Soil organic carbon source and stability rapid evaluation method based on solid 13C nuclear magnetic resonance spectrum and intelligent analysis system

Abstract

The invention discloses a soil organic carbon source and stability rapid evaluation method based on a solid 13C nuclear magnetic resonance spectrum and an intelligent analysis system, and the method comprises the following steps: carrying out standard pretreatment on a soil sample, and detecting the soil sample by using a solid 13C nuclear magnetic resonance technology to obtain spectrum data; partitioning the obtained spectrum data according to a standard chemical shift interval, and calculating an integral area proportion of a partition corresponding to each chemical functional group; based on the molecular mixing model, constructing a linear equation set, and solving the mole fractions of the six chemical components in the soil sample by adopting a constrained least square programming algorithm; and calculating the proportions of plant source carbon, microbial source carbon and pyrolytic carbon in the soil sample according to the calculated mole fractions of the six chemical components. The carbon proportion of a plant source and a microorganism source can be effectively distinguished by combining solid 13C nuclear magnetic resonance and a molecular mixed model, and the carbon stability change in the grassland degeneration and recovery process is revealed.

Description

Technical Field

[0001] This invention relates to the field of organic geochemistry and environmental analysis technology, and particularly to a method utilizing solid-state... 13 A method and system for accurately quantifying the sources of soil organic carbon in grassland ecosystems, combining 1C nuclear magnetic resonance spectroscopy with chemometric models and intelligent algorithms. Background Technology

[0002] Soil organic carbon (SOC) is an important component of the carbon cycle in terrestrial ecosystems. Its chemical composition and biological origin determine the stability and turnover rate of carbon in the ecosystem.

[0003] In grassland ecosystems, soil organic carbon primarily originates from plant root inputs, aboveground biota, and microbial biosynthesis and decomposition products. Given the complex and intertwined carbon input pathways in grassland ecosystems, accurately distinguishing between plant-derived carbon and microbial-derived carbon has become a key scientific issue in studying soil carbon dynamics and carbon sink functions.

[0004] solid state 13 C nuclear magnetic resonance (C10) 13 CCP / MAS NMR technology can provide complete information on carbon functional groups in organic matter non-destructively, and is an important means of resolving the chemical structure of complex organic matter. The Molecular Mixing Model (MMM) proposed by Baldock et al. transforms NMR spectroscopy data into six biologically significant chemical components (carbohydrates, proteins, lignin, lipids, carbonyl compounds, and black carbon).

[0005] However, the application of this model still has the following problems: computational complexity, as MMM is a high-dimensional linear system of equations that requires specialized software for matrix solving or optimization calculations; strong subjectivity, as the model coefficients and constraint settings lack a unified standard, and the results are easily affected by human factors; and insufficient source analysis, as traditional models only provide the proportion of chemical components and cannot further reveal the biological source of organic carbon. Summary of the Invention

[0006] This invention aims to provide a comprehensive method and system for intelligent analysis of solid-state nuclear magnetic resonance (NMR) organic carbon sources in grassland ecosystems. This method automates and standardizes the processing of solid-state NMR data and further analyzes the ratio of plant-derived to microbial-derived carbon in grassland ecosystems. By combining constrained least squares programming and machine learning models, this method achieves self-learning and dynamic optimization of the model while maintaining accuracy, thereby improving the automation and reliability of determining soil organic carbon sources in grassland ecosystems.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A solid-state based 13 A rapid assessment method for soil organic carbon sources and stability using 12C nuclear magnetic resonance spectroscopy is used to quickly assess the proportion of chemical components, organic matter sources, and soil organic carbon stability in grassland ecosystem soils. The method includes the following steps: S1. Standard pretreatment of soil samples using solid-state... 13 C-NMR technology is used to detect and obtain spectral data from pretreated soil samples; S2. Divide the acquired spectral data into sections according to the standard chemical shift intervals, and calculate the integral area ratio of each section corresponding to each chemical functional group. S3. Calculate the organic carbon stability rating index based on the integral area ratio of each chemical functional group's corresponding partition, including the alkyl carbon / oxyalkyl carbon ratio and the aromaticity index. Evaluate the organic carbon stability based on the organic carbon stability analysis index. S4. Based on the molecular mixing model, a linear equation system is constructed and the constrained least squares programming algorithm is used to solve for the mole fraction of six chemical components in the soil sample. The six chemical components include carbohydrates, proteins, lignin, lipids, carbonyl compounds and black carbon. S5. Based on the calculated mole fractions of the six chemical components, calculate the proportions of plant-derived carbon, microbial-derived carbon, and pyrolytic carbon in the soil sample. The calculation formula is as follows: ; ; ; in, The percentage of carbon derived from plants, The percentage of carbon from microbial sources, The percentage of carbon produced by pyrolysis; , , , , , These are the mole fractions of carbohydrates, proteins, lignin, lipids, carbonyl compounds, and black carbon, respectively. This is the coefficient for the proportion of plant-derived lipids. This is the proportion coefficient of microbial origin in lipids. .

[0008] Preferably, step S4 specifically includes the following steps: (1) Select the coefficient matrix and establish the system of equations: , ; in, For the first The percentage of the integral area corresponding to each chemical functional group. The first element in the coefficient matrix of the molecular mixing model Line number Column elements, For the first to be tested Mole fraction of chemical components; (2) Define the objective function: ; (3) Constraints: ,and ; (4) Solution method: The sequential least squares programming algorithm is used for iterative solution, with initial values... Set as proportional vector Iterative updates until the objective function changes. Stop at this time; (5) Result Correction and Output: Set a residual threshold; if the residual norm Then the output will be the mole fraction of the six chemical components; if the residual norm Then, the coefficient matrix is ​​optimized through machine learning, and the optimized coefficient matrix is ​​used to enter the main process for calculation.

[0009] Preferably, the recalculation of the coefficient matrix through machine learning optimization includes the following steps: (1) Data input: Import measured nuclear magnetic resonance spectroscopy data and measured organic carbon source ratio; (2) Parameter initialization: using the original coefficient matrix Initial value; (3) Model training: Correction is performed using ridge regression or random forest algorithms. A new coefficient matrix is ​​obtained. ; (4) Iterative optimization: Calculate the mean squared error, and stop training when the mean squared error is less than the threshold; (5) Update the model: Replace the original matrix and proceed to the main solution process.

[0010] Preferably, in step S3, the formula for calculating the alkyl carbon / oxyalkyl carbon ratio and the aromaticity index is as follows: ; ; in, The ratio of alkyl carbon to oxyalkyl carbon is given. The aroma index; , , , , These represent the integral area percentages of functional groups alkyl carbon, O-alkyl carbon, aromatic carbon, phenolic carbon, and bis-O-alkyl carbon, respectively. This represents the total integral area of ​​each functional group partition.

[0011] Preferably, the standard pretreatment of the soil sample includes air drying, grinding, sieving, and removal of inorganic carbon and metal ions.

[0012] Furthermore, the present invention also provides a solid-state based 13 A smart analysis system for the source and stability of soil organic carbon using C1 nuclear magnetic resonance spectroscopy, comprising the following modules: Data acquisition module: Communicates with the solid-state NMR spectrometer and is used to automatically read raw NMR spectrum data; Spectral preprocessing module: used to perform baseline drift correction, noise filtering, and integration interval division; The stoichiometry module is used to solve systems of equations using a constrained least-bill programming algorithm. Model optimization module: used to automatically adjust the coefficient library of molecular mixing models based on measured data, and dynamically correct the coefficient matrix through machine learning algorithms; Organic carbon source analysis module: used to calculate the proportions of plant-derived carbon, microbial-derived carbon, and pyrolysis carbon, and output organic carbon stability analysis indicators; Results Display and Reporting Module: Used to generate standardized results reports, including chemical component ratios, organic carbon source ratios, fitting residuals, and R² values, and export them to Excel or PDF format; Cloud database module: used to upload and share diverse long-term carbon source monitoring data, and support model migration and comparison between different ecosystems.

[0013] Compared with the prior art, the present invention has the following advantages: (1) This invention is particularly applicable to ecosystem types such as alpine meadows, temperate grasslands, degraded grasslands, and artificially restored grasslands. Grassland ecosystems contain complex soil organic carbon components, including a large amount of carbon input from plant roots and microbial residues. Solid-state nuclear magnetic resonance combined with a molecular mixing model can effectively distinguish the ratio of plant-derived to microbial-derived carbon, revealing changes in carbon stability during grassland degradation and restoration. This method can be widely applied to grassland ecosystem carbon sink assessment, monitoring of degraded grassland restoration effectiveness, and accounting for carbon neutrality projects. (2) Automation and intelligence: The algorithm module can learn and optimize the coefficient library according to the sample type to improve consistency and accuracy; (3) System integration: realize full-process automation from importing spectral data to outputting carbon source results; (4) Compatible with scientific research and application: It is suitable for both scientific research and grassland carbon sink monitoring and carbon neutrality assessment projects. Attached Figure Description

[0014] Figure 1 This is the spectral partitioning diagram based on chemical shift according to the present invention.

[0015] Figure 2 This is a comparison chart of the NMR estimation and actual measurement of soil organic carbon sources according to the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] A solid-state based 13 A rapid assessment method for the source and stability of soil organic carbon using C14 nuclear magnetic resonance spectroscopy includes the following steps: In this embodiment, nine groups of grassland soil samples were collected to assess the source and stability of soil organic carbon.

[0019] 1. Sample preparation and NMR detection First, the grassland soil samples underwent standard pretreatment processes such as air drying, grinding, and sieving to remove inorganic carbon and metal ions, and then solid-state... 13 C CP / MAS NMR technology is used to acquire nuclear magnetic resonance spectral data.

[0020] 2. Spectral partitioning and integration The obtained nuclear magnetic resonance spectroscopy data were partitioned according to standard chemical shift intervals (refer to...). Figure 1 The carbon groups are typically classified into alkyl carbons, N-alkyl / methoxy carbons, O-alkyl carbons, bis-O-alkyl carbons, aromatic carbons, phenolic carbons, and carbonyl / carboxyl carbons, and the integral area percentage of the carbons in each functional group is calculated.

[0021] 3. Analysis of the stability of organic carbon The organic carbon stability rating index is calculated based on the integral area ratio of chemical functional groups, including the alkyl carbon / oxyalkyl carbon ratio and the aromaticity index. The formulas for calculating the alkyl carbon / oxyalkyl carbon ratio and the aromaticity index are as follows: ; ; in, The ratio of alkyl carbon to oxyalkyl carbon is given. The aroma index; , , , , These represent the integral area percentages of functional groups alkyl carbon, O-alkyl carbon, aromatic carbon, phenolic carbon, and bis-O-alkyl carbon, respectively. This represents the total integral area of ​​each functional group partition; Evaluation method: The relative magnitude of the above indicators is used to judge directly. The higher the value of the alkyl carbon / oxyalkyl carbon ratio and the aromaticity index, the more stable it is. For example, the stability of organic carbon can be divided into three levels: "high", "medium" and "low". The percentage of the integral area of ​​functional groups in nuclear magnetic resonance (NMR) and the stability resolution index of the nine soil samples are shown in Table 1.

[0022] 4. Model Construction and Solution Based on a molecular mixing model, a system of linear equations was constructed, and a constrained least squares programming algorithm was used to solve for the mole fraction of each chemical component in the soil sample. The chemical components included carbohydrates, proteins, lignin, lipids, carbonyl compounds, and black carbon. The specific steps included were as follows: (1) Select the coefficient matrix and establish a system of linear equations.

[0023] in, The coefficient matrix of the molecular mixing model is the matrix. The data comes from a pre-defined coefficient library (as shown in Table 2, based on Nelson & Baldock, 2005 and expandable). Let be the vector to be solved. ,in , , , , , These represent the mole fractions of six chemical components in the soil sample: carbohydrates, proteins, lignin, lipids, carbonyl compounds, and black carbon. For the measurement value vector, ,in, The molar nitrogen-carbon ratio represents the ratio of the molar mass of nitrogen to the molar mass of carbon in a soil sample. , , , , , , These represent the integral area percentages of the seven functional group partitions calculated above, namely, the integral area percentages of the partitions of alkyl carbon, N-alkyl / methoxy carbon, O-alkyl carbon, bis-O-alkyl carbon, aromatic carbon, phenolic carbon, and carbonyl / carboxyl carbon.

[0024] (2) Define the objective function The above system of linear equations can also be expressed as: , ; in, For the first The percentage of the integral area of ​​each chemical functional group. The first element in the coefficient matrix of the molecular mixing model Line number Column elements, For the first to be tested Mole fraction of chemical components; The objective function is set as follows: ; (3) Constraints: ,and ; (4) Solution method: The sequential least squares programming algorithm is used for iterative solution, with initial values... Set as proportional vector Iterative updates until the objective function changes. Stop at this time; (5) Result Correction and Output: Set a residual threshold; if the residual norm Then the output will be the mole fraction of the six chemical components; if the residual norm Then, the coefficient matrix is ​​optimized through machine learning, and the optimized coefficient matrix is ​​used to enter the main process for calculation.

[0025] The optimization of the coefficient matrix through machine learning includes the following steps: (1) Data input: Import measured nuclear magnetic resonance spectroscopy data and measured organic carbon source ratio; (2) Parameter initialization: using the original coefficient matrix Initial value; (3) Model training: Correction is performed using ridge regression or random forest algorithms. A new coefficient matrix is ​​obtained. ; (4) Iterative optimization: Calculate the mean squared error, and stop training when the mean squared error is less than the threshold; (5) Update the model: Replace the original matrix and proceed to the main solution process.

[0026] 5. Organic carbon source assessment Based on the calculated mole fractions of each chemical component, the proportions of plant-derived carbon, microbial-derived carbon, and pyrolytic carbon in the soil sample are calculated using the following formula: ; ; ; in, The percentage of carbon derived from plants, The percentage of carbon from microbial sources, The percentage of carbon produced by pyrolysis; , , , , , These are the mole fractions of carbohydrates, proteins, lignin, lipids, carbonyl compounds, and black carbon, respectively. This is the coefficient for the proportion of plant-derived lipids. This is the proportion coefficient of microbial origin in lipids. .

[0027] The mole fractions of the six chemical components in the nine soil samples and the proportions of plant-derived carbon and microbial carbon in each soil sample are shown in Table 3. The fitting results between the estimated and measured proportions of plant and microbial carbon sources in the soil samples are shown in the table below. Figure 2 As shown in the figure, the estimation results of the proportion of plant-derived carbon and microbial-derived carbon in soil using the method proposed in this invention are highly consistent with the measured values. The estimation correlation of plant-derived carbon reaches R2=0.950, while the estimation correlation of microbial-derived carbon is even higher, R2=0.972, indicating that the method can be reliably used to distinguish and quantify different sources of soil organic carbon.

[0028]

[0029] A smart analysis system for the source and stability of soil organic carbon based on solid-state 13C nuclear magnetic resonance spectroscopy, comprising the following modules: Data acquisition module: Communicates with the solid-state NMR spectrometer and is used to automatically read raw NMR spectrum data; Spectral preprocessing module: used to perform baseline drift correction, noise filtering, and integration interval division; The stoichiometry module is used to solve systems of equations using a constrained least-bill programming algorithm. Model optimization module: used to automatically adjust the coefficient library of molecular mixing models based on measured data, and dynamically correct the coefficient matrix through machine learning algorithms; Organic carbon source analysis module: used to calculate the proportions of plant-derived carbon, microbial-derived carbon, and pyrolysis carbon, and output organic carbon stability analysis indicators; Results Display and Reporting Module: Used to generate standardized results reports, including chemical component ratios, organic carbon source ratios, fitting residuals, and R² values, and export them to Excel or PDF format; Cloud database module: used to upload and share diverse long-term carbon source monitoring data, and support model migration and comparison between different ecosystems.

[0030] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A solid-state based 13 A rapid assessment method for soil organic carbon sources and stability using 12C nuclear magnetic resonance spectroscopy is provided for rapidly assessing the proportions of chemical components, organic carbon sources, and soil organic carbon stability in grassland ecosystems. Its key features include… Includes the following steps: S1. Standard pretreatment of soil samples using solid-state... 13 C-NMR technology is used to detect and obtain spectral data from pretreated soil samples; S2. Divide the acquired spectral data into sections according to the standard chemical shift intervals, and calculate the integral area ratio of each section corresponding to each chemical functional group. S3. Calculate the organic carbon stability rating index based on the integral area ratio of each chemical functional group's corresponding partition, including the alkyl carbon / oxyalkyl carbon ratio and the aromaticity index. Evaluate the organic carbon stability based on the organic carbon stability analysis index. S4. Based on the molecular mixing model, a linear equation system is constructed and the constrained least squares programming algorithm is used to solve for the mole fraction of six chemical components in the soil sample. The six chemical components include carbohydrates, proteins, lignin, lipids, carbonyl compounds and black carbon. S5. Based on the calculated mole fractions of the six chemical components, calculate the proportions of plant-derived carbon, microbial-derived carbon, and pyrolytic carbon in the soil sample. The calculation formula is as follows: ； ； ； in, The percentage of carbon derived from plants, The percentage of carbon from microbial sources, The percentage of carbon produced by pyrolysis; , , , , , These are the mole fractions of carbohydrates, proteins, lignin, lipids, carbonyl compounds, and black carbon, respectively. This is the coefficient for the proportion of plant-derived lipids. This is the proportion coefficient of microbial origin in lipids. .

2. The rapid assessment method for soil organic carbon sources and stability according to claim 1, characterized in that, Step S4 specifically includes the following steps: (1) Select the coefficient matrix and establish the system of equations: , ; in, For the first The percentage of the integral area corresponding to each chemical functional group in the partition. The first element in the coefficient matrix of the molecular mixing model Line number Column elements, For the first to be tested Mole fraction of chemical components; (2) Define the objective function: ; (3) Constraints: ,and ; (4) Solution method: The sequential least squares programming algorithm is used for iterative solution, with initial values... Set as proportional vector Iterative updates until the objective function changes. Stop at this time; (5) Result Correction and Output: Set a residual threshold; if the residual norm Then the output will be the mole fraction of the six chemical components; if the residual norm Then, the coefficient matrix is ​​optimized through machine learning, and the optimized coefficient matrix is ​​used to enter the main process for calculation.

3. The rapid assessment method for soil organic carbon sources and stability according to claim 2, characterized in that, The recalculation of the coefficient matrix through machine learning optimization includes the following steps: (1) Data input: Import measured nuclear magnetic resonance spectroscopy data and measured organic carbon source ratio; (2) Parameter initialization: using the original coefficient matrix Initial value; (3) Model training: Correction is performed using ridge regression or random forest algorithms. A new coefficient matrix is ​​obtained. ; (4) Iterative optimization: Calculate the mean squared error, and stop training when the mean squared error is less than the threshold; (5) Update the model: Replace the original matrix and proceed to the main solution process.

4. The rapid assessment method for soil organic carbon sources and stability according to claim 3, characterized in that, In step S3, the formula for calculating the alkyl carbon / oxyalkyl carbon ratio and the aromaticity index is as follows: ； ； in, The ratio of alkyl carbon to oxyalkyl carbon is given. The aroma index; , , , , These represent the integral area percentages of functional groups alkyl carbon, O-alkyl carbon, aromatic carbon, phenolic carbon, and bis-O-alkyl carbon, respectively. This represents the total integral area of ​​each functional group partition.

5. The rapid assessment method for soil organic carbon sources and stability according to claim 1, characterized in that, The standard pretreatment of soil samples includes air drying, grinding, sieving, and removal of inorganic carbon and metal ions.

6. A soil organic carbon source and stability intelligent analysis system based on the rapid assessment method for soil organic carbon source and stability according to any one of claims 1-5, characterized in that, include: Data acquisition module: Communicates with the solid-state NMR spectrometer and is used to automatically read raw NMR spectrum data; Spectral preprocessing module: used to perform baseline drift correction, noise filtering, and integration interval division; The stoichiometry module is used to solve systems of equations using a constrained least-bill programming algorithm. Model optimization module: used to automatically adjust the coefficient library of molecular mixing models based on measured data, and dynamically correct the coefficient matrix through machine learning algorithms; Organic carbon source analysis module: used to calculate the proportions of plant-derived carbon, microbial-derived carbon, and pyrolysis carbon, and output organic carbon stability analysis indicators; Results Display and Reporting Module: Used to generate standardized results reports, including chemical component ratios, organic carbon source ratios, fitting residuals, and R² values, and export them to Excel or PDF format; Cloud database module: used to upload and share diverse long-term carbon source monitoring data, and support model migration and comparison between different ecosystems.

Images