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.

Evaluation of Sweet Sauce Characteristic Taste Based on Electronic Tongue Technology

2012 ◽  
Vol 554-556 ◽  
pp. 1593-1601
Author(s):  
Ming Quan Huang ◽  
Lu Wang ◽  
Bao Guo Sun ◽  
Hong Yu Tian
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Reducing Sugars ◽  
Electronic Tongue ◽  
Amino Nitrogen ◽  
Physicochemical Characterization ◽  
Principal Component ◽  
On Line

A commercial electronic tongue (ET) with specific sensors was applied on taste distinction and physicochemical characterization of seven kinds of sweet sauces. The response signals of ET sensors were analyzed by Principal Component Analysis (PCA) and Discriminant Factor Analysis (DFA). Meanwhile, these signals were transformed into the four relative taste scores (sourness, saltiness, umami and sweetness) by macro operation, followed by comparing with the corresponding four physiochemical indexes (total acids, sodium chloride, amino nitrogen and reducing sugars) which were determined by the methods in GB/T. The results show that ET can be used to distinguish different kinds of sweet sauces according to overall taste. Moreover, the intensity order of taste scores that obtained from ET is basically matched with the sequence of the corresponding physicochemical indexes, which proves that ET technique can be an effective approach to monitor and guarantee the quality of sweet sauce on line.

Application of Visible-Near Infrared Spectra Technique Based on to the Non-Destructive Measurement of the Salmon Quality

2014 ◽  
Vol 926-930 ◽  
pp. 961-964
Author(s):  
Jiao Jiao Yin
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Principal Component ◽  
Support Vector ◽  
Analysis Method ◽  
Linear Discriminant ◽  
Near Infrared Spectra ◽  
Destructive Measurement ◽  
Non Destructive

Because the reflectivity of astaxanthin vary in different bands (mainly 400nm-600nm), so we use the visible-near infrared spectra technique to irradiate the salmon. Because in daily life, people grade the salmon flesh with a color card. In this paper, we first use principal component analysis to reduce the dimensionality of the spectral data of salmon, then use linear discriminant analysis method, least squares support vector machine classification method to distinguish the flesh quality. The correct classification rates are 60%and73.3%. The results show that we can use visible – near infrared spectra to distinguish the quality of the salmon which doesn’t be dissected.

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 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 Species Classification and Quality Assessment of Cangzhu (Atractylodis Rhizoma) by High-Performance Liquid Chromatography and Chemometric Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Yong-Gang Xia ◽  
Bing-You Yang ◽  
Qiu-Hong Wang ◽  
Jun Liang ◽  
Di Wang ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Discriminant Analysis ◽  
Quality Assessment ◽  
High Performance ◽  
Principal Component ◽  
Chemometric Methods ◽  
Species Classification ◽  
Linear Discriminant

Fast and sensitive high-performance liquid chromatography (HPLC) coupled with chemometric methods was utilized to assist in the quality assessment of Cangzhu (Atractylodis Rhizoma). By comparative analysis of chromatographic profiles, twelve common peaks were selected for multivariate analysis. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of the chromatographic data demonstrated that 16 batches of Cangzhu samples could be welldifferentiated and categorized into two groups, which were closely related to their species (Atractylodes chinensis and A. lancea). By loading plots of PCA and OPLS-DA, the “common peaks” 2, 10, and 12 were defined as “marker peaks,” which were identified as atractylodinol, (4E,6E,12E)-tetradecatriene-8,10-diyne-1,3-diyl diacetate, and atractylodin, respectively. These three “marker peaks” were then simultaneously quantified for further controlling the quality of Cangzhu, which showed acceptable linearity, both intraday and interday precisions (RSD ≤ 2.30%), repeatability (RSD ≤ 2.82%), and the recoveries of the three analytes in the range of 96.57–100.16%, with RSDs less than 1.46%. Finally, linear discriminant analysis (LDA) was successfully used to build predictive models of the group membership based on the contents of three marker peaks. Results of the present study demonstrated that HPLC-based metabolic profiling coupled with chemometric methods and quantificational determination was a very flexible, reliable, and effective way for homogeneity evaluation and quality assessment of traditional Chinese medicine.

scholarly journals The use of electronic tongue (e-tongue) as a simple and rapid method for honey authentication

Food Research ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 453-460
Author(s):  
L.A. Lestari ◽  
K. Triyana ◽  
A.K. Hanifah ◽  
R.A. Wildiana
Keyword(s):  
High Performance ◽  
Food Industry ◽  
Prediction Models ◽  
Sugar Content ◽  
Electronic Tongue ◽  
Rapid Test ◽  
Botanical Origin ◽  
Skilled Personnel ◽  
Measured Sample

A simple and rapid test for honey authentication is required particularly for the food industry to assure the quality of honey. The conventional methods for honey authentication are costly, requires a long waiting time to obtain results, requires highly skilled personnel, and is difficult in terms of sample preparation. The electronic tongue can be utilized as an alternative technique for honey authentication. The electronic tongue frameworks are depended on an array of sensors with low selectivity while being sensitive to several components in the measured sample. The signals gathered by the sensors are processed through pattern recognition tools to produce prediction models that permit the grouping of the samples and the measurement of a portion of their physicochemical properties. Papers that were published from 2015 to 2020 from several databases such as Google Scholar, ScienceDirect, and Pubmed were collected to obtain abstracts and original articles related to the authentication of honey-based on sugar content as well as geographical and botanical origin. This review highlighted the electronic tongue as a simple and rapid test for honey authentication, several original papers also compare the validity of electronic tongue with the high-performance liquid chromatography methods as the gold standard.

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 Volatile Profiling of Strawberry Fruits Cultivated in a Soilless System to Investigate Cultivar-Dependent Chemical Descriptors

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 768
Author(s):  
Raúl González-Domínguez ◽  
Ana Sayago ◽  
Ikram Akhatou ◽  
Ángeles Fernández-Recamales
Keyword(s):  
Volatile Compounds ◽  
Food Industry ◽  
Geographical Origin ◽  
Solid Phase ◽  
Principal Component ◽  
Linear Discriminant ◽  
Organoleptic Characteristics ◽  
Great Utility ◽  
Methyl Butyrate ◽  
Chemical Descriptors

Volatile compounds are essential for food organoleptic characteristics and of great utility for the food industry as potential markers for authenticity purposes (e.g., variety, geographical origin, adulteration). The aim of this study was to determine the characteristic volatile compounds of strawberry samples grown in a soilless system by using headspace solid phase micro-extraction coupled with gas chromatography and to investigate the influence of cultivar (Festival, Candonga, Camarosa) on this volatile profile. We observed that Festival and, to a lesser extent, Candonga varieties were characterized by the richest aroma-related profiles, including higher levels of esters, furanones and terpenes. In particular, methyl butyrate, hexyl hexanoate, linalool, geraniol and furaneol were the most abundant aromatic compounds detected in the three varieties of strawberries. Complementarily, the application of pattern recognition chemometric approaches, including principal component analysis and linear discriminant analysis, demonstrated that concentrations of specific volatiles can be employed as chemical descriptors to discriminate between strawberry cultivars. Accordingly, geraniol and hexyl hexanoate were found to be the most significant volatiles for the discrimination of strawberry varieties.

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 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.

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