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

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2565 ◽  
Author(s):  
Daniela Pauliuc ◽  
Florina Dranca ◽  
Mircea Oroian
Keyword(s):  
Zinc Oxide ◽  
Titanium Dioxide ◽  
Electronic Tongue ◽  
Principal Component ◽  
Botanical Origin ◽  
Linear Discriminant ◽  
Cheap Method ◽  
Rapid Characterization ◽  
Comparison Of The Results ◽  
Melissopalynological Analysis

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.

scholarly journals Characterization of Sicilian Honeys Pollen Profiles Using a Commercial E-Tongue and Melissopalynological Analysis for Rapid Screening: A Pilot Study

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4065 ◽  
Author(s):  
Ambra Di Rosa ◽  
Anna Marino ◽  
Francesco Leone ◽  
Giuseppe Corpina ◽  
Renato Giunta ◽  
...  
Keyword(s):  
Pilot Study ◽  
Electronic Tongue ◽  
Pollen Grain ◽  
Rapid Screening ◽  
Honey Sample ◽  
Botanical Origin ◽  
Pollen Type ◽  
Comparison Of The Results ◽  
Melissopalynological Analysis

Honey is usually classified as “unifloral” or “multifloral”, depending on whether a dominating pollen grain, originating from only one particular plant, or no dominant pollen type in the sample is found. Unifloral honeys are usually more expensive and appreciated than multifloral honeys, which highlights the importance of honey authenticity. Melissopalynological analysis is used to identify the botanical origin of honey, counting down the number of pollens grains of a honey sample, and calculating the respective percentages of the nectariferous pollens. In addition, sensory properties are also very important for honey characterization, and electronic senses emerged as useful tools for honey authentication. In this work, a comparison of the results obtained from melissopalynological analysis with those provided by a potentiometric electronic tongue is given, resulting in a 100% match between the two techniques.

scholarly journals Quality Assessment of Goldenrod, Milkweed and Multifloral Honeys Based on Botanical Origin, Antioxidant Capacity and Mineral Content

2022 ◽  
Vol 23 (2) ◽  
pp. 769
Author(s):  
Marianna Kocsis ◽  
Alexandra Bodó ◽  
Tamás Kőszegi ◽  
Rita Csepregi ◽  
Rita Filep ◽  
...  
Keyword(s):  
Antioxidant Capacity ◽  
Mineral Content ◽  
Principal Component ◽  
Pollen Spectrum ◽  
Botanical Origin ◽  
Multivariate Statistical ◽  
Zn Content ◽  
Five Elements ◽  
Multifloral Honey ◽  
Melissopalynological Analysis

The goal of the study was to evaluate the pollen spectrum, antioxidant capacity and mineral content of four Hungarian honey types, using multivariate statistical analysis. The light colored honeys were represented by milkweed honey and a multifloral (MF) honey with dominant pollen frequency of linden (MF-Tilia); the darker ones were goldenrod honey and a multifloral honey with Lamiaceae pollen majority (MF-Lamiaceae). The pollen spectrum of the samples was established with melissopalynological analysis. The absorbance of the honeys positively correlated with the antioxidant capacity determined with three of the used methods (TRC, TEAC, DPPH), but not with ORAC. The latter method correlated negatively also with other antioxidant methods and with most of the mineral values. MF-Tilia had high ORAC value, K and Na content. The MF-Lamiaceae had the highest K, Mg, P, S, Cu and Zn content, the last five elements showing strict correlation with the TRC method. The darker goldenrod honey had higher SET values and total mineral content, than the milkweed honey. The above character-sets facilitate identification of each honey type and serve as indicators of variety. The antioxidant levels and mineral content of honeys allowed their clear separation by principal component analysis (PCA).

scholarly journals Discrimination and Geographical Origin Prediction of Cynomorium songaricum Rupr. from Different Growing Areas in China by an Electronic Tongue

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Jiaji Ding ◽  
Caimei Gu ◽  
Linfang Huang ◽  
Rui Tan
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Prediction Model ◽  
Linear Discriminant Analysis ◽  
Geographical Origin ◽  
Electronic Tongue ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Discriminant ◽  
Very High

Cynomorium songaricum Rupr. is a well-known and widespread plant in China. It has very high medicinal values in many aspects. The study aimed at discriminating and predicting C. songaricum from major growing areas in China. An electronic tongue was used to analyze C. songaricum based on flavor. Discrimination was achieved by principal component analysis and linear discriminant analysis. Moreover, a prediction model was established, and C. songaricum was classified by geographical origins with 100% degree of accuracy. Therefore, the identification method presented will be helpful for further study of C. songaricum.

scholarly journals Electronic Tongue as a Correlative Technique for Modeling Cattle Meat Quality and Classification of Breeds

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2283
Author(s):  
József Surányi ◽  
John-Lewis Zinia Zaukuu ◽  
László Friedrich ◽  
Zoltan Kovacs ◽  
Ferenc Horváth ◽  
...  
Keyword(s):  
Analytical Methods ◽  
Electronic Tongue ◽  
Principal Component ◽  
Binding Activity ◽  
Quality Parameter ◽  
Water Binding ◽  
Linear Discriminant ◽  
Meat Species ◽  
Cattle Meat ◽  
Meat Samples

Discrimination and species identification of meat has always been of paramount importance in the European meat market. This is often achieved using different conventional analytical methods but advanced sensor-based methods, such as the electronic tongue (e-tongue), are also gaining attention for rapid and reliable analysis. The aim of this study was to discriminate Angus, domestic buffalo, Hungarian Grey, Hungarian Spotted cattle, and Holstein beef meat samples from the chuck steak part of the animals, which mostly contained longissimus dorsi muscles, using e-tongue as a correlative technique with conventional methods for analysis of pH, color, texture, water activity, water-holding capacity, cooking yield, water binding activity, and descriptive sensory analysis. Analysis of variance (ANOVA) was used to determine significant differences between the measured quality traits of the five-meat species after analysis with conventional analytical methods. E-tongue data were visualized with principal component analysis (PCA) before classifying the five-meat species with linear discriminant analysis (LDA). Significant differences were observed among some of the investigated quality parameter. In most cases, Hungarian Grey was most different from the other species. Using e-tongue, separation patterns could be observed in the PCA that were confirmed with 100% recognition and 97.5% prediction of all the different meat species in LDA.

scholarly journals Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1984
Author(s):  
Xiaoguang Dong ◽  
Libing Gao ◽  
Haijun Zhang ◽  
Jing Wang ◽  
Kai Qiu ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Classification Accuracy ◽  
Laying Hens ◽  
Electronic Tongue ◽  
Principal Component ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Sensory Qualities ◽  
Egg Yolks

The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) laying hens. Sensory qualities of egg yolks and albumen from three breeds were detected and discriminated based on different algorithms. Texture profile analysis (TPA) showed that the eggs from three breeds had no differences in hardness, adhesiveness, springiness, and chewiness other than cohesiveness. The smell profiles measured by electronic nose illustrated that differences existed in all 10 sensors for albumen and 8 sensors for yolks. The taste profiles measured by electronic tongue found that the main difference of egg yolks and albumen existed in bitterness and astringency. Principal component analysis (PCA) successfully showed grouping of three breeds based on electronic nose data and failed in grouping based on electronic tongue data. Based on electronic nose data, linear discriminant analysis (LDA), fine k-nearest neighbor (KNN) and linear support vector machine (SVM) were performed to discriminate yolks, albumen, and the whole eggs with 100% classification accuracy. While based on electronic tongue data, the best classification accuracy was 96.7% for yolks by LDA and fine tree, 88.9% for albumen by LDA, and 87.5% for the whole eggs by fine KNN. The experiment results showed that three breeds’ eggs had main differences in smells and could be successfully discriminated by LDA, fine KNN, and linear SVM algorithms based on electronic nose.

scholarly journals Detection of Taste Change of Bovine and Goat Milk in Room Ambient Using Electronic Tongue

2017 ◽  
Vol 17 (3) ◽  
pp. 422 ◽  
Author(s):  
Imam Tazi ◽  
Anis Choiriyah ◽  
Dwi Siswanta ◽  
Kuwat Triyana
Keyword(s):  
Food Industry ◽  
Ph Sensor ◽  
Electronic Tongue ◽  
Polymer Membranes ◽  
Goat Milk ◽  
Principal Component ◽  
Linear Discriminant ◽  
Validation Procedure ◽  
Natural Milk

An electronic tongue (e-tongue) based on an array of lipid/polymer membranes has been successfully developed for measuring the taste evolution of natural milk. The e-tongue consisted of 16 different lipid/polymer membranes combined with or without a pH sensor. The natural milk of bovine and goat were purchased from the local farming store in Malang-Indonesia. The taste measurement was carried out, from fresh (0 h) to stale (12 h), every two hours under room ambient without any treatment. The responses of the e-tongue were evaluated using a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). From PCA results, the taste of both milk samples tends to change by time although some groups show a partial overlapping. LDA results show the high precision of the e-tongue in clustering taste evolution. The correctly classified groups after the cross-validation procedure were achieved 95.7 and 87.1% for bovine and goat milk, respectively. The improvement of the classification using LDA was obtained by adding data from a pH sensor of each measurement as 100 and 98.6% for bovine and goat milk, respectively. This work indicates that the lab-made e-tongue may be useful to predict the quality of natural milk for the food industry.

scholarly journals Coupling of Sensors and Machine Learning Algorithms in the Qualitative Analysis of Wine

2021 ◽  
Vol 6 (1) ◽  
pp. 50
Author(s):  
Anna Herrera-Chacon ◽  
Inmaculada Campos ◽  
Andreu González-Calabuig ◽  
Mireia Torres ◽  
Manel del Valle
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Electronic Tongue ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Discrete Wavelet ◽  
Complex Data ◽  
Linear Discriminant ◽  
Protected Designation Of Origin ◽  
Artificial Neural

This work attempts the identification of the production year, the cultivar’s region and the aging method used in the elaboration of different Spanish red wines, all from the “tempranillo” grape variety. The identification of such characteristics relies on the use of a voltammetric electronic tongue (ET) system formed by modified graphite-epoxy electrodes (GEC) and metallic electrodes to collect a set of six voltammograms per sample, and different chemometric tools to accomplish the final identifications. A large sample set that included 199 different wine samples from commercial and own elaboration origin were analysed with the electronic tongue system, using the cyclic voltammetry technique and without any sample pre-treatment. To process the extremely complex and high-dimensionality generated data, a compression strategy was used for the acquired voltammograms, using discrete wavelet transform (DWT). This treatment reduced the information to ca. 10%, preserving significant features from the voltammetric signals. Compressed data was evaluated firstly by unsupervised methods, i.e., principal component analysis (PCA), without much success as it was found that such methods were unable to unravel the patterns contained within such complex data samples. Finally, the processed electrochemical information was evaluated by supervised methods to accomplish the proper identification; among those methods were linear discriminant analysis (LDA), supported vector machines (SVM) or artificial neural networks (ANN). The best results were obtained using artificial neural networks (ANNs), achieving 96.1% of correct classification for bottled year, 86.8% for elaboration region (protected designation of origin) and 98.6% for maturation type with or without use of wood barrel.

scholarly journals Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 265
Author(s):  
Ruchi Sharma ◽  
Wenzhe Zang ◽  
Menglian Zhou ◽  
Nicole Schafer ◽  
Lesa A. Begley ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Real Time ◽  
Rapid Assessment ◽  
Principal Component ◽  
Corticosteroid Treatment ◽  
Breath Analysis ◽  
Blood Eosinophil ◽  
Exhaled Breath ◽  
Respiratory Illnesses ◽  
Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

scholarly journals Relationships Linking the Colour and Elemental Concentrations of Blossom Honeys with Their Antioxidant Activity: A Chemometric Approach

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 702
Author(s):  
Monika Kędzierska-Matysek ◽  
Anna Teter ◽  
Małgorzata Stryjecka ◽  
Piotr Skałecki ◽  
Piotr Domaradzki ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Physicochemical Properties ◽  
Black Locust ◽  
Antioxidant Properties ◽  
Principal Component ◽  
Botanical Origin ◽  
Antioxidant Parameters ◽  
Colour Parameters ◽  
Hue Angle ◽  
Chemometric Approach

The antioxidant activity of honey depends on the botanical origin, which also determines their physicochemical properties. In this study, a multivariate analysis was used to confirm potential relationships between the antioxidant properties and colour parameters, as well as the content of seven elements in five types of artisanal honey (rapeseed, buckwheat, linden, black locust, and multifloral). The type of honey was found to significantly influence most of its physicochemical properties, colour parameters, and the content of potassium, manganese and copper. Antioxidant parameters were shown to be significantly positively correlated with redness and concentrations of copper and manganese, but negatively correlated with the hue angle and lightness. The principal component analysis confirmed that the darkest buckwheat honey had the highest antioxidant activity in combination with its specific colour parameters and content of antioxidant minerals (manganese, copper and zinc). The level of these parameters can be potentially used for the identification of buckwheat honey.

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