Raman spectrum multi-dimensional calibration algorithm based on data feedback

Abstract

The invention provides a Raman spectrum multi-dimensional calibration algorithm based on data feedback. The Raman spectrum multi-dimensional calibration algorithm comprises the following steps: selecting a reference substance; collecting data; calibrating noise of the detector; performing Raman relative wave number calibration; raman intensity calibration is carried out; performing application calibration; measurement errors caused by detector noise in the prior art are reduced; the relative wave number accuracy of Raman spectrum data is improved; the comparability of the data is enhanced; the multi-dimensional calibration algorithm is suitable for various Raman spectrum systems at the same time, has good universality, does not need to make special design changes on the existing Raman spectrum system, and has good popularization and application attributes.

Description

Technical Field

[0001] The present invention belongs to the technical field of Raman spectrum analysis, and in particular to a Raman spectrum multi-dimensional calibration algorithm based on data feedback. Background Art

[0002] Raman spectroscopy is a spectral analysis technique widely used in chemistry, biology, materials science and other fields; however, the accuracy of Raman spectroscopy data is affected by many factors, including detector noise, relative wavenumber calibration error, and intensity calibration.

[0003] Traditional calibration methods rely on specific equipment, limiting the versatility and flexibility of calibration, such as:

[0004] The application number is: CN202211267447.6, and the invention name is: A Raman spectrum wave number automatic calibration device, comprising a housing, a laser transmitter, a first reflector, a second reflector, a grating assembly, and an optical signal receiving assembly; the grating assembly comprises a grating rotation mechanism, a grating and an electric stopper, the grating rotation mechanism is assembled in the housing, and the electric stopper is assembled inside the grating rotation mechanism. The electric stopper is equipped with an optical signal triggering component, and the optical signal triggering component is assembled in the optical signal receiving assembly. The electric stopper is set to record the initial state of the grating as the zero position, and then the optical signal triggering component is assembled at a specific position of the optical signal receiving assembly; whenever the laser emitted by the laser transmitter passes through the first reflector, the grating assembly, and the second reflector, the focal point of the light processed moves to the optical signal triggering component, the electric stopper can be triggered, and the electric stopper controls the grating rotation mechanism to rotate the grating back to the zero position, so as to achieve the effect of correcting the Raman spectrum wave number deviation;

[0005] This calibration method relies on a specific structural design and cannot achieve universal calibration for ordinary Raman spectroscopy detection, making it difficult to promote and apply.

[0006] There is another calibration method in the prior art, for example: a method for calibrating a Raman spectrum detection device with application number 201611117719, comprising: calibrating the Raman spectrum detection device using a first reference sample; using the calibrated Raman spectrum detection device to detect a second reference sample having a composition different from that of the first reference sample; comparing the detected Raman spectrum of the second reference sample with the reference Raman spectrum of the second reference sample to determine whether the deviation of the characteristic peak of the detected Raman spectrum of the second reference sample is within a predetermined range; and if the deviation of the characteristic peak is within the predetermined range, the calibration process is completed, and if the deviation of the characteristic peak exceeds the predetermined range, the Raman spectrum detection device is recalibrated using the first reference sample;

[0007] However, the calibration dimensions of the patent are quite different from those of this application:

[0008] ① The 201611117719 patent involves the calibration of Raman spectrum wavenumber; this patent involves the calibration of three dimensions: detector, Raman spectrum relative wavenumber, and Raman spectrum intensity;

[0009] ② Different calibration principles: Patent 201611117719 establishes the correspondence between the pixels and wavenumbers of reference substances (such as acetonitrile), and then tests the spectral data of different reference substances for verification; the reference substances used in relative wavenumber calibration of this patent are self-luminous sources such as neon argon lamps and mercury lamps. The correspondence between pixels and wavelengths is established, and then the wavelength is converted into relative wavenumbers in combination with the excitation wavelength, which has more basic theoretical support;

[0010] ③ Neon argon lamps and mercury lamps are self-luminous sources. When measuring their spectral data, the laser is in the off state, and its spectral peak is used to establish a mapping relationship between the detector such as pixels and wavelength; Silicon (Si), when measuring its data, the laser is in the on state. In order to reversely calculate the actual excitation wavelength, for example, a 532nm laser, the actual reverse calculation may be 532.12nm; The above calibration process of this application does not require any debugging of the device-side hardware. Summary of the invention

[0011] In order to solve the above technical problems, the present invention provides a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback, which can realize multi-dimensional calibration of Raman spectroscopy system data without relying on specific equipment content. The algorithm design is scientifically optimized and suitable for promotion.

[0012] In the first aspect, a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback includes:

[0013] Step 1: Selection of reference materials;

[0014] The reference material has: clear Raman spectral characteristics, known spectral lines, and identifiable spectral peaks with known relative wave numbers;

[0015] As an example, the reference material is: a neon argon lamp, a mercury lamp, silicon and / or a standard material in ASTM E1840-96 (2022);

[0016] ASTM E1840-96(2022) is a standard for relative wavenumber calibration of Raman spectrometers. This standard provides Raman spectra of common liquid and solid chemicals, which can be used to calibrate the relative wavenumber of Raman spectrometers. This standard does not include a specific calibration procedure, but provides a series of reliable Raman spectral data.

[0017] As an example, the spectral properties of the reference material should be well documented in the scientific literature to facilitate the accuracy and traceability of the calibration process.

[0018] As an example, the known spectral lines are: the wavelengths of neon, argon, mercury, and the relative wave number of silicon.

[0019] Step 2: Data collection;

[0020] Using a Raman spectroscopy system, collecting Raman spectroscopy data for the selected reference substance; and ensuring that the conditions set by the Raman spectroscopy system when collecting the reference substance are consistent with the conditions set during the actual sample analysis;

[0021] As an example, the set conditions refer to: the excitation wavelength is set to 532nm, 633nm, 785nm or 1064nm.

[0022] Step 3: Detector noise calibration;

[0023] ① Collect the background noise data of the detector without actual samples;

[0024] As an example, the noise data includes but is not limited to: background noise and dark current.

[0025] ② Using statistical analysis methods to process the collected noise data to determine the noise model;

[0026] As an example, the statistical analysis method includes: variance analysis method or autocorrelation function method.

[0027] ③ De-noising the Raman spectral data by using the noise model to improve the quality of the Raman spectral data;

[0028] Step 4: Raman relative wave number calibration;

[0029] ① Using the known spectral lines of the reference material, a mapping relationship between signal position and wavelength (ie, absolute wavelength) is established.

[0030] ② Calculate the excitation wavelength through the relative wavenumber of the characteristic peak and perform relative wavenumber calibration accordingly;

[0031] As an example, the relative wave number of the characteristic peak adopts the relative wave number of the silicon peak: 520.7 cm -1 .

[0032] Step 5: Raman intensity calibration;

[0033] ① Using standard reference materials or standard light sources to perform mapping operations on the Raman spectrum data;

[0034] As an example, the standard reference materials are: SRM 2242a;

[0035] The SRM 2242a is a standard reference material certified by the National Institute of Standards and Technology (NIST) of the United States, specifically used for Raman intensity calibration of Raman spectrometers.

[0036] ② According to the result of the mapping operation, a calibration coefficient is obtained, and the Raman spectrum data is intensity calibrated by the calibration coefficient to ensure the accuracy and comparability of the Raman spectrum data;

[0037] Step 6: Apply calibration;

[0038] The calibration coefficients are applied to actual Raman spectral data of actual samples to improve the accuracy of the analysis.

[0039] As an example, compare the data before and after calibration, verify the calibration effect, and adjust the calibration parameters as needed.

[0040] In a second aspect, the present application also discloses an electronic device, comprising: a processor and a memory for storing instructions executable by the processor; wherein the processor is configured to execute a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback.

[0041] On the third aspect, the present application further illustrates a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback.

[0042] In a fourth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback.

[0043] Beneficial effects of the present invention:

[0044] Compared with the prior art, the multi-dimensional calibration algorithm of the present invention has the following advantages:

[0045] 1. The present invention reduces the measurement error caused by detector noise in the prior art.

[0046] 2. The present invention improves the relative wavenumber accuracy of Raman spectroscopy data.

[0047] 3. The present invention enhances the comparability of data through intensity calibration.

[0048] 4. The multi-dimensional calibration algorithm of the present invention is applicable to a variety of Raman spectroscopy systems at the same time, has good versatility, and does not require special design changes to existing Raman spectroscopy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a schematic diagram of the overall process design of a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback of the present invention.

[0050] Figure 2 This is a detector background noise data diagram (offset) of a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback of the present invention.

[0051] Figure 3 This is a data diagram of noise1 of a Raman spectrum multi-dimensional calibration algorithm based on data feedback in the present invention (data detected without laser, in order to measure camera dark noise, non-Raman spectrum, Figure 2 Similarly, it is the detection data of the camera itself, so there is no relative wave number).

[0052] Figure 4 This is a Raman spectrum data diagram of near in a Raman spectrum multi-dimensional calibration algorithm based on data feedback of the present invention. (Data before calibration, the horizontal axis is the pixel position, and has not yet been converted into relative wavenumbers through Raman calibration)

[0053] Figure 5 This is a Raman spectrum data diagram of hg in a Raman spectrum multi-dimensional calibration algorithm based on data feedback of the present invention. (Data before calibration, the horizontal axis is the pixel position, and has not yet been converted into relative wavenumbers through Raman calibration)

[0054] Figure 6 This is a Raman spectrum data diagram of the SRM of a Raman spectrum multi-dimensional calibration algorithm based on data feedback in the present invention. (Data before calibration, the horizontal axis is the pixel position, and has not yet been converted into relative wavenumbers through Raman calibration)

[0055] Figure 7 This is a standard spectrum diagram of SRM 2242a of a Raman spectrum multi-dimensional calibration algorithm based on data feedback of the present invention.

[0056] Figure 8 This is a data diagram of the si Raman spectrum of a multi-dimensional calibration algorithm of Raman spectrum based on data feedback in the present invention. (Data before calibration, the horizontal axis is the pixel position, and has not yet been converted into relative wavenumbers through Raman calibration)

[0057] Fig. 9 Schematic diagram of the relationship between signal position and wavelength mapping of a Raman spectrum multi-dimensional calibration algorithm based on data feedback of the present invention.

[0058] Fig.10 The present invention discloses a Raman spectrum multi-dimensional calibration algorithm based on data feedback, in which acetaminophen is used as a calibration spectrum line for calibrating the measured sample.

[0059] Fig.11A block diagram of an electronic device of a Raman spectrum multi-dimensional calibration algorithm based on data feedback of the present invention.

[0060] Fig.12 The block diagram is a block diagram of an electronic device according to another embodiment of a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback of the present invention. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present application, and reference is made to the accompanying drawings in the embodiments of the present application. Figures 1 to 12 As shown,

[0062] A multi-dimensional calibration algorithm for Raman spectroscopy based on data feedback, including:

[0063] Step 1: Selection of reference materials;

[0064] The reference material has: clear Raman spectral characteristics, known spectral lines, and identifiable spectral peaks with known relative wave numbers;

[0065] As an example, the reference material is: neon argon lamp, mercury lamp, silicon, SRM 2242a and / or standard materials in ASTM E1840-96 (2022).

[0066] As an example, the spectral properties of the reference material should be well documented in the scientific literature to facilitate the accuracy and traceability of the calibration process.

[0067] As an example, the known spectral lines are: the wavelengths of neon, argon, mercury, and the relative wave number of silicon.

[0068] Step 2: Data collection;

[0069] Using a Raman spectroscopy system, collecting Raman spectroscopy data for the selected reference substance; and ensuring that the conditions set by the Raman spectroscopy system when collecting the reference substance are consistent with the conditions set during the actual sample analysis;

[0070] As an example, the set conditions refer to: the excitation wavelength is set to 532nm, 633nm, 785nm or 1064nm.

[0071] Step 3: Detector noise calibration;

[0072] ① Collect the background noise data of the detector without actual samples;

[0073] When there is no actual sample: turn off the laser, collect the detector base noise under the condition of 0.1s, and record it as offset;

[0074] When there is no actual sample: turn off the laser, collect the noise accumulated by the detector for 1s under the condition of 1s, and record it as noise1;

[0075] When there is no actual sample: turn off the laser, turn on the neon-argon lamp, and obtain the atomic line spectrum under 1s conditions, which is recorded as near;

[0076] When there is no actual sample: turn off the laser, turn on the mercury lamp, and obtain the atomic line spectrum under 1s conditions, recorded as hg;

[0077] For silicon samples: turn on the laser, 6mw, 0.5s, and obtain the Raman spectrum of silicon, recorded as SI;

[0078] For the SRM 2242a sample: turn on the laser, 3mw, 0.5s, and obtain the Raman spectrum of SRM 2242a, recorded as SRM;

[0079] The dark current of the detector at any exposure time t is calculated by using the offset and noise1, which is denoted as dc. The calculation formula is as follows:

[0080] ;

[0081] As an example, the noise data includes but is not limited to: background noise and dark current.

[0082] ② Using statistical analysis methods to process the collected noise data to determine the noise model;

[0083] As an example, the statistical analysis method includes: variance analysis method or autocorrelation function method.

[0084] ③ De-noising the Raman spectral data by using the noise model to improve the quality of the Raman spectral data;

[0085] Remove the offset in the near, hg, si, and SRM signals and the dark current at the corresponding exposure time;

[0086] Step 4: Raman relative wave number calibration;

[0087] ① Using the known spectrum of the reference material, establish a mapping relationship between signal position and wavelength (i.e., absolute wavelength); Fig. 9 As shown;

[0088] ② Calculate the excitation wavelength through the relative wavenumber of the characteristic peak and perform relative wavenumber calibration accordingly;

[0089] Calculating the peak positions in the near, hg and si signals by peak finding and peak position fitting;

[0090] The peak positions in the near and hg spectra are recorded as ;

[0091] Let near and hg correspond to the wavelength of the known spectrum peaks. ;

[0092] Establishing signal position through polynomial fitting To wavelength The mapping relationship ;

[0093] Substitute the obtained sub-signal peak position of the silicon peak into the formula , we get the wavelength information corresponding to the silicon peak, recorded as , the excitation wavelength is recorded as , it is known that the relative wave number of the silicon peak is 520.7cm -1 ;

[0094] According to the formula , calculated ;

[0095] According to the above information, the relative wave number corresponding to any signal position can be obtained, which is: ;

[0096] As an example, the relative wave number of the characteristic peak adopts the relative wave number of the silicon peak: 520.7 cm -1 .

[0097] Step 5: Raman intensity calibration;

[0098] ① Using standard reference materials or standard light sources to perform mapping operations on the Raman spectrum data;

[0099] As an example, the standard reference material is: SRM 2242a.

[0100] ② According to the result of the mapping operation, a calibration coefficient is obtained, and the Raman spectrum data is intensity calibrated by the calibration coefficient to ensure the accuracy and comparability of the Raman spectrum data;

[0101] Furthermore, the calibration coefficient ;

[0102] Step 6: Apply calibration;

[0103] The calibration coefficients are applied to actual Raman spectral data of actual samples to improve the accuracy of the analysis.

[0104] As an example, compare the data before and after calibration, verify the calibration effect, and adjust the calibration parameters as needed.

[0105] Measure the Raman spectrum of any substance (i.e. actual sample) and record it as , which contains the signal location information ,Signal strength information ; Then the relative wavenumber after calibration obtained by applying the calibration coefficient is: , the calibrated intensity information is: ;

[0106] In order to better illustrate the excellence of the present invention, acetaminophen is now listed as a measured sample for calibration comparison. The results are shown in Fig.10 shown.

[0107] In a second aspect, the present application also discloses an electronic device, comprising: a processor and a memory for storing instructions executable by the processor; wherein the processor is configured to execute a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback.

[0108] On the third aspect, the present application further illustrates a non-temporary computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback.

[0109] In a fourth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute a Raman spectroscopy multi-dimensional calibration algorithm based on data feedback.

[0110] It should be noted that, for the purpose of simple description, the method embodiments are expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously; secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by the present application.

[0111] Optionally, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0112] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0113] FIG11 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0114] Reference Fig.11 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0115] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0116] The memory 804 is configured to store various types of data to support operations on the device 800; examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0117] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

[0118] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0119] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0120] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0121] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0122] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0123] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0124] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of an electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0125] Fig.121 is a block diagram of another electronic device 1900 shown in the present application. For example, the electronic device 1900 can be provided as a server.

[0126] Reference Fig.12 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0127] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0128] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0129] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0130] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0131] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0132] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed; another point, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0135] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0136] The above are only preferred embodiments of the present invention. It should be understood that the description of the above embodiments is only used to help understand the method and core ideas of the present invention, and is not used to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, etc. made within the ideas and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A Raman spectroscopy multi-dimensional calibration algorithm based on data feedback, characterized in that: include: Step 1: Selection of reference materials; The reference material has: clear Raman spectral characteristics, known spectral lines, and identifiable spectral peaks with known relative wave numbers; Step 2: Data collection; Using a Raman spectroscopy system, collecting Raman spectroscopy data for the selected reference substance; and ensuring that the conditions set by the Raman spectroscopy system when collecting the reference substance are consistent with the conditions set during the actual sample analysis; Step 3: Detector noise calibration; ① Collect the background noise data of the detector without actual samples; ② Using statistical analysis methods to process the collected noise data to determine the noise model; ③ De-noising the Raman spectral data by using the noise model to improve the quality of the Raman spectral data; Remove the offset in near, hg, si, and SRM signals and the dark current at the corresponding exposure time; Step 4: Raman relative wave number calibration; ① Using the known spectral lines of the reference material, a mapping relationship between signal position and wavelength is established; ② Calculate the excitation wavelength through the relative wavenumber of the characteristic peak and perform relative wavenumber calibration accordingly; Step 5: Raman intensity calibration; ① Using standard reference materials or standard light sources to perform mapping operations on the Raman spectrum data; ② According to the result of the mapping operation, a calibration coefficient is obtained, and the Raman spectrum data is intensity calibrated by the calibration coefficient to ensure the accuracy and comparability of the Raman spectrum data; Step 6: Apply calibration; The calibration coefficients are applied to actual Raman spectral data of actual samples to improve the accuracy of the analysis.

2. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 1, characterized in that: The reference substances are: neon argon lamp, mercury lamp, silicon, SRM 2242a and / or standard substances in ASTM E1840-96:2022.

3. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 1, characterized in that: The known spectral lines are: the wavelengths of neon, argon, mercury, and the relative wave number of silicon.

4. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 1, characterized in that: The set conditions refer to: the excitation wavelength is set to 532nm, 633nm, 785nm or 1064nm.

5. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 1, characterized in that: Collecting the background noise data of the detector means: When there is no actual sample: turn off the laser, collect the detector base noise under the condition of 0.1s, and record it as offset; When there is no actual sample: turn off the laser, collect the noise accumulated by the detector for 1s under the condition of 1s, and record it as noise1; When there is no actual sample: turn off the laser, turn on the neon-argon lamp, and obtain the atomic line spectrum under 1s conditions, which is recorded as near; When there is no actual sample: turn off the laser, turn on the mercury lamp, and obtain the atomic line spectrum under 1s conditions, recorded as hg; For silicon samples: turn on the laser, 6mw, 0.5s, and obtain the Raman spectrum of silicon, recorded as SI; For the SRM 2242a sample: turn on the laser, 3mw, 0.5s, and obtain the Raman spectrum of SRM 2242a, recorded as SRM; The dark current of the detector at any exposure time t is calculated by using the offset and noise1, which is denoted as dc. The calculation formula is as follows: 。 6. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 5, characterized in that: The noise data includes: background noise and dark current.

7. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 5, characterized in that: The statistical analysis method includes: variance analysis method or autocorrelation function method.

8. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 5, characterized in that: Calculating the peak positions in the near, hg and si signals by peak finding and peak position fitting; The peak positions in the near and hg spectra are recorded as ; Let near and hg correspond to the wavelength of the known spectrum peaks. ; Establishing signal position through polynomial fitting To wavelength The mapping relationship ; Substitute the obtained sub-signal peak position of the silicon peak into the formula , we get the wavelength information corresponding to the silicon peak, recorded as , the excitation wavelength is recorded as , it is known that the relative wave number of the silicon peak is 520.7cm -1 ; According to the formula , calculated ; According to the above information, the relative wave number corresponding to any signal position can be obtained, which is: .

9. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 8, characterized in that: The standard reference materials described are: SRM 2242a; The calibration coefficient .

10. The Raman spectroscopy multi-dimensional calibration algorithm based on data feedback according to claim 1, characterized in that: Compare the data before and after calibration, verify the calibration effect, and adjust the calibration parameters as needed; Measure the Raman spectrum of any substance and record it as , which contains the signal location information ,Signal strength information ; Then the relative wavenumber after calibration obtained by applying the calibration coefficient is: , the calibrated intensity information is: .

Images