scholarly journals Optimization of Sensors to be Used in a Voltammetric Electronic Tongue Based on Clustering Metrics

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4798
Author(s):  
Munmi Sarma ◽  
Noelia Romero ◽  
Xavier Cetó ◽  
Manel del Valle
Keyword(s):  
A Priori ◽  
Electronic Tongue ◽  
Principal Component ◽  
Canonical Variate ◽  
Ann Model ◽  
Inclusion Removal ◽  
Discrimination Ability ◽  
Improved Performance ◽  
Voltammetric Electronic Tongue ◽  
The Impact

Herein we investigate the usage of principal component analysis (PCA) and canonical variate analysis (CVA), in combination with the F factor clustering metric, for the a priori tailored selection of the optimal sensor array for a given electronic tongue (ET) application. The former allows us to visually compare the performance of the different sensors, while the latter allows us to numerically assess the impact that the inclusion/removal of the different sensors has on the discrimination ability of the ET. The proposed methodology is based on the measurement of a pure stock solution of each of the compounds under study, and the posterior analysis by PCA/CVA with stepwise iterative removal of the sensors that demote the clustering when retained as part of the array. To illustrate and assess the potential of such an approach, the quantification of paracetamol, ascorbic acid, and uric acid mixtures were chosen as the study case. Initially, an array of eight different electrodes was considered, from which an optimal array of four sensors was derived to build the quantitative ANN model. Finally, the performance of the optimized ET was benchmarked against the results previously reported for the analysis of the same mixtures, showing improved performance.

Quantifying tooth variation within a single population of Albertosaurus sarcophagus (Theropoda: Tyrannosauridae) and implications for identifying isolated teeth of tyrannosauridsThis article is one of a series of papers published in this Special Issue on the theme Albertosaurus.

2010 ◽  
Vol 47 (9) ◽  
pp. 1227-1251 ◽  
Author(s):  
Lisa G. Buckley ◽  
Derek W. Larson ◽  
Miriam Reichel ◽  
Tanya Samman
Keyword(s):  
Multivariate Analyses ◽  
Phylogenetic Signal ◽  
A Priori ◽  
Principal Component ◽  
Canonical Variate ◽  
Large Sample Size ◽  
Data Set ◽  
Theropod Dinosaurs ◽  
Degree Of Separation ◽  
Lower Maastrichtian

Documenting variation in theropod dinosaurs is usually hindered by the lack of a large sample size and specimens representing several ontogenetic stages. Here, variation within 140 disassociated and seven in situ tyrannosaur teeth from the Upper Cretaceous (lower Maastrichtian) monodominant Albertosaurus sarcophagus (Theropoda: Tyrannosauridae) bonebed is documented. This sample represents the largest data set of teeth from one population of A. sarcophagus containing both adult and juvenile specimens. Tooth variation was assessed using multivariate analyses (principal component, discriminant, and canonical variate analyses). Heterodonty in the teeth of A. sarcophagus contributes to the large amount of variation in the data set. Premaxillary teeth are significantly different from maxillary and dentary teeth, but there is no quantifiable difference between a priori identified maxillary and dentary teeth. Juvenile and adult teeth of A. sarcophagus show apparent quantitative differences that are size dependent on closer investigation, suggesting a cautious approach when interpreting multivariate analyses to identify novel tooth morphologies. Multivariate analyses on teeth of A. sarcophagus and published tooth data from other North American tyrannosaurid species reveals species-level clusters with little separation. The degree of separation among tooth clusters may reveal a phylogenetic signal in tyrannosaurid teeth.

Quality Detection of Soybean Oil with Voltammetric Electronic Tongue

2013 ◽  
Vol 680 ◽  
pp. 333-338
Author(s):  
Hong Men ◽  
Cai Wa Zhang ◽  
Yan Ping Zhang ◽  
Hong Hui Gao
Keyword(s):  
Soybean Oil ◽  
Prediction Models ◽  
Electronic Tongue ◽  
Principal Component ◽  
Sample Pretreatment ◽  
Electrode System ◽  
Partial Least Square ◽  
Least Square ◽  
Kernel Principal Component Analysis ◽  
Voltammetric Electronic Tongue

This paper proposes a reasonable methodology applied in classification and quantification techniques based on the voltammetric electronic tongue. We designed voltammetric electronic tongue oil sample pretreatment system with petroleum ether organic solvent. Through three-electrode system and cyclic voltammetry method processing blending soybean oil sample to get waveform output. Extracting crest methods as its feature extraction Pattern recognition use kernel principal component analysis and factor analysis blending on a different level of soybean oil, Partial Least Square (PLS) techniques was applied for data management and prediction models building, the prediction models are the blending ratio, the results show that voltammetric electron tongue can distinguish the quality of soybean oil.

scholarly journals Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5002 ◽  
Author(s):  
Anna Herrera-Chacón ◽  
Farzad Torabi ◽  
Farnoush Faridbod ◽  
Jahan B. Ghasemi ◽  
Andreu González-Calabuig ◽  
...  
Keyword(s):  
Metallic Nanoparticles ◽  
Simultaneous Detection ◽  
Electronic Tongue ◽  
Principal Component ◽  
Quantitative Model ◽  
Discrete Wavelet ◽  
Composite Electrodes ◽  
Voltammetric Sensors ◽  
Voltammetric Electronic Tongue

The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO3, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.

Examining the relationship between the price, the characteristics and the visitor evaluation of attributes of Italian farmhouses with an educational farm

2019 ◽  
Vol 75 (5) ◽  
pp. 827-839 ◽  
Author(s):  
Rosa Maria Fanelli
Keyword(s):  
A Priori ◽  
Principal Component ◽  
Online Reviews ◽  
Latent Factors ◽  
Content Type ◽  
Visitor Satisfaction ◽  
Main Determinants ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to examine the relationship between the price charged for a guest room in a farmhouse with an educational farm, the farmhouse characteristics and the visitor evaluation of the principal external and internal farmhouse attributes. Design/methodology/approach A large sample of 10,880 visitor reviews, extrapolated from the websites of 399 Italian farmhouses with an educational farm (FEF), was analyzed. Principal component analysis (PCA) was performed to identify the main latent dimensions of the farmhouses (visitor satisfaction with farmhouse attributes, farmhouse dimensions, visitor frequency, farmhouse services, types of accommodation and altitude) that affect the price charged for a guest room. Subsequently, multivariate regression was applied to measure the impact of these new latent factors on the price. Findings Overall, the results indicate that the price of a farmhouse with an educational farm – in the context of this niche of the Italian agritourism sector – reflects the visitor evaluation of the farmhouse attributes (especially activities and facilities available in the surrounding countryside), the farmhouse dimensions, the types of accommodation, the number of services on offer and the presence of connectivity (WI-FI). In addition, the results reveal that the price represents an important driver that guides guests in their choice of a farmhouse and that it affects visitor satisfaction with farmhouse attributes. Research limitations/implications Because of the sample chosen, the data gathered are limited to one type of organization – Italian FEF. Furthermore, it may be important to investigate in more depth some issues that remain partly unanswered that concern this niche of the Italian agritourism sector. Practical implications Thanks to the identification of latent dimensions by PCA and the examination of their impact on the farmhouse price, farmhouse operators can understand a priori the main determinants on which to focus to improve the quality of activities and facilities available in the farmhouse location to better satisfy visitor expectations. Originality/value This study provides new and practical insights into the farmhouse experience in Italian municipalities, an area where very limited research has been conducted. Indeed, this is one of the few studies to focus on online reviews to evaluate more than two farmhouse attributes and their impact on pricing.

scholarly journals Research Article. On memory, dimension, and atmospheric teleconnections

2017 ◽  
Vol 3 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Terence. J. O’Kane ◽  
Didier P. Monselesan ◽  
James S. Risbey ◽  
Illia Horenko ◽  
Christian L. E. Franzke
Keyword(s):  
A Priori ◽  
Principal Component ◽  
Teleconnection Pattern ◽  
Memory Effects ◽  
Model Parameters ◽  
Primary Role ◽  
Transient Nature ◽  
Markovian Processes ◽  
The Impact

AbstractUsing reanalysed atmospheric data and applying a data-driven multiscale approximation to non-stationary dynamical processes, we undertake a systematic examination of the role of memory and dimensionality in defining the quasi-stationary states of the troposphere over the recent decades. We focus on the role of teleconnections characterised by either zonally-oriented wave trains or meridional dipolar structures. We consider the impact of various strategies for dimension reduction based on principal component analysis, diagonalization and truncation.We include the impact of memory by consideration of Bernoulli, Markovian and non-Markovian processes. We a priori explicitly separate barotropic and baroclinic processes and then implement a comprehensive sensitivity analysis to the number and type of retained modes. Our results show the importance of explicitly mitigating the deleterious impacts of signal degradation through ill-conditioning and under sampling in preference to simple strategies based on thresholds in terms of explained variance. In both hemispheres, the results obtained for the dominant tropospheric modes depend critically on the extent to which the higher order modes are retained, the number of free model parameters to be fitted, and whether memory effects are taken into account. Our study identifies the primary role of the circumglobal teleconnection pattern in both hemispheres for Bernoulli and Markov processes, and the transient nature and zonal structure of the Southern Hemisphere patterns in relation to their Northern Hemisphere counterparts. For both hemispheres, overfitted models yield structures consistent with the major teleconnection modes (NAO, PNA and SAM), which give way to zonally oriented wavetrains when either memory effects are ignored or where the dimension is reduced via diagonalising. Where baroclinic processes are emphasised, circumpolar wavetrains are manifest.

scholarly journals Detection of Adulteration in Argan Oil by Using an Electronic Nose and a Voltammetric Electronic Tongue

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Madiha Bougrini ◽  
Khalid Tahri ◽  
Zouhair Haddi ◽  
Tarik Saidi ◽  
Nezha El Bari ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Electronic Tongue ◽  
Principal Component ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
Pattern Recognition Methods ◽  
Argan Oil ◽  
Vector Machines ◽  
Voltammetric Electronic Tongue ◽  
Ensure Product Quality

Adulteration detection of argan oil is one of the main aspects of its quality control. Following recent fraud scandals, it is mandatory to ensure product quality and customer protection. The aim of this study is to detect the percentages of adulteration of argan oil with sunflower oil by using the combination of a voltammetric e-tongue and an e-nose based on metal oxide semiconductor sensors and pattern recognition techniques. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), discriminant factor analysis (DFA), and support vector machines (SVMs). Excellent results were obtained in the differentiation between unadulterated and adulterated argan oil with sunflower one. To the best of our knowledge, this is the first attempt to demonstrate whether the combined e-nose and e-tongue technologies could be successfully applied to the detection of adulteration of argan oil.

scholarly journals Wearable Electronic Tongue for Non-Invasive Assessment of Human Sweat

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7311
Author(s):  
Magnus Falk ◽  
Emelie J. Nilsson ◽  
Stefan Cirovic ◽  
Bogdan Tudosoiu ◽  
Sergey Shleev
Keyword(s):  
Principal Component Analysis ◽  
Chemical Composition ◽  
Electronic Tongue ◽  
Principal Component ◽  
The Body ◽  
Circuit Board ◽  
Metal Electrodes ◽  
Non Invasive ◽  
Wearable Electronic ◽  
Voltammetric Electronic Tongue

Sweat is a promising biofluid in allowing for non-invasive sampling. Here, we investigate the use of a voltammetric electronic tongue, combining different metal electrodes, for the purpose of non-invasive sample assessment, specifically focusing on sweat. A wearable electronic tongue is presented by incorporating metal electrodes on a flexible circuit board and used to non-invasively monitor sweat on the body. The data obtained from the measurements were treated by multivariate data processing. Using principal component analysis to analyze the data collected by the wearable electronic tongue enabled differentiation of sweat samples of different chemical composition, and when combined with 1H-NMR sample differentiation could be attributed to changing analyte concentrations.

The Impact of Shariah Governance and Corporate Governance on the Risk Management Practices: Evidence from Local and Foreign Islamic Banks in Malaysia

2018 ◽  
Vol 15 (2) ◽  
pp. 1-20
Author(s):  
Sabri Embi ◽  
Zurina Shafii
Keyword(s):  
Risk Management ◽  
Corporate Governance ◽  
Management Practices ◽  
Principal Component ◽  
T Test ◽  
The Body ◽  
Primary Data ◽  
Islamic Banks ◽  
Shariah Governance ◽  
The Impact

The purpose of this study is to examine the impact of Shariah governance and corporate governance (CG) on the risk management practices (RMPs) of local Islamic banks and foreign Islamic banks operating in Malaysia. The Shariah governance comprises the Shariah review (SR) and Shariah audit (SA) variables. The study also evaluates the level of RMPs, CG, SR, and SA between these two type of banks. With the aid of SPSS version 20, the items for RMPs, CG, SR, and SA were subjected to principal component analysis (PCA). From the PCA, one component or factor was extracted each for the CG, SR, and RMPs while another two factors were extracted for the SA. Primary data was collected using a self-administered survey questionnaire. The questionnaire covers four aspects ; CG, SR, SA, and RMPs. The data received from the 300 usable questionnaires were subjected to correlation and regression analyses as well as an independent t-test. The result of correlation analysis shows that all the four variables have large positive correlations with each other indicating a strong and significant relationship between them. From the regression analysis undertaken, CG, SR, and SA together explained 52.3 percent of the RMPs and CG emerged as the most influential variable that impacts the RMPs. The independent t-test carried out shows that there were significant differences in the CG and SA between the local and foreign Islamic banks. However, there were no significant differences between the two types of the bank in relation to SR and RMPs. The study has contributed to the body of knowledge and is beneficial to academicians, industry players, regulators, and other stakeholders.

The Impact of Competitive Quasi-Market on Service Delivery in Benue State University, Makurdi Nigeria

GIS Business ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 85-98
Author(s):  
Idoko Peter
Keyword(s):  
Service Delivery ◽  
A Priori ◽  
Least Square ◽  
Secondary Source ◽  
State University ◽  
Ordinary Least Square ◽  
Negative Effect ◽  
The Impact ◽  
The Relationship

This research the impact of competitive quasi market on service delivery in Benue State University, Makurdi Nigeria. Both primary and secondary source of data and information were used for the study and questionnaire was used to extract information from the purposively selected respondents. The population for this study is one hundred and seventy three (173) administrative staff of Benue State University selected at random. The statistical tools employed was the classical ordinary least square (OLS) and the probability value of the estimates was used to tests hypotheses of the study. The result of the study indicates that a positive relationship exist between Competitive quasi marketing in Benue State University, Makurdi Nigeria (CQM) and Transparency in the service delivery (TRSP) and the relationship is statistically significant (p<0.05). Competitive quasi marketing (CQM) has a negative effect on Observe Competence in Benue State University, Makurdi Nigeria (OBCP) and the relationship is not statistically significant (p>0.05). Competitive quasi marketing (CQM) has a positive effect on Innovation in Benue State University, Makurdi Nigeria (INVO) and the relationship is statistically significant (p<0.05) and in line with a priori expectation. This means that a unit increases in Competitive quasi marketing (CQM) will result to a corresponding increase in innovation in Benue State University, Makurdi Nigeria (INVO) by a margin of 22.5%. It was concluded that government monopoly in the provision of certain types of services has greatly affected the quality of service experience in the institution. It was recommended among others that the stakeholders in the market has to be transparent so that the system will be productive to serve the society effectively

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

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