Mimicking Daphnia magna bioassay performance by an electronic tongue for urban water quality control

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.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as 'biohybrids', designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra musselDreissena polymorphaand water fleaDaphniaspp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system.

Electronic tongue applications for wastewater and soil analysis

Assessment of water and soil quality is critical for the health, economy, and sustainability of any community. The release of a range of life-threatening pollutants from agriculture, industries, and the residential communities themselves into the different water resources and soil requires of analytical methods intended for their detection. Given the challenge that represents coping with the monitoring of such a diverse and large number of compounds (with over 100,000 chemicals registered, yet in continuous increase), holistic solutions such as electronic tongues (ETs) are emerging as a promising tool for a sustainable, simple, and green monitoring of soil and water resources. In this direction, this review aims to present and critically provide an overview of the basic concepts of ETs, followed by some relevant applications recently reported in the literature in environmental analysis, more specifically, the monitoring of water and wastewater, their quality and the detection of water pollutants as well as soil analysis.

Analytical Figures of Merit for Multisensor Arrays

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

Electronic Tongue Recognition with Feature Specificity Enhancement

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.

Electronic Tongues for Inedible Media

“Electronic tongues”, “taste sensors”, and similar devices (further named as “multisensor systems”, or MSS) have been studied and applied mostly for the analysis of edible analytes. This is not surprising, since the MSS development was sometimes inspired by the mainstream idea that they could substitute human gustatory tests. However, the basic principle behind multisensor systems—a combination of an array of cross-sensitive chemical sensors for liquid analysis and a machine learning engine for multivariate data processing—does not imply any limitations on the application of such systems for the analysis of inedible media. This review deals with the numerous MSS applications for the analysis of inedible analytes, among other things, for agricultural and medical purposes.

Rapid Evaluation of Integral Quality and Safety of Surface and Waste Waters by a Multisensor System (Electronic Tongue)

The paper describes a wide-range practical application of the potentiometric multisensor system (MS) (1) for integral safety evaluation of a variety of natural waters at multiple locations, under various climatic conditions and anthropogenic stress and (2) for close to real consistency evaluation of waste water purification processes at urban water treatment plants. In total, 25 natural surface water samples were collected around St. Petersburg (Russia), analyzed as is, and after ultrasonic treatment. Toxicity of the samples was evaluated using bioassay and MS. Relative errors of toxicity assessment with MS in these samples were below 20%. The system was also applied for fast determination of integral water quality using chemical oxygen demand (COD) values in 20 samples of water from river and ponds in Kolkata (India) and performed with an acceptable precision of 20% to 22% in this task. Furthermore, the MS was applied for fast simultaneous evaluation of COD, biochemical oxygen demand, inorganic phosphorous, ammonia, and nitrate nitrogen at two waste water treatment plants (over 320 samples). Reasonable precision (within 25%) of such analysis is acceptable for rapid water safety evaluation and enables fast control of the purification process. MS proved to be a practicable analytical instrument for various real-world tasks related to water safety monitoring.

Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/ tempcode.html.

Moving your laboratories to the field--Advantages and limitations of the use of field portable instruments in environmental sample analysis

The recent rapid progress in technology of field portable instruments has increased their applications in environmental sample analysis. These instruments offer a possibility of cost-effective, non-destructive, real-time, direct, on-site measurements of a wide range of both inorganic and organic analytes in gaseous, liquid and solid samples. Some of them do not require the use of reagents and do not produce any analytical waste. All these features contribute to the greenness of field portable techniques. Several stationary analytical instruments have their portable versions. The most popular ones include: gas chromatographs with different detectors (mass spectrometer (MS), flame ionization detector, photoionization detector), ultraviolet-visible and near-infrared spectrophotometers, X-ray fluorescence spectrometers, ion mobility spectrometers, electronic noses and electronic tongues. The use of portable instruments in environmental sample analysis gives a possibility of on-site screening and a subsequent selection of samples for routine laboratory analyses. They are also very useful in situations that require an emergency response and for process monitoring applications. However, quantification of results is still problematic in many cases. The other disadvantages include: higher detection limits and lower sensitivity than these obtained in laboratory conditions, a strong influence of environmental factors on the instrument performance and a high possibility of sample contamination in the field. This paper reviews recent applications of field portable instruments in environmental sample analysis and discusses their analytical capabilities.