The trouble with plasmons: Removing noise from surface plasmon resonance imaging

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  • Published: Apr 2, 2012
  • Author: Jon Evans
  • Channels: Laboratory Informatics
thumbnail image: The trouble with plasmons: Removing noise from surface plasmon resonance imaging

Oscillate together

The trouble with plasmons: Removing noise from surface plasmon resonance imaging  

Surface plasmons are certainly one of the physical world's more intriguing entities. Produced by the collective oscillation of electrons at the surface of metals, they allow light to perform some seemingly impossible feats, such as pass through gaps that are smaller than their wavelengths or even physically blocked.

These impressive powers come from the fact that surface plasmons interact with light at frequencies that match their own oscillations, allowing them to guide light through holes that should be too small and around physical blockages. Most metals interact with light at infrared frequencies, but certain metals, such as gold, interact with light at visible frequencies. Indeed, this is why gold glitters, because its surface plasmons are very good at scattering visible light.

Scientists have recently begun to take advantage of this ability to develop novel detectors. For the scattering of light by surface plasmons on gold surfaces is very sensitive and can be influenced by any molecules that stick to the gold, offering a way to detect these molecules and measure their concentrations.

 

Islands of gold

Known as surface plasmon resonance imaging (SPRi), the idea is to attach some kind of receptor molecule to gold 'islands' deposited on the bottom of a tiny channel and then pass a liquid sample over it, using either a pump or electro-osmotic flow (see All that glitters). If an analyte in the sample binds with the receptor molecule, this is recorded as a change in the scattering of light shone on the gold. The end result is a visual image of the surface of the channels, in which peaks represent analytes binding with receptors on the islands. 

Although SPRi has several advantages, being a label-free detection technique that is ideally suited for use on microfluidic chips, it suffers from a lot of background noise, reducing its sensitivity. On the visual image, this means that the individual peaks are not always easy to distinguish from each other, while the surface is often not flat, making it difficult to compare the sizes of the different peaks and accurately measure the analyte concentrations.

Now, however, a team of chemists at the Chinese Academy of Science's Institute of Chemistry in Beijing, led by Yi Chen, have come up with a mathematical process for removing much of this noise.

 

Flat surfaces

The process consists of two distinct steps. The first step employs wavelet transform to remove random noise from the SPRi signal and thus make the individual peaks easier to distinguish from each other. The second step employs least-square fitting to remove the background noise, thereby flattening the surface.

Wavelet transform works by using small waves known as wavelets to extract the low- and high-frequency parts of the SPRi signal, retaining the general trends and the fine details but discarding the rest, including much of the random noise. Least-square fitting works by creating a mathematical model of the uneven surface from seven randomly-selected data points on that surface and then subtracting the model from the actual uneven surface. The end result is a much flatter surface.

To test this process, Chen and his team removed the noise produced when detecting the model protein bovine serum albumin (BSA) by SPRi. They found that the process was able to double the signal-to-noise ratio, while greatly flattening the surface, allowing the BSA concentration to be measured much more accurately.

Interestingly, however, they found that the process produced much better results if the wavelet transform step was performed before the least-square fitting step. When they tried it the other way round, the random noise interfered with the surface flattening, showing that the random noise needs to be removed first.

Related Links

Chemometrics and Intelligent Laboratory Systems (Article in Press): "Post-experimental denoising and background subtraction of surface plasmon resonance images for better quantification"


Article by Jon Evans

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

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