Optical emission spectrometer and method for analyzing spectral peaks

Abstract

A method of analyzing spectral peaks obtained by an optical spectrometer is provided. The method includes imaging a spectral peak using an optics assembly and a detector of the optical spectrometer to generate a detected image of the spectral peak. An image correction function is applied to the detected image of the spectral peak to obtain a corrected image of the spectral peak, wherein the image correction function increases the spectral power density of the spectral peak in the corrected image of the spectral peak. Information representing the spectral peak is extracted from the corrected image of the spectral peak.

Description

Technical Field

[0001] The present disclosure relates to optical spectrometry. In particular, the present disclosure relates to the analysis of spectral peaks obtained by an optical spectrometer. Background Art

[0002] Optical spectrometry, such as optical emission spectrometry, is an analytical technique for analyzing a sample. In optical emission spectrometry, a plasma source, for example, can be used to excite the sample. The excited atoms of the sample emit light, where the wavelength of the emitted light is characteristic of the atoms present in the sample. Thus, the light emitted by the sample includes a plurality of spectral peaks, where each spectral peak corresponds to a specific energy level transition in the atoms. By detecting the presence of spectral peaks at specific wavelengths, the presence of elements in the sample can be determined. In addition, the intensity of each spectral peak can be used to analyze the concentration of elements within the sample.

[0003] Various optical spectrometry techniques involve imaging spectral peaks on a detector. Typically, some form of optical component is used to direct the light to be analyzed onto the detector. For example, an optical emission spectrometer (OES) typically includes an echelle grating to diffract the light into a two-dimensional spectrum, which is imaged by an array detector. In such a system, spectral peaks corresponding to narrow wavelengths may appear as bright "spots" on the detector. The wavelength of a spectral peak can be inferred from its position on the detector.

[0004] Thus, an optical spectrometer, such as an optical emission spectrometer, can generate a plurality of spectral peaks when analyzing a sample. Part of the process of analyzing the plurality of spectral peaks involves identifying spectral peaks from the measurement data. The identification process typically involves fitting a curve to the measurement data in order to identify the peak positions (and associated wavelengths) and peak intensities. The peak wavelengths and peak intensities can be used to determine the elements present in the sample and the relative amounts of each element.

[0005] Accordingly, the present disclosure seeks to provide a method for analyzing spectral peaks that addresses at least one problem associated with existing techniques, or at least provides a commercially useful alternative thereto. Summary of the Invention

[0006] According to a first aspect of the present disclosure, there is provided a method for analyzing spectral peaks obtained by an optical spectrometer. The method comprises:

[0007] imaging the spectral peaks using an optical device assembly and a detector of the optical spectrometer to generate a detected image of the spectral peaks;

[0008] applying an image correction function to the detected image of the spectral peaks to obtain a corrected image of the spectral peaks, wherein the image correction function increases the spectral power density of the spectral peaks in the corrected image of the spectral peaks; and

[0009] Extract information representing the spectral peak from the corrected image of the spectral peak.

[0010] The inventors of the present invention have recognized that the optical component of the spectrometer introduces optical distortion into the image of the spectral peak recorded by the detector of the spectrometer. For example, the optical distortion may actually blur the image incident on the detector or add other noise. The effect of this optical distortion is to distribute the energy associated with the spectral emission across the wavelength range. In fact, the spectral power density of the detected spectral peak is reduced. The present invention provides an image correction function to correct the optical distortion introduced by the optical component of the spectrometer. Specifically, the image correction function is configured to correct the detected image by increasing the spectral power density of the spectral peak.

[0011] It should be understood that the inventors of the present invention are applying an image correction function to correct the two-dimensional image of the spectral peak. Thus, the method of the first aspect aims to capture the energy associated with the spectral peak that has been distorted in two dimensions. That is, the image correction function is configured to correct the "blur" of the spectral peak by transferring energy from the edge of the spectral peak towards the center of the peak. Once the corrected image is obtained, information representing the spectral peak can be extracted from the corrected image. It should be understood that the extracted information can then be subjected to further information processing steps, such as curve fitting, identification of elements present in the sample, etc.

[0012] In some embodiments, applying the image correction function to the detected image of the spectral peak includes:

[0013] Obtaining a predicted image correction function of the spectral peak based on the optical characteristics of the optical component of the spectrometer and the detector; and

[0014] Generating a predicted image of the spectral peak based on the predicted image correction function; and generating the calibrated image correction function based on the predicted image correction function and the comparison between the predicted image of the spectral peak and the detected image of the spectral peak,

[0015] wherein applying the calibrated image correction function to the detected image of the spectral peak to obtain the corrected image of the spectral peak.

[0016] Accordingly, the inventors of the present invention have recognized that various image correction functions can be applied to increase the spectral power density of spectral peaks. The inventors of the present invention have recognized that while an initial prediction of an image correction function representing the distortion introduced by an optical component assembly can be made, the initially predicted image correction function typically does not fully capture the random noise present in the detected image. Thus, in order to improve the corrected image of the obtained spectral peak (e.g., further increase the spectral power density), in some embodiments, the method can further calibrate the image correction function. The calibration can be performed by using the predicted image correction function to generate a predicted image of the spectral peak that will be obtained by an optical spectrometer and comparing the predicted image with the detected image. Based on any differences between the predicted image and the detected image, the method can then generate a calibrated image correction function with improved performance (e.g., improved spectral power density) (e.g., by iteratively predicting the image correction function).

[0017] In some embodiments, obtaining a corrected image of a spectral peak using a calibrated image correction function includes deconvolving the detected image of the spectral peak with the calibrated image correction function to obtain the corrected image of the spectral peak. Thus, in some embodiments, the predicted image correction function is a predicted point spread function (PSF), and the calibrated image correction function is a calibrated point spread function (PSF). Accordingly, convolution can be used to generate the predicted image from the predicted PSF. Subsequently, deconvolution can be used to obtain the corrected image of the spectral peak from the calibrated image correction function (calibrated PSF) and the detected image.

[0018] In some embodiments, the calibrated image correction function is calculated by convolving the predicted image correction function with a noise function, wherein the noise function is calculated based on the comparison of the predicted image of the spectral peak and the detected image of the spectral peak. Thus, by taking into account the random noise affecting the optical component, the calibrated image correction function can more accurately account for the distortion present in the detected image. Accordingly, the resulting corrected image can more accurately reflect the spectral peak (spectral image) being measured.

[0019] In some embodiments, the noise function includes an all - one matrix (sometimes referred to as an identity matrix). In some embodiments, the noise function can be an all - one matrix of variable size. By changing the size (order) of the noise function, the influence of the noise function when calculating the calibrated image correction function can be adjusted to more accurately reflect the noise present in the optical spectrometer.

[0020] In some embodiments, a blind deconvolution process can be used to generate the calibrated image correction function / corrected image.

[0021] In some embodiments, an image regularization process may be used to formulate an image correction function. That is, the image degradation process may be expressed in the following form:

[0022] y = Hx + n

[0023] where y is the detected image, x is the original (undistorted image), H is the linear degradation operator, and n is the noise vector. The objective function for x is defined based on different constraint sets. Then, a solution is obtained by minimizing the objective function (image correction function) with respect to x. Thus, an image correction function may be obtained through the image regularization process to determine the corrected image y.

[0024] In some embodiments, the calibrated image correction function is calculated iteratively. In some embodiments, the iteration is performed until the difference between the predicted image of the spectral peak and the detected image of the spectral peak is below a predetermined threshold. For example, the difference between the predicted image and the detected image may be quantified, and subsequent iterations of the calibrated image function may seek to reduce the quantified difference between the predicted image and the detected image. In some embodiments, the iteration may be performed until the quantified difference is below a predetermined threshold. Thus, each calibrated image correction function may achieve a certain level of improvement based on the threshold. Additionally or alternatively, in some embodiments, the iteration may be performed no more than a predetermined number of times to prevent the iteration from running for, e.g., an extended period of time.

[0025] In some embodiments, an image hashing algorithm is used to calculate the difference between the predicted image of the spectral peak and the detected image of the spectral peak. For example, a perceptual image hashing algorithm or a wavelet image hashing algorithm may be used.

[0026] In some embodiments in which a noise function is used, the order of the noise function is increased in each iteration of the calculation of the calibrated image correction function.

[0027] In some embodiments, imaging a spectral peak includes selecting a sub-region of a detector to form a detector image of the spectral peak, wherein a predicted image correction function and a predicted image are generated for the selected sub-region of the detector. For example, in some embodiments, the detector of an optical spectrometer can be an array detector including a two-dimensional pixel arrangement. Such an array detector can instantaneously record multiple spectral peaks at multiple different positions on the detector. Thus, it should be understood that in some embodiments, the image correction function can be applied only to a local region of the detector. The local region of the detector can be a region of the detector proportional to the size of the spectral peak of interest. For example, the area of the local region of the detector may not be greater than 300%, 250%, 200%, 175%, 150%, or 125% of the area of the spectral peak of interest. That is, in some embodiments, selecting a sub-region of the detector can include selecting a sub-region of the detector where only a single spectral peak is present, such that the detected image includes a single spectral peak.

[0028] In some embodiments where the optical spectrometer includes an array detector (the array detector includes a two-dimensional pixel array), the area of each sub-region of the detector may not be greater than 2,500 pixels (e.g., 50×50 pixels). In some embodiments, the area of each sub-region of the detector may not be greater than 2000 pixels, 1600 pixels, 900 pixels, 600 pixels, or 400 pixels. It should be understood that the sub-region can be square, rectangular, or any other shape. Preferably, the sub-region is square.

[0029] In some embodiments, the sub-region of the detector can be selected based on the wavelength of interest (e.g., the wavelength of interest specified by the user). Such a wavelength of interest can correspond to a spectral emission / spectral peak associated with the analyte of interest. Then, the sub-region of the detector can be selected centered on the pixel of the detector associated with the specified wavelength. For example, the size of the sub-region can be at least: 10×10 pixels, 20×20 pixels, or 40×40 pixels, where the center of the sub-region is located at the pixel of the detector associated with the specified wavelength.

[0030] In some embodiments, the optical device assembly includes a diffraction grating configured to diffract spectral peaks of different wavelengths to different sub-regions of the detector. In other embodiments, it should be understood that other optical components can be provided as part of the optical device assembly to generate the spectral peaks incident on the detector.

[0031] In some embodiments, the optical device assembly includes a slit and a diffraction element. The diffraction element may be configured to distribute spectral peaks to be analyzed across a detector. In some embodiments, the diffraction element may include a diffraction grating, a diffraction prism, or any other suitable optical component. In some embodiments, the diffraction element may be provided by a plurality of (diffractive) optical components. The slit may define a relatively narrow opening through which light is transmitted. The slit may have a known fixed width, or in some embodiments, the width of the slit may be adjusted by the optical device assembly. In some embodiments, the slit may be a pinhole slit having a fixed known diameter. In some embodiments, the optical characteristics of the optical device assembly may include the width of the slit or the diameter of the pinhole slit. Thus, in some embodiments, the width of the slit may be used to determine a predicted image correction function.

[0032] In some embodiments, the optical device assembly is capable of operating in a first configuration having a first set of optical characteristics or in a second configuration having a second set of optical characteristics. The different optical characteristics may be used, for example, to change the resolution of an optical spectrometer or to increase the exposure time of a detector. The different optical characteristics may also be used to improve the calibration of the optical spectrometer. For example, the optical device assembly in the first configuration (i.e., in the standard mode) may be used to image a spectral peak to generate a detected image of the spectral peak, and the optical device assembly in the second configuration (i.e., in the enhanced mode) may be used to image the (same) spectral peak to generate a calibrated detected image of the spectral peak. A calibrated image correction function may be generated based on the predicted image correction function and a comparison of the predicted image of the spectral peak with the calibrated detected image of the spectral peak. Then, the calibrated image correction function is applied to the detected image of the spectral peak (i.e., the image obtained in the standard mode) to obtain a corrected image of the spectral peak. Thus, a calibrated detected image with increased resolution may be obtained to calibrate the spectral peak, where the calibrated image correction function may then be applied to the spectral peak imaged in the "standard" mode.

[0033] In some embodiments, the optical spectrometer is an optical emission spectrometer. Thus, imaging a spectral peak includes exciting a sample to be analyzed, where spectral emission from the sample is directed by the optical device assembly to a detector. In some embodiments, a plasma source may be used to excite the sample to be analyzed.

[0034] In some embodiments, the optical spectrometer can be an optical absorption spectrometer. For example, the optical absorption spectrometer can include a light source, such as a hollow cathode lamp (HCL) or a deuterium lamp. In some embodiments, the light from the light source can pass through the sample and be transmitted to the detector, where light of some wavelengths may be absorbed by the sample. Thus, in the case of optical absorption spectrometry, the spectral peaks to be analyzed correspond to the absorption peaks that can be extracted from the detected image. That is, in the detected image, the spectral peaks may appear as the absence of light (e.g., the minimum or valley in a graph of wavelength versus intensity). However, it should be understood that when the detected image is corrected and the absorption information is extracted from the corrected image, the method according to the present disclosure can still be applied.

[0035] According to a second aspect of the present disclosure, there is provided a controller for an optical spectrometer configured to analyze spectral peaks. The controller is configured to:

[0036] Cause the spectrometer to image the spectral peaks using the optical component assembly and the detector of the spectrometer to generate a detected image of the spectral peaks;

[0037] Apply an image correction function to the detected image of the spectral peaks to obtain a corrected image of the spectral peaks, wherein the image correction function increases the spectral power density of the spectral peaks in the corrected image of the spectral peaks; and

[0038] Extract information representing the spectral peaks from the corrected image of the spectral peaks.

[0039] Thus, it should be understood that the controller of the second aspect can be used to cause the optical spectrometer to perform the method of the first aspect. Thus, it should be understood that the controller of the second aspect can be combined with any optional features and associated advantages of the first aspect discussed above.

[0040] According to a third aspect, there is provided an optical spectrometer including: a light source configured to output light; a detector; an optical component assembly configured to direct the light from the light source to the detector; and a controller according to the second aspect. In some embodiments, the optical spectrometer can be an optical emission spectrometer, wherein the light source includes a sample that is excited to emit light (spectral emission), and the emitted spectral emission is detected by the detector.

[0041] According to a fourth aspect of the present disclosure, there is provided a computer program including instructions for causing the optical spectrometer of the third aspect to perform the steps of the method of the first aspect. Thus, it should be understood that the computer program of the fourth aspect can be combined with any optional features and associated advantages of the first aspect, second aspect, or third aspect discussed above.

[0042] According to a fifth aspect of the present disclosure, there is provided a computer-readable medium having stored thereon the computer program of the fourth aspect. Thus, it should be understood that the computer-readable medium of the fifth aspect may incorporate any optional features and associated advantages of the first, second, third, or fourth aspects discussed above. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention may be practiced in many ways and will now be described by way of example only and with reference to the accompanying drawings, in which:

[0044] - Figure 1 shows a schematic diagram of an optical spectroscopy system according to an embodiment of the present disclosure;

[0045] - Figure 2 shows a schematic diagram of an optical device assembly of an optical spectroscopy system according to an embodiment of the present disclosure;

[0046] - Figure 3 shows a schematic diagram of a two-dimensional detector according to an embodiment of the present disclosure;

[0047] - Figure 4 shows a block diagram of a method of analyzing spectral peaks according to an embodiment of the present disclosure;

[0048] - Figure 5a depicts a detected image of a spectral peak according to the present disclosure;

[0049] - Figure 5b depicts a corrected image of a spectral peak according to the present disclosure;

[0050] - Figure 5c shows, respectively, for Figure 5a and Figure 5b graphs of the intensity of spectral peaks extracted along the stages of the detector for the detected image and the corrected image;

[0051] - Figure 6 shows a block diagram of steps for obtaining a corrected image correction function according to an embodiment of the present disclosure;

[0052] - Figure 7 shows a block diagram of steps for obtaining a corrected image correction function according to another embodiment of the present disclosure;

[0053] - Figure 8 shows a graphical representation of a possible method for calculating a predicted image according to the present disclosure;

[0054] - Figure 9a depicts a detected image of another spectral peak according to the present disclosure;

[0055] -Figure 9b depicts Figure 9a an image of the calibrated PSF of the detected image of

[0056] - Figure 9c depicts Figure 9a a corrected image of the spectral peak of

[0057] - Figure 9d shows a graph of the intensity of the spectral peaks extracted along the stages of the detector for the detected image of Figure 9a and Figure 9c the corrected image, respectively;

[0058] - Figure 10a shows a view of the slit of the optical spectrometry system in a first position; and

[0059] - Figure 10b shows a further view of the entrance slit in a second position;

[0060] - Figure 11 shows a view of a part of the optical spectrometry system including the detector and a part of the optical device assembly including the slit. DETAILED DESCRIPTION

[0061] According to an embodiment of the present disclosure, an optical spectrometry system 10 is provided. The optical spectrometry system 10 is configured to perform an optical spectrometry method on a sample to generate a sample spectrum. The optical spectrometry system 10 can also analyze the spectral peaks of the sample spectrum according to the method of the present disclosure. A schematic view of the optical spectrometry system 10 is shown in Figure 1 . As shown in Figure 1 , the optical spectrometry system 10 includes a light source 11, an optical arrangement 12, a detector 13, a processor (μP) 14, a memory 15, and an input / output (I / O) unit 16.

[0062] In an Figure 1 embodiment, the light source 11 is a plasma source, such as an inductively coupled plasma (ICP) source. Thus, Figure 1The optical spectroscopy system 10 can be an optical emission spectroscopy system 10. In other embodiments, the light source 11 can be a furnace or any other high-temperature light source that generates an excited state substance suitable for use in optical emission spectroscopy. Alternatively, other optical spectroscopy systems 10 can provide a light source 11 suitable for the optical spectroscopy method being performed. The light source 11 can be configured to receive a sample to be analyzed using the optical spectroscopy system 10. For example, in the case where the light source 11 is a plasma source, the sample can be introduced into the plasma, where the sample interacts with the plasma. A sample in aqueous solution form can be directly introduced into the plasma source, while a solid sample can be introduced using, for example, laser ablation or vaporization.

[0063] In Figure 1 embodiments, the optical device assembly 12 can include a slit, an echelle grating, and a prism (and / or additional gratings) to produce a two-dimensional image of the light generated by the light source 11 (and the sample, if present). Examples of the optical device assembly 12 are discussed in more detail below. The two-dimensional image is formed on the detector 13. In such an arrangement, it should be understood that the optical device assembly 12 is configured to direct the radiation from the light source 11 to the detector such that the radiation is suitable for being detected by the detector 13.

[0064] In Figure 1 embodiments, the detector 13 can be a CCD (charge-coupled device) array. A typical CCD array can have at least approximately 1024×1024 pixels (1 million pixels). The CCD array can be arranged to produce spectral intensity values corresponding to the measured light amount of the echelle grating spectrum and to transfer the spectral values to the processor 14. Thus, the detector 13 can be a multi-channel detector configured to detect multiple different wavelengths. The detector 13 (as in Figure 1 embodiments) can be configured to detect a two-dimensional spectrum. In other embodiments, the detector 13 can be a CMOS or CID detector.

[0065] The processor 14 (controller) can include a commercially available microprocessor, etc. The memory 15 can be a suitable semiconductor memory and can be used to store instructions that allow the processor 14 to execute embodiments of the method according to the present disclosure. The processor 14 and the memory 15 can be configured to control the optical spectroscopy system 10 to execute the method according to the embodiments of the present disclosure. Thus, the memory 15 can include instructions that, when executed by the processor 14, cause the optical spectroscopy system 10 to execute the method according to the embodiments of the present disclosure.

[0066] The optical spectroscopy system 10 can be configured to generate a sample spectrum by introducing a sample into a light source 11. The light generated by the light source 11 interacts with the sample, and the spectral emission, which is a characteristic of the sample, is emitted by the sample. The spectral emissions from the light source 11 and the sample are guided by an optical arrangement 12 to a detector 13. The echelle grating (or other diffractive element) of the optical component 12 diffracts the spectral emissions of different wavelengths by different amounts, such that spectral peaks associated with each spectral emission are detected at different positions on the detector 13. Thus, it should be understood that the optical distortion introduced into the original image by the optical component (e.g., the image of the slit is distorted by downstream optical components) will vary with the wavelength of the spectral peak.

[0067] Figure 2 An example of the optical component 12 according to the present disclosure is shown. Figure 2 The optical component includes a slit 30, a first mirror 31, a second mirror 32, an echelle grating 33, a prism 34, a third mirror 35, and a lens 36. As Figure 2 shown, the light to be analyzed enters the optical component 12 via the slit 30. Thus, the detector 13 effectively images the slit 30, and the image of the slit incident on the detector 13 is distorted by the optical component 12. Thus, the image correction function according to the present disclosure attempts to recreate the image of the slit by inverting the distortion introduced by the optical component 12.

[0068] It should be understood that the various mirrors 31, 32, 35, the prism 34, and the lens 36 are well known to those skilled in the art and will not be discussed further herein. The echelle grating 33 is configured to diffract light so as to distribute the spectrum to be analyzed across the detector 13. It should be understood that Figure 2 the optical component 12 shown is only one example of the optical component 12 that can be used in combination with the present disclosure. That is, the method disclosed herein can be applied to any suitable optical component 12 of an optical spectrometer.

[0069] Figure 3 Shows Figure 1 a schematic diagram of the two-dimensional detector 13 of the embodiment of. Figure 3 The two-dimensional detector 13 of is formed by a pixel array, but each pixel is not shown separately in Figure 3 herein. Figure 3 Includes a schematic representation (dashed line) of order 20 of the light diffracted by the echelle grating and the prism that is imaged on the detector 13. Each order 20 corresponds to a different wavelength range, and the wavelength varies in the transverse direction along each order. For example, in Figure 3 the embodiment of, the wavelength of the diffracted light can increase from left to right along each order. The starting wavelength can also increase from order a) to order i). Figure 3Four detailed views of exemplary single - spectral emissions are also shown, which are imaged by groups of pixels of the detector at different positions on the detector. It should be understood that the peak shape of each spectral emission is different based on the optical aberration of the optical arrangement 12. In some embodiments, the optical arrangement 12 may cause light of a certain wavelength to be diffracted to a single position or multiple positions on the detector 13. Thus, in some embodiments, spectral emissions may appear at multiple positions on the detector 13.

[0070] The detector 13 is configured to output the recorded intensity of each pixel of the detector 13 to the processor 14 for further analysis.

[0071] Next, method 100 for analyzing spectral peaks obtained by an optical spectrometer will be referred to Figure 4 and described. This method will be described with reference to Figure 1 the optical spectrometry system 10, but it should be understood that method 100 is not limited to the above - mentioned optical spectrometry system 10. Alternatively, method 100 can be performed by any other processor provided with an image of the spectral peaks generated by the optical spectrometry system 10.

[0072] In Figure 4 step 101 shown, a detected image of the spectral peak is obtained. It should be understood that the detector 13 of the optical spectrometry system 10 is configured to image multiple spectral peaks simultaneously. Thus, in Figure 1 and Figure 4 embodiments, the full - frame image acquired by the detector is cropped to focus on a smaller region where spectral peaks are present. Thus, imaging the spectral peaks may include selecting a sub - region of the detector (corresponding to a sub - region of the detected image) to form the detected image of the spectral peak. For example, the sub - region of the detector may include an area of no more than 2500 pixels. In Figure 3 embodiments, the sub - region of the detector 13 that generates the detected image has an area of 40×40 pixels. In fact, a 40×40 pixel sub - region can be selected from the full - frame image to be used as the detected image for method 100.

[0073] Preferably, the spectral peak of interest is positioned towards the center of the detected image. In some embodiments, the center of gravity of the spectral peak of interest can be used to position the spectral peak towards the center of the detected image. Alternatively, the sub - region of the detector used to form the detected image can be manually selected by the user of the spectral system 10. For example, in some embodiments, the user can specify the wavelength of interest (e.g., the wavelength corresponding to the spectral emission of interest), and the processor 14 can select pixels along the stage of the detector 13 associated with the wavelength, where the sub - region of the detector 13 is selected based on the selected pixels. For example, the selected pixels may be located at the center of the sub - region.

[0074] In some embodiments, the detected image of the spectral peak may be subjected to one or more preprocessing steps. For example, a background correction step may be performed on the detected image of the spectral peak before applying an image correction function. The background correction step may include removing the background noise present in the detected image based on a measurement of the background noise in the image. Background correction of the detected image may be performed to remove spectral emissions associated with the plasma (e.g., the spectral emission of Ar in the case of using an Ar plasma) to reduce or remove shot noise from the detected image.

[0075] In step 103, an image correction function is applied to the detected image to generate a corrected image of the spectral peak. The image correction function is configured to increase the spectral power density of the spectral peak in the corrected image of the spectral peak. Thus, the correction process transfers power from the edges of the peak in the detected image towards the center, such that the corrected image of the spectral peak more accurately represents the spectral power of the spectral emission being imaged.

[0076] Further details of applying the image correction function in step 103 are provided below.

[0077] In step 105, information representing the spectral peak is extracted from the corrected image of the spectral peak. For example, in some embodiments, the processor 14 (as Figure 1 shown) may extract the intensity information of the pixels along one stage 20 of the detector 13 (as Figure 3 shown) to form a graph of intensity versus pixel number. Based on the known relationship between pixel position and wavelength, the processor can then infer the wavelength and intensity information about the spectral peak.

[0078] For example, Figure 5a , Figure 5b and Figure 5c show examples of the detected image, the corrected image, and the graphs of the spectral peaks extracted from each of these two images for comparison. As Figure 5c shown, graphs of the intensity of the spectral peak in each image versus pixel position (i.e., wavelength) are plotted. It should be understood according to Figure 5c that the image correction function increases the spectral power density of peak b). Thus, the method of analyzing the spectral peak more accurately characterizes the intensity associated with the spectral emission. It should also be understood that, relative to the detected peak (peak a) with a full width at half maximum (FWHM) of 3.38 pixels, the FWHM of the corrected peak (peak b) is reduced to 1.84 pixels.

[0079] Next, the steps of applying the image correction function 103 will be discussed in more detail.

[0080] Figure 6A block diagram showing a possible image correction function according to an embodiment of the present disclosure. Figure 6 The method can be performed by Figure 1 and Figure 3 an optical spectroscopy system of

[0081] In Figure 6 the embodiment of Figure 1 as shown, the processor 14 assumes that the detected image is a degraded representation of the image of the slit 30. The degradation can be represented by a point spread function (PSF). Thus, the PSF can be used to describe the response of the imaging system (optical component 12) to a point light source. By determining the PSF representing the image degradation of the optical component 12, deconvolution can be used to deblur the detected image to generate a corrected image.

[0082] For the optical component 12, ideal models of various optical components (e.g., mirrors 31, 32, 35, echelle grating 33, prism 34, and lens 36) are well known to those skilled in the art. Thus, a person skilled in the art can generate an initial theoretical model of the optical component to obtain the predicted PSF of the optical component. Thus, in step 121, the controller obtains the predicted image correction function (predicted PSF) of the spectral peak based on the optical characteristics of the spectrometer's optical component and the detector. For example, an optical simulation computer program (e.g., Zemax) can be used to generate the predicted PSF. In the case where there is a diffraction element (e.g., echelle grating 33) in the optical component 12, the predicted PSF can depend on the wavelength of the spectral peak being analyzed. Thus, the obtained predicted PSF can depend on the detector position of the spectral peak. For example, the predicted PSF can be obtained based on the detector position at the center of the detected image.

[0083] In step 123, the controller 14 generates a predicted image of the spectral peak based on the predicted image correction function. For example, Figure 2 in the embodiment of

[0084] where the slit 30 is provided, knowledge of the optical properties of the slit 30 and the predicted PSF can be used to generate the predicted image by convolving the predicted PSF with the prediction of the original image (in this case, the theoretical image (O) of the slit 30). Figure 6In some embodiments, a hashing algorithm can be used to compare the two images. In some embodiments, a perceptual image hashing algorithm or a wavelet image hashing algorithm can be used. The hashing algorithm outputs a numerical value indicating the difference between the predicted image and the detected image.

[0085] For example, in some embodiments, the hashing algorithm can first scale the image to grayscale, e.g., an 8×8 image (image hashing with other pixel sizes can be used). Then, an analysis can be performed on each of the 64 pixels in order to assign a binary value of 1 or 0 to each pixel. These 64 bits form the output of the image algorithm (image hash). The analysis performed on each pixel depends on the type of image hashing algorithm applied. For example, in the average image hashing algorithm, if the pixel intensity is greater than or equal to the average pixel intensity of the image, a binary value of 1 is output, otherwise a binary 0 is output. In the perceptual image hashing algorithm, a discrete cosine transform can be applied to the 8×8 image, where binary values of 1 and 0 are assigned based on each pixel being respectively greater than at least the average frequency or less than the average frequency. For the wavelet image hashing algorithm, a discrete wavelet transform can be applied to the 8×8 image, and then binary values are assigned based on the transformed image.

[0086] The image hashes generated for each of the predicted image and the detected image can be compared in order to evaluate the difference between the images. Thus, a bit-by-bit comparison of the image hash values (e.g., 64-bit binary numbers) provides an assessment of image similarity. Thus, the image hash comparison provides a numerical value indicating the difference between the two images (the number of different bits, or expressed as a percentage of the total number of bits in the image hash) (a higher value indicates a greater difference between the images).

[0087] In step 127, a calibrated image correction function (calibrated PSF) is generated based on the predicted image correction function (predicted PSF) of the optical device assembly 12 and the comparison of the predicted image of the spectral peak with the detected image of the spectral peak. For example, the output of the hashing algorithm can be used to calibrate the predicted PSF to more closely reflect the image degradation of the optical device assembly 12. Thus, the predicted PSF can be calibrated based on the output of the image hashing algorithm (or other image comparison algorithms) to produce a calibrated PSF.

[0088] In Figure 6 some embodiments, the inventors of the present invention have recognized that one source of error between the predicted PSF of the optical device assembly 12 (i.e., the PSF generated according to the theoretical model of the optical components) and the actual PSF can be characterized as a noise function. Therefore, by convolving the predicted PSF with the noise function, a calibrated PSF can be generated. In Figure 6In an embodiment, the noise function can be a low-rank Poisson noise, which represents the photon noise present in the optical device assembly 12. In some embodiments, the noise function can be represented by a full-one block matrix, further modified by Poisson noise terms. For a detected image with a relatively large amount of shot noise / Poisson noise, including Poisson noise terms in the noise function allows the calibrated PSF to more accurately reflect the optical properties of the optical spectrometer. By incorporating Poisson noise terms, the noise function can conform to the Poisson distribution:

[0089]

[0090] The noise function can be generated based on a comparison of the predicted image correction function and the detected image. Thus, a calibrated image correction function can be obtained by convolving the predicted PSF with the noise function generated based on this comparison, where the calibrated PSF more closely matches the actual PSF of the optical device assembly (relative to the predicted PSF).

[0091] Once the calibrated image correction function is generated, in step 129, a corrected image of the spectral peak is obtained using the calibrated image correction function. In Figure 6 an embodiment (where the calibrated image correction function is the PSF (calibrated PSF)), the corrected image can be obtained by deconvolving the detected image of the spectral peak using the calibrated PSF to obtain the corrected image of the spectral peak. Alternatively, other image correction algorithms that utilize the PSF to correct the image can be applied to obtain the corrected image. For example, the Richardson-Lucy algorithm can be applied using the corrected PSF to obtain the corrected image.

[0092] In some embodiments, it may be preferable to iteratively calculate the calibrated image correction function (calibrated PSF). Thus, Figure 7 FIG. shows a block diagram of an iterative method for step 103 of applying the image correction function.

[0093] In Figure 7 the iterative method, the comparison of the predicted image and the detected image can prompt the processor 14 to determine whether the predicted PSF should be further iterated (step 126). Thus, in the case where the comparison output in step 125 indicates a numerical value of the difference between the predicted image and the detected image, the processor 14 can compare this numerical value with a predetermined threshold. In the case where the numerical value exceeds the predetermined threshold, the method proceeds to step 128 to iterate the predicted image correction function.

[0094] In step 128, the predicted PSF can be iterated in a manner similar to step 127 of calculating the calibrated PSF discussed above. Thus, the predicted PSF (P) can be convolved with a noise function N of variable sizem Perform a convolution to iterate the predicted PSF (P). In some embodiments, the noise function N m is an all - one block matrix.

[0095] Thus, in Figure 7 embodiments, the predicted PSF (P1) of the first iteration can be calculated by taking the initial predicted PSF (P0) and convolving it with an all - one block matrix J2 of order 4 (sometimes called an identity matrix, but do not confuse it with the unit matrix) (i.e., a 2×2 identity matrix). That is, the first iteration of the predicted PSF can be calculated as:

[0096] P1 = P0 * J2. (1)

[0097] In the above equation 1, the symbol * is understood to represent the convolution of the predicted PSF P0 with the identity matrix (a matrix) J2. For each iteration of the predicted PSF P n the order of the square identity matrix increases (i.e., an (n + 1)x(n + 1) square matrix). That is, the order of the square identity matrix for each iteration is (n + 1) 2 . Thus, a noise function of variable size is convolved with the initial prediction of the PSF P0 in order to generate a new predicted PSF. Therefore, for the nth iteration of the predicted PSF (P n ), the predicted PSF can be calculated as:

[0098] P n = P0 * J n+1

[0099] In the above example, an all - one matrix is used to modify the predicted PSF (P0). In fact, the iterative method attempts to adapt the predicted PSF to the actual optical distortion present in the optical device assembly by assuming that the difference can be characterized by a noise function. In Figure 7 embodiments, it is assumed that the noise function is represented by an image transform equivalent to box blur. Although the above example uses a box blur noise function, other image correction matrices can be used in addition to or as an alternative to the all - one matrix (box blur matrix). For example, alternative noise functions can include: Gaussian blur, unsharp masking, or other similar techniques. It should be understood that the application of any function can also include performing image normalization on the function. Figure 8 A figure is provided that graphically shows how to determine the predicted image to compare with the detected image (D).

[0100] Iterations can be performed until the difference between the predicted image and the detected image (calculated in step 125, as described above for Figure 6falls below a predetermined threshold. That is, the predicted image and the detected image are similar enough to indicate that the currently predicted PSF (P n ) is suitable for use as a calibrated PSF. Thus, in Figure 7 step 127, the calibrated image correction function can be determined based on the current iteration of the predicted PSF (P n ). That is, in Figure 7 the embodiment, the calibrated image correction function is the current iteration of the predicted PSF (P n ).

[0101] Once the calibrated image correction function is determined, a corrected image is generated in step 129. Step 129 can be performed as described above with respect to Figure 6 for example, using Richardson-Lucy restoration to obtain the corrected image.

[0102] As an example, Figure 9a shows a detected image of a spectral peak obtained by an optical spectrometer. Figure 9b shows an image of a predicted point spread function obtained by an optical spectrometer. By performing the above method (e.g., Figure 7 the method), a calibrated PSF is obtained, and the calibrated PSF is used to generate a corrected image using Richardson-Lucy restoration, as Figure 9c shown. A graph of the intensity of the spectral peak along the stages of the detector can be extracted from the corrected image, as Figure 9d shown. Figure 9d Equivalent information extracted from the detected image for comparison is also shown. As can be seen in Figure 9d , the peak intensity of the corrected peak has increased by 3.48 times, and the FWHM of the peak has decreased from 2.99 pixels to 1.96 pixels. According to Figures 9a to 9d it should also be understood that there is a second lower-intensity peak in the detected image. The method of analyzing the spectral peak has improved the FWHM of the spectral peak, such that the separation between the two peaks has increased. Thus, it should be understood that the method of the present disclosure may be particularly beneficial for analyzing at least partially overlapping spectral peaks.

[0103] It should be understood that other methods can also be utilized to obtain the calibrated image correction function. Specifically, methods related to Figure 7An iterative method similar to the iterative method disclosed in [reference]. For example, a blind deconvolution process can be used to obtain a calibrated image correction function from a predicted image correction function. In such a blind convolution process, a predicted image is calculated using the predicted PSF and the object image (O). Then, the resulting predicted image is compared with the detected image. Based on this comparison, corrections to the object image (o) and the predicted PSF are calculated, and this correction is used to generate a new predicted image. The same correction is applied to the predicted PSF, thereby generating a new PSF in each iterative estimate.

[0104] In some embodiments, an image regularization process can be used in step 103 to apply the image correction function. That is, the image degradation process can be represented in the following form:

[0105] y = Hx + n

[0106] where y is the detected image, x is the original (undistorted image), H is the linear degradation operator, and n is the noise vector. The objective function for x is defined based on different sets of constraints. Then, a solution is obtained by minimizing the objective function (image correction function) with respect to x. Thus, an image correction function can be obtained through an image regularization process to determine the corrected image y.

[0107] In some embodiments, the above method can also be performed by an optical absorption spectrometer. Specifically, for absorption spectroscopy, the absorption spectral peaks may exist as minima in the detected image. However, a similar image correction method can be applied to more accurately extract information from the absorption spectrum.

[0108] Therefore, according to the present disclosure, a method for analyzing spectral peaks is provided. It should be understood that the methods discussed above can be performed by the controller 14 of an optical spectrometer.

[0109] In Figure 6 and Figure 7 of the above embodiments, in step 123, the prediction of the original image for generating the predicted image is the theoretical image (O) of the slit 30. Thus, the theoretical image (O) of the slit can be defined by one or more optical characteristics of the slit (e.g., the width of the slit 30). For example, in Figure 10a a schematic diagram of the slit 30 is provided. The slit 30 is defined by two opposite (longitudinal) edges 38a, 38b of the slit member 30a. The two edges 38a, 38b are disposed on opposite sides of the slit 30 and extend parallel to each other such that the width of the slit 30 is constant along the length of the slit.

[0110] It should be understood that in other embodiments, other optical components and associated optical characteristics can be used to define the original image. For example, Figure 10bA diagram showing the pinhole slit 39. The pinhole slit 39 is a circular opening formed in the pinhole member 39a. The circular opening has a constant (known) diameter such that the theoretical image of the pinhole slit 39 can be defined by the diameter of the pinhole slit 39. In some embodiments, the diameter of the pinhole slit can be from about 1 μm to 3 μm. Preferably, as Figure 10b shown, the diameter of the pinhole slit is less than the width of the slit 30.

[0111] In some embodiments, the optical components used to define the optical characteristics of the optical device assembly 12 can be changed in order to improve the accuracy of the calibrated image correction function. As Figure 10a , Figure 10b and Figure 11 shown, the optical device assembly 12 can include the slit 30 and the pinhole slit 39. As according to Figure 10a and Figure 10b it should be understood that the pinhole slit 39 may be capable of moving between a first position (standard mode) and a second position (enhanced mode), in the first position, the slit 30 is unobstructed, in the second position, the pinhole slit 39 obscures the slit such that light is transmitted through the pinhole slit 39. Thus, in the second position, the center of the pinhole slit 39 is aligned with the center of the slit 30. Thus, the optical device assembly 12 can operate in standard mode or enhanced mode, wherein the optical characteristics vary between two different settings.

[0112] The enhanced mode of the optical device assembly 12 can be used to improve the generation of the calibrated image correction function. When operating in the enhanced mode, the optical device assembly 12 exposes the detector 14 to light via the pinhole slit 39 to obtain a calibrated detected image (i.e., the pinhole slit 39 is in the second position). Since the light intensity is lower, the time (exposure time) taken to obtain the calibrated detected image may increase relative to the exposure time of the image obtained in the standard mode. Then, based on the predicted image correction function (for the optical device assembly 12 including the pinhole slit 39) and the comparison of the predicted image of the spectral peak with the calibrated detected image of the spectral peak, the calibrated image correction function can be determined following the above method. Due to the longer exposure time, the calibrated image correction function can more accurately determine the required correction to the detected image.

[0113] Then, the calibrated image correction function determined in the enhanced mode can be applied to the detected image obtained in the standard mode. That is, after obtaining the calibrated image correction function, the pinhole slit 39 can be moved back to the first position, and the detected image can be obtained in the standard mode (using the slit 30). Then, the calibrated image correction function can be applied to the detected image of the spectral peak to obtain a corrected image of the spectral peak.

Claims

1. A method for analyzing spectral peaks obtained by an optical spectrometer, the method comprising: imaging the spectral peaks using an optical component assembly and a detector of the optical spectrometer to generate a detected image of the spectral peaks; applying an image correction function to the detected image of the spectral peaks to obtain a corrected image of the spectral peaks, wherein the image correction function increases the spectral power density of the spectral peaks in the corrected image of the spectral peaks; and extracting information representing the spectral peaks from the corrected image of the spectral peaks.

2. The method according to claim 1, wherein applying an image correction function to the detected image of the spectral peaks comprises: obtaining a predicted image correction function of the spectral peaks based on optical characteristics of the optical component assembly and the detector of the spectrometer; generating a predicted image of the spectral peaks based on the predicted image correction function; and generating a calibrated image correction function based on the predicted image correction function and a comparison between the predicted image of the spectral peaks and the detected image of the spectral peaks, wherein the calibrated image correction function is applied to the detected image of the spectral peaks to obtain a corrected image of the spectral peaks.

3. The method according to claim 2, wherein obtaining a corrected image of the spectral peaks using the calibrated image correction function comprises deconvolving the detected image of the spectral peaks using the calibrated image correction function to obtain a corrected image of the spectral peaks.

4. The method according to claim 2 or claim 3, wherein the calibrated image correction function is calculated by convolving the predicted image correction function with a noise function, wherein the noise function is calculated based on the comparison between the predicted image of the spectral peaks and the detected image of the spectral peaks.

5. The method according to claim 4, wherein the noise function includes a matrix of all ones.

6. The method according to any one of claims 2 to 5, wherein the calibrated image correction function is calculated iteratively until the difference between the predicted image of the spectral peaks and the detected image of the spectral peaks is below a predetermined threshold.

7. The method according to claim 6, wherein the difference between the predicted image of the spectral peaks and the detected image of the spectral peaks is calculated using an image hashing algorithm.

8. The method according to any one of claims 6 to 7, wherein in each iteration of the calculation of the calibrated image correction function, the order of the noise function is increased.

9. The method according to any one of claims 1 to 8, wherein imaging the spectral peaks comprises selecting a sub-region of the detector to form the detected image of the spectral peaks; and wherein the predicted image correction function and the predicted image are generated for the selected sub-region of the detector.

10. The method according to claim 9, wherein The optical device assembly includes a diffraction grating configured to diffract spectral peaks of different wavelengths to different sub-regions of the detector.

11. The method according to any one of claims 1 to 10, wherein The spectrometer is an optical emission spectrometer.

12. The method according to any one of claims 1 to 11, wherein The detected image of the spectral peak includes a first spectral peak and a second spectral peak, wherein the first spectral peak and the second spectral peak overlap.

13. The method according to any one of claims 1 to 12, wherein The predicted image correction function is a predicted point spread function; and The calibrated image correction function is a calibrated point spread function.

14. The method according to any one of claims 1 to 13, wherein The optical device assembly includes a slit and a diffraction element configured to distribute the spectral peak to be analyzed across the detector.

15. The method according to any one of claims 1 to 14, wherein The optical device assembly is capable of operating in a first configuration having a first set of optical characteristics or in a second configuration having a second set of optical characteristics.

16. The method according to claim 15, which depends on claim 2, wherein The optical device assembly in the first configuration is used to image the spectral peak to generate the detected image of the spectral peak; and The optical device assembly in the second configuration is used to image the spectral peak to generate a calibrated detected image of the spectral peak, wherein The calibrated image correction function is generated based on the predicted image correction function and the comparison between the predicted image of the spectral peak and the calibrated detected image of the spectral peak; and The calibrated image correction function is applied to the detected image of the spectral peak to obtain a corrected image of the spectral peak.

17. A controller for an optical spectrometer configured to analyze spectral peaks, wherein the controller is configured to: Cause the spectrometer to image a spectral peak using an optical device assembly and a detector of the spectrometer to generate a detected image of the spectral peak; Apply an image correction function to the detected image of the spectral peak to obtain a corrected image of the spectral peak, wherein the image correction function increases the spectral power density of the spectral peak in the corrected image of the spectral peak; and Extract information representing the spectral peak from the corrected image of the spectral peak.

18. An optical spectrometer, the optical spectrometer includes A light source configured to output light; A detector; An optical device assembly configured to direct light from the light source to the detector; and The controller according to claim 17.

19. A computer program comprising instructions for causing the controller according to claim 17 or the optical spectrometer according to claim 15 to perform the method according to any one of claims 1 to 16.

20. A computer-readable medium having stored thereon the computer program according to claim 19.

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