Technique potential for classification of honey by electronic tongue

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.

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

Raspberry, Rape, Thyme, Sunflower and Mint Honeys Authentication Using Voltammetric Tongue

The aim of this study was to authenticate five types of Romanian honey (raspberry, rape, thyme, sunflower and mint) using a voltammetric tongue (VE tongue) technique. For the electronic tongue system, six electrodes (silver, gold, platinum, glass, zinc oxide and titanium dioxide) were used. The results of the melissopalynological analysis were supplemented by the data obtained with the electronic voltammetric tongue system. The results were interpreted by means of principal component analysis (PCA) and linear discriminant analysis (LDA). In this way, the usefulness of the working electrodes was compared for determining the botanical origin of the honey samples. The electrodes of titanium dioxide, zinc oxide, and silver were more useful, as the results obtained with these electrodes showed that it was achieved a better classification of honey according to its botanical origin. The comparison of the results of the electronic voltammetric tongue technique with those obtained by melissopalynological analysis showed that the technique was able to accurately classify 92.7% of the original grouped cases. The similarity of results confirmed the ability of the electronic voltammetric tongue technique to perform a rapid characterization of honey samples, which complements its advantages of being an easy-to-use and cheap method of analysis.

Impedimetric electronic tongue based on molybdenum disulfide and graphene oxide for monitoring antibiotics in liquid media

Antibiotics are considered emerging pollutants which indiscriminate use has led to the development of antibiotic-resistant bacteria, while their improper disposal has caused adverse effects to the environment and human health. Thus, the development of devices or techniques capable of detecting antibiotics with high sensitivity, low detection limits, and reasonable cost becomes of prime importance. In this work, an electronic tongue (e-tongue) based on molybdenum disulfide (MoS₂) and graphene oxide (GO) was developed and employed to detect four distinct antibiotics, namely cloxacillin benzathine, erythromycin, streptomycin sulfate, and tetracycline hydrochloride. The five sensing units of the e-tongue were obtained using the drop-casting method to modify gold interdigitated electrodes with MoS₂ and GO. Using Principal Component Analysis to process the experimental data allowed the e-tongue to recognize samples contaminated with distinct antibiotics at varied concentrations from 0.5 to 5.0 nmol L^-1. Analyses with real samples were also performed using river water and human urine and the electronic tongue was able to differentiate the samples at a nanomolar level. The proposed system represents a sensitive and low-cost alternative for antibiotic analyses in different liquid media.

Using an automatic pulse voltammetric electronic tongue to verify the origin of honey from Spain, Honduras, and Mozambique

BACKGROUND

The growing need to classify the origin of honey in a simple way is leading to the development of affordable analytical equipment that is in-line and manageable, enabling rapid on-site screening. The aim of this work was therefore to evaluate whether an electronic tongue (made of four metallic electrodes: Ir, Rh, Pt, Au), based on potential multistep pulse voltammetry with electrochemical polishing, is able to differentiate between honey samples from Spain, Honduras, and Mozambique.

RESULTS

It was demonstrated, for the first time, that automatic pulse voltammetry, in combination with principal component analysis (PCA) statistical analysis, was able to differentiate honey samples from these three countries. A partial least squares (PLS) analysis predicted the level of certain physicochemical parameters, the best results being for conductivity and moisture with correlation coefficients of 0.948 and 0.879, whereas the weakest correlation was for the sugars.

CONCLUSION

The tool proposed in this study could be applied to identify the country origin of the three types of multifloral honey considered here. It also offers promising perspectives for expanding knowledge of the provenance of honey. All of this could be achieved when a comprehensive database with the information generated by this electronic tongue has been created. © 2019 Society of Chemical Industry.

Honey Evaluation Using Electronic Tongues: An Overview

Honey-rich composition in biologically active compounds makes honey a food products highly appreciated due to the nutritional and healthy properties. Food-manufacturing is very prone to different types of adulterations and fraudulent labelling making it urgent to establish accurate, fast and cost-effective analytical techniques for honey assessment. In addition to the classical techniques (e.g., physicochemical analysis, microscopy, chromatography, immunoassay, DNA metabarcoding, spectroscopy), electrochemical based-sensor devices have arisen as reliable and green techniques for food analysis including honey evaluation, allowing in-situ and on-line assessment, being a user-friendly procedure not requiring high technical expertise. In this work, the use of electronic tongues, also known as taste sensor devices, for honey authenticity and assessment is reviewed. Also, the versatility of electronic tongues to qualitative (e.g., botanical and/or geographical origin assessment as well as detection of adulteration) and quantitative (e.g., assessment of adulterants levels, determination of flavonoids levels or antibiotics and insecticides residues, flavonoids) honey analysis is shown. The review is mainly focused on the research outputs reported during the last decade aiming to demonstrate the potentialities of potentiometric and voltammetric multi-sensor devices, pointing out their main advantages and present and future challenges for becoming a practical quality analytical tool at industrial and commercial levels.

Food analysis using artificial senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.

An embedded Simplified Fuzzy ARTMAP implemented on a microcontroller for food classification

In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food.

Diverse applications of electronic-nose technologies in agriculture and forestry

Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

Electronic noses and tongues: applications for the food and pharmaceutical industries

The electronic nose (e-nose) is designed to crudely mimic the mammalian nose in that most contain sensors that non-selectively interact with odor molecules to produce some sort of signal that is then sent to a computer that uses multivariate statistics to determine patterns in the data. This pattern recognition is used to determine that one sample is similar or different from another based on headspace volatiles. There are different types of e-nose sensors including organic polymers, metal oxides, quartz crystal microbalance and even gas-chromatography (GC) or combined with mass spectroscopy (MS) can be used in a non-selective manner using chemical mass or patterns from a short GC column as an e-nose or "Z" nose. The electronic tongue reacts similarly to non-volatile compounds in a liquid. This review will concentrate on applications of e-nose and e-tongue technology for edible products and pharmaceutical uses.