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Seggio M, Arcadio F, Radicchi E, Cennamo N, Zeni L, Bossi AM. Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor. ACS OMEGA 2024; 9:18984-18994. [PMID: 38708270 PMCID: PMC11064004 DOI: 10.1021/acsomega.3c09485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 05/07/2024]
Abstract
Nano- and microplastic particles are a global and emerging environmental issue that might pose potential threats to human health. The present work exploits artificial intelligence (AI) to identify nano- and microplastics in water by monitoring the interaction of the sample with a sensitive surface. An estrogen receptor (ER) grafted onto a gold surface, realized on a nonexpensive and easy-to-produce plastic optical fiber (POF) platform in order to excite a surface plasmon resonance (SPR) phenomenon, has been developed in order to carry out a "smart" sensitive interface (ER-SPR-POF interface). The ER-SPR-POF interface offers output data useful for exploiting a machine learning-based approach to achieve nano- and microplastic particle sensors. This work developed a proof-of-concept sensor through a training phase carried out by different particles, in terms of materials and size. The experimental results have demonstrated that the proposed "smart" ER-SPR-POF interface combined with AI can be used to identify the kind of particles in terms of the materials (polystyrene; poly(methyl methacrylate)) and size (20 μm; 100 nm) with an accuracy of 90.3%.
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Affiliation(s)
- Mimimorena Seggio
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
| | - Francesco Arcadio
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Eros Radicchi
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
| | - Nunzio Cennamo
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Luigi Zeni
- Department
of Engineering, University of Campania Luigi
Vanvitelli, via Roma 29, 81031 Aversa, Italy
| | - Alessandra Maria Bossi
- Department
of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
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Tibaduiza D, Anaya M, Gómez J, Sarmiento J, Perez M, Lara C, Ruiz J, Osorio N, Rodriguez K, Hernandez I, Sanchez C. Electronic Tongues and Noses: A General Overview. BIOSENSORS 2024; 14:190. [PMID: 38667183 PMCID: PMC11048215 DOI: 10.3390/bios14040190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/06/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024]
Abstract
As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.
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Affiliation(s)
- Diego Tibaduiza
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Maribel Anaya
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Johan Gómez
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Juan Sarmiento
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Maria Perez
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Cristhian Lara
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Johan Ruiz
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Nicolas Osorio
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Katerin Rodriguez
- Departamento de Ingeniería Química y Ambiental, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Isaac Hernandez
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
| | - Carlos Sanchez
- Departamento de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (M.A.); (J.G.); (J.S.); (M.P.); (C.L.); (J.R.); (N.O.); (I.H.); (C.S.)
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Leon-Medina JX, Anaya M, Tibaduiza DA. New Electronic Tongue Sensor Array System for Accurate Liquor Beverage Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:6178. [PMID: 37448027 DOI: 10.3390/s23136178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023]
Abstract
The use of sensors in different applications to improve the monitoring of a process and its variables is required as it enables information to be obtained directly from the process by ensuring its quality. This is now possible because of the advances in the fabrication of sensors and the development of equipment with a high processing capability. These elements enable the development of portable smart systems that can be used directly in the monitoring of the process and the testing of variables, which, in some cases, must evaluated by laboratory tests to ensure high-accuracy measurement results. One of these processes is taste recognition and, in general, the classification of liquids, where electronic tongues have presented some advantages compared with traditional monitoring because of the time reduction for the analysis, the possibility of online monitoring, and the use of strategies of artificial intelligence for the analysis of the data. However, although some methods and strategies have been developed, it is necessary to continue in the development of strategies that enable the results in the analysis of the data from electrochemical sensors to be improved. In this way, this paper explores the application of an electronic tongue system in the classification of liquor beverages, which was directly applied to an alcoholic beverage found in specific regions of Colombia. The system considers the use of eight commercial sensors and a data acquisition system with a machine-learning-based methodology developed for this aim. Results show the advantages of the system and its accuracy in the analysis and classification of this kind of alcoholic beverage.
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Affiliation(s)
- Jersson X Leon-Medina
- Department of Mechanical and Mechatronics Engineering, Universidad Nacional de Colombia-Sede Bogotá, Bogotá 111321, Colombia
- Control, Data and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d'Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), 08019 Barcelona, Spain
| | - Maribel Anaya
- Department of Electrical and Electronic Engineering, Universidad Nacional de Colombia-Sede Bogotá, Bogotá 111321, Colombia
| | - Diego A Tibaduiza
- Department of Electrical and Electronic Engineering, Universidad Nacional de Colombia-Sede Bogotá, Bogotá 111321, Colombia
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Tong Q, Liu Z, Lu F, Feng Z, Wan Q. A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal. SENSORS (BASEL, SWITZERLAND) 2022; 22:6108. [PMID: 36015870 PMCID: PMC9413349 DOI: 10.3390/s22166108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
The transient pulses caused by local faults of rolling bearings are an important measurement information for fault diagnosis. However, extracting transient pulses from complex nonstationary vibration signals with a large amount of background noise is challenging, especially in the early stage. To improve the anti-noise ability and detect incipient faults, a novel signal de-noising method based on enhanced time-frequency manifold (ETFM) and kurtosis-wavelet dictionary is proposed. First, to mine the high-dimensional features, the C-C method and Cao's method are combined to determine the embedding dimension and delay time of phase space reconstruction. Second, the input parameters of the liner local tangent space arrangement (LLTSA) algorithm are determined by the grid search method based on Renyi entropy, and the dimension is reduced by manifold learning to obtain the ETFM with the highest time-frequency aggregation. Finally, a kurtosis-wavelet dictionary is constructed for selecting the best atom and eliminating the noise and reconstruct the defective signal. Actual simulations showed that the proposed method is more effective in noise suppression than traditional algorithms and that it can accurately reproduce the amplitude and phase information of the raw signal.
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Affiliation(s)
- Qingbin Tong
- School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
- Beijing Rail Transit Electrical Engineering Technology Research Center, Beijing 100044, China
| | - Ziyu Liu
- School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Feiyu Lu
- School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Ziwei Feng
- School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Qingzhu Wan
- School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
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Tai HY, Lin JJ, Huang YH, Shih PJ, Wang IJ, Yen JY. Correlation between corneal dynamic responses and keratoconus topographic parameters. J Int Med Res 2022; 50:3000605221108100. [PMID: 35766023 PMCID: PMC9247384 DOI: 10.1177/03000605221108100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Objective To investigate the correlation between corneal biomechanical properties and
topographic parameters using machine learning networks for automatic
severity diagnosis and reference benchmark construction. Methods This was a retrospective study involving 31 eyes from 31 patients with
keratonus. Two clustering approaches were used (i.e., shape-based and
feature-based). The shape-based method used a keratoconus benchmark
validated for indicating the severity of keratoconus. The feature-based
method extracted imperative features for clustering analysis. Results There were strong correlations between the symmetric modes and the
keratoconus severity and between the asymmetric modes and the location of
the weak centroid. The Pearson product-moment correlation coefficient (PPMC)
between the symmetric mode and normality was 0.92 and between the asymmetric
mode and the weak centroid value was 0.75. Conclusion This study confirmed that there is a relationship between the keratoconus
signs obtained from topography and the corneal dynamic behaviour captured by
the Corvis ST device. Further studies are required to gather more patient
data to establish a more extensive database for validation.
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Affiliation(s)
- Hsi-Yun Tai
- Department of Mechanical Engineering, National Taiwan University, Taipei
| | - Jun-Ji Lin
- Department of Mechanical Engineering, National Taiwan University, Taipei
| | - Yi-Hung Huang
- Department of Mechanical Engineering, National Taiwan University, Taipei
| | - Po-Jen Shih
- Department of Biomedical Engineering, National Taiwan University, Taipei
| | - I-Jong Wang
- Department of Ophthalmology, National Taiwan University Hospital, Taipei
| | - Jia-Yush Yen
- Department of Mechanical Engineering, National Taiwan University, Taipei
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A Feature Engineering-Assisted CM Technology for SMPS Output Aluminium Electrolytic Capacitors (AEC) Considering D-ESR-Q-Z Parameters. Processes (Basel) 2022. [DOI: 10.3390/pr10061091] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Recent research has seen an interest in the condition monitoring (CM) approach for aluminium electrolytic capacitors (AEC), which are present in switched-mode power supplies and other power electronics equipment. From various literature reviews conducted and from a failure mode effect analysis (FMEA) standpoint, the most critical and prone to fault component with the highest percentage is mostly capacitors. Due to its long-lasting ability (endurance), CM offers a better paradigm for AEC due to its application. However, owing to severe conditions (over-voltage, mechanical stress, high temperature) that could occur during use, they (capacitors) could be exposed to early breakdown and overall shutdown of the SMPS. This study considered accelerated life testing (electrical stress and long-term frequency testing) for the component due to its endurance in thousands of hours. We have set up the experiment test bench to monitor the critical electrical parameters: dissipation factor (D), equivalent series resistance (ESR), quality factor (Q), and impedance (Z), which would serve as a health indicator (HI) for the evaluation of the AECs. Time-domain features were extracted from the measured data, and the best features were selected using the correlation-based technique.
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Avian C, Prakosa SW, Faisal M, Leu JS. Estimating finger joint angles on surface EMG using Manifold Learning and Long Short-Term Memory with Attention mechanism. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103099] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Wei C, Wang H, Lu Y, Hu F, Feng N, Zhou B, Jiang D, Wang Z. Recognition of lower limb movements using empirical mode decomposition and k-nearest neighbor entropy estimator with surface electromyogram signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Schackart KE, Yoon JY. Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors. SENSORS (BASEL, SWITZERLAND) 2021; 21:5519. [PMID: 34450960 PMCID: PMC8401027 DOI: 10.3390/s21165519] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/09/2021] [Accepted: 08/13/2021] [Indexed: 01/06/2023]
Abstract
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.
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Affiliation(s)
- Kenneth E. Schackart
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA;
| | - Jeong-Yeol Yoon
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA;
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, USA
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Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting. SENSORS 2021; 21:s21082748. [PMID: 33924654 PMCID: PMC8069796 DOI: 10.3390/s21082748] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 11/25/2022]
Abstract
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.
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Sensors for Structural Health Monitoring and Condition Monitoring. SENSORS 2021; 21:s21051558. [PMID: 33668107 PMCID: PMC7956286 DOI: 10.3390/s21051558] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/20/2021] [Indexed: 11/16/2022]
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