An electronic tongue for simultaneous determination of Ca2+, Mg2+, K+ and NH4+ in water samples by multivariate calibration methods

Application of the Electrospinning Technique in Electrochemical Biosensors: An Overview

Electrospinning is a cost-effective and flexible technology for producing nanofibers with large specific surface areas, functionalized surfaces, and stable structures. In recent years, electrospun nanofibers have attracted more and more attention in electrochemical biosensors due to their excellent morphological and structural properties. This review outlines the principle of electrospinning technology. The strategies of producing nanofibers with different diameters, morphologies, and structures are discussed to understand the regulation rules of nanofiber morphology and structure. The application of electrospun nanofibers in electrochemical biosensors is reviewed in detail. In addition, we look towards the future prospects of electrospinning technology and the challenge of scale production.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

A Simultaneous Determination Method for the Analysis of Chloride and Nitrate Ions in Air Samples by PLS1

This study describes a multi-ion-selective electrode system for the simultaneous determination of nitrate and chloride ions in air samples by using multivariate calibration methods. The measurement system was constituted of two ion-selective electrodes, an Ag/AgCl double-junction reference electrode and a multi-potentiometer. The measurements were performed at pH 5.0 acetic acid/sodium acetate buffer. The obtained data were evaluated by using Partial Least Squares (PLS1). The system was used to analyze the synthetic samples and fume-hood samples in terms of the amount of chloride and nitrate. The percentage recovery values obtained from fume-hood samples were 93.8% ± 3.8 and 102.4% ± 2.5 for chloride and nitrate, respectively. The presented system could be an easy-to-use approach for monitoring the amount of chloride and nitrate species in the scope of occupational health and safety analysis.

Online Rapid Detection Method of Fertilizer Solution Information Based on Characteristic Frequency Response Features

Online rapid detection of a fertilizer solution's type and concentration is crucial for intelligent water and fertilizer machines to realize intellectual precision variable fertilization. In this paper, a cylindrical capacitance sensor was designed based on the dielectric properties of the fertilizer solution, and an online rapid detection method of fertilizer type and concentration was proposed based on the characteristic frequency response mode. Three fertilizer solutions, potassium chloride, calcium superphosphate, and urea, were used as test objects. Ten concentrations of each fertilizer solution in the 10~100 g/L range were taken as the test fertilizer solution. Then, under the action of a series of sine wave excitation signals from 1 kHz to 10 MHz, the sensor's amplitude-frequency/phase-frequency response data were obtained. The detection strategy of 'first type, then concentration' was adopted to realize rapid online detection of fertilizer type and concentration. Experimental results indicated that the maximum relative error of the sensor stability test was 0.72%, and the maximum error of concentration detection was 7.26%. Thus, the intelligent water and fertilizer machine can give feedback on the information of a fertilizer solution in real-time during the process of precise variable fertilization, thus improving the intelligence of water and fertilizer machines.

Potentiometric Electronic Tongue for Quantitative Ion Analysis in Natural Mineral Waters

The present paper addresses the development and use of a new potentiometric electronic tongue for both qualitative and quantitative characterization of natural mineral waters. The electronic tongue is particularly related to the conductivity and ion content of/in the water sample. The analytical system is based on six ion-selective electrodes whose membranes are formulated to provide either cationic or anionic response and considering plasticizers with different dielectric constants (bis(2-ethylhexyl) sebacate, 2-nitrophenyl octyl ether or tricresylphosphate), while keeping the polymeric matrix, i.e., poly(vinyl chloride). Notably, the absence of any ionophore in the membrane provides a general response profile, i.e., no selectivity toward any special ion, which is convenient for the realization of an effective electronic tongue. The dynamic response of the tongue toward water samples of different chemical compositions and geographical locations has been obtained. At the optimized experimental conditions, the tongue presents acceptable repeatability and reproducibility (absence of hysteresis). The principal component analysis of the final potential values observed with the six electrodes allows for the differentiation and classification of the samples according to their conductivity, which is somehow related to the mineralization. Moreover, quantitative determination of the six main ions in the water samples (i.e., chloride, nitrate, hydrogen carbonate, sulfate, sodium, calcium, and magnesium) is possible by means of a simple linear calibration (and cross-validation) model.

Unlocking All-Solid Ion Selective Electrodes: Prospects in Crop Detection

This paper reviews the development of all-solid-state ion-selective electrodes (ASSISEs) for agricultural crop detection. Both nutrient ions and heavy metal ions inside and outside the plant have a significant influence on crop growth. This review begins with the detection principle of ASSISEs. The second section introduces the key characteristics of ASSISE and demonstrates its feasibility in crop detection based on previous research. The third section considers the development of ASSISEs in the detection of corps internally and externally (e.g., crop nutrition, heavy metal pollution, soil salinization, N enrichment, and sensor miniaturization, etc.) and discusses the interference of the test environment. The suggestions and conclusions discussed in this paper may provide the foundation for additional research into ion detection for crops.

A smart diagnostic tool based on deep kernel learning for on-site determination of phosphate, calcium, and magnesium concentration in a hydroponic system

Calcium, phosphate, and magnesium are essential nutrients for plant growth. The in situ determination of these nutrients is an important task for monitoring them in a closed hydroponic system where the nutrient elements need to be individually quantified based on ion-selective electrode (ISE) sensing. The accuracy issue of calcium ISEs due to interference, drift, and ionic strength, and the unavailability of phosphate and magnesium ISEs makes the development of these ion detecting tools hard to set up in a hydroponic system. This study modeled and evaluated a smart tool for recognising three ions (calcium, phosphate, and magnesium) based on the automatic multivariate standard addition method (AMSAM) and deep kernel learning (DKL) model. The purpose was to improve the accuracy of calcium ISEs, determining phosphate through cobalt electrochemistry, and soft sensing of magnesium ions. The model provided better performance in on-site detecting and measuring those ions in a lettuce hydroponic system achieving root mean square errors (RMSEs) of 12.5, 12.1, and 7.5 mg L-1 with coefficients of variation (CVs) below 5.0%, 7.0%, and 10% for determining Ca2+, H2PO4 -, and Mg2+ in the range of 150-250, 100-200, and 20-70 mg L-1 respectively. Furthermore, the DKL was implemented for the first time in the third platform (LabVIEW) and deployed to determine three ions in a real on-site hydroponic system. The open architecture of the SDT allowed posting the measured results on a cloud computer. This would help growers monitor their plants' nutrients conveniently. The informative data about the three mentioned ions that have no commercial sensors so far, could be adapted to the other components to develop a fully automated fertigation system for hydroponic production.