Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors
The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most existing multi-mode sensors rely on the separate ana...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/15/3/148 |
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| author | Xue Zou Xiaohong Wang Jinchun Tu Delun Chen Yang Cao |
| author_facet | Xue Zou Xiaohong Wang Jinchun Tu Delun Chen Yang Cao |
| author_sort | Xue Zou |
| collection | DOAJ |
| description | The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most existing multi-mode sensors rely on the separate analysis of each mode signal, which can easily lead to sensor failure when the deviation between different mode results is too large. In this study, we propose a multi-mode sensor based on Prussian Blue (PB) for ascorbic acid (AA) detection. We innovatively integrate back-propagation artificial neural networks (BP ANNs) to comprehensively process the three collected signal data sets, which successfully solves the problem of sensor failure caused by the large deviation of signal detection results, and greatly improves the prediction accuracy, detection range, and anti-interference of the sensor. Our findings provide an effective solution for optimizing the data analysis of multi-modal sensors, and show broad application prospects in bioanalysis, clinical diagnosis, and related fields. |
| format | Article |
| id | doaj-art-4f448201a11d4593a2b62b1aaaf998a2 |
| institution | Kabale University |
| issn | 2079-6374 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj-art-4f448201a11d4593a2b62b1aaaf998a22025-08-20T03:43:37ZengMDPI AGBiosensors2079-63742025-02-0115314810.3390/bios15030148Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode SensorsXue Zou0Xiaohong Wang1Jinchun Tu2Delun Chen3Yang Cao4State Key Laboratory of Marine Resource Utilization in South China Sea, College of Material Science and Engineering, Hainan University, Haikou 570228, ChinaSchool of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, College of Material Science and Engineering, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, College of Material Science and Engineering, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, College of Material Science and Engineering, Hainan University, Haikou 570228, ChinaThe detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most existing multi-mode sensors rely on the separate analysis of each mode signal, which can easily lead to sensor failure when the deviation between different mode results is too large. In this study, we propose a multi-mode sensor based on Prussian Blue (PB) for ascorbic acid (AA) detection. We innovatively integrate back-propagation artificial neural networks (BP ANNs) to comprehensively process the three collected signal data sets, which successfully solves the problem of sensor failure caused by the large deviation of signal detection results, and greatly improves the prediction accuracy, detection range, and anti-interference of the sensor. Our findings provide an effective solution for optimizing the data analysis of multi-modal sensors, and show broad application prospects in bioanalysis, clinical diagnosis, and related fields.https://www.mdpi.com/2079-6374/15/3/148BP ANNmulti-mode sensorPB |
| spellingShingle | Xue Zou Xiaohong Wang Jinchun Tu Delun Chen Yang Cao Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors Biosensors BP ANN multi-mode sensor PB |
| title | Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors |
| title_full | Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors |
| title_fullStr | Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors |
| title_full_unstemmed | Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors |
| title_short | Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors |
| title_sort | back propagation artificial neural network enhanced accuracy of multi mode sensors |
| topic | BP ANN multi-mode sensor PB |
| url | https://www.mdpi.com/2079-6374/15/3/148 |
| work_keys_str_mv | AT xuezou backpropagationartificialneuralnetworkenhancedaccuracyofmultimodesensors AT xiaohongwang backpropagationartificialneuralnetworkenhancedaccuracyofmultimodesensors AT jinchuntu backpropagationartificialneuralnetworkenhancedaccuracyofmultimodesensors AT delunchen backpropagationartificialneuralnetworkenhancedaccuracyofmultimodesensors AT yangcao backpropagationartificialneuralnetworkenhancedaccuracyofmultimodesensors |