Array Of Ion-selective Electrodes Research Articles

Statistical generation of training sets for measuring NO3(-), NH4(+) and major ions in natural waters using an ion selective electrode array.

Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples. A novel signal processor, underpinned by an appropriate sensor array, is proposed that overcomes previous limitations by explicitly integrating basic chemical constraints (e.g., charge balance). This work further presents a rationalized process for the development of such in situ instrumentation for NO3(-) and NH4(+), including a statistical-modeling strategy for instrument design, training/calibration, and validation. Statistical analysis reveals that historical concentrations of major ionic constituents in natural waters across New England strongly covary and are multi-modal. This informs the design of a statistically appropriate training set, suggesting that the strong covariance of constituents across environmental samples can be exploited through appropriate signal processing mechanisms to further improve estimates of minor constituents. Two artificial neural network architectures, one expanded to incorporate knowledge of basic chemical constraints, were tested to process outputs of a multi-sensor array, trained using datasets of varying degrees of statistical representativeness to natural water samples. The accuracy of ANN results improves monotonically with the statistical representativeness of the training set (error decreases by ∼5×), while the expanded neural network architecture contributes a further factor of 2-3.5 decrease in error when trained with the most representative sample set. Results using the most statistically accurate set of training samples (which retain environmentally relevant ion concentrations but avoid the potential interference of humic acids) demonstrated accurate, unbiased quantification of nitrate and ammonium at natural environmental levels (±20% down to <10 μM), as well as the major ions Na(+), K(+), Ca(2+), Mg(2+), Cl(-), and SO4(2-), in unprocessed samples. These results show promise for the development of new in situ instrumentation for the support of scientific field work.

Automated Lettuce Nutrient Solution Management Using an Array of Ion-Selective Electrodes

Automated sensing and control of macronutrients in hydroponic solutions would allow more efficient management of nutrients for crop growth in closed systems. This article describes the development and evaluation of a computer-controlled nutrient management system with an array of ion-selective electrodes (ISEs) and fertilizer pumps that could effectively manage concentrations of NO3, K, and Ca ions in closed hydroponic systems. A fertilizer dosing algorithm was developed to calculate the volumes of individual nutrient stock solutions to be supplied based on the measurement of present concentrations in a mixing tank. In a five-step spiking test, the system was able to formulate five different concentrations of NO3 and Ca ions comparable to the target concentrations, showing almost 1:1 relationships between the actual and target values. However, actual K concentrations were prepared almost 40% higher than target concentrations due to low K estimates. The use of a two-point normalization method in conjunction with ISEs was effective in minimizing signal drifts resulting from in-line measurement of ion concentrations in the closed system. The automated nutrient management system was used to grow lettuce in a greenhouse with the ebb and flow method. After water was automatically added to the mixing tank to maintain the level of water within 10% of the total volume of the tank, the amounts of three different salts, i.e., KNO3, KH2PO4, and Ca(NO3)2, were variably supplied to the mixing tank based on real-time measurement of concentrations. The three ions were automatically controlled to reach target concentrations of 280, 140, and 70 mg L-1 within errors of -7.7 ±28.1, 20.8 ±28.5, and -5.6 ±8.2 mg L-1 for NO3, K, and Ca ions, respectively.

A Sparsity-Based Method for Blind Compensation of a Memoryless Nonlinear Distortion: Application to Ion-Selective Electrodes

In this paper, we propose a method for blind compensation of a memoryless nonlinear distortion. We assume as prior information that the desired signal admits a sparse representation in a transformed domain that should be known in advance. Then, given that a nonlinear distortion tends to generate signals that are less sparse than the desired one, our proposal is to build a compensating function model that gives rise to a maximally sparse signal. The implementation of this proposal has, as central elements, a criterion built upon an approximation of the $\ell _{0}$ -norm, the use of polynomial functions as compensating structures, and an optimization strategy based on sequential quadratic programming. We provide a theoretic analysis for an $\ell _{0}$ -norm criterion and results considering synthetic data. We also employ the method in an actual application related to chemical analysis via ion-selective electrode arrays.

Application of mathematical models and genetic algorithm to simulate the response characteristics of an ion selective electrode array for system recalibration

With proper training procedures, including the use of a sufficiently large training sample set, ISE array with Back-Propagation Neural Networks (BPNNs) can be used to simultaneously determine the concentrations for multiple ions in environmental water. However, it is not practical for end-users to re-measure the whole training sample set – dozens of samples – to re-build the BPNN model every time after a long storage or measurement period, since both the sensitivity and selectivity of ISEs would be gradually degraded over time. In this study, instead of using BPNNs, we developed and patented some mathematical models to describe the based characteristics of the ISE array's response, including the sensitivity and selectivity. The developed mathematical equations can be used to simulate the response values of ISE array, and to determine the analytes' concentrations from unknown samples. By measuring only a couple of standard solutions, which are practical to the end users, the ISE array can be automatically recalibrated using genetic algorithm (GA). This research is based on an array of Prussian blue modified Carbon Paste Electrodes (PB-CPEs), and the interferences between nitrate (NO3) and chloride (Cl) was used as a case study.

Application of Blind Source Separation Methods to Ion-Selective Electrode Arrays in Flow-Injection Analysis

As shown recently, the interference problem typical of ion-selective electrodes can be dealt with via smart arrays adjusted by blind source separation methods. In this letter, we resume this study and show that such an approach can be applied even when faced with a limited number of samples acquired through flow-injection analysis.

Discrimination of Soils and Assessment of Soil Fertility Using Information from an Ion Selective Electrodes Array and Artificial Neural Networks

AbstractMultichannel sensor measurements combined with advanced treatment is the departure point for a new concept in sensorics, the electronic tongue. Our setup worked with an array of 20 ion selective electrodes plus an artificial neural network used as a pattern recognition method applied to soil analysis. With this design, we got a versatile tool which was able to perform qualitative and quantitative determinations. As first application, the qualitative discrimination between six distinct soil types based on their extractable components was attempted. The procedure was simplified to a single extraction step before measurements. Water, a BaCl2 saline solution and an acetic acid extract were evaluated as extracting agents. The best performance was reached with the acetic acid extraction method with a correct classification rate and sensitivity both of 94%, and a specificity of 100%. In addition, a quantitative determination of several physicochemical properties of agricultural interest, such as organic carbon content and selected cations (like K+ or Mg2+) and anions (like NO3− or Cl−) was also demonstrated, showing satisfactory agreement with the reference methods.

Application of neural networks with novel independent component analysis methodologies for the simultaneous determination of cadmium, copper, and lead using an ISE array

The paper introduces a novel chemometric strategy based on independent component analysis (ICA) coupled with a back‐propagation neural network. In this approach, one of the most popular ICA methods, the fast fixed‐point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. As a case study, an ion‐selective electrode (ISE) array, consisting of three working electrodes and one reference electrode, was used for the simultaneous determination of three heavy metals (cadmium, copper, and lead) in aqueous solutions, which are normally prone to severe interferences. The robustness and appropriateness of the approach were assessed using the average mean of relative error (MRE) of triplicated external validation. After configuration and optimization, the average MRE for Cu was &lt;5%. For the determination of Cd and Pb, whose ISEs normally cannot tolerate Cu ions even at the microgram per liter levels, the MREs were 8%. This article demonstrated that this approach can be applied to the detection of heavy metal contamination in industrial wastewater with prediction accuracies comparable with other popular quantitative chemometric neural network methods. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis

The development of chemical sensor arrays based on Blind Source Separation (BSS) provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE). The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data.

Beer classification by means of a potentiometric electronic tongue

In this work, an electronic tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, principal component analysis (PCA), which allowed identifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the correct recognition of sample varieties (81.9% accuracy). In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. In this respect, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show an ET quantitative application, beer alcohol content was predicted from the array data employing an artificial neural network model (root mean square error for testing subset was 0.131 abv).

Automated sensing of hydroponic macronutrients using a computer-controlled system with an array of ion-selective electrodes

Highlights An ISE-based, automated test stand for direct measurement of hydroponic nutrients is developed. The sensitivity and selectivity of PVC membranes for NO3, K, Ca, and Mg are tested. Tested...

Automated sensing of hydroponic macronutrients using a computer-controlled system with an array of ion-selective electrodes

Automated sensing of macronutrients in hydroponic solutions would allow more efficient management of nutrients for crop growth in closed hydroponic systems. Ion-selective electrodes (ISEs) are a promising approach because they can directly measure the analyte with a wide range of sensitivity and are small and portable. However, signal drift and reduced accuracy over time are major concerns with an in-line management system based on continuous immersion of ISEs in a solution. An ISE-based, computer-controlled measurement system for automatic sampling, calibration, and electrode rinsing is a viable technique to improve accuracy and precision in the determination of nutrient concentration. This study reports on development and evaluation of such a system for direct measurement of macronutrients in recirculating hydroponic solutions. The sensitivity and selectivity of PVC membrane-based ISEs fabricated with TDDA–NPOE, valinomycin, and calcium ionophore II were satisfactory for measuring NO3–N, K, and Ca concentrations in single-ion solutions over ranges typical of hydroponic solutions. However, an acceptable Mg membrane was not identified. An ISE array incorporating the selected membranes, in combination with a computer-controlled measurement system, was evaluated for the direct measurement of NO3–N, K, and Ca concentration in paprika hydroponic nutrient solutions. Using previously developed baseline correction and two-point normalization methods, ISE-measured NO3–N and K concentrations in spiked or diluted hydroponic nutrient samples were strongly related to those determined using standard laboratory instruments (R2=0.85). However, the tested Ca electrode did not provide satisfactory results due to reduced sensitivity and poor selectivity in the hydroponic solution. The approach used in this research, where a base solution is used for both referencing the baseline and rinsing electrodes and two calibration solutions are injected to implement the two-point normalization method, could be used in an automated sensing system for hydroponic nutrients in greenhouses. Additional research is required to identify ion-selective membranes for Ca and Mg ions in hydroponic solutions.

Effect of lead accumulation in maize leaves on their chemical images created by a flow-through electronic tongue

A flow-through electronic tongue based on miniaturized ion-selective electrode array was used for the classification of the maize leaf samples (Zea mays) exposed to media containing Pb(II) ions. The system provided a good recognition of the extracts from the plant leaves treated with solutions of varying concentrations of Pb(NO3)2. Additionally, samples derived from specific segments of the maize leaf, representing different developmental stages of cells, were also discriminated. The presented results showed that the developed sensor array combined with Partial Least Squares-Discriminant Analysis (PLS-DA) technique allowed to recognize the plant samples on the basis of the changes in the ionic composition of the leaf homogenates.

A calcium ion-selective electrode array for monitoring the activity of HepG2/C3As in a microchannel

Variations in calcium ion (Ca^(2+)) concentrations play a crucial role in cell–cell communication; periodic changes act as cellular signals. In this study, we developed a solid-state calcium ion-selective electrode (Ca^(2+) ISE) integrated in a microchannel to measure extracellular Ca^(2+) variations; an ISE has a high detection sensitivity (as low as 10^(−9) M) and selectivity against potassium ions (K^+). The sensor was used to monitor the cellular activities of HepG2/C3As cultured in a microchannel. We measured a periodically oscillating extracellular Ca^(2+) concentration that was superimposed on a gradual increase in the Ca^(2+) concentration after potassium chloride (KCl) stimulation. The oscillation period was in the range of 30–50 s, which was in good agreement with the literature.

Classification of amino acids and oligopeptides with the use of multi-mode chemical images obtained with ion selective electrode array

The analysis of amino acids and oligopeptides as important bioanalytes is a significant task in medical diagnostics and proteomic research and currently it is based on chromatographic and electrophoretic methods. In this paper, an approach based on sensor array coupled with titration is proposed for effective recognition of 5 amino acids and 4 oligopeptides. It is shown, that the increase of information gained during measurements of sensors’ signals in various pH allows for capturing more data on interaction of amino acids and oligopeptides with various polymeric membranes of potentiometric electrodes used in the array, and that leads to the lowering of classification errors in partial least squares analysis.

Recognition of Mineral Water with an Electronic Tongue Based on PCA and PNN

Electronic Tongue is a kind of intelligent equipment which is used to distinguish tastes. An electronic tongue composed of a sensor array of ion-selective electrodes has been developed and used for the qualitative analysis of five different brands of mineral water. The acquired original data has been optimized by principal component analysis (PCA) and then the probabilistic neural network (PNN) model is designed to process the data. The application results show that the performance of the proposed method has surpasses the traditional BP neural network algorithm, the speed of recognition is fast and the accuracy rate can reach 100%, which gives the electronic tongue system good practicability and feasibility.

An Electronic Tongue System for Recognition of Mineral Water Based On Principle Component Analysis and Wavelet Neural Network

Electronic Tongue is a kind of intelligent equipment which is used to distinguish tastes. An electronic tongue made by a sensor array of ion-selective electrodes (ISE) has been developed and used for the qualitative analysis of five different kinds of mineral water. The acquired original data has been optimized by the principle component analysis (PCA) and independent component analysis (ICA). Then a wavelet neural network (WNN) model was designed based on the local optimalizing searching characteristic of BP neural network and an appropriate set of the parameters. The application results show that the performance of the proposed method surpasses the traditional BP algorithm. It can improve convergence and the learning capability of the network, and gives the Electronic Tongue a higher aggregate classification rate.

A Dataset for the Design of Smart Ion-Selective Electrode Arrays for Quantitative Analysis

Smart sensors arrays (SSAs) provide a flexible approach to deal with the interference problem typical of ion-selective electrodes (ISEs). The development of the core of a SSA, the signal processing algorithm, often requires a dataset containing input-output measurements. Motivated by that, this letter presents a set of experiments with arrays of ISEs. The acquired dataset is publicly available in a web page where published results with these data can be added for benchmarking.

Wet Chemistry experiments on the 2007 Phoenix Mars Scout Lander mission: Data analysis and results

Chemical analyses of three Martian soil samples were performed using the Wet Chemistry Laboratories on the 2007 Phoenix Mars Scout Lander. One soil sample was obtained from the top ∼2 cm (Rosy Red) and two were obtained at ∼5 cm depth from the ice table interface (Sorceress 1 and Sorceress 2). When mixed with water in a ∼1:25 soil to solution ratio (by volume), a portion of the soil components solvated. Ion concentrations were measured using an array of ion selective electrodes and solution conductivity using a conductivity cell. The measured concentrations represent the minimum leachable ions in the soil and do not take into account species remaining in the soil. Described is the data processing and analysis for determining concentrations of seven ionic species directly measured in the soil/solution mixture. There were no significant differences in concentrations, pH, or conductivity, between the three samples. Using laboratory experiments, refinement of the surface calibrations, and modeling, we have determined a pH for the soil solution of 7.7(±0.3), under prevalent conditions, carbonate buffering, and PCO2 in the cell headspace. Perchlorate was the dominant anion in solution with a concentration for Rosy Red of 2.7(±1) mM. Equilibrium modeling indicates that measured [Ca2+] at 0.56(±0.5) mM and [Mg2+] at 2.9(±1.5) mM, are consistent with carbonate equilibrium for a saturated solution. The [Na+] and [K+] were 1.4(±0.6), and 0.36(±0.3) mM, respectively. Results indicate that the leached portion of soils at the Phoenix landing site are slightly alkaline and dominated by carbonate and perchlorate. However, it should be noted that there is a 5–15 mM discrepancy between measured ions and conductivity and another species may be present.

A Bayesian Nonlinear Source Separation Method for Smart Ion-Selective Electrode Arrays

Potentiometry with ion-selective electrodes (ISEs) provides a simple and cheap approach for estimating ionic activities. However, a well-known shortcoming of ISEs regards their lack of selectivity. Recent works have suggested that smart sensor arrays equipped with a blind source separation (BSS) algorithm offer a promising solution to the interference problem. In fact, the use of blind methods eases the time-demanding calibration stages needed in the typical approaches. In this work, we develop a Bayesian source separation method for processing the outputs of an ISE array. The major benefit brought by the Bayesian framework is the possibility of taking into account some prior information, which can result in more realistic solutions. Concerning the inference stage, it is conducted by means of Markov chain Monte Carlo (MCMC) methods. The validity of our approach is supported by experiments with artificial data and also in a scenario with real data.

Response of a copper(II) and iron(III) ion-selective electrode bielectrode array in saline media

A bielectrode array comprising a jalpaite membrane (i.e., Ag 1.5Cu 0.5S) copper(II) ion-selective electrode (ISE) and chalcogenide glass membrane (i.e., Fe 2.5(Se 60Ge 28Sb 12) 97.5) iron(III) ISE has been assembled by individually wiring each solid-state sensor into a single electrode body. Furthermore, a dual metal ion buffer calibration standard incorporating copper(II) and iron(III) coordinating ligands to regulate the levels of free copper(II) and iron(III) in the buffer has been developed to enable simultaneous calibration of the bielectrode ISE array. In this work, the bielectrode ISE array has been employed in the continuous flow analysis (CFA) of free copper(II) and iron(III) in seawater media. It is shown that the individual electrodes displayed Nernstian response in the metal ion buffer calibration standard over a wide dynamic range (viz., 10 −15 to 10 −5 M aCu 2+ and 10 −21 to 10 −11 M aFe 3+), and the results of repetitive CFA analyses of free copper(II) and iron(III) in seawater are commensurate with the typical values found in coastal seawater samples. Clearly, the bielectrode ISE array may be used in the simultaneous analysis of free copper(II) and iron(III) in seawater without fear of cross-interference between the solid-state sensors.