Application of an e-tongue to the analysis of monovarietal and blends of white wines

Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection

Food safety is a major concern that has an impact on the national economy and people's lives. The food industry has grown in quality and innovation in tandem with the rapid development of the economy and society. The emergence of new food technologies, as well as changes in dietary habits, has increased public concern about food safety. With the emergence of various counterfeit and substandard products, food quality and safety testing have become even more important. Traditional testing methods rely on sensory analysis and physical and chemical analysis. This approach is subjective and poorly adapted to the general public. It requires a high level of technical operation and is difficult to carry out on a large scale. To address this situation, this paper proposes an intelligent approach to food safety quality testing. The core idea is, first, to use sensors to collect data on the various components of the sample to be tested. Second, the random forest (RF) model used in this paper is trained. Third, the trained model is used to classify and identify the test samples. Based on the classification results, a conclusion is drawn as to whether the food product is a variant or a counterfeit. The advantage of this study is that the training model used is a weighted RF algorithm based on mutual information. The correlation between any two decision trees is calculated using mutual information, and for the more correlated decision trees, only the one with the highest evaluation accuracy is retained to form a new RF, and the evaluation accuracy is converted into voting weights, resulting in an RF model with less redundancy and higher evaluation accuracy. The experimental results show that the method used in this paper can successfully identify spoiled or counterfeit products and has good practicality.

The State of Lifelong Learning in Service Robots:

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

Modern bio and chemical sensors and neuromorphic devices based on organic semiconductors

This review summarizes and highlights the current state-of-the-art of research on chemical sensors and biosensors in liquid environment and neuromorphic devices based on electrolyte-gated organic transistors with the active semiconductor layer of organic π-conjugated materials (small molecules, oligomers and polymers). The architecture and principles of operation of electrolyte-gated organic transistors and the main advantages and drawbacks of these devices are considered in detail. The criteria for the selection of organic semiconductors for these devices are presented. The causes of degradation of semiconductor layers and ways of their elimination are discussed. Examples of the use of electrolyte-gated organic transistors as bio and chemical sensors, artificial synapses and computing devices are given.

The bibliography includes 132 references.

Electrochemical Sensors Coupled with Multivariate Statistical Analysis as Screening Tools for Wine Authentication Issues: A Review

Consumers are increasingly interested in the characteristics of the products they consume, including aroma, taste, and appearance, and hence, scientific research was conducted in order to develop electronic senses devices that mimic the human senses. Thanks to the utilization of electroanalytical techniques that used various sensors modified with different electroactive materials coupled with pattern recognition methods, artificial senses such as electronic tongues (ETs) are widely applied in food analysis for quality and authenticity approaches. This paper summarizes the applications of electrochemical sensors (voltammetric, amperometric, and potentiometric) coupled with unsupervised and supervised pattern recognition methods (principal components analysis (PCA), linear discriminant analysis (LDA), partial least square (PLS) regression, artificial neural network (ANN)) for wine authenticity assessments including the discrimination of varietal and geographical origins, monitoring the ageing processes, vintage year discrimination, and detection of frauds and adulterations. Different wine electrochemical authentication methodologies covering the electrochemical techniques, electrodes types, functionalization sensitive materials and multivariate statistical analysis are emphasized and the main advantages and disadvantages of using the proposed methodologies for real applications were concluded.

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.

Microfluidic-controlled optical router for lab on a chip

In multiplexed analysis, lab on a chip (LoC) devices are advantageous due to the low sample and reagent volumes required. Although optical detection is preferred for providing high sensitivity in a contactless configuration, multiplexed optical LoCs are limited by the technological complexity for integrating multiple light sources and detectors in a single device. To address this issue, we present a microfluidic-controlled optical router that enables measurement in four individual optical channels using a single light source and detector, and without movable parts. The optofluidic device is entirely fabricated in polydimethylsiloxane (PDMS) by soft-lithography, compatible with standard microfabrication technologies, enabling monolithic integration in LoCs. In the device, in-coupled light from an optical fiber is collimated by a polymeric micro-lens and guided through a set of four sequentially connected micro-chambers. When a micro-chamber is filled with water, light is transmitted to the next one. If it is empty of liquid, however, total internal reflection (TIR) occurs at the PDMS-air interface, re-directing the light to the output optical fiber. The router presents high performance, with low cross-talk (<2%) and high switching frequencies (up to 0.343 ± 0.006 Hz), and provides a stable signal for up to 91% of the switching time. With this miniaturized, low-cost, simple and robust design, we expect the current technology to be integrated in the new generation of multiplexed photonic LoCs for biomarker analysis, even at the point of care.

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.

Assessing White Wine Viscosity Variation Using Polarized Laser Speckle: A Promising Alternative to Wine Sensory Analysis

In this paper, we report measurements of wine viscosity, correlated to polarized laser speckle results. Experiments were performed on white wine samples produced with a single grape variety. Effects of the wine making cellar, the grape variety, and the vintage on wine Brix degree, alcohol content, viscosity, and speckle parameters are considered. We show that speckle parameters, namely, spatial contrast and speckle decorrelation time, as well as the inertia moment extracted from the temporal history speckle pattern, are mainly affected by the alcohol and sugar content and hence the wine viscosity. Principal component analysis revealed a high correlation between laser speckle results on the one hand and viscosity and Brix degree values on the other. As speckle analysis proved to be an efficient method of measuring the variation of the viscosity of white mono-variety wine, one can therefore consider it as an alternative method to wine sensory analysis.

Portable Electronic Tongue Based on Microsensors for the Analysis of Cava Wines

Cava is a quality sparkling wine produced in Spain. As a product with a designation of origin, Cava wine has to meet certain quality requirements throughout its production process; therefore, the analysis of several parameters is of great interest. In this work, a portable electronic tongue for the analysis of Cava wine is described. The system is comprised of compact and low-power-consumption electronic equipment and an array of microsensors formed by six ion-selective field effect transistors sensitive to pH, Na⁺, K⁺, Ca²⁺, Cl⁻, and CO₃²⁻, one conductivity sensor, one redox potential sensor, and two amperometric gold microelectrodes. This system, combined with chemometric tools, has been applied to the analysis of 78 Cava wine samples. Results demonstrate that the electronic tongue is able to classify the samples according to the aging time, with a percentage of correct prediction between 80% and 96%, by using linear discriminant analysis, as well as to quantify the total acidity, pH, volumetric alcoholic degree, potassium, conductivity, glycerol, and methanol parameters, with mean relative errors between 2.3% and 6.0%, by using partial least squares regressions.

Alcohol, Tannins, and Mannoprotein and their Interactions Influence the Sensory Properties of Selected Commercial Merlot Wines: A Preliminary Study

The objective of this study was to assess the influence of the interaction among alcohol, tannins, and mannoproteins on the aroma, flavor, taste, and mouthfeel characteristics of selected commercial Merlot wines. Merlot wines (n = 61) were characterized for wine chemistry parameters, including pH, titratable acidity, alcohol, glucose, fructose, tannin profile, total proteins, and mannoprotein content. Agglomerative clustering of these physicochemical characteristics revealed 6 groups of wines. Two wines were selected from each group (n = 12) and profiled by a trained sensory evaluation panel. One wine from each group was evaluated using the electronic tongue (e-tongue). Sensory evaluation results showed complex effects among tannins, alcohol, and mannoproteins on the perception of most aromas, flavors, tastes, and mouthfeel attributes (P < 0.05). The e-tongue showed distinct differences among the taste attributes of the 6 groups of wines as indicated by a high discrimination index (DI = 95). Strong correlations (r(2) > 0.930) were reported between the e-tongue and sensory perception of sweet, sour, bitter, burning, astringent, and metallic. This study showed that interactions among wine matrix components influence the resulting sensory perceptions. The strong correlation between the e-tongue and trained panel evaluations indicated the e-tongue can complement sensory evaluations to improve wine quality assessment.

Monitoring and evaluation of alcoholic fermentation processes using a chemocapacitor sensor array

The alcoholic fermentation of Savatiano must variety was initiated under laboratory conditions and monitored daily with a gas sensor array without any pre-treatment steps. The sensor array consisted of eight interdigitated chemocapacitors (IDCs) coated with specific polymers. Two batches of fermented must were tested and also subjected daily to standard chemical analysis. The chemical composition of the two fermenting musts differed from day one of laboratory monitoring (due to different storage conditions of the musts) and due to a deliberate increase of the acetic acid content of one of the musts, during the course of the process, in an effort to spoil the fermenting medium. Sensor array responses to the headspace of the fermenting medium were compared with those obtained either for pure or contaminated samples with controlled concentrations of standard ethanol solutions of impurities. Results of data processing with Principal Component Analysis (PCA), demonstrate that this sensing system could discriminate between a normal and a potential spoiled grape must fermentation process, so this gas sensing system could be potentially applied during wine production as an auxiliary qualitative control instrument.

A novel low-cost sensor prototype for monitoring temperature during wine fermentation in tanks

This paper presents a multipurpose and low cost sensor for temperature control over the wine fermentation process, in order to steadily communicate data through wireless modules in real time to a viticulturist's mobile or fixed device. The advantage of our prototype is due to the fact that it will be used by small winemakers in the “Ribera del Duero” area, and as it is a cheaper sensor and easy to use for the control and monitoring of the grape fermentation process, it will probably be used by other business men with the same necessities in the region. The microcontroller MSP430G2553 is among the components that make up the sensor, that are integrated onto a motherboard. It communicates with the RN-42 Bluetooth module through an UART interface. After verifying that all elements are working correctly, the parts are assembled to form the final prototype. This device has been tested in a winery in the region, fulfilling the initial project specifications.

Voltamperometric discrimination of urea and melamine adulterated skimmed milk powder

Nitrogen compounds like urea and melamine are known to be commonly used for milk adulteration resulting in undesired intoxication; a well-known example is the Chinese episode occurred in 2008. The development of a rapid, reliable and economic test is of relevance in order to improve adulterated milk identification. Cyclic voltammetry studies using an Au working electrode were performed on adulterated and non-adulterated milk samples from different independent manufacturers. Voltammetric data and their first derivative were subjected to functional principal component analysis (f-PCA) and correctly classified by the KNN classifier. The adulterated and non-adulterated milk samples showed significant differences. Best results of prediction were obtained with first derivative data. Detection limits in milk samples adulterated with 1% of its total nitrogen derived from melamine or urea were as low as 85.0 mg · L(-1) and 121.4 mg · L(-1), respectively. We present this method as a fast and robust screening method for milk adulteration analysis and prevention of food intoxication.

Bioinspired sensor systems

This editorial summarizes and classifies the contributions presented by different authors to the special issue of the journal Sensors dedicated to Bioinspired Sensor Systems. From the coupling of sensor arrays or networks, plus computer processing abilities, new applications to mimic or to complement human senses are arising in the context of ambient intelligence. Principles used, and illustrative study cases have been presented permitting readers to grasp the current status of the field.