The measurement principles, working parameters and configurations of voltammetric electronic tongues and its applications for foodstuff analysis

Wei, Zhenbo; Yang, Yulin; Wang, Jun; Zhang, Weilin; Ren, Qifeng

doi:10.1016/j.jfoodeng.2017.08.005

Cited by 46 publications

(31 citation statements)

References 204 publications

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“…In addition, spectrophotometric [32] is another very popular means of measurement. Each type of electronic tongue can utilize different types of working electrodes, such as bare electrodes, modified electrodes and biosensors [33]. Typically, bare electrodes are gold, silver, and palladium; the modified electrodes are carbon paste processed by double phthalocyanine compounds or conductive polymers; biosensors are carbon biocomposite or graphene electrode containing enzyme and different metal catalysts.…”

Section: Electronic Tongue Systemmentioning

confidence: 99%

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Zhong

Zhang

et al. 2019

Applied Sciences

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Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are "hand-crafted", which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency.

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Section: Electronic Tongue Systemmentioning

confidence: 99%

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Zhong

Zhang

et al. 2019

Applied Sciences

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“…Considering that the sensor array can generate a great amount of complex data, the application of multivariate pattern recognition techniques is mandatory as commonly used for data processing [11,14].…”

Section: Introductionmentioning

confidence: 99%

“…For the sensing field, carbon paste electrodes (CPE) present interesting characteristics as low background currents, versatility and simplicity of construction and/or modification, and low cost [15]. Several works report the use of non-modified [16] and modified carbon paste electrodes for ET application [14,17]. Modifiers such as metal complexes [18], metal oxide nanoparticles [19], polypyrrole [20], phthalocyanines [18b, 21] and carbon nanotubes [22] have been successfully used for carbon paste construction in ET.…”

Section: Introductionmentioning

confidence: 99%

Voltammetric Electronic Tongue Based on Carbon Paste Electrodes Modified with Biochar for Phenolic Compounds Stripping Detection

et al. 2019

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Biochar is a charcoal produced from the biomass pyrolysis process that presents a highly porous and functionalized surface. In the present work an array of carbon paste electrodes (CPE) made of different forms of carbon (graphite, carbon nanotubes and activated biochar) was evaluated in the development of an electronic tongue for discrimination and stripping voltammetric determination of catechol (CAT), 4‐ethylcatechol (4‐EC) and 4‐ethylguaiacol (4‐EG) phenolic compounds. Morphological characterization of carbon materials and electrodes surfaces was performed by scanning electron microscopy (SEM) and semi‐quantitative elemental composition by energy dispersive spectroscopy (EDS). Electrochemical Impedance Spectroscopy (EIS) measurements were used for electrochemical characterization of electrodes. Cyclic voltammetry measurements were performed for the phenolic compounds evaluated using different concentrations. Principal component analysis (PCA) was performed to evaluate the qualitative analysis. Quantitative data modeling was done using artificial neural networks (ANN). The proposed sensor array presented analytical potentiality allowing the distinction and determination of CAT, 4‐EC and 4‐EG by using chemometric processing. The method showed sensibility, reproducibility and a good linearity (R2>0.9940) for three compounds evaluated. Spontaneous preconcentration of three compounds was possible using all three sensors, which can allow the application of these as passive samplers for remote determinations of phenolic compounds in wine and food samples.

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“…An electronic tongue (e-tongue) is a multisensory array mostly based on electroanalytical methods and multivariate statistical techniques to facilitate information visualization in a qualitative and/or quantitative way. E-tongues are promising analytical devices having simple operation, fast response, low cost, easy integration with other systems (microfluidic, optical, etc) to enable miniaturization and provide a high sensitivity for measurements in complex liquid media, providing an interesting alternative to address many of the existing environmental monitoring challenges, specifically relevant emerging pollutants such as heavy metals and toxins.Chemistry (IUPAC) [9], with different detection methods reported in the literature, commonly based on voltammetric, potentiometric, impedimetric, and biosensing detection [10][11][12][13][14]. Here, we will focus on e-tongue devices devoted to heavy metal/toxin detection in liquid systems.…”

mentioning

confidence: 99%

“…Chemistry (IUPAC) [9], with different detection methods reported in the literature, commonly based on voltammetric, potentiometric, impedimetric, and biosensing detection [10][11][12][13][14]. Here, we will focus on e-tongue devices devoted to heavy metal/toxin detection in liquid systems.…”

mentioning

confidence: 99%

Heavy Metal/Toxins Detection Using Electronic Tongues

2019

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The growing concern for sustainability and environmental preservation has increased the demand for reliable, fast response, and low-cost devices to monitor the existence of heavy metals and toxins in water resources. An electronic tongue (e-tongue) is a multisensory array mostly based on electroanalytical methods and multivariate statistical techniques to facilitate information visualization in a qualitative and/or quantitative way. E-tongues are promising analytical devices having simple operation, fast response, low cost, easy integration with other systems (microfluidic, optical, etc) to enable miniaturization and provide a high sensitivity for measurements in complex liquid media, providing an interesting alternative to address many of the existing environmental monitoring challenges, specifically relevant emerging pollutants such as heavy metals and toxins.

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The measurement principles, working parameters and configurations of voltammetric electronic tongues and its applications for foodstuff analysis

Cited by 46 publications

References 204 publications

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Voltammetric Electronic Tongue Based on Carbon Paste Electrodes Modified with Biochar for Phenolic Compounds Stripping Detection

Heavy Metal/Toxins Detection Using Electronic Tongues

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