Raman spectroscopy has numerous applications in the field of biology. One such application is the simultaneously measurement of the concentration of multiple biochemical components in low volume aqueous mixtures, for example, a single drop of blood serum. Over twenty years ago, it was shown for the first time that it was possible to estimate the concentration of glucose, urea, and lactic acid in mixture by combining Raman Spectroscopy with Partial Least Squares Regression analysis. This was followed by numerous contributions in the literature designed to increase the number of components and reduce the limits of concentration that could be simultaneously measured using Raman spectroscopy, by developing various optical architectures to maximise the signal to noise ratio. The aim of this paper is to demonstrate the potential of a confocal Raman microscopy system for multicomponent analysis for the case of physiologically relevant mixtures of glucose, urea, and lactic acid.
Raman microspectroscopy is an optoelectronic technique that can be used to evaluate the chemical composition of bio- logical samples. Raman spectroscopy has been shown to be a powerful classification tool for the investigation of various cancer related diseases including bladder, breast and cervical cancer. Raman scattering is an inherently weak process with approximately 1 in 107 photons undergoing scattering. For this reason, noise from the recording system can have a significant impact on the quality of the signal and its suitability for classification. Different camera settings when obtain- ing spectra from charge-coupled devices can result in significantly different noise performance. This paper provides an investigation into practical aspects of retrieving the signal from the charge-coupled device, and the effects of integration time and multiple acquisitions on the signal to noise ratio of Raman spectra, with a particular focus on biological sam- ples. The main sources of noise are shot noise, CCD dark current, readout noise, and cosmic ray artefacts. Shot noise and dark current noise are time dependent and so there are practical considerations when choosing an integration time. Readout noise is inherent in each individual recording, which may be compounded when averaging spectra together. Our results demonstrate that read parameters and read modes can greatly influence the signal to noise ratio. We also discuss experimental conditions and processing methods that can mitigate these effects.
Raman micro-spectroscopy is an optoelectronic technique that can be used to evaluate the chemical composition of biological samples and has been shown to be a powerful diagnostic tool for the investigation of various cancer related diseases including bladder, breast, and cervical cancer. Raman scattering is an inherently weak process with approximately 1 in 107 photons undergoing scattering and for this reason, noise from the recording system can have a significant impact on the quality of the signal, and its suitability for diagnostic classification. The main sources of noise in the recorded signal are shot noise, CCD dark current, and CCD readout noise. Shot noise results from the low signal photon count while dark current results from thermally generated electrons in the semiconductor pixels. Both of these noise sources are time dependent; readout noise is time independent but is inherent in each individual recording and results in the fundamental limit of measurement, arising from the internal electronics of the camera. In this paper, each of the aforementioned noise sources are analysed in isolation, and used to experimentally validate a mathematical model. This model is then used to simulate spectra that might be acquired under various experimental conditions including the use of different cameras, different source wavelength, and power etc. Simulated noisy datasets of T24 and RT112 cell line spectra are generated based on true cell Raman spectrum irradiance values (recorded using very long exposure times) and the addition of simulated noise. These datasets are then input to multivariate classification using Principal Components Analysis and Linear Discriminant Analysis. This method enables an investigation into the effect of noise on the sensitivity and specificity of Raman based classification under various experimental conditions and using different equipment.
Raman spectroscopy is a powerful tool for analyzing the composition of biological samples in terms of biomolecular content. Over the past two decades there has been considerable interest in the application of Raman to measuring the concentration of the various constituents in a multicomponent mixture. This is achieved by first building a database of the Raman spectra of the individual components in a pure form. Following this a least squares algorithms is applied to find a best fit that accounts for the spectrum of the mixture. The weights returned by a partial least squares algorithm indicate the relative concentration of each component. Of particular interest has been application of the method to estimate the concentration of various analytes in blood and urine samples, including glucose. In this paper we briefly review the subject of multicomponent analysis by Raman Spectroscopy in terms of experimental methodology, limits of measurement, and applications