Large Amplitude Pulse Voltammetry Research Articles

Electronic Tongue Recognition with Feature Specificity Enhancement.

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.

Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins

In the study, the voltammetric electronic tongue based on three nanocomposites modified electrodes was applied for the identification of rice wines of different geographical origins. The nanocomposites were prepared by gold and copper nanoparticles in the presence of conducting polymers (polymer sulfanilic acid, polymer glutamic acid) and carboxylic multi - walled carbon nanotubes. The modified electrodes showed high sensitivity to guanosine - 5' - monophosphate disodium salt, tyrosine and gallic acid which have good correlation with the geographical origins of rice wines. Scanning electron microscopy was performed to display the surface morphologies of the nanocomposites, and cyclic voltammetry was applied to study the electrochemical behaviors of the taste substances on the electrode surfaces. Four types of electrochemical parameters (pH, scan rates, accumulation potentials and time) were optimized for getting a low limit of the detection of each taste substance. The geographical information of rice wines was obtained by the modified electrodes based on two types of multi - frequency large amplitude pulse voltammetry, and “area method” was applied for extracting the feature data from the original information obtained. Based on the area feature data, principal component analysis, locality preserving projection (LPP), and linear discriminant analysis were applied for the classification of the rice wines of different geographical origins, and LPP presented the best results; extreme learning machine (ELM) and alibrary for support vector machines were applied for predicting the geographical origins of rice wines, and ELM performed better.

A humid electronic nose based on pulse voltammetry: A proof-of-concept design

We report herein the design, manufacture and use of a “humid electronic nose” prototype based on voltammetric techniques. It consists of an array of four working electrodes (i.e., Au, Pt, Ir and Rh) housed inside a homemade stainless steel cylinder, in contact with a fabric mesh made of nylon damped with a NaCl aqueous solution, used as the supporting humid membrane. The “humid electronic nose” was tested for the discrimination of different samples displaying different volatile compounds. The samples chosen involve aqueous solutions of different simple volatile products (i.e., ammonia, acetone, acetic acid and 6-amino-1-hexanol) and different food samples (i.e., onion, coffee and Roquefort cheese). Under working conditions, the volatile compounds from the corresponding sample were generated in the measurement chamber and were partially dissolved in the damped nylon fabric, which was in contact with the set of electrodes. It was envisioned that provided different samples offer different vapours, the application of a suitable set of pulses to the electrodes will differentiate the samples. This proof-of-concept study employed a Large Amplitude Pulse Voltammetry (LAPV) waveform. The increment for the potential steps was of 200mV and potentials ranged from +1 to −1V with each pulse applied for 50ms. PCA studies from the response obtained by the “humid electronic nose” discriminated the different samples studied. The neural network Self Organized Map (SOM) was also used to analyze the electrochemical data obtained from the “humid electronic nose”.

The evaluation of sugar content and firmness of non-climacteric pears based on voltammetric electronic tongue

The sugar content and firmness of non-climacteric pear of different cultivars were studied by a voltammetric electronic tongue (VE-tongue). The VE-tongue self-developed in this study comprised six working electrodes (gold, silver, platinum, palladium, tungsten, and titanium electrode), an Ag/AgCl reference electrode, and a platinum auxiliary electrode. The multi-frequency large amplitude pulse voltammetry (MLAPV) was applied to the working electrodes as the scanning potential waveform,and it consisted of four frequency segments of 1Hz, 10Hz, 100Hz, and 1000Hz. In this study, five cultivars of pear from different geographical origins were tested by VE-tongue, and the firmness and sugar content of pears were tested by the traditional methods. The characteristic data (the maximum and minimum values) obtained by VE-tongue were compressed by principal component analysis (PCA), and the principal components (PCs) were taken as the input variables of principal component regression (PCR), partial least squares regression (PLSR), and least squared-support vector machines (LS-SVMs) to predict sugar content and firmness. All the models showed good results, and LS-SVM preformed best in the prediction.

Sinusoidal envelope voltammetry as a new readout technique for electronic tongues

We introduce a novel voltammetric method, so-called sinusoidal envelope voltammetry, for use in electronic tongues. Fourier transformation was used to transform the data of the signal from the time domain to the frequency domain. The four taste substances, acesulfame potassium, monosodium glutamate, potassium chloride and tartaric acid, are shown to exhibit abundant frequency characteristics in the power spectrum of a Fourier transformation. This indicates that the power spectrum from sinusoidal envelope voltammetry can be used as fingerprints of samples for classification. Principal component analysis along with discrimination index and multi-frequency large amplitude pulse voltammetry as a reference technique is used to evaluate the separation ability of sinusoidal envelope voltammetry. The score plots of the method for the four taste substances and for the five brands of Jiafan rice wine show better discrimination ability than multi-frequency large amplitude pulse voltammetry. Sinusoidal envelope voltammetry is considered to be a promising technique for use in voltammetric electronic tongues.

Classification and prediction of rice wines with different marked ages by using a voltammetric electronic tongue

A voltammetric electronic tongue (VE-tongue) was developed to discriminate the difference between Chinese rice wines in this research. Three types of Chinese rice wine with different marked ages (1, 3, and 5 years) were classified by the VE-tongue by principal component analysis (PCA) and cluster analysis (CA). The VE-tongue consisted of six working electrodes (gold, silver, platinum, palladium, tungsten, and titanium) in a standard three-electrode configuration. The multi-frequency large amplitude pulse voltammetry (MLAPV), which consisted of four segments of 1 Hz, 10 Hz, 100 Hz, and 1000 Hz, was applied as the potential waveform. The three types of Chinese rice wine could be classified accurately by PCA and CA, and some interesting regularity is shown in the score plots with the help of PCA. Two regression models, partial least squares (PLS) and back-error propagation-artificial neural network (BP–ANN), were used for wine age prediction. The regression results showed that the marked ages of the three types of Chinese rice wine were successfully predicted using PLS and BP–ANN.

Classification of monofloral honeys by voltammetric electronic tongue with chemometrics method

A voltammetric electronic tongue (VE-tongue) based on multifrequency large amplitude pulse voltammetry (MLAPV) was developed to classify monofloral honeys of seven kinds of floral origins. The VE-tongue was composed of six working electrodes (gold, silver, platinum, palladium, tungsten, and titanium) in a standard three-electrode configuration. The applied waveform of MLAPV was composed of four individual frequencies: 1 Hz, 10 Hz, 100 Hz, and 1000 Hz. Two eigenvalues (the maximum value and the minimum value) of each cycle were extracted for building the first database (FDB); four eigenvalues (the maximum value, the minimum value, and two inflexion values) were exacted for building the second database (SDB). The two databases were analyzed by three-pattern recognition techniques: principal component analysis (PCA), discriminant function analysis (DFA) and cluster analysis (CA), respectively. It was possible to discriminate the seven kinds of honeys of different floral origins completely based on FDB and SDB by PCA, DFA and CA, and FDB was certificated as an efficient database by contrasting with the SDB. Moreover, the effective working electrodes and frequencies were picked out as the best experimental project for the further study.

Detection of antibiotic residues in bovine milk by a voltammetric electronic tongue system

A voltammetric electronic tongue (VE-tongue) was developed to detect antibiotic residues in bovine milk. Six antibiotics (Chloramphenicol, Erythromycin, Kanamycin sulfate, Neomycin sulfate, Streptomycin sulfate and Tetracycline HCl) spiked at four different concentration levels (0.5, 1, 1.5 and 2 maximum residue limits (MRLs)) were classified based on VE-tongue by two pattern recognition methods: principal component analysis (PCA) and discriminant function analysis (DFA). The VE-tongue was composed of five working electrodes (gold, silver, platinum, palladium, and titanium) positioned in a standard three-electrode configuration. The Multi-frequency large amplitude pulse voltammetry (MLAPV) which consisted of four segments (1 Hz, 10 Hz, 100 Hz and 1000 Hz) was applied as potential waveform. The six antibiotics at the MRLs could not be separated from bovine milk completely by PCA, but all the samples were demarcated clearly by DFA. Three regression models: Principal Component Regression Analysis (PCR), Partial Least Squares Regression (PLSR), and Least Squares-Support Vector Machines (LS-SVM) were used for concentrations of antibiotics prediction. All the regression models performed well, and PCR had the most stable results.

Classification and prediction of Chinese rice wines with different marked ages by using a voltammetric electronic tongue

A voltammetric electronic tongue (VE-tongue) was developed to discriminate the difference between Chinese rice wines in this research. Three types of Chinese rice wines with different marked ages (1, 3 and 5 years) were classified based on VE-tongue by Principal component analysis (PCA), Cluster analysis (CA). The VE-tongue consisted of six working electrodes (gold, silver, platinum, palladium, tungsten, and titanium) in a standard three-electrode configuration. The Multi-frequency large amplitude pulse voltammetry (MLAPV) which consisted of four segments of 1 Hz, 10 Hz, 100 Hz and 1000 Hz was applied as potential waveform. Three types of Chinese rice wines could be classified completely by PCA and CA, and some interesting regularity is shown in the score plots with the help of PCA. Two regression models: Partial Least Squares (PLS) and Back-error propagation–Artificial neural network (BP–ANN) were used for wine age prediction. The regression analysis showed that the marked ages of the three types of Chinese rice wine were successfully predicted by PLS and BP–ANN.

Multifrequency large amplitude pulse voltammetry: A novel electrochemical method for electronic tongue

A novel electrochemical method, multifrequency large amplitude pulse voltammetry (MLAPV) for electronic tongue was introduced in this paper. It was constructed by several large amplitude pulse voltammetry (LAPV) with deferent step length. The applied waveform of MLAPV comprises three individual frequency segments, 1 Hz, 10 Hz and 100 Hz. The electronic tongue based on MLAPV was constructed by several metallic working electrodes, such as platinum, gold, titanium, nickel, palladium, a Ag/AgCl reference electrode and a pillar platinum electrode as counter electrode for standard three-electrode systems. Principal component analysis (PCA), a kind of multivariate data analysis (MVDA) was used for processing the data from the electronic tongue. In the present study, six Chinese distilled spirits and seven Longjing teas were analyzed by the electronic tongue based on MLAPV and were successfully discriminated by the working electrodes at different frequency segments. The results showed that frequency segments on the working electrode played a key role in discriminating various samples. MLAPV made the same metallic working electrodes have different separation ability at each segments. Better discrimination ability can be achieved by the combination of working electrodes with specific frequency segments. The present work showed that MLAPV is a useful method to reveal the specific frequency segment for each working electrode in various analysis systems.

A flow injection voltammetric electronic tongue applied to paper mill industrial waters

A voltammetric electronic tongue with automated operation based on the flow injection (FIA) technique was applied to the characterization of wastewaters coming from the paper mill industry. A metallic multielectrode array – formed by platinum, gold and rhodium electrodes – was employed as the detection system, while the measurements were based on large amplitude pulse voltammetry (LAPV). LAPV consisted in scans of pulses from to 0 to 1.8V at 0.2V steps. Five current values were recorded for each pulse, so a set of 300 current values (three electrodes×20 pulses×five values) was recorded for each sample. Samples were first discriminated using Principal Component Analysis (PCA), while Artificial Neural Networks were used for the characterization and prediction of chemical oxygen demand, conductivity and pH. The system may be used for the quick identification and monitoring of the quality of used waters in these industrial facilities.

Discrimination of tea by means of a voltammetric electronic tongue and different applied waveforms

A new sensor technology, an electronic tongue based on voltammetry has been developed at Linköping University. Three different metallic working electrodes are used in combination with a set of voltage “pulses”, a waveform, to separate different samples. In this paper, three different waveforms are investigated. This is done through a study with nine different teas. Multivariate data analysis ((MVDA), principal component analysis (PCA)) is used to evaluate the data (the recorded current responses). The waveforms are large amplitude pulse voltammetry (LAPV), small amplitude pulse voltammetry (SAPV), and staircase voltammetry. Each method discriminated between the tea samples to some extent, but differently from each other. Best discrimination is achieved when the combination LAPV and staircase are merged together. When SAPV is included in the combination a worse separation is observed. It is clearly the case that more waveforms do not automatically lead to more information.