A simple electronic tongue

Kumar, Ritesh; Bhondekar, Amol P.; Kaur, Ramneek; Vig, Saru; Sharma, Anupma; Kapur, Pawan

doi:10.1016/j.snb.2012.06.031

Cited by 24 publications

(12 citation statements)

References 39 publications

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“…The fundamentals of this method, and various applications for predicting chemical compound concentration, have been presented elsewhere [6,8,[11][12][13][14][15]. The algorithm attracted huge interest among scientists because it is based on a very simple idea and leads to high performance in numerous practical applications [14,[16][17][18]. The algorithm was developed originally for pattern recognition by learning from exemplary data belonging to two opposite sets.…”

Section: Svm Algorithmmentioning

confidence: 99%

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Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Lentka¹,

Smulko²,

Ionescu³

et al. 2015

Metrology and Measurement Systems

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This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.

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Section: Svm Algorithmmentioning

confidence: 99%

“…There are many papers discussing the use of the SVM method for data processing in gas sensing [6,12,[16][17][18]. We should emphasize the necessity of careful selection of LS-SVM model parameters to obtain high accuracy of gas concentration prediction.…”

Section: Application Of the Ls-svm Algorithmmentioning

confidence: 99%

Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Lentka¹,

Smulko²,

Ionescu³

et al. 2015

Metrology and Measurement Systems

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“…For the electronic nose data, PCA and CA were used for discriminating and forming clusters of the seasoning samples based on the quality grade. PCA is an unsupervised technique that can calculate the principal components having the largest variance in order to reduce the dimensionality in data sets and allow the visualization of clusters (Alasalvar et al 2012;Kumar et al 2012). CA is an unsupervised classification technology that can cluster similar measurements from all samples Liu et al 2012).…”

Section: Discussionmentioning

confidence: 99%

Quality Evaluation of Beef Seasonings Using Gas Chromatography-Mass Spectrometry and Electronic Nose: Correlation with Sensory Attributes and Classification According to Grade Level

Tian

Lan

et al. 2014

Food Anal. Methods

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To evaluate the quality of beef seasonings, the aroma profiles of high-grade, medium-grade, and lowergrade beef seasonings were characterized by sensory evaluation, gas chromatography-mass spectrometry (GC-MS) and an electronic nose. Sensory evaluation indicated significant differences in the three grades of beef seasonings whose highgrade samples got the highest score of the aroma properties of beef, fat, vegetable, soy sauce, and overall flavor and the lowest score of the aroma of gamey. A total of 57 volatile compounds that included aldehydes, acids, alkenes, nitrogenand sulfur-containing compounds, phenols, alcohols, and ketones were identified by solid-phase micro-extraction gas chromatography-mass spectrometry (SPME/GC-MS). The volatile compounds showed good correlation with seven sensory attributes according to the partial least square regression (PLSR) models. Electronic nose data were processed by principal component analysis (PCA) and cluster analysis (CA). According to the obtained results, electronic nose and SPME/ GC-MS combined with multivariate statistical analysis were effective identification tools for the quality discrimination of beef seasonings.

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“…The screen-printed electrodes (SPEs) have been designed for production of disposable biosensors and chemical sensor [142]. SPE are simple, low cost, portable, small sized suitable to miniaturization and capable of mass production [143].…”

Section: Electrochemical Mip Sensor For Detection Of Various Small Momentioning

confidence: 99%

Ultrasensitive Bioaffinity Electrochemical Sensors: Advances and New Perspectives

Alizadeh

Salimi

2018

Electroanalysis

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This article reviews recent advances and developments in the field of bioaffinity electrochemical sensors with emphasis on a subclass including immunosensors, aptasensors, genosensors and molecularly imprinted sensosr. Recent insights into novel fabrication methodologies and electrochemical techniques have resulted in the demonstration of biosensors able to detection of wide range of target analyte. It has been shown that due to their high specificity and sensitivity, they represent a great tool in practical applications. Furthermore, at last couple decades nanotechnology has become very popular in the sensor fields due to the high capacity of nanomaterials to immobilize of bioaffinity agents and also their inhibiting effects on denaturation of enzymes and other bioaffinity systems, which they could well have beneficial effects for the performance of these sensors. The main focus of this review is to discuss methods for immobilizing bioreceptors onto a solid support, introduction of nanomaterials for improve sensitivity of biosensor and various electrochemical sensing platforms. The use of biosensor in these applications is most appropriate, because the analysis of trace substances in medical and healthcare applications, environmental science, and food industries is so important. In addition, recent advances in nanomaterial‐mediated bioaffinity sensors used for onset biosensing, point‐of‐care testing and healthcare management are also highlighted.

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A simple electronic tongue

Cited by 24 publications

References 39 publications

Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Quality Evaluation of Beef Seasonings Using Gas Chromatography-Mass Spectrometry and Electronic Nose: Correlation with Sensory Attributes and Classification According to Grade Level

Ultrasensitive Bioaffinity Electrochemical Sensors: Advances and New Perspectives

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