Optimal linear data analysis for surface plasmon resonance biosensors
Optimal linear data analysis for surface plasmon resonance biosensors
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DOI：
10.1016/s0925-4005(98)00316-5
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发表时间：
1999-01-25
影响因子：
8.4
通讯作者：
Yee, SS
中科院分区：
文献类型：
--
作者：
Chinowsky, TM;Jung, LS;Yee, SS
表面等离子体共振生物传感器通过分析测量的反射光谱中的微小变化来测量生物膜的厚度或分子浓度。在这篇文章中，我们描述了线性频谱分析技术，旨在产生具有最大可能信噪比的测量结果。我们展示了如何在适当的假设下推导出用于测量任何系统参数的最优分析方法，以及如何使多个参数的测量独立以换取信噪比的降低。与使用模拟数据的两种传统数据分析方法(二次拟合法和质心法)相比，线性方法的信噪比提高了30%。在实际的硫醇结合数据应用中，线性方法的信噪比比质心法高46%，比二次拟合法高65%。通过使用线性方法的能力来抑制由光源亮度变化引起的噪声，实现了这种程度的降噪。(C)1999 Elsevier Science S.A.保留所有权利。
Surface plasmon resonance biosensors measure the thickness or molecular concentration of a biolayer by analyzing small changes in measured reflection spectra. In this paper, we describe linear spectral analysis techniques designed to produce measurements with the maximum possible signal-to-noise ratio. We show how, under appropriate assumptions, an optimal analysis method may be derived for measuring any system parameter, and how measurements of multiple parameters may be made independent in exchange for an decrease in signal to noise ratio. Compared to two conventional data analysis techniques (quadratic fit and centroid methods) using simulated data, the linear techniques show a 30% increase in signal to noise ratio. In application to actual thiol binding data, the linear method yields a signal to noise ratio 46% greater than that of the centroid method and 65% greater than that of the quadratic fit method. This level of noise reduction was achieved by using the ability of the linear methods to reject noise caused by light source brightness variations. (C) 1999 Elsevier Science S.A. All rights reserved.