Systems and methods for improved quantification with polymer chromatography

Abstract

A method for determining a chemical or structural property of a liquid sample includes irradiating a flow cell containing a liquid mixture of the sample dissolved in a solvent with a first light beam and a second light beam at a first time step and a second time step; detecting the first light beam and the second light beam after the first light beam and the second light beam have passed through the flow cell at the first time step and the second time step to determine a first interferogram and a second interferogram; performing a Fourier transform on the first interferogram and the second interferogram to determine first single beam data and second single beam data; determining a first signal value and a second signal value of the first single-beam data and the second single-beam data at a first frequency; adjusting each signal value of the second single beam data based on a ratio of the second signal value to the first signal value to determine adjusted second single beam data; and determining a characteristic of the sample based on the adjusted second single beam data.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. application serial number 63 / 405,136, filed on September 9, 2022, entitled “SYSTEMS AND METHODS FOR IMPROVED QUANTIFICATION WITH POLYMERCHROMATOGRAPHY,” the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present specification relates to polymer chromatography, and more particularly, to systems and methods for improved quantitation using polymer chromatography. Background Art

[0004] Polymer chromatography is a technique that can be used to measure the chemical or structural properties of polymers, such as molecular weight distribution (MWD), chemical composition distribution (CCD), or component concentrations of a polymer sample. In particular, polymer chromatography can be used to measure the MWD, CCD, or other chemical or structural properties of polyolefins such as polyethylene. For example, polyolefins such as polyethylene and polypropylene contain only hydrogen and carbon atoms. Other types of polyolefins, such as ethylene vinyl acetate copolymers, have other types of atoms, such as oxygen atoms in addition to hydrogen and carbon atoms.

[0005] One type of polymer chromatography is gel permeation chromatography (GPC), which is a type of size exclusion chromatography (SEC). In GPC, a sample solution is dissolved in a solvent or solvent mixture. The sample solution is injected and passed through one or more elution columns including porous particles. Larger sized polymer chains will only diffuse in and out of larger pores and will elute from the column faster, while smaller sized polymer chains will diffuse in and out of larger and smaller pores and elute from the column more slowly. Therefore, GPC separates a polymer into its components based on the hydrodynamic size of the components of the polymer.

[0006] As the polymer components elute from the column, a detector can be used to detect these components. One type of detection mechanism that can be used is Fourier transform infrared spectroscopy (FTIR). The FTIR system projects a beam of light consisting of multiple wavelengths onto the sample. The system then measures the sample's absorbance of the beam. This process is repeated with multiple beams containing different combinations of wavelengths. By analyzing the data using Fourier analysis and apodization functions, the sample's absorbance spectrum (or transmittance spectrum) can be calculated, which can be used to determine various chemical or structural properties of the sample's constituent components.

[0007] As described above, flow cell FTIR can be used to determine the properties of liquid samples, such as dissolved samples as part of a GPC system. In particular, the dissolved sample can pass through the flow cell after passing through one or more columns. Then, light can be projected on the flow cell to measure the absorbance properties of the liquid. However, in order to be used for GPC, polyolefins can only be dissolved in a few solvent types at high temperatures, such as 1,2,4-trichlorobenzene (TCB), o-dichlorobenzene (ODCB) or xylene. These solvents absorb many of the same light frequencies as polyolefins. Therefore, when analyzing polyolefins, typical FTIR detectors will produce data with a low signal-to-noise ratio (SNR), thereby reducing their effectiveness for GPC. For example, tetrachloroethylene (TCE) is transparent at the CH frequency, but has poor solubility for many high-density polyethylenes and is therefore not widely used. Filter-based IR detectors (e.g., PolymerChar TM ), which lacks the flexibility of light frequency selection for analysis compared to FTIR. Therefore, there is a need for an improved flow cell FTIR detector with high SNR when analyzing polyolefins. Summary of the invention

[0008] In one embodiment, a method for determining one or more chemical or structural characteristics of a liquid sample mixture is provided. The method may include irradiating a flow cell with a first plurality of light beams at a first time step and a second plurality of light beams at a second time step, the flow cell containing a liquid mixture of the sample dissolved in a solvent. The method may also include detecting the first plurality of light beams after the first plurality of light beams have passed through the flow cell at the first time step to determine a first interference pattern, and detecting the second plurality of light beams after the second plurality of light beams have passed through the flow cell at the second time step to determine a second interference pattern. The method may also include performing a Fourier transform on the first interference pattern to determine first single beam data associated with the liquid mixture, and performing a Fourier transform on the second interference pattern to determine second single beam data associated with the liquid mixture. The method may also include: determining a first signal value of the first single beam data at a first frequency; determining a second signal value of the second single beam data at the first frequency; determining a first ratio of the second signal value to the first signal value; and adjusting each signal value of the second single beam data based on the first ratio to determine adjusted second single beam data. The method may also include determining the one or more chemical or structural properties of the sample based on the adjusted second single beam data.

[0009] In another embodiment, a method may include: receiving a first data set including a series of data points; performing a first denoising algorithm on the first data set to obtain a first denoised data set; calculating a first derivative of the first denoised data set and a second derivative of the first denoised data set; and calculating a relative information density at each data point of the first denoised data set. The relative information density may include the absolute value of the first derivative divided by the maximum absolute value of the first derivative across all of the data points of the first denoised data set and the absolute value of the second derivative divided by the sum of the maximum absolute value of the second derivative across all of the data points of the first denoised data set. The method may also include: enhancing the first data set based on the relative information density and an enhancement factor by inserting data points into the first data set to obtain an enhanced data set; performing the first denoising algorithm on the enhanced data set to obtain an enhanced denoised data set; and removing data points corresponding to the data points added to the first data set from the enhanced denoised data set to obtain a second denoised data set.

[0010] In another embodiment, a system for determining one or more chemical or structural characteristics of a liquid sample may include: a light source; one or more elution columns; a flow cell containing a liquid mixture of the sample dissolved in a solvent after passing through the one or more elution columns; and a detector. The light source may illuminate the flow cell with a first plurality of light beams at a first time step and a second plurality of light beams at a second time step. The detector may detect the first plurality of light beams after the first plurality of light beams have passed through the flow cell at the first time step to determine a first interference pattern, and detect the second plurality of light beams after the second plurality of light beams have passed through the flow cell at the second time step to determine a second interference pattern. The detector may also perform a Fourier transform on the first interference pattern to determine a first single beam data associated with the liquid mixture, and perform a Fourier transform on the second interference pattern to determine a second single beam data associated with the liquid mixture. The detector can also determine a first signal value of the first single beam data at a first frequency; determine a second signal value of the second single beam data at the first frequency; determine a first ratio of the second signal value to the first signal value; adjust each signal value of the second single beam data based on the first ratio to determine adjusted second single beam data; and determine the one or more chemical or structural properties of the sample based on the adjusted second single beam data. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The embodiments illustrated in the drawings are illustrative and exemplary in nature and are not intended to limit the present disclosure. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, in which similar structures are indicated by similar reference numerals and in which:

[0012] Figure 1 schematically depicts an exemplary FTIR detector according to one or more embodiments shown and described herein;

[0013] Figure 2 schematically depicts another exemplary FTIR detector according to one or more embodiments shown and described herein;

[0014] Figure 3 Depicted is a schematic diagram of a system according to one or more embodiments shown and described herein. Figure 1 and Figure 2 A schematic diagram of a computing device;

[0015] Figure 4 Schematically depicts a Figure 3 a plurality of storage modules of a computing device;

[0016] Figure 5A Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 An exemplary interferogram generated by an FTIR detector;

[0017] Figure 5B Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 An exemplary single beam signal generated by an FTIR detector;

[0018] Figure 5C Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 Example transmittance generated by an FTIR detector;

[0019] Figure 6 Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 An exemplary chromatogram generated by an FTIR detector;

[0020] Figure 7 Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 Example transmittance data recorded by an FTIR detector;

[0021] Fig. 8A Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 An exemplary chromatogram generated by an FTIR detector;

[0022] Figure 8B Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 An exemplary roughly de-noised signal generated by an FTIR detector;

[0023] Figure 8C Depicted Figure 8B The first derivative of an exemplary coarse denoised signal of ;

[0024] Fig.8D Depicted Figure 8B The second derivative of an exemplary coarse denoised signal of ;

[0025] Fig.9A Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 Example relative information density generated by an FTIR detector;

[0026] Fig. 9B Depicted Fig.9A Another view of the relative information density of

[0027] Fig. 9C Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 An exemplary enhanced signal generated by an FTIR detector of

[0028] Fig.9D Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 An exemplary de-noised enhanced signal generated by an FTIR detector;

[0029] Fig.9E Depicts a method that can be implemented according to one or more embodiments shown and described herein. Figure 1 and Figure 2 An exemplary final de-noised signal generated by an FTIR detector;

[0030] Fig.10 depicts a flowchart of an exemplary method for performing internal normalization according to one or more embodiments shown and described herein; and

[0031] Fig.11 1 depicts a flow chart of an exemplary method of performing denoising according to one or more embodiments shown and described herein. DETAILED DESCRIPTION

[0032] Embodiments disclosed herein describe systems and methods for improving quantification with polymer chromatography. In particular, embodiments disclosed herein describe a flow cell FTIR detector capable of recording data with improved SNR suitable for measuring properties of polyolefins as part of a polymer chromatography experiment.

[0033] Now turning to the attached figure, Figure 1 An exemplary flow cell FTIR detector 100 is shown according to embodiments described herein. Figure 1 The flow cell FTIR detector 100 includes an emission source 102 , a beam splitter 104 , a fixed mirror 106 , a movable mirror 108 , a first focusing mirror 110 , a flow cell 112 , a second focusing mirror 114 , a detector 116 , an elution column 125 , and a computing device 130 .

[0034] The emission source 102 can output a light beam 118 containing multiple component wavelengths. In the illustrated example, the emission source 102 outputs infrared (IR) light. However, in other examples, the emission source 102 can output other types of light.

[0035] IR light 118 emitted by emission source 102 may be projected onto beam splitter 104. Beam splitter 104 may reflect a first portion of light 118 as light beam 120 toward fixed mirror 106, and transmit and project a second portion of light 118 as light beam 122 onto movable mirror 108. In the illustrated example, the beam splitter directs half of light 118 toward fixed mirror 106 and half of light 118 toward movable mirror 108. However, in other examples, the beam splitter may split light 118 at other ratios.

[0036] The fixed mirror 106 can be positioned at a fixed distance from the beam splitter 104. Light 120 can be projected onto the fixed mirror 106 and reflected back toward the beam splitter 104. The movable mirror 108 can be positioned at a variable distance from the beam splitter 104. Light 122 can be projected onto the movable mirror 108 and reflected back toward the beam splitter 104. By changing the position of the movable mirror 108 relative to the beam splitter 104 during operation of the FTIR detector 100, the phase of the light 122 when it is projected onto the beam splitter 104 can be changed.

[0037] When reflected beams 120 and 122 are projected onto beam splitter 104, the reflected beams may be combined into beam 124 and directed toward detector 116. By changing the position of movable mirror 108 during operation of FTIR detector 100, the interference pattern between reflected beam 120 and reflected beam 122 may be varied, thereby producing an interference pattern when combined beam 124 is projected onto detector 116.

[0038] The first focusing mirror 110 can focus the light 124 onto the flow cell 112. When measuring the molecular weight of a sample, the larger the flow cell 112, the wider the peak of the signal detected by the detector 116 will be. However, as this peak becomes wider, the separation efficiency decreases, thereby limiting the spectral resolution of the data and / or the separation resolution of the polymer chromatography. Therefore, it may be desirable to have a smaller flow cell 112. Therefore, the first focusing mirror 110 focuses the light 124 onto the flow cell 112 so that the light is focused on the sample in the flow cell 112.

[0039] As discussed above, the flow cell 112 can be used to hold a sample to be analyzed. In particular, the sample to be analyzed can be dissolved in a solvent, and the combined mixture can flow through the flow cell 112 during operation of the flow cell FTIR detector 100. As discussed above, when analyzing data, using a smaller flow cell results in better separation efficiency. As discussed above, when the sample placed in the flow cell 112 is a polyolefin, the SNR of the detected data tends to be less than that of other samples due to the type of solvent in which the polyolefin can be dissolved. In addition, using a smaller flow cell 112 (e.g., having a shorter optical path length) means using a smaller volume of liquid, which may also result in a lower SNR for the detected data. In conventional FTIR detectors, the SNR may be too low to effectively analyze the chemical or structural characteristics of polyolefins in chlorinated solvents such as TCB or ODCB. However, the denoising techniques disclosed herein allow for the detection of denoising data in a more accurate manner. Figure 1 Polyolefins in chlorinated solvents such as TCB or ODCB can be directly analyzed in the FTIR detector 100 of the present invention. In some examples, the denoising techniques disclosed herein can be used with non-chlorinated solvents (e.g., xylene, decalin, and fatty alcohols) that can dissolve polyolefins, with improved polymer chromatography quantification.

[0040] Figure 2 Another exemplary flow cell FTIR detector 200 is shown. The construction of the flow cell FTIR detector 200 is similar to Figure 1 The flow cell FTIR detector 100 is constructed as described above, except that a flow cell 113 is used instead of the flow cell 112. In particular, Figure 2 The flow cell 113 is connected to a plurality of different elution columns. Thus, the valve 202 can be controlled to allow liquid to flow through the flow cell 113 from a particular elution column. Figure 2 In the example of FIG. 1 , valve 202 is connected to three elution columns 125, 126, 127. However, in other examples, valve 202 can be connected to any number of elution columns. Figure 2In the example of FIG. 1 , valves can individually control the flow of liquid from each of the elution columns 125, 126, 127 through the flow cell 113. Thus, different liquids can flow through the flow cell 113 at different times during an FTIR experiment.

[0041] Return to reference Figure 1 , the second focusing mirror 114 can focus the light 124 passing through the flow cell 112 onto the detector 116. In an embodiment, the first focusing mirror 110 and the second focusing mirror 114 can be placed at the same distance from the flow cell 112 on opposite sides. In addition, the first focusing mirror 110 and the second focusing mirror 114 can have the same focal length. However, in some examples, the first focusing mirror 110 and the second focusing mirror 114 can have different focal lengths and / or can be positioned at different distances from the flow cell 112.

[0042] The detector 116 can detect the light 124 that passes through the second focusing mirror 114. In particular, when the moving mirror 108 is moving, the detector 116 can measure the intensity of the light 124 at multiple time steps. Therefore, the detector 116 can measure the intensity of the light 124 caused by various interference patterns between the reflected light 120 and the reflected light 122 to generate an interference pattern. Then, the detector 116 can perform a Fourier transform on the interference pattern to obtain a single beam spectrum. By comparing the first single beam spectrum obtained when the sample is present in the flow cell 112 with the background single beam spectrum (for example, when only the solvent is present in the flow cell 112 and there is no sample), the absorbance spectrum of the sample can be determined. The chemical or structural characteristics (for example, MWD and CCD) of the sample can be determined based on the determined absorbance spectrum, as discussed in further detail below.

[0043] exist Figure 1 In the example of , a single detector 116 is shown. However, in other examples, multiple detectors can be used. For example, in addition to the FTIR detector, a light scattering detector can also be used. In these examples, the ratio between the data captured by the multiple detectors can be determined in order to measure additional characteristics of the sample, such as the absolute Mw or viscosity of the eluent.

[0044] Elution column 125 can be used to separate the components of the polymer sample to be analyzed. In the illustrated example, elution column 125 includes gel particles as a part of the GPC system. However, in other examples, elution column 125 can include other materials and can use other mechanisms to separate polymer samples. In the illustrated example, when the liquid mixture containing the liquid polymer sample dissolved in the solvent passes through elution column 125, the components of the sample are separated based on their hydrodynamic size. In particular, the larger polymer chain is eluted by column 125 before the smaller polymer component. Therefore, when the different components of the sample pass through flow cell 112 at different times, time series data can be determined. Then the chemical or structural characteristics of the sample can be determined based on the time series data, as discussed in further detail below. In the illustrated example, when the liquid mixture containing the liquid polymer sample dissolved in the solvent mixture passes through elution column 125, the components of the sample are separated based on their hydrodynamic size, as discussed above.

[0045] Elution column 125 can be used to separate the components of the polymer sample to be analyzed. In the illustrated example, elution column 125 includes surface inert particles. However, in other examples, elution column 125 can include other materials and can use other mechanisms to separate polymer samples. For example, separation based on crystallization can be used to obtain chemical composition distribution, and crystallization elution fractionation (CEF) or temperature rise elution fractionation (TREF) can be used to perform chemical composition distribution analysis (CCD). In some examples, interaction chromatography can be used, such as thermal gradient interaction chromatography or solvent gradient liquid chromatography. In the illustrated example, when the liquid mixture containing the liquid polymer sample dissolved in the solvent passes through elution column 125 and the components of the sample are separated based on their hydrodynamic size, time series data can be determined when different components of the sample pass through flow cell 112 at different times. Then the chemical or structural characteristics of the sample can be determined based on the time series data, as discussed in further detail below.

[0046] The elution column 125 can be used to separate components of a polymer sample that has a certain amount of cross-linking (e.g., insoluble in the solvent). For example, the elution column 125 can separate a cross-linked polyethylene gel from non-cross-linked polyethylene molecules. In the illustrated example, the elution column 125 includes one or more filters. In the illustrated example, when a liquid mixture containing a polymer sample dissolved in a solvent passes through the elution column 125 and the components of the sample are separated based on their ability to pass through the filter, time series data can be determined when different components of the sample pass through the flow cell 112 at different times. The chemical or structural characteristics of the non-cross-linked components can then be determined based on the time series data, as discussed in further detail below.

[0047] exist Figure 1In the example of FIG. 1 , a single elution column 125 is shown. However, it should be understood that in other examples, multiple elution columns may be used. For example, in Figure 2 In the example shown in FIG. 2 , the flow cell FTIR detector 200 includes three elution columns 125 , 126 , and 127 .

[0048] Return to reference Figure 1 , computing device 130 may be communicatively coupled to detector 116 and may perform data analysis and / or control one or more operations of detector 116 based on data received by detector 116. In the illustrated example, computing device 130 is physically present in the same device as detector 116. However, in other examples, computing device 130 may be a remote computing device (e.g., a cloud server). In some examples, computing device 130 may be part of detector 116. Figure 3 Computing device 130 is discussed in greater detail.

[0049] Figure 3 Schematically depicts Figure 1 and Figure 2 1. An exemplary configuration of a computing device 130. In the illustrated example, the computing device 130 includes one or more processors 302, a communication path 304, one or more memory modules 306, a data storage component 308, and network interface hardware 310, the details of which will be described in the following paragraphs.

[0050] Each of the one or more processors 302 may be any device capable of executing machine-readable and executable instructions. Thus, each of the one or more processors 302 may be a controller, an integrated circuit, a microchip, a computer, or any other physical or cloud-based computing device. Algorithms (including training models, signal preprocessing, and noise removal methods discussed below) may be executed by one or more processors 302. One or more processors 302 are coupled to a communication path 304, which provides signal interconnectivity between various modules of the computing device 130. Thus, the communication path 304 may couple any number of processors 302 to each other in a communication manner, and allow the modules coupled to the communication path 304 to operate in a distributed computing environment. Specifically, each module in the module may operate as a node that can send and / or receive data. As used herein, the term "coupled in a communication manner" means that data signals can be exchanged with each other via coupled components, such as, for example, electrical signals via a conductive medium, electromagnetic signals via air, optical signals via optical waveguides, and the like.

[0051] Thus, the communication path 304 may be formed by any medium capable of transmitting a signal, such as, for example, a conductive wire, a conductive trace, an optical waveguide, etc. In some embodiments, the communication path 304 may facilitate wireless signals, such as WiFi, Near Field Communication (NFC), etc.) transmission. In addition, the communication path 304 can be formed by a combination of media capable of transmitting signals. In one embodiment, the communication path 304 includes a combination of conductive traces, conductive lines, connectors, and buses that cooperate to allow the transmission of electrical data signals to components such as processors, memories, sensors, input devices, output devices, and communication devices. In addition, it should be noted that the term "signal" means a waveform (e.g., electrical, optical, magnetic, mechanical, or electromagnetic) that can be propagated through a medium, such as DC, AC, sine waves, triangle waves, square waves, vibrations, etc.

[0052] The computing device 130 includes one or more memory modules 306 coupled to the communication path 304. The one or more memory modules 306 may include RAM, ROM, flash memory, a hard drive, or any device capable of storing machine-readable and executable instructions so that the machine-readable and executable instructions can be accessed by one or more processors 302. The machine-readable and executable instructions may include logic or algorithms written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL), such as, for example, machine language that can be directly executed by a processor, or assembly language, object-oriented programming (OOP), scripting language, microcode, etc., which can be compiled or assembled into machine-readable and executable instructions and stored on one or more memory modules 306. Alternatively, the machine-readable and executable instructions may be written in a hardware description language (HDL) (such as logic implemented via a field programmable gate array (FPGA) configuration or an application-specific integrated circuit (ASIC) or its equivalent). Therefore, the methods described herein can be implemented in any conventional computer programming language as pre-programmed hardware elements or a combination of hardware components and software components. The following is in conjunction with Figure 4 Memory module 306 is discussed in greater detail.

[0053] Still reference Figure 3 , the exemplary computing device 130 includes a data storage component 308. The data storage component 308 may store data received from the detector 116 or data generated by the computing device 130. The data storage component 308 may also store other data used by various components of the computing device 130.

[0054] Still reference Figure 3, the computing device 130 includes a network interface hardware 310 for communicatively coupling the computing device 130 to the detector 116. Therefore, the network interface hardware 310 can send data to the detector 116 and / or receive data from the detector. The network interface hardware 310 may include a wired and / or wireless connection to the detector 116. In other examples, the network interface hardware 310 can send data to other computing devices and / or receive data from other computing devices. The network interface hardware 310 can be communicatively coupled to the communication path 304 and can be any device capable of transmitting and / or receiving data via a network. Therefore, the network interface hardware 310 may include a communication transceiver for sending and / or receiving any wired or wireless communication. For example, the network interface hardware 310 may include an antenna, a modem, a LAN port, a Wi-Fi card, a WiMax card, a mobile communication hardware, a near field communication hardware, a satellite communication hardware and / or any wired or wireless hardware for communicating with the detector 116 and / or other networks and / or devices.

[0055] Reference now Figure 4 , the one or more memory modules 306 include a data receiving module 400, an interference pattern determination module 402, a single beam determination module 404, a transmittance determination module 406, a chromatogram determination module 408, a data normalization module 410, a rough denoising module 412, a first-order derivative determination module 414, a second-order derivative determination module 416, a relative information density determination module 418, a data enhancement module 420, and a data reduction module 422. Each of the data receiving module 400, the interference pattern determination module 402, the single beam determination module 404, the transmittance determination module 406, the chromatogram determination module 408, the data normalization module 410, the rough denoising module 412, the first-order derivative determination module 414, the second-order derivative determination module 416, the relative information density determination module 418, the data enhancement module 420, and the data reduction module 422 may be a program module in the form of an operating system, an application program module, and other program modules stored in the one or more memory modules 306. Such program modules may include, but are not limited to, routines, subroutines, programs, objects, components, data structures, etc., for performing particular tasks or executing particular data types, as will be described below.

[0056] The data receiving module 400 can receive data from the detector 116. In particular, the data receiving module 400 can receive light intensity data indicating the intensity of light projected onto the detector 116 after passing through the flow cell 112. In operation, a liquid sample (which may be referred to herein as a liquid mixture) dissolved in a solvent is eluted from the elution column 125. When the eluted mixture leaves the column 125, it passes through the flow cell 112. As discussed above, the elution column 125 causes the components of the mixture to be eluted at different times based on their hydrodynamic sizes. In particular, the larger components of the mixture are eluted first, while the smaller components of the mixture are eluted subsequently.

[0057] As the mixture passes through the flow cell 112, the movable mirror 108 can continuously move back and forth, so that different interference patterns are generated when the reflected light beams 120 and 122 are recombined at the beam splitter 104. When the movable mirror 108 is moved, the different interference patterns cause the combined light beam 124 to have different frequency components. As the movable mirror 108 moves, the detector 116 can continuously measure the intensity of the light beam 124 projected on the detector 116 after passing through the flow cell 112, thereby collecting multiple data points. This data can then be transmitted by the detector 116 to the computing device 130 and can be received by the data receiving module 400.

[0058] As disclosed herein, the interference pattern determination module 402 can determine a plurality of interference patterns based on the data received by the data receiving module 400. When the moving mirror 108 has completed one complete movement path, a complete cycle of constructive and destructive interference can occur between the reflected light beams 120 and 122. The intensity of the light beam 124 projected on the detector 116 during this cycle comprises the interference pattern. Therefore, after the data receiving module 400 receives data from the detector including one complete channel of light intensity data from the moving mirror 108, the interference pattern determination module 402 can determine the interference pattern based on the received data. Figure 5A An exemplary interference pattern is shown in .

[0059] In addition, as discussed above, different components of the liquid mixture elute from the column 125 and pass through the flow cell 112 at different times. Therefore, when the liquid mixture flows through the flow cell 112, the mobile mirror 108 can continue to move through multiple cycles of its movement path. Each time the mobile mirror 108 moves through a cycle of its movement path, the interference pattern determination module 402 can determine the interference pattern based on the data received by the data receiving module 400 during the movement path of the mobile mirror 108. Therefore, when different components of the liquid mixture are present in the flow cell 112, the interference pattern determination module 402 can determine multiple interference patterns at multiple time steps. Therefore, the different interference patterns determined by the interference pattern determination module 402 can be used to determine the chemical or structural characteristics of different components of the sample, as disclosed herein. Therefore, the different interference patterns determined by the interference pattern determination module 402 can be used to determine the concentrations of different components of the sample, as disclosed herein.

[0060] Return to reference Figure 4 , the single beam determination module 404 can determine the single beam data based on each interference pattern determined by the interference pattern determination module 402. In particular, the single beam determination module 404 can determine the single beam data by performing Fourier transform on the interference pattern determined by the interference pattern determination module 402 using any method for performing Fourier transform. Figure 5B An exemplary single wave beam is shown in FIG. The single beam as determined by the single beam determination module 404 indicates the intensity of light projected onto the detector 116 after passing through the flow cell 112 at different frequencies. Figure 5B In the example, compared to the cm -1 The measured wave number plots the intensity of the light.

[0061] In an embodiment, the detector 116 can record one set of measurements when the liquid mixture comprising the sample dissolved in the solvent elutes from the column 125, and another set of measurements when only the solvent elutes from the column 125. The measurements recorded when only the solvent elutes from the column 125 can be used as a background signal, and the measurements recorded when the mixture elutes from the column 125 can be compared to the background signal, as explained below.

[0062] Still reference Figure 4, the transmittance determination module 406 can determine the transmittance associated with the sample, as disclosed herein. As discussed above, the detector 116 can record data when a liquid mixture containing a sample dissolved in a solvent elutes from the column 125, and can also record data when only the solvent elutes from the column 125. The data receiving module 400 can receive two sets of data from the detector. The interference pattern determination module 402 can then calculate the interference pattern for each set of data, and the single beam determination module 404 can calculate a single beam for each set of data. The transmittance determination module 406 can then calculate the ratio between the single beam of data collected when the sample mixture elutes from the column 125 and the single beam of data collected when only the solvent elutes from the column 125 (i.e., the ratio between the sample data and the background data). This ratio may include the transmittance of the sample. Figure 5C 4. Exemplary transmittances that may be determined by the transmittance determination module 406 are shown. The transmittance determination module 406 may determine the transmittance of each individual beam determined by the individual beam determination module 404. Thus, as different components of the sample elute from the column 125, the transmittance determination module 406 may determine multiple transmittances at different time steps.

[0063] Return to reference Figure 4 The chromatogram determination module 408 can determine a chromatogram based on the multiple transmittances determined by the transmittance determination module 406. In particular, when the sample mixture is eluted by the column 125, the chromatogram determination module 408 can determine the absorbance curve of the sample within a specific wavelength band, as disclosed herein.

[0064] As described above, the transmittance determination module 406 can determine a plurality of transmittances associated with the sample at a plurality of time steps as the sample mixture elutes from the column 125. The chromatogram determination module 408 can first convert the transmittance associated with the sample determined by the transmittance determination module 406 to an absorbance associated with the sample using known techniques. The chromatogram determination module 408 can perform this conversion for any number of transmittances determined by the transmittance determination module 406. The chromatogram determination module 408 can then determine a chromatogram based on the determined absorbances, as disclosed herein.

[0065] As discussed above, the transmittance determination module 406 can determine the transmittance associated with the sample at multiple time steps as the sample mixture elutes from the column 125. Accordingly, the chromatogram determination module 408 can determine the absorbance associated with the sample at multiple time steps as the sample mixture elutes from the column 125. The chromatogram determination module 408 can then select a particular wavelength range of interest (e.g., 720-700 cm) for which to determine the chromatogram. -1 ). For example, the chromatogram determination module 408 can select a wavelength range associated with known components of the sample.

[0066] The chromatogram determination module 408 can then determine the absorbance within this wavelength range for each of the absorbances associated with the sample to generate a chromatogram. That is, as the sample mixture elutes from the column 125, the chromatogram determination module 408 can create a chromatogram including the absorbance associated with the sample within the selected wavelength range of interest at multiple time steps. Figure 6 An exemplary chromatogram that may be determined by the chromatogram determination module 408 is shown in FIG. 4 , which plots absorbance versus retention time in minutes.

[0067] The chromatogram determination module 408 can determine a chromatogram associated with the sample, as discussed above. However, as discussed above, the chromatogram is based on the ratio of the sample signal to the background signal. And as discussed above, for polyolefins, the solvents in which these polyolefins can be dissolved absorb many of the same wavelengths as the polyolefins themselves, making it difficult to distinguish the sample signal from the background signal. For example, Figure 7 Graph 702 shows the background signal of the solvent alone, while graph 704 shows the signal from the sample dissolved in the solvent. Figure 7 As can be seen in the , all signals overlap except for a small wavelength range. Therefore, the SNR for such samples is small. Therefore, any changes in the measurement caused by fluctuations over time in the instrument temperature, input light intensity, etc., can be extremely detrimental to the measurement accuracy. Therefore, two denoising techniques are disclosed herein for improving the SNR when performing FTIR experiments involving polyolefins using the techniques described herein.

[0068] The first denoising technique that can be performed is an internal normalization procedure. Figure 4 , the data normalization module 410 can perform this internal normalization procedure, as disclosed herein. Initially, a baseline wavelength can be selected at which the sample of interest is known to have little or no absorbance. For example, the sample of interest can have a known absorbance at the baseline wavelength that is below a predetermined threshold. Therefore, any absorbance detected at the baseline wavelength is only due to the solvent itself. This baseline wavelength can be selected by the user based on known characteristics of the sample of interest.

[0069] Once the baseline wavelength is selected, the data normalization module 410 can determine the absorbance value of the first single beam signal determined by the single beam determination module 404 at the baseline wavelength. The data normalization module 410 can then store this absorbance value as the baseline absorbance. Since each subsequent single beam signal is determined by the single beam determination module 404 at a subsequent time step, the data normalization module 410 can determine the absorbance value at the baseline wavelength. Because the baseline wavelength is selected at a wavelength without sample contribution, the absorbance value at the baseline wavelength should not change over time. However, due to changes in instrument temperature, input light intensity, and other factors, changes may be introduced due to these factors. Therefore, it is assumed that any changes in the signal value at the baseline wavelength are introduced due to noise rather than signal. Therefore, each subsequent single beam signal can be adjusted based on the absorbance value measured at the baseline wavelength, as disclosed herein.

[0070] In particular, for subsequent single beam signals following an initial single beam signal, all data points of the subsequent single beam signals may be proportionally scaled based on a ratio of absorbance values ​​at the baseline wavelength. For example, if the absorbance value at the baseline wavelength of the first single beam signal has a first value, and the absorbance value at the baseline wavelength of the second single beam signal at a subsequent time step is 2% greater than the first value, it may be assumed that this 2% increase is caused by noise rather than signal. Therefore, the data normalization module 410 may reduce each data point of the second single beam signal by 2% to offset the effect of this noise. The data normalization module 410 may perform this technique for each single beam signal determined by the single beam determination module 404. Therefore, the data normalization module 410 may determine an adjusted single beam signal that more accurately reflects the signal generated by the sample rather than noise. The chromatogram determination module 408 may then determine a chromatogram based on the adjusted single beam signal to generate more accurate data with a greater SNR.

[0071] In some examples, the data normalization module 410 can perform internal normalization based on two baseline wavelengths. For example, two different baseline wavelengths can be selected where it is known that the sample of interest has little or no absorbance. For example, two baseline wavelengths can be selected where no measurable difference is observed between the sample of interest and the solvent. In addition, the transmittance values ​​at the two baseline wavelengths and the wavelengths themselves can be substantially different. For example, the transmittance at one baseline wavelength can be less than 50% of the transmittance at another baseline wavelength.

[0072] The data normalization module 410 can then determine and store the absorbance value of the first single beam signal at each of the two baseline wavelengths. As each subsequent single beam signal is determined by the single beam determination module 404 at a subsequent time step, the data normalization module 410 can determine the absorbance value of the single beam signal at each selected wavelength in the two selected baseline wavelengths. The data normalization module 410 can then scale the absorbance values ​​of the subsequent single beam data at the two baseline wavelengths based on the ratio between the absorbance value of the first single beam signal and the absorbance value of the subsequent single beam data at the corresponding baseline wavelength (e.g., using linear regression). The data normalization module 410 can then scale all other data points at other wavelengths of the subsequent single beam signal based on the amount of scaling performed at the two baseline wavelengths (e.g., using linear interpretation).

[0073] In some examples, data normalization module 410 can perform internal normalization based on three or more baseline wavelengths in a similar manner. In some examples, data normalization module 410 can perform internal normalization based on transmittance instead of absorbance.

[0074] In addition to the internal normalization procedure discussed above, a second denoising technique may be performed as a supplement to or in place of the internal normalization procedure, as disclosed herein. In the illustrated example, this second denoising technique may be performed on the chromatogram determined by the chromatogram determination module 408 . Fig. 8A An exemplary chromatogram on which this denoising technique can be performed is shown. In other examples, the second denoising technique can be performed on a chromatogram determined by another detector or another type of detector. Examples of other types of detectors that can be used include light scattering detectors, viscometers, and differential reflectance detectors, among others. In some examples, the second denoising technique can be performed on data other than chromatograms (e.g., any equally spaced one-dimensional data).

[0075] Return to reference Figure 4 , the coarse denoising module 412 performs coarse denoising on the chromatogram determined by the chromatogram determination module 408 using a known denoising algorithm. In the illustrated example, the coarse denoising module 412 performs coarse denoising by calculating the wavelet transform of the chromatogram using a àtrous algorithm and then combining the wavelet frequency components above the cutoff frequency for the wavelet transform to generate a coarse denoised signal associated with the chromatogram. In an embodiment, the cutoff frequency for the wavelet transform can be set by a user. In other examples, other denoising algorithms can be used to determine the coarse denoised signal associated with the chromatogram. The wavelet transform can be repeated several times, for example 2-10 times or more.

[0076] Figure 8B It shows that the rough denoising module 412 can be used to Fig. 8A An example rough denoised signal generated by a chromatogram of . As can be seen in the figure, Figure 8B An exemplary rough denoised signal ratio of Fig. 8A The chromatogram is less noisy. Figure 8B The peaks 800 and 802 of the roughly denoised signal are not particularly sharp. Therefore, the additional denoising techniques described below can improve and preserve the sharp peaks of the chromatogram.

[0077] Return to reference Figure 4 The first-order derivative determination module 414 may calculate a first-order derivative of the coarse denoised signal generated by the coarse denoising module 412 , and the second-order derivative determination module 416 may calculate a second-order derivative of the coarse denoised signal generated by the coarse denoising module 412 . Figure 8B An exemplary first-order derivative of a roughly denoised signal is shown in Figure 8C In, and Figure 8B An exemplary second derivative of a roughly denoised signal is shown in Fig.8D middle.

[0078] Return to reference Figure 4 , the relative information density determination module 418 can determine the relative information density at each point of the coarse denoising signal determined by the coarse denoising module 412 based on the first-order derivative and the second-order derivative determined by the first-order derivative determination module 414 and the second-order derivative determination module 416, respectively, as disclosed herein. In particular, the relative information density determination module 418 can determine the relative information density using the following techniques.

[0079] For each point of the coarse denoised signal, the relative information density determination module 418 can determine the first-order derivative at the data point and the second-order derivative at the data point. The relative information density determination module 418 can also determine the maximum absolute value of the first-order derivative across the entire coarse denoised signal and the maximum absolute value of the second-order derivative across the entire coarse denoised signal.

[0080] For a particular point of the coarse denoised signal being considered, the relative information density determination module 418 can then determine the sum of the first-order derivative of the coarse denoised signal at that point divided by the maximum absolute value of the first-order derivative and the second-order derivative at that point divided by the maximum absolute value of the second-order derivative. This sum can include the relative information density of one point of the coarse denoised signal. The relative information density determination module 418 can similarly calculate the relative information density at each point of the coarse denoised signal to generate the relative information density of the entire coarse denoised signal. The relative information density determined by the relative information density determination module 418 can be used as an enhancement level, as described below. Fig.9A Exemplary relative information densities or enhancement levels determined by relative information density determination module 418 are shown. Fig. 9B Shows Fig.9AAn amplified version of an exemplary relative information density or enhancement level.

[0081] Return to reference Figure 4 , the data enhancement module 420 can perform data enhancement of the original chromatogram, as disclosed herein. In particular, the data enhancement module 420 can enhance the original chromatogram based on the relative information density calculated by the relative information density determination module 418, generating a roughly denoised signal from the original chromatogram. Enhancing the chromatogram includes inserting data points between existing data points of the chromatogram. As determined by the relative information density determination module 418, the number of data points to be inserted is proportional to the relative information density at each point of the chromatogram. In particular, the number of data points to be inserted at any point of the chromatogram is equal to the relative information density at that point multiplied by the enhancement factor. The enhancement factor can be any number greater than or equal to 1 and can be set by a user.

[0082] The data enhancement module 420 can enhance the chromatogram by inserting an appropriate number of data points (based on the relative information density and enhancement factor) between each existing data point using various techniques (e.g., linear interpolation, nearest neighbor, cubic spline, best fit curve, etc.) to generate an enhanced signal. Fig. 9C An enhanced signal that may be generated by the data enhancement module 420 is shown.

[0083] Return to reference Figure 4 After the data enhancement module 420 generates the enhanced signal, as discussed above, the coarse denoising module 412 can perform a known denoising algorithm on the enhanced signal to generate a denoised enhanced signal. The coarse denoising module 412 can generate the denoised enhanced signal using the same denoising algorithm used to generate the coarse denoised signal discussed above. Fig.9D An exemplary denoised enhanced signal that may be generated by the data enhancement module 420 is shown.

[0084] Return to reference Figure 4 , the data reduction module 422 may reduce the denoised enhanced signal by removing data points from the denoised enhanced signal corresponding to the data points added by the data enhancement module 420 to generate a final denoised signal. Fig.9E An exemplary final denoised signal that may be generated by the data reduction module 422 is shown. As can be seen in the figure, Fig.9E The peaks 900 and 902 of the final denoised signal are Figure 8B The peaks 800 and 802 of the roughly denoised signal are sharper.

[0085] Fig.10A flow chart of an exemplary method for performing internal standardization is depicted. At step 1000, the data receiving module 400 receives data from the detector 116. In particular, when the moving mirror 108 moves back and forth and the light beam 124 passes through the flow cell 112, the data receiving module 400 can receive light intensity data from the detector 116. In an embodiment, the data receiving module 400 can receive a set of data recorded when a sample dissolved in a solvent is eluted from the column 125 through the flow cell 112 and another set of data recorded when only the solvent is eluted from the column 125 through the flow cell 112.

[0086] At step 1002, the interference pattern determination module 402 determines an initial interference pattern based on the data received by the data receiving module 400. In particular, the interference pattern determination module 402 may determine an interference pattern including light intensities received by the detector 116 when the moving mirror 108 moves through one complete cycle causing constructive and destructive interference between the light beams 120 and 122 while a sample diluted in a solvent is eluted from the column 125 through the flow cell 112. The interference pattern determination module 402 may also determine an interference pattern based on data received when only the solvent is eluted from the column 125 through the flow cell 112 to obtain a background interference pattern.

[0087] At step 1004, the single beam determination module 404 determines single beam data based on the initial interferogram determined by the interferogram determination module 402. In particular, the single beam determination module 404 may determine the single beam data by performing Fourier transform on the initial interferogram determined by the interferogram determination module 402 with or without an apodization function. The single beam determination module 404 may also determine background single beams by performing Fourier transform on the background interferogram.

[0088] At step 1006, the transmittance determination module 406 determines the transmittance based on the single beam data determined by the single beam determination module 404. In particular, the transmittance determination module 406 can determine the transmittance by calculating the ratio between the single beam data associated with the sample and the background single beam. Then at step 1008, the chromatogram determination module 408 can determine the absorbance based on the transmittance determined by the transmittance determination module 406.

[0089] At step 1010, the data normalization module 410 determines the absorbance value determined by the chromatogram determination module 408 at the baseline wavelength. The baseline wavelength can be selected by the user at a wavelength at which the sample is known to have little or no absorbance. As discussed above, in some examples, the data normalization module 410 can perform internal normalization on transmittance values ​​rather than absorbance values.

[0090] At step 1012, the interference pattern determination module 402 determines a subsequent interference pattern based on the subsequent data received by the data receiving module 400. That is, as described above, as the sample mixture elutes from the column 125, the movable mirror 108 may make multiple passes over time in order to obtain data as different components of the sample elute. Therefore, after determining the interference pattern associated with the first pass of the movable mirror 108, the interference pattern determination module 402 may determine the interference pattern associated with the subsequent pass of the movable mirror 108. This may generate an interference pattern at a later time step than the initial interference pattern.

[0091] At step 1014, the single beam determination module 404 determines single beam data based on the subsequent interferogram determined by the interferogram determination module 402. At step 1016, the transmittance determination module 406 determines a subsequent transmittance based on the single beam data determined by the single beam determination module 404. At step 1018, the chromatogram determination module 408 determines a subsequent absorbance based on the transmittance determined by the transmittance determination module 406.

[0092] At step 1020, the data normalization module 410 determines the value of the subsequent absorbance at the baseline wavelength. The data normalization module 410 then determines the ratio of the subsequent absorbance value at the baseline wavelength to the initial absorbance value at the baseline wavelength. The data normalization module 410 then scales each data point of the subsequent single beam data based on this ratio to determine an adjusted single beam signal. For example, if the subsequent absorbance at the baseline wavelength increases by 1% over the baseline absorbance, each data point of the subsequent single beam is reduced by 1%. Alternatively, if the subsequent absorbance at the baseline wavelength decreases by 1% over the baseline absorbance, each data point of the subsequent single beam is increased by 1%. This can reduce the impact of noise introduced by changes in instrument conditions over the time span of conducting the experiment.

[0093] At step 1022, the interference pattern determination module 402 determines whether additional data needs to be processed. That is, the interference pattern determination module 402 can determine whether an interference pattern has been determined for all data received by the data receiving module 400 or whether additional interference patterns need to be calculated. If additional data needs to be processed (yes at step 1022), control returns to step 1012 and the interference pattern for the next time step of the experiment is determined. Alternatively, if additional data does not need to be processed (no at step 1022), then at step 1024, the chromatogram determination module 408 determines a chromatogram based on the multiple adjusted single beam signals determined by the data normalization module 410.

[0094] Fig.11A flow chart of an exemplary method for performing denoising as disclosed herein is depicted. At step 1100, the coarse denoising module 412 performs denoising on the chromatogram determined by the chromatogram determination module 408 using a known denoising algorithm to generate a coarse denoised signal. In the illustrated example, the coarse denoising module 412 performs a wavelet transform using a porous algorithm and combining wavelet frequency components above a cutoff frequency. However, in other examples, other denoising algorithms may be performed.

[0095] At step 1102 , the first order derivative determination module 414 calculates the first order derivative of the coarse denoised signal determined by the coarse denoising module 412 . At step 1104 , the second order derivative determination module 416 calculates the second order derivative of the coarse denoised signal determined by the coarse denoising module 412 .

[0096] At step 1106, the relative information density determination module 418 determines the relative information density at each point of the coarse denoised signal based on the first-order derivative and the second-order derivative determined by the first-order derivative determination module 414 and the second-order derivative determination module 416, respectively. In particular, for each point of the coarse denoised signal, the relative information density determination module 418 may calculate the sum of the first-order derivative divided by the maximum absolute value of the first-order derivative and the second-order derivative divided by the maximum absolute value of the second-order derivative.

[0097] At step 1108, the data enhancement module 420 performs data enhancement of the chromatogram determined by the chromatogram determination module 408 to generate an enhanced signal. In particular, for each point of the chromatogram, the data enhancement module 420 can insert a number of data points between adjacent existing data points, which is equal to the information density at the point multiplied by the user-defined enhancement factor. The data enhancement module 420 can use a variety of techniques (e.g., linear interpolation, nearest neighbor, cubic spline, best fit curve, etc.) to insert these data points.

[0098] At step 1110, the coarse denoising module 412 performs denoising on the enhanced signal using a known denoising algorithm, thereby generating a denoised enhanced signal. Then at step 1112, the data reduction module 422 reduces the denoised enhanced signal. In particular, the data reduction module 422 can reduce the data points of the denoised enhanced signal corresponding to the data points added by the data enhancement module 420.

[0099] It should now be understood that the embodiments described herein relate to systems and methods for improving quantitative analysis using polymer chromatography. The polymer sample can be dissolved in a solvent and eluted from an elution column by a flow cell while emitting infrared light on the flow cell. The light transmitted through the flow cell can be detected by an FTIR detector. In order to improve the signal-to-noise ratio of the collected data, an internal standardization procedure can be performed, and a denoising procedure can be performed.

[0100] Internal standardization procedures can adjust the data collected during the experimental process based on the signal recorded at the wavelength where the known sample does not provide any signal. This can reduce the impact of the changes in temperature, light intensity, environment and other factors during the experimental process. De-noising signals can perform data processing to reduce noise and maintain the peaks in the chromatogram. Therefore, technology described herein can allow the chemical or structural characteristics of polyolefins to be measured more accurately in polymer chromatography experiments.

[0101] It should be noted that the term "chemical or structural properties" includes, but is not limited to, the chemical composition, molecular weight, intrinsic viscosity, chain ends, unsaturation, long chain branching and corresponding distribution of a sample, and / or the chemical composition and structural properties of the components of the sample of interest.

[0102] It should be noted that the terms "substantially" and "about" may be used herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. These terms are also used herein to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue.

[0103] Although specific embodiments have been illustrated and described herein, it will be appreciated that various other changes and modifications may be made without departing from the spirit and scope of the claimed subject matter. In addition, although various aspects of the claimed subject matter have been described herein, such aspects need not be utilized in combination. It is therefore contemplated that the appended claims cover all such changes and modifications within the scope of the claimed subject matter.

Claims

1. A method for determining one or more chemical or structural properties of a liquid sample, the method comprising: illuminating a flow cell containing a liquid mixture of the sample dissolved in a solvent with a first plurality of light beams at a first time step and with a second plurality of light beams at a second time step; detecting the first plurality of light beams after the first plurality of light beams have traversed the flow cell at the first time step to determine a first interference pattern, and detecting the second plurality of light beams after the second plurality of light beams have traversed the flow cell at the second time step to determine a second interference pattern; performing a Fourier transform on the first interferogram to determine first single beam data associated with the liquid mixture, and performing a Fourier transform on the second interferogram to determine second single beam data associated with the liquid mixture; determining a first signal value of the first single beam data at a first frequency; determining a second signal value of the second single-beam data at the first frequency; determining a first ratio of the second signal value to the first signal value; adjusting each signal value of the second single beam data based on the first ratio to determine adjusted second single beam data; as well as The one or more chemical or structural properties of the sample are determined based on the adjusted second single beam data. The method of claim 1 , wherein the sample comprises a polyolefin.

3. The method according to claim 1, further comprising: determining a third signal value of the first single beam data at a second frequency; determining a fourth signal value of the second single-beam data at the second frequency; determining a second ratio of the fourth signal value to the third signal value; and adjusting each signal value of the second single beam data based on the first ratio and the second ratio.

4. The method of claim 1, wherein at least one of the one or more chemical or structural properties comprises a molecular weight distribution.

5. The method of claim 1, wherein at least one of the one or more chemical or structural properties comprises a chemical composition distribution. 6 . The method according to claim 1 , wherein the first frequency is a baseline frequency at which the signal value of the first single-beam data caused by the sample is lower than a predetermined threshold.

7. The method of claim 1, further comprising illuminating the flow cell with a third plurality of light beams having the flow cell, the flow cell containing the solvent but not the sample; detecting the third plurality of light beams after the third plurality of light beams have passed through the flow cell containing the solvent but not the sample to determine a background interference pattern; performing a Fourier transform on the background interferogram to determine background single beam data; and The one or more chemical or structural characteristics of the sample are determined based on a ratio of the adjusted second single-beam data to the background single-beam data.

8. A method comprising: receiving a first data set comprising a series of data points; executing a first denoising algorithm on the first data set to obtain a first denoised data set; Calculating a first-order derivative of the first denoised data set and a second-order derivative of the first denoised data set; Calculating a relative information density at each data point of the first denoised data set, the relative information density comprising a sum of an absolute value of the first-order derivative divided by a maximum absolute value of the first-order derivative across all the data points of the first denoised data set and an absolute value of the second-order derivative divided by a maximum absolute value of the second-order derivative across all the data points of the first denoised data set; enhancing the first data set based on the relative information density and the enhancement factor by inserting data points into the first data set to obtain an enhanced data set; executing the first denoising algorithm on the enhanced data set to obtain an enhanced denoised data set; as well as Data points corresponding to the data points added to the first data set are removed from the enhanced denoised data set to obtain a second denoised data set.

9. The method according to claim 8, further comprising: irradiating a flow cell containing a liquid mixture of a sample dissolved in a solvent with a plurality of light beams after the liquid mixture has passed through one or more elution columns; detecting the plurality of light beams after the plurality of light beams have traversed the flow cell at a plurality of time steps to determine a plurality of interference patterns; performing a Fourier transform on each of the plurality of interference patterns to determine a plurality of single beam signals associated with the liquid mixture at the plurality of time steps; determining a chromatogram as the first data set based on the plurality of single beam signals; as well as One or more chemical or structural properties of the sample are determined based on the second de-noised data set.

10. The method of claim 8, wherein the first denoising algorithm comprises a wavelet transform. The method of claim 8 , wherein the first denoising algorithm comprises a porous algorithm.

12. The method of claim 8, further comprising enhancing the first data set using linear interpolation.

13. The method of claim 8, further comprising enhancing the first data set using nearest neighbor interpolation.

14. The method of claim 8, further comprising enhancing the first data set using cubic spline interpolation.

15. A system for determining one or more chemical or structural properties of a liquid sample, the system comprising: light source; one or more elution columns; a flow cell containing a liquid mixture of the sample dissolved in a solvent after passing through the one or more elution columns; and Detector; wherein the light source is configured to illuminate the flow cell with a first plurality of light beams at a first time step and with a second plurality of light beams at a second time step; and Wherein the detector is configured as: detecting the first plurality of light beams after the first plurality of light beams have traversed the flow cell at the first time step to determine a first interference pattern, and detecting the second plurality of light beams after the second plurality of light beams have traversed the flow cell at the second time step to determine a second interference pattern; performing a Fourier transform on the first interferogram to determine first single beam data associated with the liquid mixture, and performing a Fourier transform on the second interferogram to determine second single beam data associated with the liquid mixture; determining a first signal value of the first single beam data at a first frequency; determining a second signal value of the second single-beam data at the first frequency; determining a first ratio of the second signal value to the first signal value; adjusting each signal value of the second single beam data based on the first ratio to determine adjusted second single beam data; and The one or more chemical or structural properties of the sample are determined based on the adjusted second single beam data. 16 . The system of claim 15 , wherein the first frequency is a baseline frequency at which the signal value of the first single-beam data caused by the sample is below a predetermined threshold.

17. The system of claim 15, further comprising illuminating the flow cell with a third plurality of light beams having the flow cell, the flow cell containing the solvent but not the sample; detecting the third plurality of light beams after the third plurality of light beams have passed through the flow cell containing the solvent but not the sample to determine a background interference pattern; performing a Fourier transform on the background interferogram to determine background single beam data; and The one or more chemical or structural characteristics of the sample are determined based on a ratio of the adjusted second single-beam data to the background single-beam data.

18. The system of claim 15, wherein the detector is further configured to: determining a chromatogram based at least on the first signal beam data and the second single beam data; executing a first denoising algorithm on the chromatogram to obtain a first denoised chromatogram; calculating a first derivative of the first denoised chromatogram and a second derivative of the first denoised chromatogram; calculating a relative information density at each data point of the first denoised chromatogram, the relative information density comprising a sum of an absolute value of the first-order derivative divided by a maximum absolute value of the first-order derivative across all the data points of the first denoised chromatogram and an absolute value of the second-order derivative divided by a maximum absolute value of the second-order derivative across all the data points of the first denoised chromatogram; enhancing the chromatogram based on the relative information density and the enhancement factor by inserting data points into the chromatogram to obtain an enhanced chromatogram; executing the first denoising algorithm on the enhanced chromatogram to obtain an enhanced denoised chromatogram; removing data points from the enhanced denoised chromatogram corresponding to the data points added to the chromatogram to obtain a second denoised chromatogram; and The one or more chemical or structural characteristics of the sample are determined based on the second denoised chromatogram.

19. The system of claim 18, wherein the first denoising algorithm comprises a wavelet transform.

20. The system of claim 18, wherein the first denoising algorithm comprises a porous algorithm.

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