Electronic Tongue Signals Research Articles

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus.

Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)-transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods - the Markov transition field, short-time Fourier transform and recurrence plot - were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness. The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices. The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry.

Ancient tea plants black tea taste determinants and their changes over manufacturing processes

Ancient tea plants black tea (ATBT) has outstanding sweet-mellow taste, but what contributes to its quality remains unclear. We tested 36 freeze and 9 finished tea sampled from five processing stages for ATBT by electronic tongue (ET), high performance liquid chromatography, and gas chromatography-tandem mass spectrometry. ET and chromatographic data revealed variation of sensory characteristics and chemical compounds among the five processing steps of ATBT. Through the correlation analysis and principal component analysis, it was concluded that ester-catechins and soluble sugars content in tea leaves had significant effect with the bitterness and sweetness in tea infusions (p < 0.05). Catechins, theaflavins, soluble sugars, and ET signals was considered an important information for determining the processing steps, and it had an impact on the taste of the tea infusions. These findings indicated that accurate processing control is effective approach to maintaining chemical constituents of contributes to taste quality in ATBT.

Analysis and comparison of the quality and flavour of traditional and conventional dry sausages collected from northeast China

In this study, the physicochemical properties and flavour profile of traditional dry sausages (T-SH, T-DXAL, T-HG, T-MDJ, T-HRB) collected from various wet markets were compared with those of conventional dry sausages (C-QL, C-ND, C-YSD, C-YC, C-HRL) collected from various food companies in northeast China. Traditional dry sausages were characterised by a low moisture content, a low water activity, and a high shear force after a long fermentation time compared with conventional dry sausages. Electronic nose and electronic tongue signals combined with chemometrics methods were applied for a comprehensive qualitative analysis of the odour and taste of dry sausages. A total of 61 volatile compounds were identified using gas chromatography–mass spectrometry, and the multivariate chemometrics analysis confirmed the difference in volatile compounds between traditional and conventional samples. Moreover, the sensory evaluation revealed that conventional dry sausages lacked the characteristic fermented flavour of traditional dry sausages.

Synergetic application of E-tongue and E-eye based on deep learning to discrimination of Pu-erh tea storage time

This study proposed an efficient approach that an electronic tongue (ET) and an electronic eye (EE) combined with a deep learning algorithm were jointly leveraged to recognition Pu-erh tea. A one-dimensional convolutional neural network (1-D CNN) and a two-dimensional convolutional neural network (2-D CNN) were designed and optimized for the feature extraction of ET and EE signals, respectively. Then, a feature-level fusion strategy was introduced to address the feature vectors extracted from the different types of CNN models. To highlight the effect of data fusion, a backpropagation neural network (BPNN), a classifier similar to the fully connected layers of CNN models, was employed. Meanwhile, the Bayesian optimization algorithm (BOA) was employed for hyperparameter optimization of the identification models. The experimental results showed that the feature fusion strategy assimilated the merits of the ET and EE and gained better Pu-erh tea identification performance than an independent intelligent sensory system combined with CNN model. The results demonstrate that the feature-level fusion based on deep learning algorithm gained the best accuracy on the test set, with a precision, a recall, an F1-score and an AUC score of 99.07%, 99.2%, 0.992 and 0.994, respectively. This study shows that the simultaneous utilization of an ET and an EE combined with deep learning algorithm could function as a rapid detection method for discriminating the storage time of Pu-erh tea.

Applications of electronic tongue system for quantification of safranal concentration in saffron (Crocus sativus L.)

In the present research the electronic tongue system was proposed consisted of an array of 4 voltammetric sensors to discriminate saffron samples based on the safranal concentration. Different samples were provided by blending saffron samples with distilled water and pure safranal with ethanol. The provided signals by the electronic tongue system were analyzed using the PCA and ANN methods. The total variance among safranal concentrations by PCA method were 90% and 100% in pure safranal and saffron samples, respectively. Also, it was revealed that the electronic tongue could provide good prediction for safranal concentration in saffron as 98.81% accuracy by means of optimized ANN model. Results showed that there are some relationships between HPLC and electronic tongue signals to determine safranal concentration levels in different saffron samples.

Tea Quality Prediction by Sparse Modeling of Electronic Tongue Signals

In this paper, we present an efficient technique of electronic tongue (ET) signal processing using the sparse decomposition method for the prediction of tea quality. Sparse model coefficients of an under complete dictionary are considered as characteristic attributes of the ET signals obtained for characterization of tea samples. The effectiveness of the proposed method is established by performing experiment on three different types of pulse voltammetry. The ability of the sparse coefficients, as features of ET signal, is tested by employing three different classifiers. High classification accuracy of all the classifiers on three types of voltammetric measurement data validates the usefulness of this technique. The present model-based the tea quality prediction method is very fast, simple, and straightforward. This tea quality prediction method is able to address the problem more efficiently in comparison to other techniques found in the literature.

Data fusion of electronic eye and electronic tongue signals to monitor grape ripening

Two separate artificial sensors, an electronic eye (EE) and an electronic tongue (ET), were recently developed to monitor grape ripening based on the analysis of must. The aim of this research is to exploit the complementary information obtained by means of EE and ET sensing systems using different data fusion strategies, in order to develop an integrated device able to quickly and easily quantify the physico-chemical parameters that are used to assess phenolic ripeness. To this purpose, both low-level and mid-level data fusion approaches were investigated. Partial Least Squares (PLS) regression was applied to the fused data, with the aim of relating the information brought by the two sensors with twelve physico-chemical parameters measured on the must samples by standard analytical methods. The results achieved with mid-level data fusion outperformed those obtained using EE and ET separately, and highlighted that both the artificial sensors have made a significant contribution to the prediction of each one of the considered physico-chemical parameters.

Feature Fusion for Prediction of Theaflavin and Thearubigin in Tea Using Electronic Tongue

Liquor characteristics of black cut, tear, and curl tea mostly depend on two biochemical components like theaflavin (TF) and thearubigin (TR). Evaluation of tea quality can be done efficiently by estimating the concentration of TF and TR without using biochemical tests as it takes much time, which requires laborious effort for sample preparation, storage, and measurement. Moreover, the required instruments for this test are very costly. In this paper, we have proposed an efficient method of TF and TR prediction in a given tea sample using electronic tongue (ET) signal. Combinations of transformed features, like discrete cosine transform, Stockwell transform (ST), and singular value decomposition, of ET signals are fused to develop regression models to predict the contents of TF, TR, and TR/TF. Three different regression models such as artificial neural network, vector-valued regularized kernel function approximation, and support vector regression are used to evaluate the performance of the proposed method. High prediction accuracy using fusion of features ensures the effectiveness of the proposed method for prediction of TF and TR using ET signals.

Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals

In this paper, a novel method to model the responses of electronic tongue (ET) sensors using autoregressive (AR) and AR moving average techniques is presented. The transient response of each electrode present in the sensor array of an ET is characterized with tea samples of different qualities. Models coefficients are used as the characteristics features of the ET response corresponding to the tea samples. Three different classifiers, namely, artificial neural network, vector valued regularized kernel function approximation, and one-versus-one support vector machine, are employed to evaluate the performance of these features to discriminate the quality of black tea. Experimental results on three types of voltammetric measurement data show that the proposed method may be very useful for prediction of tea quality. The present model-based classification method is very straightforward and provides better or similar performance compared with some other methods proposed in the literature for ET signal classification.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactory–gustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

A Novel Technique of Black Tea Quality Prediction Using Electronic Tongue Signals

Electronic tongue (ET) system is under extensive development for automatic analysis and prediction of quality of different industrial end products. Each sensor in an ET system generates a specific electronic response in presence of different organic or inorganic compounds in the sample. The vital part of the ET system is the discrimination of the complex pattern generated by the sensor array. In this paper, a novel technique of black tea quality estimation is using the ET signals. A moving window is used to extract discrete wavelet transform coefficients from the transient response of ET. The energy in different frequency bands are used as the features of the ET signal for different positions of the window. The prediction of a new sample is performed by the highest score obtained by a particular class by testing all the patterns generated by windowing ET signal. The performance of the proposed technique is verified to estimate black tea quality using two kernel classifiers, namely support vector machine and recently proposed vector valued regularized kernel function approximation method. High prediction accuracy of both the classifiers confirms the effectiveness of the proposed technique of tea quality estimation using ET signals.

Application of a voltammetric electronic tongue and near infrared spectroscopy for a rapid umami taste assessment

The relationships between sensory attribute and analytical measurements, performed by electronic tongue (ET) and near-infrared spectroscopy (NIRS), were investigated in order to develop a rapid method for the assessment of umami taste. Commercially available umami products and some aminoacids were submitted to sensory analysis. Results were analysed in comparison with the outcomes of analytical measurements. Multivariate exploratory analysis was performed by principal component analysis (PCA). Calibration models for prediction of the umami taste on the basis of ET and NIR signals were obtained using partial least squares (PLS) regression. Different approaches for merging data from the two different analytical instruments were considered. Both of the techniques demonstrated to provide information related with umami taste. In particular, ET signals showed the higher correlation with umami attribute. Data fusion was found to be slightly beneficial – not so significantly as to justify the coupled use of the two analytical techniques.

English

Electronic nose (e-nose) and electronic tongue (e-tongue) instrumental systems were designed to crudely mimic human olfactory and taste sensory organs and are composed of an array of sensors. Complex data sets from electronic nose and electronic tongue signals combined with multivariate statistics represent rapid and efficient tools for classification, discrimination, recognition and identification of samples, as well as for the prediction of concentrations of different compounds. A wide variety of sensors can be employed into the design of these instrumental systems, especially that of electronic tongues, offering numerous practical applications. In this study, characteristics of sensors and possibilities of electronic nose and electronic tongue applications in the dairy industry were reviewed.

Application of a GA–PLS strategy for variable reduction of electronic tongue signals

A genetic algorithm (GA) combined with partial least squares (PLS) regression was applied in order to reduce the considerable number of variables of electrochemical signals recorded by a voltammetric electronic tongue. The algorithm was specifically designed for improving the analysis of data matrices with high collinearity, providing good solutions in terms of both predictive ability and interpretability and, at the same time, minimising the risk of overfitting.The variable reduction was carried out within a wine characterisation performed with an electronic tongue based on voltammetric sensors. In more detail, the aim was the evaluation of differences among wines aged by means of two different processes: the traditional ageing process in oak wood barrels and an alternative method based on the use of stainless steel tanks and oak wood chips or staves. The results show that the variables selected are at least as good as the full signals to separate the two different classes, even though the number of parameters has been reduced from 1980 to 280 variables; furthermore, the models obtained are simpler and more easily interpretable. In fact, it is important to point out that the variables selected by the genetic algorithm are related to the electrochemical activity of the polyphenolic fraction of wine.

Use of an electronic tongue to analyze mold growth in liquid media

The feasibility of employing an electronic tongue to measure the growth of mold in a liquid medium was studied. We used the electronic tongue developed at Linköping University, which is based on pulsed voltammetry and consists of an array of different metal electrodes. Instead of focusing on a single parameter, this device provides information about the condition or quality of a sample or process. Accordingly, the data obtained are complex, and multivariate methods such as principal component analysis (PCA) or projection to latent structures (PLS) are required to extract relevant information. A gas chromatographic technique was developed to measure ergosterol content in mold biomass and was subsequently used as a reference method to investigate the ability of the electronic tongue to measure the growth of mold in liquid media. The result shows that the electronic tongue can monitor mold growth in liquids. In PLS analysis, the electronic tongue signals correlate well with the amount of ergosterol in the mold biomass as well as the microbially induced changes in the pH of the medium.