NDLI: Support Vector Regression for quantitative determination of human chorionic gonadotropin concentration from gold immunochromatographic strip

Content Provider	IEEE Xplore Digital Library
Author	Jiang Haiyan Du Min
Copyright Year	2011
Description	Author affiliation: College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China (Jiang Haiyan) \|\| College of Physics and Information Engineering, Fuzhou University, Fuzhou, China (Du Min)
Abstract	There are several methods for quantitative determination of human chorionic gonadotropin (hCG), among these methods, the gold immunochromatographic assay has the advantages of simple operation, low costs and short operation time. However, this assay can only get qualitative or semi-quantitative results when observed directly with naked eyes. By combining the fuzzy C-means algorithm and the Support Vector Regression (SVR), this paper presents a rapid quantitative determination method of hCG immunochromatographic assay strip. In this paper, SVR is used to predict the hCG concentration. In the experiment, the data derived from 35 strips CCD image are used for SVR model training; on the other hand, the data derived from other 18 strips are used for model testing. The results show that the SVR yields a good result, the total MSE of the 18 strips is 10.2. The CV is 7.07%. This method is proved to be practical and objective, as well as enhances the detection sensitivity in some extent.
Starting Page	518
Ending Page	522
File Size	468473
Page Count	5
File Format	PDF
ISBN	9781424493517
e-ISBN	9781424493524
DOI	10.1109/BMEI.2011.6098318
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Strips Gold Image segmentation human chorionic gonadotropin Support Vector regression Gold immunochromatographic assay Feature extraction Data models Immune system
Content Type	Text
Resource Type	Article

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