Organoleptic Analysis of Drinking Water Using an Electronic Tongue Based on Electrochemical Microsensors

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.

Recent Applications of Potentiometric Electronic Tongue and Electronic Nose in Sensory Evaluation

Electronic tongue (e-tongue) and electronic nose (e-nose) have been widely used to determine food products' taste, aroma, and flavor profiles. Several researchers and industries have recently attempted to find relationships between these e-senses and human sensory panels to ultimately replace sensory panels or use them as a viable alternative to timeconsuming and expensive traditional sensory evaluation (e.g., consumer acceptance testing or descriptive sensory analysis). This study investigated the recent applications of e-tongue and e-nose in the food and beverages sectors and their relationships with human sensory panels, including a trained sensory panel and naïve consumers. According to several studies, the e-tongue, e-nose, or a combination of e-tongue and e-nose can be an effective and powerful tool for rapid assessment of sensory profiles and quality detection with significant correlations with human sensory data. These instruments are also often reported to be more sensitive to detect subtle changes/differences that the human panel cannot detect. Future trends and projections of the e-tongue and e-nose with limitations are also discussed.

Recent advances in neuromorphic transistors for artificial perception applications: FOCUS ISSUE REVIEW

Conventional von Neumann architecture is insufficient in establishing artificial intelligence (AI) in terms of energy efficiency, computing in memory and dynamic learning. Delightedly, rapid developments in neuromorphic computing provide a new paradigm to solve this dilemma. Furthermore, neuromorphic devices that can realize synaptic plasticity and neuromorphic function have extraordinary significance for neuromorphic system. A three-terminal neuromorphic transistor is one of the typical representatives. In addition, human body has five senses, including vision, touch, auditory sense, olfactory sense and gustatory sense, providing abundant information for brain. Inspired by the human perception system, developments in artificial perception system will give new vitality to intelligent robots. This review discusses the operation mechanism, function and application of neuromorphic transistors. The latest progresses in artificial perception systems based on neuromorphic transistors are provided. Finally, the opportunities and challenges of artificial perception systems are summarized.

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.

Digital Taste in Mulsemedia Augmented Reality: Perspective on Developments and Challenges

Digitalization of human taste has been on the back burners of multi-sensory media until the beginning of the decade, with audio, video, and haptic input/output(I/O) taking over as the major sensory mechanisms. This article reviews the consolidated literature on augmented reality (AR) in the modulation and stimulation of the sensation of taste in humans using low-amplitude electrical signals. Describing multiple factors that combine to produce a single taste, various techniques to stimulate/modulate taste artificially are described. The article explores techniques from prominent research pools with an inclination towards taste modulation. The goal is to seamlessly integrate gustatory augmentation into the commercial market. It highlights core benefits and limitations and proposes feasible extensions to the already established technological architecture for taste stimulation and modulation, namely, from the Internet of Things, artificial intelligence, and machine learning. Past research on taste has had a more software-oriented approach, with a few trends getting exceptions presented as taste modulation hardware. Using modern technological extensions, the medium of taste has the potential to merge with audio and video data streams as a viable multichannel medium for the transfer of sensory information.

Real-Time Edge Neuromorphic Tasting From Chemical Microsensor Arrays

Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of miniaturization, energy autonomy, and real time operation. Toward this goal, we present the first implementation of artificial taste running on neuromorphic hardware for continuous edge monitoring applications. We used a solid-state electrochemical microsensor array to acquire multivariate, time-varying chemical measurements, employed temporal filtering to enhance sensor readout dynamics, and deployed a rate-based, deep convolutional spiking neural network to efficiently fuse the electrochemical sensor data. To evaluate performance we created MicroBeTa (Microsensor Beverage Tasting), a new dataset for beverage classification incorporating 7 h of temporal recordings performed over 3 days, including sensor drifts and sensor replacements. Our implementation of artificial taste is 15× more energy efficient on inference tasks than similar convolutional architectures running on other commercial, low power edge-AI inference devices, achieving over 178× lower latencies than the sampling period of the sensor readout, and high accuracy (97%) on a single Intel Loihi neuromorphic research processor included in a USB stick form factor.

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R² ≥ 0.9998).