An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution
In this work, an integrated microfluidic microwave array sensor is proposed for the enrichment and detection of mixed biological solution. In individuals with urinary tract infections or intestinal health issues, the levels of white blood cells (WBCs) and <i>Escherichia coli</i> (<i&g...
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MDPI AG
2025-01-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/15/1/45 |
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| author | Sen Yang Yanxiong Wang Yanfeng Jiang Tian Qiang |
| author_facet | Sen Yang Yanxiong Wang Yanfeng Jiang Tian Qiang |
| author_sort | Sen Yang |
| collection | DOAJ |
| description | In this work, an integrated microfluidic microwave array sensor is proposed for the enrichment and detection of mixed biological solution. In individuals with urinary tract infections or intestinal health issues, the levels of white blood cells (WBCs) and <i>Escherichia coli</i> (<i>E. coli</i>) in urine or intestinal extracts can be significantly elevated compared to normal. The proposed integrated chip, characterized by its low cost, simplicity of operation, fast response, and high accuracy, is designed to detect a mixed solution of WBCs and <i>E. coli</i>. The results demonstrate that microfluidics could effectively enrich WBCs with an efficiency of 88.3%. For WBC detection, the resonance frequency of the sensing chip decreases with increasing concentration, while for <i>E. coli</i> detection, the capacitance value of the sensing chip increases with elevated concentration. Furthermore, the measurement data are processed using machine learning. Specifically, the WBC measurement data are subjected to a further linear fitting. In addition, the prediction model for <i>E. coli</i> concentration, employing four different algorithms, achieves a maximum accuracy of 95.24%. Consequently, the proposed integrated chip can be employed for the clinical diagnosis of WBCs and <i>E. coli</i>, providing a novel approach for medical and biological research involving cells and bacteria. |
| format | Article |
| id | doaj-art-5a1c4ed381bc43fea68bfc46b17c2fb2 |
| institution | Kabale University |
| issn | 2079-6374 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj-art-5a1c4ed381bc43fea68bfc46b17c2fb22025-01-24T13:25:32ZengMDPI AGBiosensors2079-63742025-01-011514510.3390/bios15010045An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological SolutionSen Yang0Yanxiong Wang1Yanfeng Jiang2Tian Qiang3School of Integrated Circuits, Jiangnan University, Wuxi 214122, ChinaSchool of Integrated Circuits, Jiangnan University, Wuxi 214122, ChinaSchool of Integrated Circuits, Jiangnan University, Wuxi 214122, ChinaSchool of Integrated Circuits, Jiangnan University, Wuxi 214122, ChinaIn this work, an integrated microfluidic microwave array sensor is proposed for the enrichment and detection of mixed biological solution. In individuals with urinary tract infections or intestinal health issues, the levels of white blood cells (WBCs) and <i>Escherichia coli</i> (<i>E. coli</i>) in urine or intestinal extracts can be significantly elevated compared to normal. The proposed integrated chip, characterized by its low cost, simplicity of operation, fast response, and high accuracy, is designed to detect a mixed solution of WBCs and <i>E. coli</i>. The results demonstrate that microfluidics could effectively enrich WBCs with an efficiency of 88.3%. For WBC detection, the resonance frequency of the sensing chip decreases with increasing concentration, while for <i>E. coli</i> detection, the capacitance value of the sensing chip increases with elevated concentration. Furthermore, the measurement data are processed using machine learning. Specifically, the WBC measurement data are subjected to a further linear fitting. In addition, the prediction model for <i>E. coli</i> concentration, employing four different algorithms, achieves a maximum accuracy of 95.24%. Consequently, the proposed integrated chip can be employed for the clinical diagnosis of WBCs and <i>E. coli</i>, providing a novel approach for medical and biological research involving cells and bacteria.https://www.mdpi.com/2079-6374/15/1/45microwave array sensormicrowave detectionmicrofluidic chipenrichment and detectionmixed biological solution |
| spellingShingle | Sen Yang Yanxiong Wang Yanfeng Jiang Tian Qiang An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution Biosensors microwave array sensor microwave detection microfluidic chip enrichment and detection mixed biological solution |
| title | An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution |
| title_full | An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution |
| title_fullStr | An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution |
| title_full_unstemmed | An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution |
| title_short | An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution |
| title_sort | integrated microfluidic microwave array sensor with machine learning for enrichment and detection of mixed biological solution |
| topic | microwave array sensor microwave detection microfluidic chip enrichment and detection mixed biological solution |
| url | https://www.mdpi.com/2079-6374/15/1/45 |
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