New Opportunities of Electrochemistry for Monitoring, Modulating, and Mimicking the Brain Signals

In vivo electrochemistry is a powerful key for unlocking the chemical consequences in neural networks of the brain. The past half-century has witnessed the technology revolutionization in this field along with innovations in electrochemical concepts, principles, methods, and devices. Present applications of electrochemical approaches have extended from measuring neurochemical concentrations to modulating and mimicking brain signals. In this Perspective, newly reported strategies for tackling long-standing challenges of in vivo electrochemical brain monitoring (i.e., basal level measurement, electroactivity dependence, in vivo stability, neuron compatibility, multiplexity, and implantable device fabrication) are highlighted. Moreover, recent progress on neuromodulation tools and neuromorphic devices in electrochemical frameworks is introduced. A glimpse of future opportunities for electrochemistry in brain research is offered at last.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Simultaneous identification and quantification of nitro-containing explosives by advanced chemometric data treatment of cyclic voltammetry at screen-printed electrodes

The simultaneous determination of three nitro-containing compounds found in the majority of explosive mixtures, namely hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), 2,4,6-trinitrotoluene (TNT) and pentaerythritol tetranitrate (PETN), is demonstrated using both qualitative and quantitative approaches involving the coupling of electrochemical measurements and advanced chemometric data processing. Voltammetric responses were obtained from a single bare screen-printed carbon electrode (SPCE), which exhibited marked mix-responses towards the compounds examined. The responses obtained were then preprocessed employing discrete wavelet transform (DWT) and the resulting coefficients were input to an artificial neural network (ANN) model. Subsequently, meaningful data was extracted from the complex voltammetric readings, achieving either the correct discrimination of the different commercial mixtures (100% of accuracy, sensitivity and specificity) or the individual quantification of each of the compounds under study (total NRMSE of 0.162 for the external test subset).

Dyes assay for measuring physicochemical parameters

A combination of selective fluorescent dyes has been developed for simultaneous quantitative measurements of several physicochemical parameters. The operating principle of the assay is similar to electronic nose and tongue systems, which combine nonspecific or semispecific elements for the determination of diverse analytes and chemometric techniques for multivariate data analysis. The analytical capability of the proposed mixture is engendered by changes in fluorescence signal in response to changes in environment such as pH, temperature, ionic strength, and presence of oxygen. The signal is detected by a three-dimensional spectrofluorimeter, and the acquired data are processed using an artificial neural network (ANN) for multivariate calibration. The fluorescence spectrum of a solution of selected dyes allows discreet reading of emission maxima of all dyes composing the mixture. The variations in peaks intensities caused by environmental changes provide distinctive fluorescence patterns which can be handled in the same way as the signals collected from nose/tongue electrochemical or piezoelectric devices. This optical system opens possibilities for rapid, inexpensive, real-time detection of a multitude of physicochemical parameters and analytes of complex samples.

Does chemometrics enhance the performance of electroanalysis?

This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of approximately +/-10% is acceptable.

High-dimensional model representation of cyclic voltammograms

Digital simulation costs present an obstacle on the way to high-speed, real-time, on-line theoretical analysis of experimental data in cyclic voltammetry. To overcome this difficulty, we propose to use solution mapping based on a correlated, hierarchical expansion of multivariate functions, known as high-dimensional model representation (HDMR). The nonlinear dependencies of the simulated voltammograms on multiple model parameters are represented in the form of compact look-up tables, from which approximate voltammograms can be calculated rapidly by interpolation, for any model parameter combinations from a predefined domain. Most importantly, the HDMR does not suffer from the problem of the exponential growth of the look-up tables with the number of model parameters. The creation of a solution map requires a single effort of simulating many voltammograms. However, once the map is prepared, it can be stored and reused many times without the need to repeat costly simulations. HDMR maps are created and examined for five examples of cyclic voltammetry models at planar macroelectrodes in a one-dimensional spatial geometry under pure diffusion transport conditions. The usefulness of the maps for rapid visualization and exploration of the effects of the parameters on the voltammograms and for rapid simultaneous estimation of many parameters from cyclic voltammetric data is demonstrated through computational experiments.

Resolving neurotransmitters detected by fast-scan cyclic voltammetry

Carbon-fiber microelectrodes are frequently used as chemical sensors in biological preparations. In this work, we evaluated the ability of cyclic voltammograms recorded at fast-scan rates to resolve neurochemicals when analyzed by principal component regression. A calibration set of 30 cyclic voltammograms was constructed from 9 different substances at a variety of concentrations. The set was reduced by principal component analysis, and it was found that 99.5% of the variance in the data could be captured with five principal components. This set was used to evaluate cyclic voltammograms obtained with one or two compounds present in solution. In most cases, satisfactory predictions of the identity and concentration of analytes were obtained. Chemical dynamics were also resolved from a set of fast-scan cyclic voltammograms obtained with the electrode implanted in a region of a brain slice that contains dopaminergic terminals. Following stimulation, principal component regression of the data resolved the changes in dopamine and pH that were evoked. In a second test of the method, vesicular release was measured from adrenal medullary cells and the data were evaluated with a calibration set composed of epinephrine and norepinephrine. Cells that secreted one or the other were identified. Overall, the results show that principal component regression with appropriate calibration data allows resolution of substances that give overlapping cyclic voltammograms.

Simultaneous determination of phenolic compounds by means of an automated voltammetric “electronic tongue”

This contribution describes the simultaneous determination of three phenolic compounds, o-cresol, p-chlorophenol and 4-chloro-3-methylphenol, using direct oxidation and amperometric detection coupled by signal deconvolution, accomplished via chemometric methods. Direct oxidation of phenolic compounds is performed at the surface of an epoxy-graphite transducer, by linear scan voltammetry. Due to strong signal overlapping, artificial neural networks (ANNs) were used during data treatment, in a combination of chemometrics and electrochemical sensors known as an "electronic tongue". To calibrate this system properly, a total of 80 mixed samples were prepared automatically by employing a sequential injection analysis (SIA) system designed to automatically generate the information needed to train the network. The phenolic compound concentration varied from 1 to 70 microM for o-cresol, from 0.5 microM to 140 microM for p-chlorophenol and from 1 microM to 100 microM for 4-chloro-3-methylphenol. A good prediction capability was obtained, with correlation coefficients >0.964 when the obtained values were compared with those expected for a set of 24 external test samples not used for training. The results presented here indicate that this technique is a simple and robust analytical method of environmental interest.

Determination of phenolic compounds by a polyphenol oxidase amperometric biosensor and artificial neural network analysis

The determination of phenolic compounds is significant given its toxicity, even at very low concentration levels. Amperometric determination of phenols is a simple technique available. Direct oxidation of phenols can be used, but another possibility is the use of polyphenol oxidase (tyrosinase) enzyme biosensors that oxidises the phenolic compounds into their corresponding quinones. Reduction of the resulting quinones accomplishes the amplification of the amperometric signal, as long as the result of the reduction process is the corresponding cathecol, this being able to be oxidised again by the polyphenol oxidase immobilized on the surface of the biosensor. In this communication, simultaneous determination of different phenols was carried out combining biosensor measurements with chemometric tools, in what is known as electronic tongue. The departure information used was the overlapped reduction voltammogram generated with the amperometric biosensor based on polyphenol oxidase. Artificial Neural Networks (ANN) were used for extraction and quantification of each compound. Phenol, cathecol and m-cresol formed the three-analyte study case resolved in this work. Good prediction ability was attained, and so, the separate quantification of these three phenols was accomplished.