulation frequency, and serotonin reuptake inhibition. The RPV-PLSR approach can be generalized to other electrochemically active neurotransmitters and provides a feedback pipeline for future optimization of multi-analyte, fit-for-purpose waveforms and machine learning approaches to data analysis.

Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry

Electrochemical sensors and biosensors have been successfully used in a wide range of applications, but systematic optimization and nonlinear relationships have been compromised for electrode fabrication and data analysis. Machine learning and experimental designs are chemometric tools that have been proved to be useful in method development and data analysis. This minireview summarizes recent applications of machine learning and experimental designs in electroanalytical chemistry. First, experimental designs, e.g., full factorial, central composite, and Box-Behnken are discussed as systematic approaches to optimize electrode fabrication to consider the effects from individual variables and their interactions. Then, the principles of machine learning algorithms, including linear and logistic regressions, neural network, and support vector machine, are introduced. These machine learning models have been implemented to extract complex relationships between chemical structures and their electrochemical properties and to analyze complicated electrochemical data to improve calibration and analyte classification, such as in electronic tongues. Lastly, the future of machine learning and experimental designs in electrochemical sensors is outlined. These chemometric strategies will accelerate the development and enhance the performance of electrochemical devices for point-of-care diagnostics and commercialization.

Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative comparison of experimental data with extensive simulated data sets derived from models proposed to describe complex electrochemical reactions. While the methodology is generic to all forms of dynamic electrochemistry, including the widely used direct current cyclic voltammetry, this review highlights advances achievable in the parameterisation of large amplitude alternating current voltammetry. One significant advantage this technique offers in terms of data analysis is that Fourier transformation provides access to the higher order harmonics that are almost devoid of background current. Perspectives on the technical advances needed to develop intelligent data analysis strategies and make them generally available to users of voltammetry are provided.

Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu²⁺ contents on the SWASV signals of Pb²⁺ was investigated, and a nonlinear relationship between Pb²⁺ concentration and the peak currents of Pb²⁺ and Cu²⁺ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb²⁺ and Cu²⁺) and one output (i.e., Pb²⁺ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R²) and root-mean-square error (RMSE). Results showed that the SVR model with R² and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb²⁺ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring.