Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task

Leon-Medina, Jersson X.; Anaya, Maribel; Pozo, Francesc; Burgos, Diego Alexander Tibaduiza

doi:10.3390/s20174834

Cited by 28 publications

(26 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yan et al [ 24 ] provide a review article on the feature extraction methods for Enose data. For non-linear feature extraction of Etongue data, Leon-Medina et al [ 46 ] give a great comparison of seven manifold learning methods.…”

Section: Electrochemical Bioreceptor-free Biosensorsmentioning

confidence: 99%

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Schackart

Yoon

2021

Sensors

View full text Add to dashboard Cite

Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

show abstract

Section: Electrochemical Bioreceptor-free Biosensorsmentioning

confidence: 99%

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Schackart

Yoon

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Because the data acquired in the wind-turbine foundation come from several sensors, they can have different magnitudes; therefore, scaling the data is key before analyzing it. Among all existing normalization techniques, the mean-centered group-scaling method (MCGS) [ 19 , 37 , 38 , 39 ] demonstrated very accurate behavior.…”

Section: Data Preprocessing: Training Data Preparationmentioning

confidence: 99%

“…The main goal of data-driven algorithms is to analyze large or complex sensor networks that provide multivariate information using ML approaches. These complex sensor networks can be found in some SHM solutions [ 15 , 16 ], classification of gases by means of electronic noses [ 17 , 18 ], and classification of liquids by means of electronic tongues [ 19 ], among others. A common problem for data-driven algorithms is that data captured by the network of sensors have a high dimensionality [ 20 ], and therefore, algorithms are employed to handle and process this large amount of information.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Leon-Medina

Anaya

Mariné

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification was developed in this study. The proposed methodology involves several stages: (1) data acquisition, (2) data arrangement, (3) data normalization through the mean-centered unitary group-scaling method, (4) linear feature extraction, (5) classification using the extreme gradient boosting machine learning classifier, and (6) validation applying a 5-fold cross-validation technique. The linear feature extraction capabilities of principal component analysis are employed; the original data of 58,008 features is reduced to only 21 features. The methodology is validated with an experimental test performed in a small-scale wind-turbine foundation structure that simulates the perturbation effects caused by wind and marine waves by applying an unknown white noise signal excitation to the structure. A vibration-response methodology is selected for collecting accelerometer data from both the healthy structure and the structure subjected to four different damage scenarios. The datasets are satisfactorily classified, with performance measures over 99.9% after using the proposed damage classification methodology.

show abstract

“…The data set obtained from an MLAPV (multifrequency large amplitude pulse voltammetry) electronic tongue device comes from various types of sensors and their magnitudes can have different scales [1]. These signals are characterized by having high dimensionality [2]. This can cause problems in Machine Learning models, both in pattern recognition and in the accuracy of data classification [3].…”

Section: Introductionmentioning

confidence: 99%

Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

Mendez-Lopez

Leon-Medina

Burgos

2021

The 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry

Self Cite

View full text Add to dashboard Cite

Electronic tongue type sensor arrays are made of different materials with the property of capturing signals independently by each sensor. The signals captured when conducting electrochemical tests often have high dimensionality, which increases when performing the data unfolding process. This unfolding process consists of arranging the data coming from different experiments, sensors, and sample times, thus the obtained information is arranged in a two-dimensional matrix. In this work, a description of a tool for the analysis of electronic tongue signals is developed. This tool is developed in Matlab® App Designer, to process and classify the data from different substances analyzed by an electronic tongue type sensor array. The data processing is carried out through the execution of the following stages: (1) data unfolding, (2) normalization, (3) dimensionality reduction, (4) classification through a supervised machine learning model, and finally (5) a cross-validation procedure to calculate a set of classification performance measures. Some important characteristics of this tool are the possibility to tune the parameters of the dimensionality reduction and classifier algorithms, and also plot the two and three-dimensional scatter plot of the features after reduced the dimensionality. This to see the data separability between classes and compatibility in each class. This interface is successfully tested with two electronic tongue sensor array datasets with multi-frequency large amplitude pulse voltammetry (MLAPV) signals. The developed graphical user interface allows comparing different methods in each of the mentioned stages to find the best combination of methods and thus obtain the highest values of classification performance measures.

show abstract

Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task

Cited by 28 publications

References 59 publications

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning

Contact Info

Product

Resources

About