Identification of monofloral honey using voltammetric electronic tongue

Development of a low-cost electrochemical sensor for monitoring components in wastewater treatment processes

Anaerobic digestion (AD) is a complex biological process widely used to decompose various types of organic matter, as well as to produce some metabolites and biogas. Diverse microorganism groups cooperate in many intricate metabolic routes so that organic matter can be degraded. However, any imbalance on these routes can lead to process instability or even failure. Therefore, a proper monitoring system, as well as a good understanding of the process, are key steps to improve performance and stability. Several mathematical models have been developed to represent AD. Despite this, process monitoring is mostly conducted by analytical methods, whose equipment is either expensive or the analyses are time-consuming, which may be a hindrance to low-budget developments. The objective of this study was to develop a low-cost electrochemical sensor to monitor components in wastewater treatment plants. Hundreds of synthetically supplemented sugarcane vinasse and synthetic domestic sewage samples were characterised. The obtained signals were used to calibrate principal component regression, partial least square and artificial neural network estimation models. The predictable variables were chemical oxygen demand, volatile fatty acids, sodium bicarbonate, beef extract, and lipids, and their R2 ranged from 0.84 to 0.99, depending on the component.

New Electronic Tongue Sensor Array System for Accurate Liquor Beverage Classification

The use of sensors in different applications to improve the monitoring of a process and its variables is required as it enables information to be obtained directly from the process by ensuring its quality. This is now possible because of the advances in the fabrication of sensors and the development of equipment with a high processing capability. These elements enable the development of portable smart systems that can be used directly in the monitoring of the process and the testing of variables, which, in some cases, must evaluated by laboratory tests to ensure high-accuracy measurement results. One of these processes is taste recognition and, in general, the classification of liquids, where electronic tongues have presented some advantages compared with traditional monitoring because of the time reduction for the analysis, the possibility of online monitoring, and the use of strategies of artificial intelligence for the analysis of the data. However, although some methods and strategies have been developed, it is necessary to continue in the development of strategies that enable the results in the analysis of the data from electrochemical sensors to be improved. In this way, this paper explores the application of an electronic tongue system in the classification of liquor beverages, which was directly applied to an alcoholic beverage found in specific regions of Colombia. The system considers the use of eight commercial sensors and a data acquisition system with a machine-learning-based methodology developed for this aim. Results show the advantages of the system and its accuracy in the analysis and classification of this kind of alcoholic beverage.

Identification of Forsythia suspensa (Thunb.) Vahl in different harvest periods using intelligent sensory technologies, HPLC characteristic fingerprint coupled with chemometrics

INTRODUCTION

Forsythia suspensa (Thunb.) Vahl (FS), the fruit of Oleaceae plants, as a large part of traditional Chinese medicine, is classified as "Qingqiao (Q)" and "Laoqiao (L)" based on the harvest time. Because the maturation of FS is a gradual process, its accurate identification based on different maturity levels is an important issue.

OBJECTIVES

We suggest colorimetric, electronic tongue, and high-performance liquid chromatography (HPLC) characteristic fingerprints to discriminate FS in different harvest periods.

MATERIAL AND METHODS

First, FS fruits from different harvest times were collected, and then, their colour parameters, E-tongue sensory properties, HPLC characteristic fingerprints, and contents of nominal ingredients were determined. Finally, multivariate statistical analyses, including three-dimensional scatter plots, hierarchical cluster, principal component, linear discriminant, similarity, and partial least squares discriminant analyses were performed.

RESULTS

The results demonstrated that the three experimental techniques could effectively discriminate FS based on different harvest times with 100% accuracy. Under the qualitative conditions, nine common peaks were identified in the HPLC fingerprints of 60 samples, among which, six peaks [variable importance in projection (VIP) > 1] could be used as index peaks for qualitative identification. In fact, the contents of quality marker components, including forsythin, phillygenin, rutin and forsythoside A, were significant different (P < 0.001) at different harvest times. Interestingly, the quality markers not only accurately reflected the maturity of FS but also showed close correlations with the colour parameters and sensory E-tongue responses.

CONCLUSION

In our present investigation, bionic technologies, including a colorimeter, E-tongue analysis, and HPLC characteristic fingerprints, combined with chemometrics, were employed to develop a novel and accurate method for discriminating FS based on different harvest times.

Voltammetric E-Tongue for Honey Adulteration Detection

The aim of this study is to establish the usefulness of an electronic tongue based on cyclic voltammetry e-tongue using five working electrodes (gold, silver, copper, platinum and glass) in honey adulteration detection. Authentic honey samples of different botanical origin (acacia, tilia, sunflower, polyfloral and raspberry) were adulterated with agave, maple, inverted sugar, corn and rice syrups in percentages of 5%, 10%, 20% and 50%. The silver and copper electrodes provided the clearest voltammograms, the differences between authentic and adulterated honey samples being highlighted by the maximum current intensity. The electronic tongue results have been correlated with physicochemical parameters (pH, free acidity, hydroxymethylfurfural content—5 HMF and electrical conductivity—EC). Using statistical methods such as Linear discriminant analysis (LDA) and Support vector machines (SVM), an accuracy of 94.87% and 100% respectively was obtained in the calibration step and 89.65% and 100% respectively in the validation step. The PLS-R (Partial Least Squares Regression) model (constructed from the minimum and maximum current intensity obtained for all electrodes) was used in physicochemical parameters prediction; EC reached the highest regression coefficients (0.840 in the calibration step and 0.842 in the validation step, respectively), being followed by pH (0.704 in the calibration step and 0.516 in the validation step, respectively).

Improvement strategies of food supply chain through novel food processing technologies during COVID-19 pandemic

Coronavirus disease-19 (COVID-19) is a contagious disease caused by a novel corona virus (SARS-CoV-2). No medical intervention has yet succeeded, though vaccine success is expected soon. However, it may take months or years to reach the vaccine to the whole population of the world. Therefore, the technological preparedness is worth to discuss for the smooth running of food processing activities. We have explained the impact of the COVID-19 pandemic on the food supply chain (FSC) and then discussed the technological interventions to overcome these impacts. The novel and smart technologies during food processing to minimize human-to-human and human-to-food contact were compiled. The potential virus-decontamination technologies were also discussed. Finally, we concluded that these technologies would make food processing activities smarter, which would ultimately help to run the FSC smoothly during COVID-19 pandemic.

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.

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.

Lavandula Essential Oils: A Current Review of Applications in Medicinal, Food, and Cosmetic Industries of Lavender

The global essential oil market has been steadily increasing in size over the past few years, and is estimated to reach ca. $7.5 billion USD per annum by 2018. Lavenders ( Lavandula; Lamiaceae) contribute significantly to this market, yielding ca. 1500 tons of essential oils which are primarily used in cosmetics, personal care products, and medicines. Recent literature indicates that these oils may also have applications in food preservation and pest control, among others. The medicinal and pharmaceutical properties of lavenders are chiefly due their essential oils, in particular the major essential oil constituents linalool and linalyl acetate, although certain activities have been attributed to the phenolic compounds. In addition, there is evidence that the major and minor essential oil constituents act synergistically to provide various biological effects. A substantial amount of current research focuses on evaluating the biological activities of lavender essential oils for potential use in traditional and complementary medicine, food systems, cosmetic and fragrance formulations, and insect control products. This review examines recent progress in these areas, and highlights the current and future implications for these economically and medicinally valuable plants.

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.

Application of Electronic Nose Systems on Animal-Source Food

Electronic nose, which is designed to perceive artificially the odour-active molecules in a sample headspace, has seen an increased use in the food industry as a rapid and reliable tool for quality assessment, classification, and authentication of several food items. The use of chemometrics and pattern recognition methods, together with gas sensors, emerged to be a very powerful analytical approach. In this chapter, an overview of the recent achievements in the field of electronic nose applications on animal-source food is given. Moreover, the authors deal with the recent research trends to overcome the actual sensor shortcomings, including sensor fusion techniques and their applications to evaluate animal-source foods and novel electronic nose systems.

Emerging approach for analytical characterization and geographical classification of Moroccan and French honeys by means of a voltammetric electronic tongue

Moroccan and French honeys from different geographical areas were classified and characterized by applying a voltammetric electronic tongue (VE-tongue) coupled to analytical methods. The studied parameters include color intensity, free lactonic and total acidity, proteins, phenols, hydroxymethylfurfural content (HMF), sucrose, reducing and total sugars. The geographical classification of different honeys was developed through three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cluster analysis (HCA). Honey characterization was achieved by partial least squares modeling (PLS). All the PLS models developed were able to accurately estimate the correct values of the parameters analyzed using as input the voltammetric experimental data (i.e. r>0.9). This confirms the potential ability of the VE-tongue for performing a rapid characterization of honeys via PLS in which an uncomplicated, cost-effective sample preparation process that does not require the use of additional chemicals is implemented.

Antioxidant activity and physico-chemical parameters for the differentiation of honey using a potentiometric electronic tongue

BACKGROUND

This work evaluates the capacity of a potentiometric electronic tongue to differentiate between types of honey (orange blossom, rosemary, thyme, sunflower, winter savory and honeydew honey) according to their antioxidant level. The classical procedures used to evaluate the antioxidant potential of honey are inappropriate for in situ monitoring on reception of batches of raw honey in the packaging industry. They are also destructive, time-consuming and very tedious, requiring highly expert analysts and specialised equipment.

RESULTS

The electronic tongue system made with Ag, Ni, Co, Cu and Au was able to not only differentiate between types of honey but also to predict their total antioxidant capacity. The discrimination ability of the system was proved by means of a fuzzy ARTMAP type ANN, with 100% classification success. A prediction multiple linear regression model showed that the best correlation coefficient was for antioxidant activity (0.9666), then for electrical conductivity (0.8959) and to a lesser extent for a_w , moisture and colour.

CONCLUSION

The proposed measurement system could be a quick, easy option for the honey packaging sector to provide continuous in-line information about a characteristic as important as the antioxidant level. © 2016 Society of Chemical Industry.

Sensor-array-based evaluation and grading of beef taste quality

A sensor array composed of ion electrodes including 2 glass electrodes, 3 liquid-membrane electrodes and 7 insoluble salt electrodes was built. Before detection, the working electrodes were activated as required in activate fluids, and the stability states of sensors were analyzed in deionized water. Beef samples were evaluated after all working electrodes stabilized. The response signals from the samples were recorded by an electrochemical workstation and used as the evaluation results. A beef taste sensory evaluation criterion was built and used into sensory evaluation of beef samples. The samples were scored with quality grades according to this criterion, and the results were compared with the results of the sensor array in evaluation of beef broth samples. The evaluation results were processed by principal component analysis and used to build a beef taste quality evaluation model based on artificial neural networks. Tests show this model has an accuracy of 90% in classification of beef taste quality grades.

Discrimination of Rice with Different Pretreatment Methods by Using a Voltammetric Electronic Tongue

In this study, an application of a voltammetric electronic tongue for discrimination and prediction of different varieties of rice was investigated. Different pretreatment methods were selected, which were subsequently used for the discrimination of different varieties of rice and prediction of unknown rice samples. To this aim, a voltammetric array of sensors based on metallic electrodes was used as the sensing part. The different samples were analyzed by cyclic voltammetry with two sample-pretreatment methods. Discriminant Factorial Analysis was used to visualize the different categories of rice samples; however, radial basis function (RBF) artificial neural network with leave-one-out cross-validation method was employed for prediction modeling. The collected signal data were first compressed employing fast Fourier transform (FFT) and then significant features were extracted from the voltammetric signals. The experimental results indicated that the sample solutions obtained by the non-crushed pretreatment method could efficiently meet the effect of discrimination and recognition. The satisfactory prediction results of voltammetric electronic tongue based on RBF artificial neural network were obtained with less than five-fold dilution of the sample solution. The main objective of this study was to develop primary research on the application of an electronic tongue system for the discrimination and prediction of solid foods and provide an objective assessment tool for the food industry.

Assessment of taste attributes of peanut meal enzymatic-hydrolysis hydrolysates using an electronic tongue

Peanut meal is the byproduct of high-temperature peanut oil extraction; it is mainly composed of proteins, which have complex tastes after enzymatic hydrolysis to free amino acids and small peptides. The enzymatic hydrolysis method was adopted by using two compound proteases of trypsin and flavorzyme to hydrolyze peanut meal aiming to provide a flavor base. Hence, it is necessary to assess the taste attributes and assign definite taste scores of peanut meal double enzymatic hydrolysis hydrolysates (DEH). Conventionally, sensory analysis is used to assess taste intensity in DEH. However, it has disadvantages because it is expensive and laborious. Hence, in this study, both taste attributes and taste scores of peanut meal DEH were evaluated using an electronic tongue. In this regard, the response characteristics of the electronic tongue to the DEH samples and standard five taste samples were researched to qualitatively assess the taste attributes using PCA and DFA. PLS and RBF neural network (RBFNN) quantitative prediction models were employed to compare predictive abilities and to correlate results obtained from the electronic tongue and sensory analysis, respectively. The results showed that all prediction models had good correlations between the predicted scores from electronic tongue and those obtained from sensory analysis. The PLS and RBFNN prediction models constructed using the voltage response values from the sensors exhibited higher correlation and prediction ability than that of principal components. As compared with the taste performance by PLS model, that of RBFNN models was better. This study exhibits potential advantages and a concise objective taste assessment tool using the electronic tongue in the assessment of DEH taste attributes in the food industry.