Array Of Ion-selective Electrodes Research Articles

Decision-tree-based ion-specific dosing algorithm for enhancing closed hydroponic efficiency and reducing carbon emissions.

The maintenance of ion balance in closed hydroponic solutions is essential to improve the crop quality and recycling efficiency of nutrient solutions. However, the absence of robust ion sensors for key ions such as P and Mg and the coupling of ions in fertilizer salts render it difficult to effectively manage ion-specific nutrient solutions. Although ion-specific dosing algorithms have been established, their effectiveness has been inadequately explored. In this study, a decision-tree-based dosing algorithm was developed to calculate the optimal volumes of individual nutrient stock solutions to be supplied for five major nutrient ions, i.e., NO3, K, Ca, P, and Mg, based on the concentrations of NO3, K, and Ca and remaining volume of the recycled nutrient solution. In the performance assessment based on five nutrient solution samples encompassing the typical concentration ranges for leafy vegetable cultivation, the ion-selective electrode array demonstrated feasible accuracies, with root mean square errors of 29.5, 10.1, and 6.1 mg·L-1 for NO3, K, and Ca, respectively. In a five-step replenishment test involving varying target concentrations and nutrient solution volumes, the system formulated nutrient solutions according to the specified targets, exhibiting average relative errors of 10.6 ± 8.0%, 7.9 ± 2.1%, 8.0 ± 11.0%, and 4.2 ± 3.7% for the Ca, K, and NO3 concentrations and volume of the nutrient solution, respectively. Furthermore, the decision tree method helped reduce the total fertilizer injections and carbon emissions by 12.8% and 20.6% in the stepwise test, respectively. The findings demonstrate that the decision-tree-based dosing algorithm not only enables more efficient reuse of nutrient solution compared to the existing simplex method but also confirms the potential for reducing carbon emissions, indicating the possibility of sustainable agricultural development.

A multi-ion interference decoupling model based on ion-selective electrode arrays.

Compared to traditional liquid-junction ion-selective electrodes, solid-contact ion-selective electrodes (SC-ISEs) have attracted much attention and undergone rapid development due to their compactness and ease of integration. However, the application and widespread use of SC-ISEs are limited by their non-ideal selectivity and susceptibility to signal drift. Although the principles of artificial neural network (ANN) methods have shown significant progress in partially resolving the selectivity issue, they generally require extensive calibration steps and computational resources to implement. As a result, numerical computation models are more practical and economical, but existing approaches often overlook experimental phenomena and have relatively complex modeling principles. In this study, we propose a proportional factor model based on the trend of SC-ISEs affected by the multiple ions, along with a scalable dynamic correction procedure to improve its robustness. This model utilizes an estimated response surface method to solve nonlinear equations, requiring fewer calibration experiments. It accurately extracts the concentrations of multiple target ions in the presence of multi-ion interference and dynamically adjusts the model parameters for different types of ISEs. Additionally, we design a multi-channel SC-ISE array as a carrier for theoretical validation. In a case study, we demonstrate the feasibility of decoupling multiple ions using the SC-ISE array, obtaining concentrations of calcium, magnesium, sodium, and potassium ions, and verify the accuracy of the multi-ion detection system for real-world water samples.

Microfluidic-based ion-selective thermoplastic electrode array for point-of-care detection of potassium and sodium ions.

A microfluidic paper-based thermoplastic electrode (TPE) array has been developed for point-of-care detection of Na+ and K+ ions using a custom-made portable potentiometer. TPEs were fabricated using polystyrene as the binder and two different types of graphite to compare theelectrode performance. The newly designed TPE array embedded in a polymethyl methacrylate chip consists of two working electrodes modified with carbon black nanomaterial and an ion-selective membrane, and an all-solid-state reference electrode modified with Ag/AgCl ink and poly(butyl methacrylate-co-methyl methacrylate) membrane via drop-casting. Ion-selective membrane compositions and conditioning steps were optimized. Under optimized conditions, ion-selective TPEs demonstrated fast response time (4s) and good stability. The TPE array demonstrated a Nernstian behavior for K+ with a sensitivity of 59.2 ± 0.2mV decade-1 and near-Nernstian response for Na+ with a sensitivity of 54.0 ± 1.1mV decade-1 in the range 10-1 - 10-4M and 1 - 10-3M, respectively. The detection limits were 1 × 10-5M and 1 × 10-4M for K+ and Na+, respectively. In addition, a K+ and Na+ selective microfluidic paper-based analytical device (µPAD) was applied to artificial serum analysis and found in good agreement with average recoveries of 101.3% and 99.7%, respectively, suggesting that the developed ISE array is suitable for detection of sodium and potassium in complex matrix.

Ion composition profiling and pattern recognition of vegetable sap using a solid-contact ion-selective electrode array

This is the first study to develop an effective tool for plant sap analysis based on a solid-contact ion-selective electrode (SCISE) array, which has the advantages of on-site, direct and fast analysis. SCISEs are all-solid-state ion-selective electrodes with a conducting polymer for ion-to-electron transduction. With the conducting polymer solid-contact, the electrodes perform high stability and short response time (<30 s) during potentiometric sensing. The developed SCISE array consists of potassium (SEN = 51.9 mV/decade), sodium (64.2 mV/decade), ammonium (59.3 mV/decade), calcium (32.1 mV/decade), and magnesium (33.0 mV/decade) selective electrodes. To verify the application, seven types of fresh crude vegetable leaf juices (ion concentration range: 10−3–10−4 M) were measured with the array, and the result was compared with ion chromatography. It was found that the array was able to obtain the unique, distinguishable ion composition profile as a radar plot of each vegetable sap, implying the fingerprint application of the present technology. Furthermore, applying principle component analysis (PCA) and K-means clustering, lettuces grown in different environments (deficiency either in potassium or in nitrate) are able to be discriminated. In summary, we demonstrate a tool for on-site, high-throughput and direct plant sap analysis based on SCISE array. Moreover, it could combine with pattern recognition and become a promising tools which could provide a diagnosis of the nutritive status of vegetables.

Home Detection Technique for Na+ and K+ in Urine Using a Self-Calibrated all-Solid-State Ion-Selective Electrode Array Based on Polystyrene-Au Ion-Sensing Nanocomposites.

An all-solid-state ion-selective electrode (ASS-ISE) array that is portable and easily miniaturized can meet the needs of home sensing devices for long-term health monitoring. However, their stability and accuracy are affected by the multistep modification required for ASS-ISE manufacturing and the complex background signal of real samples. In this study, a four-channel ISE array with the integration of a calibration channel has been developed based on polystyrene-Au (PS-Au) ion-sensing nanocomposites (PS-Au ISE array) for the home detection of Na+ and K+. The nanocomposites combine target recognition function and ion-electron transduction function and could be modified on the channel surface by direct drop-casting, thus simplifying the preparation process and then improving the stability. Meanwhile, the integrated calibration channel could automatically deduct complex background signals in real sample analysis and thus improve the accuracy. As a result, the proposed self-calibrated PS-Au ISE array showed a near Nernstian behavior for Na+ and K+ in the range of 1 × 10-2 M-1 × 10-4 M, and the detection limits were 6.8 × 10-5 M and 5.5 × 10-5 M in artificial urine. The linear equations can be obtained according to the slopes and intercepts of Na+ and K+, and thus, the concentration of the target ions can be directly read out by combining this PS-Au ISE array with the smart electronic device. Furthermore, the detection results of Na+ and K+ in human urine agreed well with those obtained by ICP-AES, suggesting that this proposed self-calibrated PS-Au ISE array is very suitable for home smart sensing devices, facilitating the health monitoring.

Rapid Discrimination of Beer Flavors Using Ion-Selective Electrode Array System Combined with Chemometrics

Rapid discrimination of beer with different flavors was performed by ion-selective electrode array and chemometrics. Ion-selective electrode array consisted of twelve ion-selective electrodes. Six varieties of beer 10 types of each with different flavors from Tsingtao Brewery were investigated. Potentiometric measurements were conducted in presence of beer to obtain the output potential values. Principal component analysis (PCA) was applied as a data compaction technique to reduce complexity of the potential data. Linear discriminant analysis (LDA) and least-squares support vector machine (LS-SVM) methods were used to develop discriminant models. The LS-SVM model achieved a more satisfying performance with an identification rate of 95.0% than the LDA model. For validating reliability, the developed method and sensory evaluation method were used for discriminating the beer samples. The LS-SVM model with an identification rate of 98.3% was better than the sensory evaluation method with an identification rate of 89.2%. The prepared sensor array can rapidly and accurately discriminate beer samples with different flavors and quality.

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution.

Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic strength make them difficult to apply in a hydroponic system. To minimize these deficiencies, we combined the multivariate standard addition (MSAM) sampling technique with the deep kernel learning (DKL) model for a six ISEs array to increase the prediction accuracy and precision of eight ions, including , , , , , , , and . The enhanced data feature based on feature enrichment (FE) of the MSAM technique provided more useful information to DKL for improving the prediction reliability of the available ISE ions and enhanced the detection of unavailable ISE ions (phosphate and magnesium). The results showed that the combined MSAM–feature enrichment (FE)–DKL sensing structure for validating ten real hydroponic samples achieved low root mean square errors (RMSE) of 63.8, 8.3, 29.2, 18.5, 11.8, and 8.8 with below 8% coefficients of variation (CVs) for predicting nitrate, ammonium, potassium, calcium, sodium, and chloride, respectively. Moreover, the prediction of phosphate and magnesium in the ranges of 5–275 mg·L−1 and 10–80 had RMSEs of 29.6 and 8.7 respectively. The results prove that the proposed approach can be applied successfully to improve the accuracy and feasibility of ISEs in a closed hydroponic system.

Optical Sensing with a Potentiometric Sensing Array by Prussian Blue Film Integrated Closed Bipolar Electrodes.

The simultaneous optical readout of a potentiometric sensor array of ion-selective electrodes (ISEs) based on PVC membranes is described here for the first time. The optical array consists of electrochromic Prussian Blue (PB) films in multiple closed ion-selective bipolar electrodes (BPEs), which gives a physical separation between the optical detection and sample compartments. The potential-dependent turnover of PB generates Prussian White (PW). A near-Nernstian response of the PB film is confirmed by colorimetric absorbance experiments as a function of applied potential. In the combined bipolar electrode cell, the overall potential is kept constant with a single potentiostat over the entire array where each PB spot indicates the potential change of an individual connected potentiometric probe. For cation-selective electrodes, the absorbance or blue intensity of the connected PB film is enhanced with increasing target cation activity. The colorimetric absorbance changes are simultaneously followed by a digital camera and analyzed by Mathematica software. A multiple cation-BPE array allows one to achieve simultaneous quantitative analysis of potassium, sodium, and calcium ions, demonstrated here in highly colored fruit juices. Mass transport at the PB thin film is shown not to be rate-limiting. The measuring ranges can be tuned in a wide range by potential control. The PB film exhibits greatly improved reproducibility and stability as compared to previous work with a ferroin redox probe confined in a thin solution layer.

Multivariate limit of detection for non-linear sensor arrays

With the increased development of low-cost and miniature devices, sensors are increasingly being deployed as arrays of redundant sensors. However, little work has been done characterizing properties of these arrays. Here, we develop and test a Bayesian algorithm for estimating the limit of detection of sensor arrays. The algorithm is applicable for single sensors as well as sensor arrays, and works by reducing a vector in the signal domain to a univariate response in the measurand domain. We show that the new algorithm can reproduce results from a benchmark algorithm for single sensors, and then demonstrate the benefit of adding additional sensors to an array. Then, we provide guidelines that achieve numerical stability while minimising computational cost. Finally, we provide a real-world example using an array of ion-selective electrodes measuring carbonate in seawater. This application demonstrates how incorporation of a set of individual low-quality sensors into an array leads to a substantially reduced LOD that clearly meets the demands of the application.

Hybrid Signal-Processing Method Based on Neural Network for Prediction of NO3, K, Ca, and Mg Ions in Hydroponic Solutions Using an Array of Ion-Selective Electrodes.

In closed hydroponics, fast and continuous measurement of individual nutrient concentrations is necessary to improve water- and nutrient-use efficiencies and crop production. Ion-selective electrodes (ISEs) could be one of the most attractive tools for hydroponic applications. However, signal drifts over time and interferences from other ions present in hydroponic solutions make it difficult to use the ISEs in hydroponic solutions. In this study, hybrid signal processing combining a two-point normalization (TPN) method for the effective compensation of the drifts and a back propagation artificial neural network (ANN) algorithm for the interpretation of the interferences was developed. In addition, the ANN-based approach for the prediction of Mg concentration which had no feasible ISE was conducted by interpreting the signals from a sensor array consisting of electrical conductivity (EC) and ion-selective electrodes (NO3, K, and Ca). From the application test using 8 samples from real greenhouses, the hybrid method based on a combination of the TPN and ANN methods showed relatively low root mean square errors of 47.2, 13.2, and 18.9 mg∙L−1 with coefficients of variation (CVs) below 10% for NO3, K, and Ca, respectively, compared to those obtained by separate use of the two methods. Furthermore, the Mg prediction results with a root mean square error (RMSE) of 14.6 mg∙L−1 over the range of 10–60 mg∙L−1 showed potential as an approximate diagnostic tool to measure Mg in hydroponic solutions. These results demonstrate that the hybrid method can improve the accuracy and feasibility of ISEs in hydroponic applications.

Application of Ion Selective Electrode array to simultaneously determinate multi-free ions in solution

Abstract An ion-selective electrode (ISE) array, probeCARE™ was developed for real-time measurement of multi-free ions in solution. The technique includes algorithms that have been developed to allow real-time measurement of a multitude of free ions simultaneously in solution. The ISE array system allows common ions: sodium (Na + ), Potassium (K + ), calcium (Ca2＋), nitrate (NO 3 − ) and chloride (Cl − ) to be measured in complex solution matrices. This is of value to agricultural irrigators and for monitoring water quality in lakes and streams. It can be adopted for the benefit of fertigation application, and water qualities are monitoring to ensure environmental protection authority (EPA) standards are achieved. The prediction accuracies of ISEs can be negatively impacted by pH, ionic strength and the interferences from other undesired ions. The ISE array system contained a self-developed algorithm to eliminate the interferences from the major ions existed in nature water. On the purpose of validating the prediction accuracy, 96 samples based on 22 different real water backgrounds were employed. Several chemicals were spiked into these samples for simulating the fertilizers and other water quality hazards. Based on the experiment results, it can be concluded that the system can strong ability to simultaneously determinate multi-free ions, regardless the pH, ionic strength and the interferences from major ions in nature water.

Ultrasensitive micro ion selective sensor arrays for multiplex heavy metal ions detection

Trace heavy metal ions are probable carcinogens with concentrations at ppb levels. Ion selective electrode (ISE) technology is a conventional and widely used method to detect various ions with the advantages as rapid measurement, low power consumption and low cost. However, the detection limit of a conventional bulky ISE device is only able to reach ppm level so far, not sensitive enough for practically detecting heavy metal ions. A micro ion selective electrode (μISE) array which can realize multiplex detection of heavy metal ions on one chip is reported in this paper. Its detection limit for Pb2+, Cd2+, AsO2−, and Hg2+ can reach 1, 3, 10, and 1 ppb, respectively, within the permissible limits of drinking water. The micrometer scale device arrays from microfabrication processes demonstrated enhanced sensitivity, uniformity, and reduced response time (18 s) compared to conventional ISE. Our devices showed superb selectivity, and the performance dependence on temperature and pH values was investigated as well.

(Invited) Electrochemical Measurement Approaches for Planetary Exploration

2018 marks the 10-year anniversary of the landing of the NASA Phoenix spacecraft on the ice-rich northern plains of Mars. The Phoenix Lander carried the Wet Chemistry Laboratory (WCL), a component instrument of the Microscopy, Electrochemistry, and Conductivity Analyzer (MECA) and was used to perform a comprehensive electrochemical characterization of martian soil in an aqueous solution. Designed to characterize the habitability of the martian surface, WCL contained an array of ion selective electrodes, a conductivity cell, a platinum electrode to measure redox potential, electrodes to perform perform cyclic voltammetry, chronopotentiometry and anodic stripping voltammetry, as well as a set of chemical reagents used to perform a sulfate titration and sample acidification. The measurements made with WCL resulted in a number of major discoveries, including the detection of percent levels of perchlorate on the martian surface, and represent the first, and to date, the only time electrochemical methods have been used for planetary exploration beyond Earth. NASA is now embarking on a new era of astrobiology exploration as plans are being formulated for missions to Ocean Worlds of the outer solar system, including Europa, an ice covered Jovian moon. Based on lessons learned during the development of WCL and its operation on the surface of Mars, we will discuss new design approaches and strategies that will enable the implementation of autonomous microfluidic electrochemical sensor arrays during upcoming missions to these yet to be explored solar system environments.

Simultaneous determination of copper and zinc in brass samples by PCR and PLS1 methods using a multiple ion-selective electrode array

A method was developed to determine copper and zinc simultaneously in brass sample using a multiple ion-selective electrode array system and multivariate calibration techniques. The multiple ion-selective electrode array system was consisted of one pH electrode and eight ion selective electrodes which were formed using commercial ionophores and which are mainly sensitive copper and zinc. PCR, PLS1 which are multivariate calibration techniques were used to calibrate the multiple ion-selective electrode array system using five level multivariate calibration design and PCR method was selected as most suitable method. The measurements were performed at pH 10 in ammonia-ammonium chloride buffer. Copper and zinc content of a brass sample were determined by the calibrated multiple ion-selective electrode array system and the composition of brass sample was also determined by gravimetric method. The percentage amount of copper and zinc in brass sample were 64 ± 1 and 26 ± 2, respectively using the gravimetric method. The percentage results obtained in brass samples for copper and zinc were 63 ± 3 and 24 ± 3 by PCR method and 60 ± 5 and 20 ± 7 by PLS1 method, respectively. The obtained results were calculated for a 95% confidence level.

Dissolution studies of metamizole sodium and pseudoephedrine sulphate dosage forms − comparison and correlation of electronic tongue results with reference studies

This work reports a critical evaluation of the results of the release of active substances (APIs) from novel pharmaceutical formulations provided by an electronic tongue system (ET). Detailed dissolution studies of modified-release granules used in pharmacotherapy containing metamizole sodium and pseudoephedrine sulphate were carried out. The impact of the dissolution-modifying excipients (carmellose sodium and hypromellose) on the dissolution process as well as on the outcomes of the sensor array of ion-selective electrodes was investigated. The obtained dissolution profiles were compared and correlated with those registered during the reference studies performed according to the pharmacopoeial method. It was pointed out that the proper evaluation of the efficiency of the release modification requires the examination of dosage forms as well as physical mixtures of API and excipient. Moreover, the results obtained using potentiometric ET were complementary to the classical methodology. Their partial inconsistency, remarked during several experiments, should be interpreted with caution owing to simultaneous sensing of APIs and excipients by the sensors and their various performances (i.e. selectivity and sensitivity) towards these components.

An Embedded System for Automated Hydroponic Nutrient Solution Management

Abstract. An ideal closed hydroponic system would include automated preparation of nutrient solutions, including reuse of the drain solution with replenishment of fertilizers and water in proper ratios based on real-time measurement of various nutrient concentrations in the drain solution. The electrical conductivity (EC) based systems commonly used for managing hydroponic nutrients in closed systems do not allow individual corrections to each nutrient deficient for crop growth. To improve the performance of a PC-based nutrient management system originally developed in our previous study, this article describes the development and evaluation of a WinCE-based embedded system with robustness and an easy-to-use user interface, consisting mainly of a micro-control unit (MCU) based embedded nutrient controller and an array of ion-selective electrodes (ISEs) that can automatically control the nutrient replenishment events based on real-time measurements of NO3, K, and Ca concentrations. To overcome the problem of decoupled replenishment among the three nutrients found in the previous study, a robust fertilizer dosing algorithm was designed that can separately calculate the volumes of six nutrient stock solutions to be supplied in a sequential order according to specific ions. In a five-step spiking test, the use of a two-point normalization method prior to each measurement sequence coupled with the robust nutrient dosing algorithm enabled the system to formulate five different concentrations of NO3 and K ions comparable to the target concentrations. However, the five different Ca concentrations prepared were almost 33% higher than the target concentrations because of low Ca estimates due to a change in the sensitivity of the Ca ISE between the different background solutions used for calibration and measurement. This could be overcome with the use of the same background solution or a pre-sensitivity compensation when an initial measurement is made. In a lettuce cultivation test conducted with the ebb-and-flow method over 27 days, the nutrient control performance of the embedded system was improved as compared to the previously developed PC-based system while maintaining the target concentrations of 436, 117, and 80 mg L-1 within errors of -15.0 ±18.1, -5.0 ±10.0, and -9.2 ±4.1 mg L-1 for NO3, K, and Ca ions, respectively. The use of the developed system in closed crop cultivation proved to be feasible for managing individual nutrients in reused solution at desired concentration levels, resulting in the potential of maximizing the efficiency of nutrient use. Keywords: Automatic control, Closed system, Dosing algorithm, Embedded system, Greenhouse, Ion sensor, Nutrients.

Novel recalibration methodologies for ion‐selective electrode arrays in the multi‐ion interference scenario

Because of the deterioration of sensitivity and selectivity of ion‐selective electrode (ISE) arrays after a long period of storage or measurement, the recalibration of ISE arrays is a major challenge, which significantly affects the practical application of ISE arrays. Although approaches using artificial neural networks (ANN) to solve the problem have been widely studied, it is impractical for end‐users to frequently remeasure dozens of samples to rebuild an ANN model for measuring of ISE array. In this study, a system of equations was developed to simulate the ISE array response for the scenario of multiple interfering ions. By fitting the coefficients for the system of equations using optimization algorithms, such as genetic algorithms (GA), the ISE array can be recalibrated with only a couple of selected samples. As a case study, a solid‐state ISE array for the detection of 4 exchangeable cations was applied. The deterioration of sensitivity and selectivity of the ISE array over time was investigated. The simplified methodology for calibrating this ISE array was applied and demonstrated in this study.

Simultaneously determining multi-metal ions using an ion selective electrode array system

Abstract The electronic tongue (ET) system contains an ion selective electrode (ISE) array, with a signal recording system and data processing methodology. It is developed for simultaneous determination of multiple free ions dissolved in solutions for real-time in situ monitoring. This article outlines an application of the ET system to determine chloride (Cl) simultaneously, and multi cationic metal ions, including calcium (Ca), cadmium (Cd), copper (Cu) and lead (Pb). In this study, Principal Component Analysis (PCA) was applied as a data compaction technique to reduce the complexity of data from the ISE array without losing much information. A patent data preprocessing method, genetic ICA, was utilized to improve the prediction accuracy of ANN. Moreover, Orthogonal Experiment Design was employed for minimizing the number of training samples without losing any quality characteristics for the specific ions. Using our methodology means that results can be obtained rapidly and accurately without disturbing the natural speciation. The robustness and appropriateness of the approach were assessed using the mean relative error (MRE). When configured and optimized, the ET system can quantify the concentration of the four desired metals and chloride with the average MRE being lower than 0.10.

Flexible Ion Sensors for Bodily Fluids

This work presents an array of ion selective electrodes combined with a solid state reference electrode for analyzing of potassium and sodium ions in fluid. The sensors was fabricated using screen printing and drop casting techniques. The ion selective electrodes were made from a stack of screen printed AgCl electrode and solid state polyhydroxyethylmethacrylate hydrogel internal electrolyte layers, which were preconditioned in 0.1M KCl. The sensitivity of the sensors to potassium and sodium ions was achieved by casting a plasticized PVC based ion selective membrane, which contained an optimal ionophore to polymer ratio, directly on the hydrogel layer. The reference electrode was made on another AgCl electrode, which was covered with the same hydrogel. For both ions, sensitivities of 50mV per decade were achieved in a concentration range 10-3 - 1M. In this concentration range, the potassium sensor had no cross sensitivity to sodium ions and vice versa.

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.