Mineral peak correction-based soil organic carbon functional group differential spectrum quantitative analysis method

Abstract

The invention relates to a soil organic carbon functional group differential spectrum quantitative analysis method based on mineral peak correction. The method comprises the following steps: dividing a to-be-detected soil sample meeting a preset standard into two parts, firing one of the two parts to form an original soil sample and an ash content sample, collecting a functional group characteristic signal from the sample through a Fourier transform infrared spectrometer, generating an original soil spectrum and an ash content spectrum, and calculating the content of the ash content in the original soil spectrum and the ash content spectrum according to the original soil spectrum and the ash content spectrum. The method comprises the following steps: carrying out spectrum pretreatment on an original soil spectrum and an ash spectrum, carrying out spectrum subtraction treatment after pretreatment to obtain a differential spectrum, carrying out peak area integration and proportion calculation on the differential spectrum, and establishing a reliable correlation between the peak area of the differential spectrum and the absolute content of organic carbon so as to directly output the absolute content of a target carbon functional group. By adopting the method, the defect that the traditional spectral analysis method can only be qualitative or semi-quantitative can be effectively overcome, the anti-interference capability and the standardization degree are improved, and the method has better repeatability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of quantitative analysis of soil organic carbon functional groups, and particularly relates to a soil organic carbon functional group difference spectrum quantitative analysis method based on mineral peak correction. BACKGROUND

[0002] The chemical structure of soil organic carbon (SOC) is the key to determining its stability and soil function. Fourier transform infrared spectroscopy (FTIR) is a fast and non-destructive analysis technique that is widely used to characterize the chemical structural characteristics of SOC. However, the complexity of soil composition and the interference of mineral components result in overlapping of mineral and organic functional group characteristic peaks in the mid-infrared region (4000-400 cm -1 ), which affects the detection sensitivity of the target wave number and greatly limits the application of FTIR in precise quantitative analysis of organic carbon functional groups.

[0003] In the traditional technology, in order to solve the problem of mineral interference, a method of removing organic matter by high-temperature calcination and then subtracting the mineral background (ash spectrum) from the original soil spectrum by spectral difference is proposed to enhance the signal of organic carbon characteristic peaks.

[0004] However, these methods have the defect of insufficient quantitative ability: the existing methods focus more on qualitative or semi-quantitative analysis, and lack reliable quantitative models that associate the difference spectrum organic peak area with the absolute content of organic carbon, so they cannot calculate the absolute content of specific functional groups (such as aliphatic carbon and aromatic carbon) ). SUMMARY

[0005] Therefore, it is necessary to provide a new method that can effectively reduce mineral interference and realize quantitative analysis of SOC functional groups.

[0006] In a first aspect, the application provides a soil organic carbon functional group difference spectrum quantitative analysis method based on mineral peak correction, comprising:

[0007] The soil sample to be measured that meets the preset particle size screening standard is divided into two parts to obtain an original soil sample and a soil sample to be treated, and the soil sample to be treated is subjected to calcination treatment according to a preset condition to obtain an ash sample;

[0008] Based on a Fourier transform infrared spectrometer, the original soil sample and the ash sample are subjected to spectral determination treatment by using a potassium bromide pressing method to obtain a preliminary original soil spectrum and a preliminary ash spectrum;

[0009] The preliminary raw soil spectrum and the preliminary ash spectrum are subjected to spectral pretreatment to obtain the raw soil spectrum and the ash spectrum;

[0010] The raw soil spectrum and the ash spectrum are subjected to spectral subtraction processing to obtain a difference spectrum;

[0011] The target organic carbon functional group absorption peak in the difference spectrum is subjected to peak area integration processing to obtain a peak area integration result, and based on the peak area integration result, the target functional group carbon content is calculated in combination with the difference value of the organic carbon content of the raw soil sample and the ash sample.

[0012] Further, the target organic carbon functional group absorption peak in the difference spectrum is subjected to peak area integration processing to obtain a peak area integration result, and based on the peak area integration result, the target functional group carbon content is calculated in combination with the difference value of the organic carbon content of the raw soil sample and the ash sample, including:

[0013] The target organic carbon functional group absorption peak in the difference spectrum is subjected to peak area integration to obtain a target functional group peak area; wherein the wave number of the target organic carbon functional group absorption peak includes 、 and ;

[0014] All target functional group peak areas are summed to obtain a total peak area;

[0015] The difference value of the organic carbon content of the raw soil sample and the ash sample is obtained, and based on the difference value of the organic carbon content and the total peak area, the target functional group carbon content is calculated using the following formula:

[0016]

[0017] wherein, is the target functional group carbon content, is the target functional group peak area, is the difference value of the organic carbon content, is the total peak area.

[0018] Further, the raw soil spectrum and the ash spectrum are subjected to spectral subtraction processing to obtain a difference spectrum, including:

[0019] The ash spectrum is set as a background spectrum, and an initial value of a subtraction coefficient is set;

[0020] Based on the initial value of the subtraction coefficient and the ash spectrum set as the background spectrum, the raw soil spectrum is subjected to subtraction processing to obtain a subtraction spectrum;

[0021] The quartz mineral peak signal intensity at and the signal intensity of the quartz mineral peak at 798 cm-1;

[0022] Based on the signal intensity monitoring result, the difference coefficient is iteratively adjusted until the signal intensity of the quartz mineral peak at 798 cm-1 and the signal intensity of the quartz mineral peak at 798 cm-1 satisfy the preset requirement, and the iteration is stopped to obtain an iteration result. the signal intensity of the quartz mineral peak at 798 cm-1 and the signal intensity of the quartz mineral peak at 798 cm-1 satisfy the preset requirement, and the iteration is stopped to obtain an iteration result.

[0023] The difference spectrum corresponding to the iteration result is determined as the difference spectrum.

[0024] Further, the method further comprises:

[0025] The peak area of the target functional group absorption peak and the peak area of the quartz mineral peak in the original soil spectrum and the ash spectrum are integrated respectively, and the relative peak area of the target functional group absorption peak and the relative peak area of the quartz mineral peak are calculated; wherein the wave number of the quartz mineral peak includes and , and the relative peak area is the proportion of each peak area to the total integral area of the corresponding spectrum;

[0026] Based on the stability of the quartz mineral peak before and after ashing, the difference spectrum factor is calculated according to the relative peak area of the quartz mineral peak;

[0027] For each target organic carbon functional group, the corrected relative peak area is calculated according to the relative peak area of the target functional group absorption peak and the difference spectrum factor; and all corrected relative peak areas are summed to obtain a total corrected peak area;

[0028] Based on the total corrected peak area, combined with the difference value of the organic carbon content of the original soil sample and the ash sample, the new target functional group carbon content is calculated using the following formula:

[0029]

[0030] wherein, is the new target functional group carbon content, is the corrected relative peak area, is the total corrected peak area, is the difference value of the organic carbon content;

[0031] Based on the new target functional group carbon content, the reliability of the target functional group carbon content is verified to obtain a verification result, and based on the verification result, an analysis report of the functional group carbon content and the difference is generated.

[0032] Further, the preliminary original soil spectrum and the preliminary ash spectrum are subjected to spectral pretreatment to obtain the original soil spectrum and the ash spectrum, including:

[0033] ​Baseline correction is performed on the preliminary raw soil spectrum and the preliminary ash spectrum to obtain a corrected raw soil spectrum and a corrected ash spectrum.

[0034] The corrected raw soil spectrum and the corrected ash spectrum are denoised to obtain the raw soil spectrum and the ash spectrum.

[0035] In a second aspect, the present application also provides a soil organic carbon functional group difference spectrum quantitative analysis device based on mineral peak correction, comprising:

[0036] A sample preparation module is configured to divide a soil sample to be tested that meets a preset particle size screening standard into two parts to obtain a raw soil sample and a soil sample to be processed, and perform a burning treatment on the soil sample to be processed according to a preset condition to obtain an ash sample.

[0037] A spectrum generation module is configured to perform spectrum determination treatment on the raw soil sample and the ash sample based on a Fourier transform infrared spectrometer by using a potassium bromide pressing method to obtain a preliminary raw soil spectrum and a preliminary ash spectrum.

[0038] A spectrum processing module is configured to perform spectrum pretreatment on the preliminary raw soil spectrum and the preliminary ash spectrum to obtain a raw soil spectrum and an ash spectrum.

[0039] A difference spectrum acquisition module is configured to perform spectrum subtraction treatment on the raw soil spectrum and the ash spectrum to obtain a difference spectrum.

[0040] A functional group carbon content calculation module is configured to perform peak area integration treatment on a target organic carbon functional group absorption peak in the difference spectrum to obtain a peak area integration result, and calculate a target functional group carbon content based on the peak area integration result and a difference value of organic carbon contents of the raw soil sample and the ash sample.

[0041] In a third aspect, the present application also provides a computer device, comprising a memory and a processor, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement any one of the soil organic carbon functional group difference spectrum quantitative analysis methods based on mineral peak correction described in the embodiments of the present application.

[0042] In a fourth aspect, the present application also provides a computer readable storage medium, wherein the computer readable storage medium stores at least one program code, and the program code is loaded and executed by a processor to implement any one of the soil organic carbon functional group difference spectrum quantitative analysis methods based on mineral peak correction described in the embodiments of the present application.

[0043] The soil organic carbon functional group difference spectrum quantitative analysis method based on mineral peak correction can effectively overcome the defects of traditional spectrum analysis methods that can only be qualitative or semi-quantitative, improve the anti-interference ability and standardization degree, and has good repeatability. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0045] Figure 1 A flowchart of a soil organic carbon functional group difference spectrum quantitative analysis method based on mineral peak correction in an embodiment is shown in the figure.

[0046] Figure 2 A flowchart of a step of performing spectrum difference processing on the original soil spectrum and the ash spectrum to obtain a difference spectrum in an embodiment is shown in the figure.

[0047] Figure 3 A structural diagram of a soil organic carbon functional group difference spectrum quantitative analysis device based on mineral peak correction in an embodiment is shown in the figure. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0049] In an embodiment, a soil organic carbon functional group difference spectrum quantitative analysis method based on mineral peak correction is provided. The method is applied to a terminal in this embodiment, and it can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is realized through the interaction of the terminal and the server. As shown in the figure, the method in this embodiment includes the following steps: Figure 1

[0050] ​Step S101, divide the to-be-tested soil sample meeting the preset particle size screening standard into two parts to obtain an original soil sample and a to-be-treated soil sample, and perform a burning treatment on the to-be-treated soil sample according to a preset condition to obtain an ash sample.

[0051] The preset particle size screening standard refers to a screening basis and rule about the particle diameter size in a material separation, product production or the like, and the to-be-tested soil sample is separated from mixed particle materials by defining a target particle size range, an allowed deviation, a screening precision and the like. The preset condition includes a burning at 550 degrees Celsius for 4 hours, and the temperature and time parameters are optimized based on the principle that organic matter is completely oxidized and mineral phases remain stable, so as to ensure that organic carbon is completely removed and mineral skeletons such as quartz and clay minerals are not damaged.

[0052] Exemplarily, the to-be-tested soil sample ground and passed through a 0.25 mm sieve is divided into two parts, one of which is used as the original soil sample to retain the complete organic and inorganic components, and the other is burned in a muffle furnace at 550 degrees Celsius for 4 hours to remove all organic carbon content in the soil, and the relative absorption peak area of the mineral peak does not change during the burning, so that the ash sample containing only mineral components is obtained. The above operation needs to be performed in a dry environment to prevent water from introducing additional absorption peaks, and the burning process needs to strictly control the heating rate and cooling conditions to avoid mineral phase change. The muffle furnace is a commonly used high-temperature heating device in the laboratory, which can burn, ash, melt or heat treat the sample in a controllable high-temperature environment, and can avoid direct contact between the sample and the heating element to reduce pollution or excessive oxidation.

[0053] Step S102, based on the Fourier transform infrared spectrometer, the potassium bromide pressing method is used to perform spectral determination processing on the original soil sample and the ash sample respectively to obtain a preliminary original soil spectrum and a preliminary ash spectrum.

[0054] Among them, Fourier Transform Infrared Spectroscopy (FTIR) is an analytical instrument that combines infrared spectroscopy with Fourier transform mathematical methods. It is used to analyze the chemical structure, functional group composition, and purity of substances by detecting their absorption, reflection, or transmission characteristics to infrared light of different wavelengths. Infrared spectroscopy is an analytical technique based on the vibrational-rotational energy level transitions of molecules. Its principle is that when infrared light of a specific wavelength irradiates a substance, the molecules absorb infrared light energy matching their own vibrational / rotational frequencies, causing the molecular energy levels to transition from the ground state to an excited state. By detecting the absorption intensity of light at different wavelengths, an infrared absorption spectrum reflecting the molecular structure characteristics can be obtained, and thus... Qualitative, quantitative, or structural analysis of substances is achieved; the Fourier transform mathematical method refers to decomposing a complex signal, which can be a waveform in the time dimension, an image in the spatial dimension, etc., into a superposition of countless sine / cosine waves (i.e., simple harmonic signals) with different frequencies and amplitudes; the potassium bromide pellet method is one of the most commonly used solid sample preparation techniques in infrared spectroscopy analysis, which involves mixing a trace amount of solid sample with inert potassium bromide powder and pressing it into a transparent sheet for infrared light transmission detection; spectral measurement and processing is an experimental operation that utilizes the absorption, emission, or scattering characteristics of substances to electromagnetic waves (light) of different wavelengths to analyze information such as the composition, structure, and content of substances.

[0055] For example, before performing spectral measurements, potassium bromide was dried at 105°C for 12 hours to remove moisture and prevent interference from the hydroxyl absorption peak. Soil samples were mixed with potassium bromide at a mass ratio of 1:100, ground evenly in an agate mortar, and then pressed into transparent tablets to ensure optical path consistency and transmittance. Parameters were then set for the Fourier transform infrared spectrometer (Nicolet 6700), including the scan range. Resolution The number of scans was 64, and these parameters were used to optimize the signal-to-noise ratio and resolution balance. Based on the pre-set parameters of the Fourier transform infrared spectrometer, and using prepared potassium bromide, the original soil samples and ash samples were spectrally measured separately using the potassium bromide pellet method. Each sample was measured three times, and the average value was taken to reduce random errors. The final outputs were preliminary original soil spectra and preliminary ash spectra, both in wavenumber-absorbance matrix form, including composite signals of organic and mineral components. Atmospheric and atmospheric signals needed to be subtracted in real time during the measurement process. Background. The wavelength and intensity calibration of a Fourier transform infrared spectrometer can be performed by verifying accuracy using standard samples. The Nicolet 6700 is a classic Fourier transform infrared spectrometer from Thermo Fisher Scientific, primarily used for chemical structure analysis, qualitative and quantitative detection of components, and is a commonly used analytical instrument in scientific research, industrial quality control, and other fields.

[0056] Step S103: Perform spectral preprocessing on the preliminary original soil spectrum and preliminary ash spectrum to obtain the original soil spectrum and ash spectrum.

[0057] Among them, spectral preprocessing is a key preliminary step in spectral analysis, which aims to eliminate systematic errors introduced by the instrument and the sample, including baseline correction and noise reduction.

[0058] For example, baseline correction and noise reduction are performed on the preliminary original soil spectrum and preliminary ash spectrum to obtain the original soil spectrum and ash spectrum.

[0059] Step S104: Perform spectral subtraction on the original soil spectrum and ash spectrum to obtain the difference spectrum.

[0060] Among them, spectral subtraction is a commonly used spectral analysis technique that highlights the characteristic spectrum of the target substance by subtracting interference signals. Essentially, it uses the superposition and linear relationship of spectra to achieve signal purification.

[0061] For example, by using ash spectroscopy as a mineral background, interference signals are subtracted from the original soil spectrum to obtain a difference spectrum that enhances the characteristic peaks of organic functional groups.

[0062] Step S105: Perform peak area integration on the absorption peak of the target organic carbon functional group in the difference spectrum to obtain the peak area integration result. Based on the peak area integration result, combined with the difference in organic carbon content between the original soil sample and the ash sample, calculate the carbon content of the target functional group.

[0063] Peak area integration is an operation used for quantitative detection in analytical chemistry such as chromatography and spectroscopy. It calculates the area enclosed by the response peak and the baseline to achieve a precise correlation between the content of the target substance in the sample.

[0064] For example, the peak area of ​​the absorption peak of the target organic carbon functional group in the difference spectrum is integrated to obtain the peak area of ​​each functional group. Based on the peak area of ​​each functional group and the difference in organic carbon content between the original soil sample and the ash sample, the carbon content of the target functional group is calculated. The integration needs to verify the correctness of the baseline, and the average of three measurements is used to reduce errors during the calculation. The target functional groups include... (Aliphatic CH) (Aromatic C=C) and (Methyl / methylene CH).

[0065] In this embodiment, a portion of the soil sample to be tested, meeting preset standards, is burned to form the original soil sample and ash sample. Functional group characteristic signals are collected from the sample using a Fourier transform infrared spectrometer to generate a spectrum. The spectrum is preprocessed and then subjected to spectral difference subtraction to obtain the difference spectrum. Subsequently, peak area integration and ratio calculations are performed on the difference spectrum to establish a reliable correlation between the peak area and the absolute content of organic carbon, thus directly outputting the absolute content of aliphatic and aromatic carbon. This effectively overcomes the limitations of traditional spectroscopic analysis methods, which can only provide qualitative or semi-quantitative results, improves anti-interference capabilities and standardization, and exhibits good repeatability.

[0066] In one embodiment, the absorption peak of the target organic carbon functional group in the difference spectrum is processed by peak area integration to obtain the peak area integration result. Based on the peak area integration result, combined with the difference in organic carbon content between the original soil sample and the ash sample, the carbon content of the target functional group is calculated, including:

[0067] Step S201: Integrate the peak area of ​​the absorption peak of the target organic carbon functional group in the difference spectrum to obtain the peak area of ​​the target functional group; wherein, the wavenumber of the absorption peak of the target organic carbon functional group includes , and .

[0068] The absorption peaks of the target organic carbon functional groups correspond to specific chemical bond vibrations. The region is an aliphatic CH stretching vibration. The vibration is an aromatic C=C stretching vibration. The position represents the CH bending vibration of methyl and methylene groups.

[0069] For example, based on the Beer-Lambert law in infrared spectroscopy, the integration function of spectral processing software (such as Omnic 8.0) is used to first determine the integration boundary of each absorption peak. This boundary is typically referenced to the baseline and extended by a certain wavenumber range (e.g., 10%) according to the peak's full width at half maximum (FWHM) to ensure complete peak area capture. Then, difference spectral data in wavenumber-absorbance matrix form is loaded, the start and end points of each peak are identified, and a numerical integration method, such as the trapezoidal rule, is used. The integration formula is: , and For the boundary of integration, For absorbance, the peak area values ​​of each target functional group were calculated, including , and Among them, the Beer-Lambert law states that the degree to which a substance absorbs infrared light of a specific wavelength is positively correlated with the concentration of the substance and the path length of light propagation in the substance; that is, the area of ​​the absorption peak is proportional to the concentration of functional groups. Spectral processing software is a type of professional tool specifically used for spectral data acquisition, processing, analysis, and visualization, serving scientific research and industrial scenarios related to spectral technologies such as infrared spectroscopy and Raman spectroscopy. Omnic 8.0 refers to the operation and data analysis software for Fourier transform infrared spectrometers (FTIR) launched by Bruker.

[0070] Step S202: Sum the peak areas of all target functional groups to obtain the total peak area.

[0071] For example, based on the additivity of the spectral signals, the peak areas of each functional group are obtained. , and To sum them up, the formula is: ,in, This represents the total peak area. For example, during data processing, the validity of each peak area value needs to be verified. This includes checking if they are positive and free of outliers, such as negative values ​​due to integration boundary errors, and ensuring that all peak areas are based on the same integration parameters and baseline correction conditions to maintain consistency. The additivity of the spectral signals means that the total peak area represents the overall absorption signal intensity of all target organic carbon functional groups in the difference spectrum, reflecting the total amount of organic carbon.

[0072] Step S203: Obtain the difference in organic carbon content between the original soil sample and the ash sample, and calculate the target functional group carbon content based on the difference in organic carbon content and the total peak area using the following formula:

[0073]

[0074] in, The target functional group carbon content, The target functional group peak area. This represents the difference in organic carbon content. This represents the total peak area.

[0075] For example, the organic carbon content of the original soil sample and the ash sample is determined by an elemental analyzer, and the difference in organic carbon content between the original soil sample and the ash sample is calculated based on the measured organic carbon content. The calculation formula is as follows: ,in, This represents the difference in organic carbon content. This represents the organic carbon content of the original soil sample. This represents the organic carbon content of the ash sample. The determination needs to be repeated three times and the average value taken to reduce error. For example, based on a proportional distribution model, the difference in organic carbon content is used to determine the optimal allocation for each functional group (e.g., ...). , and ), substitute the corresponding Value, such as correspond Perform multiplication and division operations to calculate the carbon content of the target functional group, which can be expressed in units of... The calculation process must ensure unit consistency and verify the reasonableness of the results; for example, the sum of the carbon content of each functional group should be close to ΔSOC. The proportional allocation model refers to distributing ΔSOC according to the relative proportion of each functional group's peak area in the total peak area, thereby converting the spectral signal into absolute content.

[0076] In this embodiment, the absolute carbon content of each target functional group is calculated based on a proportional distribution model by integrating the peak areas of the absorption peaks of the target organic carbon functional groups in the difference spectrum and summing the peak areas of each target functional group. This effectively overcomes the limitations of traditional FTIR methods, which can only provide qualitative or semi-quantitative results, and enables the direct output of the absolute content of functional groups such as aliphatic carbon and aromatic carbon. The results are intuitive, accurate, and reproducible.

[0077] In one embodiment, such as Figure 2 As shown, the original soil spectrum and ash spectrum were subjected to spectral subtraction to obtain the difference spectrum, which includes:

[0078] Step S301: Set the ash spectrum as the background spectrum and set the initial value of the difference coefficient.

[0079] Among them, the ash spectrum refers to the spectrum that retains only the mineral components after the incineration treatment. Since the organic matter has been removed, it can be used as an ideal background reference to eliminate mineral interference in the original soil spectrum. The reduction coefficient k is a parameter that adjusts the degree of reduction. The initial value is usually set to 1 as the starting point for iterative optimization to ensure that the reduction process starts from the standard state and avoids initial deviation.

[0080] For example, in spectral processing software (such as Omnic 8.0), ash spectral data is loaded and designated as the background spectrum; at the same time, the initial value of the difference coefficient is set.

[0081] Step S302: Based on the initial value of the subtraction coefficient and the ash spectrum set as the background spectrum, the original soil spectrum is subtracted to obtain the subtracted spectrum.

[0082] For example, based on the initial value of the subtraction coefficient and the ash spectrum set as the background spectrum, the background spectrum (ash spectrum) is proportionally subtracted from the original soil spectrum to separate the organic functional group signal. The subtraction formula can be expressed as: ,in, The absorbance of the subtraction spectrum at wavenumber λ when the subtraction coefficient is i. The absorbance of the original soil spectrum at wavenumber λ. The absorbance of the original soil spectrum at wavenumber λ. This represents the subtraction coefficient. For example, the subtraction process must maintain consistent spectral resolution to avoid information loss. After wave-by-wave point vector subtraction, a complete subtracted spectral map is generated. This map initially reduces the mineral background, but residual interference may still exist, requiring further optimization. The initial value of the subtraction coefficient is 1, representing full subtraction. Wave-by-wave point vector subtraction refers to treating the original spectrum (containing the target signal + mineral interference) and the coefficient-adjusted background spectrum (containing only mineral interference) as two wavenumber-absorbance vectors, and performing subtraction at each identical wavenumber point to accurately deduct the corresponding wavenumber mineral interference signal.

[0083] Step S303: Real-time monitoring of the difference subtraction plot The signal intensity of quartz mineral peaks and The signal intensity of the quartz mineral peak was measured, and the signal intensity monitoring results were obtained.

[0084] in, and The double peaks at this point are typical absorption peaks of quartz. Because they remain stable after the removal of organic matter, they can be used as indicators of mass difference.

[0085] Specifically, settings Quartz mineral peaks and For monitoring wavenumber ranges of quartz mineral peaks, local maximum search algorithms can be used to extract peak vertices within the monitoring range, by setting... By monitoring all wavenumber points within the specified wavenumber range, the wavenumber corresponding to the maximum absorbance is identified; this point is the wavenumber of the maximum absorbance. The actual peak of the peak, similarly, in Filtering within the monitored wavenumber range The actual peak vertices of the peaks are used, and the absorbance intensities corresponding to the two actual peak vertices are taken as the signal intensity. For example, the following settings are configured: Quartz mineral peaks and The monitoring wavenumber range of the quartz mineral peak is located, and the baseline intensity within the monitoring wavenumber range is calculated. The net peak signal intensity is then obtained by subtracting the baseline intensity.

[0086]

[0087]

[0088]

[0089]

[0090] in, for The characteristic peaks of quartz correspond to the baseline absorbance intensity within the monitoring range. for The characteristic peak of quartz corresponds to the wavenumber at the left end of the monitoring interval. for The characteristic peak of quartz corresponds to the wavenumber at the right end of the monitoring interval. The absorbance intensity at the left-end wavenumber of the subtraction spectrum. The absorbance intensity at the right-end wavenumber of the subtraction spectrum; for The characteristic peaks of quartz correspond to the baseline absorbance intensity within the monitoring range. for The characteristic peak of quartz corresponds to the wavenumber at the left end of the monitoring interval. for The characteristic peak of quartz corresponds to the wavenumber at the right end of the monitoring interval. The absorbance intensity at the left-end wavenumber of the subtraction spectrum. The absorbance intensity at the right-end wavenumber of the subtraction spectrum; In the difference subtraction spectrum The net peak signal intensity of quartz characteristic peaks. for Wavenumber corresponding to the local maximum value within the monitoring range of quartz characteristic peaks For the difference subtraction spectrum in The absorbance intensity at the wavenumber, that is, the absorbance intensity corresponding to the actual peak. In the difference subtraction spectrum The net peak signal intensity of quartz characteristic peaks. for Wavenumber corresponding to the local maximum value within the monitoring range of quartz characteristic peaks For the difference subtraction spectrum in The absorbance intensity at the wavenumber, that is, the absorbance intensity corresponding to the actual peak. The interval width needs to be adjusted according to the spectral resolution; the higher the resolution, the smaller the value of Δλ. Local maximum search is a basic heuristic search algorithm that searches for a better solution in the neighborhood of the current solution until no better solution can be found. The final result is called a local maximum or local optimum, not a global maximum (global optimum).

[0091] Step S304: Based on the signal strength monitoring results, iteratively adjust the differential coefficient until... The signal intensity of quartz mineral peaks and Once the quartz mineral peak signal intensity meets the preset requirements, the iteration stops, and the iteration result is obtained.

[0092] Among them, the preset requirement is a pre-set judgment standard, which is used to trigger specific actions or filter data, such as being close to zero.

[0093] Exemplarily, exemplaryly, based on the calculated and The net peak signal intensity of the bimodal distribution can be determined by adjusting the subtraction coefficient using a trial-and-error method. After each adjustment, the subtraction is re-executed to obtain a new round of net peak signal intensity. When the net peak signal intensity of the bimodal distribution meets the preset requirements, such as being close to zero, it indicates that mineral interference has been eliminated to the greatest extent, and the iteration stops. The trial-and-error method involves trying each possible value of the subtraction coefficient, filtering the optimal solution through feedback, thus determining the approximate range of the subtraction coefficient. Each candidate coefficient is then substituted into the subtraction formula for calculation, and the results are compared to determine the final value.

[0094] Step S305: Determine the difference spectrum corresponding to the iteration result as the difference spectrum.

[0095] For example, the iteration result includes the optimal difference reduction coefficient and the corresponding difference reduction spectrum, which is used as difference spectrum data in the format of wavenumber-absorbance matrix.

[0096] In this embodiment, by setting the ash spectrum as the background spectrum and setting an initial subtraction value, the original soil spectrum is subtracted based on the initial value of the subtraction coefficient and the ash spectrum set as the background spectrum. The intensity of the quartz peak signal is monitored in real time, and the coefficient is iteratively adjusted based on the signal intensity to finally determine the difference spectrum. This method can significantly enhance the organic functional group signal, eliminate the influence of mineral overlap, provide reliable data support for subsequent accurate quantification, and improve the method's anti-interference ability and accuracy.

[0097] In one exemplary embodiment, the method further includes:

[0098] Step S401: Integrate the peak areas of the target functional group absorption peaks and quartz mineral peaks in the original soil spectrum and ash spectrum, respectively, to calculate the relative peak areas of the target functional group absorption peaks and the relative peak areas of the quartz mineral peaks; wherein, the wavenumber of the quartz mineral peaks includes... and The relative peak area is the proportion of the area of ​​each peak to the total integral area of ​​the corresponding spectrum.

[0099] Among them, the absorption peaks of the target functional groups include aliphatic CH stretching vibration at the location, Aromatic C=C stretching vibrations and The methyl / methylene CH bending vibration at the point, while the quartz mineral peak indicates... and The characteristic absorption peaks at the location; the relative peak area is defined as the proportion of each peak area to the total integral area of ​​the corresponding spectrum. This normalization process can eliminate signal fluctuations caused by differences in sample amount or tablet thickness, ensuring data comparability.

[0100] For example, the integration function of spectral processing software is used to perform full-spectrum integration on the original soil spectrum and ash spectrum to obtain the total integrated area. An integration boundary is set for each target peak, typically based on a certain wavenumber range extended from the peak's half-width at half-maximum. Numerical integration methods (such as the trapezoidal rule) are used to calculate the area of ​​each peak. The area of ​​each peak is then divided by the total integrated area to obtain the relative peak area. The data processing process must ensure consistent integration parameters to avoid baseline interference.

[0101] Step S402: Based on the stability of the quartz mineral peaks before and after ashing, the difference spectral factor is calculated according to the relative peak areas of the quartz mineral peaks.

[0102] Among them, the stability of quartz mineral peaks before and after ashing refers to the stability of quartz mineral peaks ( and As an internal standard, it remains chemically stable after being ignited at 550℃, with minimal changes in peak shape and intensity ratio, thus it can be used to quantify the contribution of mineral background; the difference spectral factor is defined as the ratio of the sum of the relative peak areas of quartz mineral peaks in the original soil spectrum to the corresponding value in the ash spectrum.

[0103] For example, based on the stability of quartz mineral peaks before and after ashing, the difference spectral factor is calculated according to the relative peak areas of the quartz mineral peaks. The calculation formula is as follows:

[0104]

[0105] Where k is the difference spectrum factor, In the original soil spectrum and The sum of the relative peak areas at each location, In the ash spectrum and The sum of the relative peak areas at each point. The calculation process requires verification that the denominator is non-zero and that the k value is expected to be in the range of 0.5-0.8 to confirm the stability of the quartz peak.

[0106] Step S403: For each target organic carbon functional group, calculate the corrected relative peak area based on the relative peak area and difference factor of the absorption peak of the target functional group; and sum all the corrected relative peak areas to obtain the total corrected peak area.

[0107] For example, for , and For each functional group, substitute the corresponding relative peak area and k value into the correction formula, perform the calculation, and obtain the corrected relative peak area of ​​the target organic carbon functional group:

[0108]

[0109] in, To correct the relative peak area, This represents the relative peak area of ​​the target functional group in the original soil. Let be the relative peak area of ​​the target functional group in the ash spectrum. For example, for all... Summing the values, we obtain the total corrected peak area:

[0110]

[0111] in, This represents the total area of ​​the correction peak. for Corrected relative peak area of ​​functional groups. for Corrected relative peak area of ​​functional groups. for Corrected relative peak area of ​​functional groups.

[0112] Step S404: Based on the total calibration peak area and combined with the difference in organic carbon content between the original soil sample and the ash sample, the carbon content of the new target functional group is calculated using the following formula:

[0113]

[0114] in, The new target is the carbon content of functional groups. To correct the relative peak area, This represents the total area of ​​the correction peak. This represents the difference in organic carbon content.

[0115] For example, the difference in organic carbon content between the original soil sample and the ash sample is obtained. For each functional group, the corresponding... and The values ​​are calculated, and multiplication and division operations are performed. The calculation process must ensure unit consistency and verify that the sum of the carbon contents of each functional group is close to ΔSOC to control error. The new target functional group carbon content is then output.

[0116] Step S405: Based on the carbon content of the new target functional group, perform reliability verification on the carbon content of the target functional group, obtain the verification results, and generate an analysis report on the carbon content and differences of the functional group based on the verification results.

[0117] Among them, reliability verification refers to using scientific methods to confirm whether the actual carbon content data of the target functional group is accurate, stable and repeatable, eliminating factors such as detection errors and sample interference, and ensuring that the data can truly reflect the carbon distribution characteristics of the functional group in the sample.

[0118] For example, the consistency and reliability of the results can be verified by calculating the deviation between the carbon content of the new target functional group (obtained from the difference factor correction method) and the carbon content of the target functional group obtained by the aforementioned method (such as the spectral difference subtraction method). The verification method considers the results reliable if the deviation is less than a set deviation threshold, such as 5%. Statistical analysis of the results from both methods is performed to generate a report including the carbon content values ​​of each functional group, explanations of the differences, and confidence intervals; the report format can be tabular or text, highlighting the differences between treatments.

[0119] In this embodiment, the relative peak areas of the target functional group absorption peaks and quartz mineral peaks in the original soil spectrum and ash spectrum are calculated. A difference spectral factor is calculated based on the relative peak area of ​​the quartz mineral peaks. Peak area correction is performed based on the difference spectral factor, and the corrected peak areas are summed. The carbon content of the target functional group is calculated according to the carbon content calculation formula, and reliability verification is performed. A difference analysis report is generated based on the verification results. This significantly improves the resistance to mineral interference, making the quantitative results of aliphatic carbon and aromatic carbon more accurate and reproducible.

[0120] In one embodiment, the preliminary original soil spectrum and preliminary ash spectrum are subjected to spectral preprocessing to obtain the original soil spectrum and ash spectrum, including:

[0121] Step S501: Baseline correction is performed on the preliminary original soil spectrum and the preliminary ash spectrum to obtain the corrected original soil spectrum and the corrected ash spectrum.

[0122] Baseline correction refers to eliminating the interference of baseline drift or baseline noise on the target signal, so that the data can more accurately reflect the real information. The baseline can be understood as the reference level of the signal, that is, the stable background signal output by the detection device when there is no target substance / target signal, such as the light intensity output of the instrument when there is no sample in spectral detection.

[0123] For example, baseline drift in infrared spectra is usually caused by sample scattering, instrument instability, or environmental factors. These non-specific absorptions distort the true absorption peak intensity. Therefore, it is necessary to fit and subtract the baseline using mathematical algorithms to ensure that the peak area integral accurately reflects the functional group concentration. Fourier transform infrared spectroscopy processing software, such as Omnic 8.0, can be used. After loading the preliminary original soil spectrum and preliminary ash spectrum, a polynomial fitting algorithm is used to automatically detect the baseline shape. This polynomial fitting algorithm constructs a polynomial curve (such as a second-order or third-order) to simulate the baseline by selecting a flat region in the spectrum without characteristic absorption as a reference point. Then, the curve is subtracted from the original spectrum to obtain the corrected original soil spectrum and ash spectrum.

[0124] Step S502: Denoise the corrected original soil spectrum and the corrected ash spectrum to obtain the original soil spectrum and the ash spectrum.

[0125] Among them, noise reduction refers to using smoothing algorithms to reduce random noise while preserving the peak shape of spectral features. This noise may come from instrument electronic interference or sample inhomogeneity.

[0126] For example, filter parameters such as window size and polynomial order are set in FTIR software, and convolution operations are performed on the corrected spectrum to calculate the smoothed value at each wavenumber point, resulting in smoothed original soil and ash spectra. Convolution is essentially a mathematical operation of "weighted summation + sliding," which can extract local features of signals or data. A filter is a device or algorithm used to process signals or data, selectively passing or suppressing certain frequency components to remove noise, extract useful information, or improve signal quality. Here, the Savitzky-Golay filter can be used; it is a polynomial-fit sliding window smoothing filter algorithm that can smooth data, suppress noise, and preserve signal trends, such as peaks, valleys, inflection points, and other detailed features, to the greatest extent possible.

[0127] In this embodiment, baseline correction and denoising of the preliminary original soil spectrum and preliminary ash spectrum can effectively improve the quality of spectral data, ensure the accuracy of subsequent subtraction and quantitative analysis, thereby providing a reliable data foundation for the entire method and enhancing anti-interference ability and result repeatability.

[0128] To further illustrate the solutions of the embodiments of this application, a specific example is provided below.

[0129] Example: Analysis of the functional group characteristics of organic carbon in black soil after sheep manure fertilization

[0130] Samples: Black soil samples (0-20cm) were taken from the sheep manure (OM) treatment group and the control (NF) group in the field experiment.

[0131] 1. Air dry the soil and grind it through a 0.25 mm sieve. Weigh 10 g of the soil and calcine it at 550℃ for 4 hours to obtain an ash sample.

[0132] 2. Accurately weigh 2 mg of original soil and ash, mix with 200 mg of dry potassium bromide, compress into tablets, and collect spectra using a Nicolet 6700 FTIR spectrometer.

[0133] 3. Calculation using Method 1:

[0134] In Omnic 8.0 software, baseline correction and smoothing were performed on the acquired infrared spectra; then, using the ash spectrum as background, the data was subtracted from the original soil spectrum, and the coefficients were adjusted to... and The bimodal signal is reduced to its lowest level, thus obtaining the difference spectrum.

[0135] In the difference spectrum, the peak areas of the absorption peaks of organic carbon functional groups are integrated and summed to obtain the OM processing: , , , ;

[0136] The SOC of the original soil treated with OM was measured by an elemental analyzer to be [value missing]. Its ash SOC is Therefore .

[0137] OM processing .

[0138] Similarly, the calculation yields and The functional group carbon content are respectively and .

[0139] 4. Calculation using Method Two:

[0140] Absorption peaks of target functional groups in the original soil spectrum and ash spectrum were analyzed respectively. , , ) and quartz mineral peaks ( , Perform peak area integration and calculate its relative peak area in the original soil spectrum: , , , The percentages were 1.033%, 48.72%, 3.27%, and 46.97%, respectively; in the ash spectrum: and The figures are 14.49% and 85.57%, respectively.

[0141] Calculate the difference spectral factor k:

[0142] Calculate the corrected relative peak area ( ):

[0143]

[0144] Calculate the carbon content of a specific functional group:

[0145] Carbon content of functional groups = Δ / ∑(Δ +Δ +Δ )*ΔSOC=1.033% / (1.033%+40.76%+3.27%)*26.69 =0.64( )

[0146] Similarly, the calculation yields and The functional group carbon content is 23.90 ( ) and 2.05 ( ).

[0147] Results: Both methods showed that sheep manure treatment significantly increased aliphatic carbon in the soil (…). , ) and aromatic carbon ( The content and trend were consistent, verifying the reliability and practicality of this method.

[0148] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0149] Based on the same inventive concept, this application also provides a soil organic carbon functional group difference spectrum quantitative analysis device based on mineral peak correction for implementing the above-mentioned method for quantitative analysis of soil organic carbon functional group difference spectrum based on mineral peak correction. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of a soil organic carbon functional group difference spectrum quantitative analysis device based on mineral peak correction provided below can be found in the limitations of the soil organic carbon functional group difference spectrum quantitative analysis method based on mineral peak correction described above, and will not be repeated here.

[0150] In one exemplary embodiment, such as Figure 3 As shown, a soil organic carbon functional group differential spectral quantitative analysis device 300 based on mineral peak correction is provided, comprising:

[0151] The sample preparation module 301 is used to divide the soil sample to be tested that meets the preset particle size screening standard into two parts to obtain the original soil sample and the soil sample to be processed, and to perform calcination treatment on the soil sample to be processed according to the preset conditions to obtain the ash sample.

[0152] The spectrum generation module 302 is used to perform spectral measurement on the original soil sample and the ash sample using the potassium bromide pellet method based on the Fourier transform infrared spectrometer, so as to obtain the preliminary original soil spectrum and the preliminary ash spectrum.

[0153] The spectral processing module 303 is used to perform spectral preprocessing on the preliminary original soil spectrum and the preliminary ash spectrum to obtain the original soil spectrum and the ash spectrum.

[0154] The difference spectrum acquisition module 304 is used to perform spectral subtraction processing on the original soil spectrum and ash spectrum to obtain the difference spectrum.

[0155] The functional group carbon content calculation module 305 is used to perform peak area integration processing on the absorption peak of the target organic carbon functional group in the difference spectrum, obtain the peak area integration result, and calculate the target functional group carbon content based on the peak area integration result and the difference in organic carbon content between the original soil sample and the ash sample.

[0156] In one embodiment, the functional group carbon content calculation module 305 is further configured to:

[0157] The peak area of ​​the target organic carbon functional group is obtained by integrating the peak area of ​​the absorption peak in the difference spectrum; wherein, the wavenumber of the absorption peak of the target organic carbon functional group includes , and ;

[0158] The total peak area is obtained by summing the peak areas of all target functional groups.

[0159] Obtain the difference in organic carbon content between the original soil sample and the ash sample. Based on the difference in organic carbon content and the total peak area, calculate the target functional group carbon content using the following formula:

[0160]

[0161] in, The target functional group carbon content, The target functional group peak area. This represents the difference in organic carbon content. This represents the total peak area.

[0162] In one embodiment, the difference spectrum acquisition module 304 is further configured to:

[0163] Set the ash spectrum as the background spectrum and set the initial value of the difference reduction coefficient;

[0164] Based on the initial value of the subtraction coefficient and the ash spectrum set as the background spectrum, the original soil spectrum is subtracted to obtain the subtracted spectrum.

[0165] Real-time monitoring of the difference graph The signal intensity of quartz mineral peaks and The signal intensity of the quartz mineral peak was measured, and the signal intensity monitoring results were obtained.

[0166] Based on the signal strength monitoring results, the differential coefficient is iteratively adjusted until... The signal intensity of quartz mineral peaks and Once the peak signal intensity of the quartz mineral meets the preset requirements, the iteration stops and the iteration result is obtained.

[0167] The difference spectrum corresponding to the iteration result is defined as the difference spectrum.

[0168] In one exemplary embodiment, the apparatus further includes:

[0169] The peak area integration module is used to integrate the peak areas of the target functional group absorption peaks and quartz mineral peaks in the original soil spectrum and ash spectrum, respectively, to calculate the relative peak areas of the target functional group absorption peaks and the relative peak areas of the quartz mineral peaks; wherein, the wavenumber of the quartz mineral peaks includes... and The relative peak area is the proportion of the area of ​​each peak to the total integral area of ​​the corresponding spectrum.

[0170] The difference spectral factor acquisition module is used to calculate the difference spectral factor based on the stability of quartz mineral peaks before and after ashing, according to the relative peak areas of the quartz mineral peaks.

[0171] The correction peak area acquisition module is used to calculate the correction relative peak area for each target organic carbon functional group based on the relative peak area and difference spectral factor of the target functional group's absorption peak; and to sum all the correction relative peak areas to obtain the total correction peak area.

[0172] The carbon content acquisition module calculates the carbon content of the new target functional group based on the total calibration peak area, combined with the difference in organic carbon content between the original soil sample and the ash sample, using the following formula:

[0173]

[0174] in, The new target is the carbon content of functional groups. To correct the relative peak area, This represents the total area of ​​the correction peak. This represents the difference in organic carbon content.

[0175] The analysis report generation module is used to perform reliability verification of the carbon content of the target functional group based on the carbon content of the new target functional group, obtain the verification results, and generate an analysis report on the carbon content and differences of the functional group based on the verification results.

[0176] In one embodiment, the spectrum generation module 302 is further configured to:

[0177] Baseline correction was performed on the preliminary original soil spectrum and the preliminary ash spectrum to obtain the corrected original soil spectrum and the corrected ash spectrum;

[0178] The original soil spectrum and the corrected ash spectrum were denoised to obtain the original soil spectrum and the ash spectrum.

[0179] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the previously described method for quantitative analysis of soil organic carbon functional groups based on mineral peak correction using differential spectral analysis.

[0180] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0181] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0182] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for quantitative analysis of functional groups of soil organic carbon based on mineral peak correction, characterized in that, The method includes: The soil sample to be tested, which meets the preset particle size screening standard, is divided into two parts to obtain the original soil sample and the soil sample to be processed. The soil sample to be processed is then subjected to incineration treatment according to preset conditions to obtain the ash sample. Based on Fourier transform infrared spectroscopy, the original soil sample and the ash sample were subjected to spectral measurement using the potassium bromide pellet method to obtain preliminary original soil spectra and preliminary ash spectra. The preliminary original soil spectrum and the preliminary ash spectrum are subjected to spectral preprocessing to obtain the original soil spectrum and ash spectrum; The original soil spectrum and the ash spectrum are subjected to spectral subtraction processing to obtain the difference spectrum; The peak area of ​​the absorption peak of the target organic carbon functional group in the difference spectrum is integrated to obtain the peak area integration result. Based on the peak area integration result and the difference in organic carbon content between the original soil sample and the ash sample, the carbon content of the target functional group is calculated.

2. The method according to claim 1, characterized in that, The peak area integration of the absorption peak of the target organic carbon functional group in the difference spectrum is performed to obtain the peak area integration result. Based on the peak area integration result and the difference in organic carbon content between the original soil sample and the ash sample, the carbon content of the target functional group is calculated, including: The peak area of ​​the target organic carbon functional group in the difference spectrum is obtained by integrating the peak area of ​​the target functional group; wherein, the wavenumber of the absorption peak of the target organic carbon functional group includes , and ; The total peak area is obtained by summing the peak areas of all the target functional groups. The difference in organic carbon content between the original soil sample and the ash sample is obtained. Based on the difference in organic carbon content and the total peak area, the target functional group carbon content is calculated using the following formula: in, The target functional group carbon content, The target functional group peak area. This represents the difference in organic carbon content. This represents the total peak area.

3. The method according to claim 1, characterized in that, The step of performing spectral subtraction processing on the original soil spectrum and the ash spectrum to obtain the difference spectrum includes: Set the ash spectrum as the background spectrum and set the initial value of the difference reduction coefficient; Based on the initial value of the subtraction coefficient and the ash spectrum set as the background spectrum, the original soil spectrum is subjected to subtraction processing to obtain a subtracted spectrum. Real-time monitoring of the difference spectrum The signal intensity of quartz mineral peaks and The signal intensity of the quartz mineral peak was measured, and the signal intensity monitoring results were obtained. Based on the signal strength monitoring results, the differential coefficient is iteratively adjusted until the signal strength monitoring results are obtained. The signal intensity of the quartz mineral peak and the Once the peak signal intensity of the quartz mineral meets the preset requirements, the iteration stops and the iteration result is obtained. The difference subtraction spectrum corresponding to the iteration result is determined as the difference spectrum.

4. The method according to claim 1, characterized in that, The method further includes: Peak area integration was performed on the target functional group absorption peak and the quartz mineral peak in the original soil spectrum and the ash spectrum, respectively, to calculate the relative peak area of ​​the target functional group absorption peak and the relative peak area of ​​the quartz mineral peak; wherein, the wavenumber of the quartz mineral peak includes and The relative peak area is the proportion of each peak area to the total integral area of ​​the corresponding spectrum; Based on the stability of the quartz mineral peaks before and after ashing, the difference spectral factor is calculated according to the relative peak areas of the quartz mineral peaks. For each target organic carbon functional group, the corrected relative peak area is calculated based on the relative peak area of ​​the absorption peak of the target functional group and the difference spectral factor; and all the corrected relative peak areas are summed to obtain the total corrected peak area. Based on the total corrected peak area and the difference in organic carbon content between the original soil sample and the ash sample, the carbon content of the new target functional group is calculated using the following formula: in, The new target is the carbon content of functional groups. To correct the relative peak area, This represents the total area of ​​the correction peak. This represents the difference in organic carbon content. Based on the carbon content of the new target functional group, the reliability of the carbon content of the target functional group is verified to obtain the verification results, and based on the verification results, an analysis report on the carbon content of the functional group and the difference is generated.

5. The method according to claim 1, characterized in that, The process of performing spectral preprocessing on the preliminary original soil spectrum and the preliminary ash spectrum to obtain the original soil spectrum and ash spectrum includes: Baseline correction is performed on the preliminary original soil spectrum and the preliminary ash spectrum to obtain the corrected original soil spectrum and the corrected ash spectrum; The original soil spectrum and the ash spectrum after correction are denoised to obtain the original soil spectrum and the ash spectrum.

6. A soil organic carbon functional group difference spectrum quantitative analysis device based on mineral peak correction, characterized in that, The device includes: The sample preparation module is used to divide the soil sample to be tested that meets the preset particle size screening standard into two parts to obtain the original soil sample and the soil sample to be processed, and to perform calcination treatment on the soil sample to be processed according to preset conditions to obtain the ash sample. The spectrum generation module is used to perform spectral measurement on the original soil sample and the ash sample respectively using a Fourier transform infrared spectrometer and a potassium bromide pellet method to obtain preliminary original soil spectrum and preliminary ash spectrum. The spectral processing module is used to perform spectral preprocessing on the preliminary original soil spectrum and the preliminary ash spectrum to obtain the original soil spectrum and the ash spectrum. The difference spectrum acquisition module is used to perform spectral subtraction processing on the original soil spectrum and the ash spectrum to obtain the difference spectrum. The functional group carbon content calculation module is used to perform peak area integration processing on the absorption peak of the target organic carbon functional group in the difference spectrum to obtain the peak area integration result, and calculate the target functional group carbon content based on the peak area integration result and the difference in organic carbon content between the original soil sample and the ash sample.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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