CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron
The Charge Coupled Device (CCD) scanner determines the concentration of the microarray by capturing the intensity of the fluorescent signal on the microarray in combination with the standard curve. Due to the characteristics of semiconductors, the CCD sensor in the scanner we designed suffers from s...
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10551909/ |
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| author | Zhenhua Gan Dongyu He Peishu Wu Baoping Xiong Nianyin Zeng Fumin Zou Feng Guo Qin Bao Fengyan Zhao |
| author_facet | Zhenhua Gan Dongyu He Peishu Wu Baoping Xiong Nianyin Zeng Fumin Zou Feng Guo Qin Bao Fengyan Zhao |
| author_sort | Zhenhua Gan |
| collection | DOAJ |
| description | The Charge Coupled Device (CCD) scanner determines the concentration of the microarray by capturing the intensity of the fluorescent signal on the microarray in combination with the standard curve. Due to the characteristics of semiconductors, the CCD sensor in the scanner we designed suffers from saturation, the non-linear relationship between photoelectric response and the light intensity collected by CCD, which poses a challenge for fitting the standard curve of microarray scanner. The Least Squares Algorithm (LSA) still has a large relative error even in the case of high-order fitting, especially in the region of the fluorescence image with small gray level. However, the standard curve is critical to the highly accurate measuring of the instrument. In view of the poor curve fitting performance of LSA, Weighted Least Squares (WLS), and Penalized Least Squares (PLS), as well as the small dataset, this paper proposes the Multi-Layer Perceptron (MLP) neural network algorithm with the minimization of relative error as the constraint, which is applied to the standard curve fitting of the scanner. The gray-level of the fluorescent probe in detection image was obtained as the data set acquired by the microarray scanner at different exposure time. And the relative error and the standard deviation of the relative errors were used as evaluation indicators. In our experiments we compared the MLP neural network with relative error minimization as the constraint with the LSA and the MLP neural network with sum of square errors (SSE) minimization as the constraint. The experimental results show that the MLP neural network constrained by minimizing the relative error has good fitting performance for the standard curve of CCD scanner, with the maximum relative error of only 0.89% while the standard deviation of relative error of only 0.25%. It can be seen that this method provides a new approach for standard curve fitting of microarray scanner. |
| format | Article |
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| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-25b3a76c08d2407f9a790cb9117b7ffd2025-01-24T00:00:41ZengIEEEIEEE Photonics Journal1943-06552024-01-0116411110.1109/JPHOT.2024.341139610551909CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer PerceptronZhenhua Gan0https://orcid.org/0000-0002-0126-0395Dongyu He1https://orcid.org/0009-0005-4386-5586Peishu Wu2https://orcid.org/0000-0001-9891-3809Baoping Xiong3https://orcid.org/0000-0003-1004-7884Nianyin Zeng4https://orcid.org/0000-0002-6957-2942Fumin Zou5https://orcid.org/0000-0002-4234-1861Feng Guo6https://orcid.org/0000-0002-5951-225XQin Bao7https://orcid.org/0009-0001-9322-9474Fengyan Zhao8Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian, ChinaDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian, ChinaDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian, ChinaFujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian, ChinaThe Charge Coupled Device (CCD) scanner determines the concentration of the microarray by capturing the intensity of the fluorescent signal on the microarray in combination with the standard curve. Due to the characteristics of semiconductors, the CCD sensor in the scanner we designed suffers from saturation, the non-linear relationship between photoelectric response and the light intensity collected by CCD, which poses a challenge for fitting the standard curve of microarray scanner. The Least Squares Algorithm (LSA) still has a large relative error even in the case of high-order fitting, especially in the region of the fluorescence image with small gray level. However, the standard curve is critical to the highly accurate measuring of the instrument. In view of the poor curve fitting performance of LSA, Weighted Least Squares (WLS), and Penalized Least Squares (PLS), as well as the small dataset, this paper proposes the Multi-Layer Perceptron (MLP) neural network algorithm with the minimization of relative error as the constraint, which is applied to the standard curve fitting of the scanner. The gray-level of the fluorescent probe in detection image was obtained as the data set acquired by the microarray scanner at different exposure time. And the relative error and the standard deviation of the relative errors were used as evaluation indicators. In our experiments we compared the MLP neural network with relative error minimization as the constraint with the LSA and the MLP neural network with sum of square errors (SSE) minimization as the constraint. The experimental results show that the MLP neural network constrained by minimizing the relative error has good fitting performance for the standard curve of CCD scanner, with the maximum relative error of only 0.89% while the standard deviation of relative error of only 0.25%. It can be seen that this method provides a new approach for standard curve fitting of microarray scanner.https://ieeexplore.ieee.org/document/10551909/Standard curve fittingMLPrelative errormicroarrayCCD scanner |
| spellingShingle | Zhenhua Gan Dongyu He Peishu Wu Baoping Xiong Nianyin Zeng Fumin Zou Feng Guo Qin Bao Fengyan Zhao CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron IEEE Photonics Journal Standard curve fitting MLP relative error microarray CCD scanner |
| title | CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron |
| title_full | CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron |
| title_fullStr | CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron |
| title_full_unstemmed | CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron |
| title_short | CCD Standard Curve Fitting for Microarray Detection Base on Multi-Layer Perceptron |
| title_sort | ccd standard curve fitting for microarray detection base on multi layer perceptron |
| topic | Standard curve fitting MLP relative error microarray CCD scanner |
| url | https://ieeexplore.ieee.org/document/10551909/ |
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