Electronic tongue applications for wastewater and soil analysis

Electrochemical Behavior of Glassy Carbon Electrodes Modified with Electropolymerized Film of N,N'-bis (2-thienylmethylene)-1,X-diaminobenzene toward Homovanillic Acid and 4-Hydroxyphenylacetic Acid

This study evaluates different electrochemical behaviors of modified glassy carbon electrodes (GCEs) for detecting urinary biomarkers related to breast cancer, namely homovanillic acid (HVA) and 4-hydroxyphenylacetic acid (4HPA). The analysis was performed in the presence of common urinary interferents, creatinine and urea. Modification of bare GCEs was done through the electropolymerization of N,N'-bis (2-thienylmethylene)-1,X-diaminobenzene (X = 2, 3, 4) isomers, so-called BTMD. The formation and characteristics of these polymeric layers were investigated using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), atomic force microscopy (AFM), and scanning electron microscopy (SEM). Differential pulse voltammetry (DPV) was used to measure responses of the electrodes  to HVA and 4HPA, assessing their sensitivity and selectivity. Results showed that the developed electrodes effectively detected both biomarkers, with peak currents increasing proportionally to biomarker concentrations and minimal interference from creatinine and urea. The modified electrodes exhibited better linearity at higher concentrations; however, saturation was observed for 4HPA at high concentrations with the p-BTMD/GCE. Each electrode displayed unique peak current, potential, and response profiles, highlighting their promise for cross-reactive sensing systems, such as electronic tongues, to analyze complex matrices such as urine.

Fluid Classification via the Dual Functionality of Moisture-Enabled Electricity Generation Enhanced by Deep Learning

Classifications of fluids using miniaturized sensors are of substantial importance for various fields of application. Modified with functional nanomaterials, a moisture-enabled electricity generation (MEG) device can execute a dual-purpose operation as both a self-powered framework and a fluid detection platform. In this study, a novel intelligent self-sustained sensing approach was implemented by integrating MEG with deep learning in microfluidics. Following a multilayer design, the MEG device including three individual units for power generation/fluid classification was fabricated in this study by using nonwoven fabrics, hydroxylated carbon nanotubes, poly(vinyl alcohol)-mixed gels, and indium tin bismuth liquid alloy. A composite configuration utilizing hydrophobic microfluidic channels and hydrophilic porous substrates was conducive to self-regulation of the on-chip flow. As a generator, the MEG device was capable of maintaining a continuous and stable power output for at least 6 h. As a sensor, the on-chip units synchronously measured the voltage (V), current (C), and resistance (R) signals as functions of time, whose transitions were completed using relays. These signals can serve as straightforward indicators of a fluid presence, such as the distinctive "fingerprint". After normalization and Fourier transform of raw V/C/R signals, a lightweight deep learning model (wide-kernel deep convolutional neural network, WDCNN) was employed for classifying pure water, kiwifruit, clementine, and lemon juices. In particular, the accuracy of the sample distinction using the WDCNN model was 100% within 15 s. The proposed integration of MEG, microfluidics, and deep learning provides a novel paradigm for the development of sustainable intelligent environmental perception, as well as new prospects for innovations in analytical science and smart instruments.

A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

Analysis of Macronutrients in Soil Using Impedimetric Multisensor Arrays

The need to increase food production to address the world population growth can only be fulfilled with precision agriculture strategies to increase crop yield with minimal expansion of the cultivated area. One example is site-specific fertilization based on accurate monitoring of soil nutrient levels, which can be made more cost-effective using sensors. This study developed an impedimetric multisensor array using ion-selective membranes to analyze soil samples enriched with macronutrients (N, P, and K), which is compared with another array based on layer-by-layer films. The results obtained from both devices are analyzed with multidimensional projection techniques and machine learning methods, where a decision tree model algorithm chooses the calibrations (best frequencies and sensors). The multicalibration space method indicates that both devices effectively distinguished all soil samples tested, with the ion-selective membrane setup presenting a higher sensitivity to K content. These findings pave the way for more environmentally friendly and efficient agricultural practices, facilitating the mapping of cropping areas for precise fertilizer application and optimized crop yield.

Simultaneous Determination of Chemical Oxygen Demand, Total Nitrogen, Ammonia, and Phosphate in Surface Water Based on a Multielectrode System

A technique for monitoring chemical oxygen demand (COD), total nitrogen (TN), ammonia (N-NH4), and phosphate (P-PO4) in surface water with a targeted signal multielectrode system (Cu, Ir, Rh, Co(OH)2, and Zr(OH)4 electrodes) is proposed for the first time. Each water quality index is specifically detected by at least two electrodes with distinct selectivity sensing mechanisms. Cyclic voltammetry and electrochemical impedance measurements are employed for multidimensional signal acquisition, complemented by normalization and Least Absolute Shrinkage and Selection Operator (LASSO) for principal feature extraction and dimension reduction. Multiple linear regression (MLR), partial least-squares (PLS), and eXtreme Gradient Boosting (XGBoost) were employed to evaluate the established prediction model. The precisions of the multielectrode system are ±10%/±5 ppm of COD, ±10%/±0.2 ppm of TN, ±5%/±0.1 ppm of N-NH4, and ±5%/±0.01 ppm of P-PO4. The analysis time of the multielectrode system is reduced from hours to minutes compared with traditional analysis, without any sample pretreatment, facilitating continuous online monitoring in the field. The developed multielectrode system offers a feasible strategy for online in situ monitoring of surface water quality.

Development an electronic nose to recognize pesticides in groundwater

Timely detection of Groundwater pollution is essential to protect human health, especially for pesticide pollution. To solve this issue, we proposed a novel solution to realize the prediction of pesticide in groundwater by using the electronic nose (e-nose). The main work of this paper was divided into three steps: 1) checking whether sample was polluted by pesticides, 2) further predicting the pesticide type, brand and pollution degree when the sample was polluted by pesticides, and 3) optimizing the sensor array. Random forest was used to complete the first step, which had the best accuracy and sensitivity of 100 %. Support vector machine was applied to complete the second step, and the accuracy reaching 98.08 %. As for the third step, recursive feature elimination was used to optimize the sensor array. After optimization, the number of sensors was reduced from 26 to 8. In addition, the e-nose developed in this paper was compared with a commercial e-nose. The results showed that the cost of the developed e-nose was much lower than that of the commercial e-nose despite its slightly weaker prediction performance. Thus, this e-nose can be employed to recognize the pesticides in groundwater, and even can be integrated into the while drilling technology to realize the in-situ detection of groundwater.

Progress in the Development of Detection Strategies Based on Olfactory and Gustatory Biomimetic Biosensors

The biomimetic olfactory and gustatory biosensing devices have broad applications in many fields, such as industry, security, and biomedicine. The development of these biosensors was inspired by the organization of biological olfactory and gustatory systems. In this review, we summarized the most recent advances in the development of detection strategies for chemical sensing based on olfactory and gustatory biomimetic biosensors. First, sensing mechanisms and principles of olfaction and gustation are briefly introduced. Then, different biomimetic sensing detection strategies are outlined based on different sensing devices functionalized with various molecular and cellular components originating from natural olfactory and gustatory systems. Thereafter, various biomimetic olfactory and gustatory biosensors are introduced in detail by classifying and summarizing the detection strategies based on different sensing devices. Finally, the future directions and challenges of biomimetic biosensing development are proposed and discussed.

New bladder cancer non-invasive surveillance method based on voltammetric electronic tongue measurement of urine

Bladder cancer (BC) is the sixth leading cause of death by cancer. Depending on the invasiveness of tumors, patients with BC will undergo surgery and surveillance lifelong, owing the high rate of recurrence and progression. In this context, the development of strategies to support non-invasive BC diagnosis is focusing attention. Voltammetric electronic tongue (VET) has been demonstrated to be of use in the analysis of biofluids. Here, we present the implementation of a VET to study 207 urines to discriminate BC and non-BC for diagnosis and surveillance to detect recurrences. Special attention has been paid to the experimental setup to improve reproducibility in the measurements. PLSDA analysis together with variable selection provided a model with high sensitivity, specificity, and area under the ROC curve AUC (0.844, 0.882, and 0.917, respectively). These results pave the way for the development of non-invasive low-cost and easy-to-use strategies to support BC diagnosis and follow-up.

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Bladder cancer (BC) and control urines were studied by voltammetric electronic tongue

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A PLSDA model was obtained with high sensitivity, specificity, and accuracy (84/88/86)

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103/122 BC urines and 7⅝5 control urines were predicted correctly

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The electronic tongue has the potential for non-invasive BC diagnostics and follow-up