Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/28—Investigating the spectrum
  • G01J3/44—Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
  • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
  • G01N21/65—Raman scattering
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/28—Investigating the spectrum
  • G01J3/44—Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
  • G01J2003/4424—Fluorescence correction for Raman spectrometry
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
  • G01N21/274—Calibration, base line adjustment, drift correction

Definitions

• This invention relates to spectroscopic apparatus and methods. It is particularly useful in Raman spectroscopy, though it can also be used in other forms of spectroscopy, e.g. narrow-line photoluminescence, fluorescence, cathode- luminescence, UV visible (UV Vis), nuclear magnetic resonance (NMR), mid infra-red (mid-IR) or near infra-red (NIR).
• narrow-line photoluminescence, fluorescence, cathode- luminescence e.g. narrow-line photoluminescence, fluorescence, cathode- luminescence, UV visible (UV Vis), nuclear magnetic resonance (NMR), mid infra-red (mid-IR) or near infra-red (NIR).
• the Raman Effect is the inelastic scattering of light by a sample.
• a sample is irradiated by monochromatic laser light and the scattered light is then dispersed by a dispersive device, such as a diffraction grating, e.g. in a monochromator, to generate a spectrum called a Raman spectrum.
• the Raman spectrum is detected by a detector such as a charge- coupled device (CCD).
• CCD charge- coupled device
• Raman effect can be used to analyse chemical compounds present in a sample.
• the detected spectrum comprises the Raman spectrum together with a background signal whose intensity, particularly for biological samples, is orders of magnitude greater than the Raman spectrum.
• This background signal is typically due to, amongst other things, the substrate supporting the sample, fluorescence and an objective lens of the Raman apparatus.
• To analyse the Raman spectrum it is often first necessary to identify a proportion of the detected spectrum that can be attributed to background sources.
• B. D. Beier and A. J. Berger, The Royal Society of Chemistry, 2009, 134, 1 198- 1202 discloses a method for automating the removal of background from a Raman signal using a polynomial fitting technique and a reference spectrum of a known spectral contaminant. In the example described, glass of a microscope slide is the known contaminant.
• the method comprises an iterative algorithm wherein, to start with, an estimate of the background component is set as the detected spectrum.
• An initial estimate is made of the concentration of the known contaminant and a polynomial is fitted to the residual between the estimated background and the estimated contribution made by the known contaminant.
• the polynomial and the estimated contribution of the known contaminant form together a current estimate of the background.
• a new estimate of the background for the next iteration is determined by comparing the current estimate to the previous estimate of the background and retaining the minimum value at each wavenumber.
• a method of estimating background radiation in spectral data comprising, iteratively, fitting a polynomial to reference data, determining an allowable deviation of the reference data from the polynomial, clipping data points of the reference data or the spectral data that are more than the allowable deviation above the polynomial to provide the reference data for the next iteration until termination criterion is met, wherein the reference data is initially based upon the spectral data.
• the polynomial tends to be fitted much more closely to the slowly varying spectrum of the background than the sudden spikes of the Raman spectrum of the sample. Accordingly, clipping data points of the reference data or spectral data that are more than the allowable deviation above the polynomial may remove the sharp Raman spectrum from the reference data whilst retaining the more slowly varying background components. In this way, it may not be necessary to make assumptions about contaminants in order to estimate the background.
• the polynomial of the final iteration may form the estimate of background radiation.
• the polynomial may be a spline curve and, in particular, a cubic spline.
• the method may comprise fitting a spline curve to the reference data using a predefined number of anchor points (knots).
• the method may be computer implemented and the number of anchor points predefined by a user.
• the number of anchor points may be predefined based on the resolution of the spectroscopy apparatus used to obtain the spectral data or the likely widths of the Raman peaks.
• Fitting of the spline curve may comprise identifying a location of the anchors. Identification of the locations of the anchors may be based upon the reference data or spectral data. The locations of the anchors for each iteration may be identified automatically using an algorithm.
• the allowable deviation is based upon an estimate of noise in the reference data.
• Noise is fluctuations in the spectral data that are relatively small compared to other features of the background, such as spectral features of the substrate and objective lens and fluorescence, and the Raman spectra of the sample.
• Such noise may be generated by, amongst other things, electronic noise in the photo-detector and processing circuitry.
• the estimate of noise may be estimated from the spectral data or the reference data.
• the noise may be estimated from an average variation, such as an RMS variation, between each point of the spectral data or reference data and its nearest neighbour(s) or an average variation, such as an RMS variation, between each local minimum of the spectral data or reference data and its nearest neighbours (local minimum in the sense that the point is less than both of its neighbours).
• the estimate of noise may be based upon a deviation of the reference data from the polynomial/estimate of background radiation. Estimates of noise based upon the spectral data may overestimate the noise because of the presence of the Raman peaks. At least a proportion of the contribution from the Raman peaks has been clipped in order to form the reference data, and therefore, estimates of noise based upon the reference data may be more accurate.
• a final/true estimate of noise is made using the reference data provided by the final iteration.
• This final/true estimate of noise may be used in further analysis of the spectral data, for example when identifying the Raman spectra/spectrum present in the spectral data.
• the estimate of noise may be used in an assessment of a fit of a model of Raman spectra to the spectral data.
• a method of estimating noise in spectral data comprising removing from the spectral data data points identified as corresponding to Raman peaks and estimating the noise in the spectral data from the remaining data points.
• Clipping may comprise decimating, ie removing, data points from the reference data or spectral data.
• clipping may comprise setting the data points to a specified value above the polynomial, preferably, a value M x the average deviation above the polynomial.
• the termination criterion may be when no clipping occurs in an iteration.
• the fitting criterion may comprise a statistical significance of a highest order polynomial coefficient of a fitted polynomial.
• the method may comprise determining the statistical significance of a highest order polynomial coefficient used to generate an estimate and generating an estimate using a higher or a lower polynomial order based upon the determined statistical significance. For example, an estimate may be generated by fitting a polynomial of order, n, to the spectral data, deleting the highest order term in the fitted polynomial and determining whether there is a statistically significant difference between the fit of the polynomial to the estimate with and without the highest order term. If there is a statistical significant difference then an estimate is generated using a higher order polynomial, such as n+1.
• an estimate is generated using a lower order polynomial, such as n-1. These steps may be repeated until a sequence of increasing or a sequence of decreasing an order of the polynomial ends (because the highest order term is no longer statistically significant in the case of a sequence of increasing polynomial order or because the highest order term is statistically significant in the case a sequence of decreasing polynomial order).
• the statistical significance of the highest order term may be determined based upon an estimate of noise in the spectral data. For example, if changes in magnitude caused by the highest order term are within the estimated noise in the spectral data.
• the fitting criterion may be a required distribution of the estimate relative to the fitted polynomial.
• the fitting criterion may relate to a comparison of a local variation between the estimate and the fitted polynomial to a global variation of the estimate to the fitted polynomial. This may be a comparison of a difference between the estimate and fitted polynomial at each point to an average variation for all of the data points and whether this falls within an acceptable threshold. If there is a region, ie two or more consecutive points, where the variations are much greater than the average, then an estimate may be generated using a higher order polynomial.
• apparatus comprising a processor, the processor arranged to carry out the methods of the first and/or second aspects of the invention.
• a data carrier having stored thereon instructions, which, when executed by a processor, cause the processor to carry out the methods of the first and/or second aspects of the invention.
• the data carrier may be a non-transient data carrier, such as volatile memory, eg RAM, non-volatile memory, eg ROM, flash memory and data storage devices, such as hard discs, optical discs, or a transient data carrier, such as an electronic or optical signal.
• volatile memory eg RAM
• non-volatile memory eg ROM
• flash memory and data storage devices, such as hard discs, optical discs, or a transient data carrier, such as an electronic or optical signal.
• a method of estimating background in spectral data comprising, iteratively, fitting an analytical curve to reference data, determining an allowable deviation of the reference data from the analytical curve, clipping data points of the reference data or the spectral data that are more than the allowable deviation above the analytical curve to provide the reference data for the next iteration until termination criterion is met, wherein the reference data is initially based upon the spectral data.
• the analytical curve may be a polynomial such as a spline curve or other suitable continuous curve that can be constructed using mathematical operations.
• apparatus comprises a Raman spectrometer connected to a computer 25 that has access to memory 29.
• an estimate of the background radiation in the spectral data is automatically made using an iterative process.
• reference data is initially set equal to the spectral data 102.
• an nth order polynomial is fitted 103.
• An order of the polynomial to be used may be preset, for example the process may be preset to be a fifth order polynomial or alternatively, as described below with reference to Figure 3, the order of the polynomial to be used for estimating the background may be determined by a suitable process.
• step 104 a deviation of each datum point of the reference data from the polynomial is determined and a root mean square (RMS) value is calculated for the deviations.
• RMS root mean square
• the reference data is then modified, in this embodiment by decimating points that are more than an allowable deviation above the polynomial.
• the allowable deviation is MxRMS, wherein M is a positive real number. M may be set by the user based upon noise in the spectral data.
• step 105 rather than decimate the reference data, the spectral data is decimated to remove data points that are more than MxRMS above the polynomial to form the i+lth reference data to which a polynomial is fitted in step 103 of the next iteration. In this way, points of the spectral data that were removed in a previous iteration may be reintroduced. To implement such a method it may be necessary to introduce termination criteria to avoid an endless loop, such as where the same points are continuously removed and then reintroduced.
• the termination criterion in step 106 is that the iterative process terminates after a set (maximum) number of iterations.
• the order of polynomial to use may be automatically determined.
• step 201 an estimate of background radiation in spectral data is determined by fitting an nth order polynomial to the spectral data. Such an estimate may be generated in the manner described with reference to Figure 2 or in an alternative manner, such as described in the prior art.
• step 202 a determination is made as to whether the fitting of the polynomial meets a fitting criterion. Three fitting criteria are described in more detail below. If the fitting meets the fitting criterion then a further estimate is generated using an n+lth order polynomial.
• an estimate is generated using an n-lth order polynomial.
• This process is repeated to generate a sequence of estimates using an ever increasing or decreasing order of polynomial until a fitting reverses the result of the determination. For example, for a generation of estimates using an increasing order of polynomials, the process is terminated when an estimate is generated by fitting a polynomial that fails to meet the fitting criterion. For a generation of estimates using a decreasing order of polynomials, the process is terminated when an estimate is generated by fitting a polynomial that meets the fitting criterion.
• the fitting criterion is a maximum number of iterations required for generating the estimate. This may act as a measure of the stability of fit.
• the fitting criterion is a statistical significance of the highest order coefficient of the fitted polynomial.
• An estimate is generated using a polynomial of order, n, a highest order term of the polynomial fitted in the final iteration is deleted and a determination is made as to whether there is a statistically significant difference between the fit of this modified polynomial to the estimate (eg the output in step 108) relative to the unmodified polynomial. If there is a statistically significant difference then an estimate is generated using a higher order polynomial, such as n+1. However, if there is no statistically significant difference then an estimate is generated using a lower order polynomial, such as n-1.
• the fitting criterion is a required distribution of the fitted polynomial relative to the estimate.
• a comparison is made of a local variation between the resolved polynomial and the estimate to a global variation of the resolved polynomial to the estimate. This may be a comparison of a difference between each point relative to an average variation for all of the data points.
• a determination is made as to whether this comparison falls within an acceptable threshold. If there is a region, ie two or more consecutive points, where the variations fall outside the threshold, then an estimate may be generated using a higher order polynomial.
• the polynomial that is fitted to the reference data is a spline curve.
• the spline curve comprises a plurality of polynomial segments connected at anchor points (also known as knots).
• anchor points also known as knots.
• the number of anchor points is defined by the user.
• the algorithm identifies locations for the anchor points based upon the reference spectrum using conventional methods and fits the polynomial segments to the anchor points at these locations.
• the locations are equally spaced along the wavenumber/frequency axis.
• the spacing of the anchor points along this axis may be defined in another way, for example, a higher density of anchor points may be used in areas of the spectra with greater variations in intensity.
• a deviation of each datum point of the reference data from the spline curve is determined and a root mean square (RMS) value is calculated for the deviations.
• the reference data is then modified, in this embodiment by decimating points that are more than an allowable deviation above the spline curve.
• the allowable deviation is MxRMS, wherein M is a positive real number. M may be set by the user based upon noise in the spectral data.
• step 306 it is determined whether a termination criterion is met, in this embodiment, whether any points have been decimated in step 305. If points were removed from the reference data, the method proceeds to the next iteration, wherein a spline curve is fitted to the modified reference data. The iterative process continues until no points are decimated in step 305. In step 308, the fitted spline curve of the final iteration is output as an estimate of the background radiation.

Landscapes

• Physics & Mathematics (AREA)
• Spectroscopy & Molecular Physics (AREA)
• General Physics & Mathematics (AREA)
• Health & Medical Sciences (AREA)
• Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
• Life Sciences & Earth Sciences (AREA)
• Chemical & Material Sciences (AREA)
• Analytical Chemistry (AREA)
• Biochemistry (AREA)
• General Health & Medical Sciences (AREA)
• Immunology (AREA)
• Pathology (AREA)
• Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
• Spectrometry And Color Measurement (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=47358630

Family Applications (1)

Country Status (5)

Families Citing this family (21)

Family Cites Families (3)

• 2012
  • 2012-10-25 GB GBGB1219189.6A patent/GB201219189D0/en not_active Ceased
• 2013
  • 2013-10-24 CN CN201380068081.2A patent/CN104870955B/zh active Active
  • 2013-10-24 EP EP13783638.3A patent/EP2912419A1/en not_active Withdrawn
  • 2013-10-24 JP JP2015538570A patent/JP6294333B2/ja active Active
  • 2013-10-24 WO PCT/GB2013/052772 patent/WO2014064447A1/en active Application Filing

Non-Patent Citations (2)

Also Published As

Similar Documents

Legal Events