Rapid characterization method of humification degree of biogas residue based on spectrum and chemical index

Abstract

This invention discloses a rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators, specifically relating to the field of spectral analysis technology. It addresses the problem of insufficient accuracy in characterizing the degree of humification caused by extracellular polymer interference in biogas residue. The method involves collecting diffuse reflectance spectral data of biogas residue samples and analyzing the morphological characteristics of the scattering background components using signal decomposition techniques to assess the occlusion intensity of extracellular polymers. When interference is present, three-dimensional fluorescence spectral data are collected, and the fluorescence intensity ratio between humic acid-like and fulvic acid-like regions is calculated. Furthermore, a clustering dispersion index is generated through phasor space mapping to comprehensively evaluate the occlusion intensity of the fluorescence signal. Simultaneously, humic component extracts are obtained through chemical extraction, and their UV-Vis spectra are measured to calculate the humification index. Finally, the humification index is correlated with spectral data and occlusion intensity to establish a correction relationship for rapidly assessing the degree of humification of biogas residue, effectively improving the accuracy and reliability of the detection results.

Description

Technical Field

[0001] This invention relates to the field of spectroscopic analysis technology, and more specifically, to a rapid characterization method for the degree of humification of biogas residue based on spectroscopic and chemical indicators. Background Technology

[0002] In the field of organic solid waste resource utilization, the degree of humification of anaerobic digestion residue is a key indicator for evaluating its stability and agricultural value. Currently, optically based spectroscopic analysis techniques have been attempted for the rapid detection of residue properties. By establishing a correlation model between spectral data and key chemical parameters of humification, the degree of humification can be indirectly characterized. These methods typically rely on the acquisition and analysis of diffuse reflectance or transmission spectra of the samples.

[0003] However, as a complex biological material, biogas residue contains extracellular polymeric substances (EPS), which are residual microbial metabolites that form a viscous, gelatinous coating on the particle surface. This gelatinous substance strongly scatters and absorbs incident light, severely interfering with the effective extraction of humic characteristic information from the spectral signal. This leads to a significant reduction in the correlation between optical measurement results and the actual degree of humification, making it difficult for the accuracy and reliability of the characterization method to meet the needs of practical applications. Summary of the Invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] Rapid characterization methods for the degree of humification of biogas residue based on spectral and chemical indicators include:

[0007] S1. Collect spectral data of biogas residue samples, including diffuse reflectance spectral information in the near-infrared and visible light bands;

[0008] S2. By decomposing the spectral data into signals and analyzing the morphological characteristics of the resulting scattering background components, the extracellular polymer shielding intensity is assessed, and the existence of extracellular polymer interference is preliminarily determined.

[0009] S3. When extracellular polymer interference is present, collect three-dimensional fluorescence spectral data of biogas residue samples and calculate the fluorescence intensity ratio between the humic acid-like region and the fulvic acid-like region in the three-dimensional fluorescence spectrum.

[0010] S4. Phasor space mapping is performed on the three-dimensional fluorescence spectral data to generate phasor point cluster dispersion. The intensity of extracellular polymeric interference on fluorescence signals is evaluated by comprehensive analysis of fluorescence intensity ratio and phasor point cluster dispersion.

[0011] S5. Chemically extract the humic component from the biogas residue sample to obtain an extract, and measure the ultraviolet-visible spectrum of the humic component extract to calculate the humification index.

[0012] S6. Correlation analysis was performed between the humification index and spectral data and the intensity of shading interference to obtain a correction relationship for rapid assessment of the humification degree of biogas residue.

[0013] Furthermore, spectral data of the biogas residue samples were collected, including:

[0014] The biogas residue sample was ground and sieved to obtain solid particles with uniform particle size.

[0015] The solid particles were placed in a constant temperature drying oven to remove free moisture and obtain a dried sample.

[0016] Fill the sample cup with the dried sample evenly and smooth the surface to ensure that the sample surface is flat and flush with the edge of the sample cup.

[0017] A diffuse reflectance spectrometer equipped with an integrating sphere was used to scan the dry sample in the visible and near-infrared bands to obtain raw diffuse reflectance spectral data.

[0018] The raw diffuse reflectance spectral data were preprocessed by diffuse reflectance conversion and standard normal variable transformation to obtain spectral data.

[0019] Furthermore, the extracellular polymeric shielding intensity is assessed by decomposing the spectral data and analyzing the morphological characteristics of the resulting scattering background components, thus preliminarily determining whether extracellular polymeric interference exists, including:

[0020] Empirical mode decomposition or variational mode decomposition is performed on the spectral data to obtain the eigenmode function components and residual trend terms containing different frequency components;

[0021] Starting from the intrinsic mode function component with the lowest frequency distribution, the components are judged sequentially towards higher frequency components. The component with the first envelope smoothness higher than the preset smoothness threshold and the fluctuation amplitude lower than the preset amplitude threshold is taken as the scattering background component representing the scattering background.

[0022] The smoothness, fluctuation amplitude, and fluctuation frequency of the envelope of the scattering background component are extracted as morphological features.

[0023] Calculate the shading intensity evaluation value based on morphological features;

[0024] The shading intensity evaluation value is compared with the preset evaluation threshold. If the shading intensity evaluation value exceeds the preset evaluation threshold, it is preliminarily determined that there is extracellular polymer interference.

[0025] Furthermore, the calculation of the occlusion intensity evaluation value based on morphological features includes: assigning preset weight coefficients to envelope smoothness, fluctuation amplitude, and fluctuation frequency respectively; calculating the weighted sum of the three morphological features after normalizing the envelope smoothness, fluctuation amplitude, and fluctuation frequency, and using the calculation result as the occlusion intensity evaluation value.

[0026] Furthermore, when extracellular polymeric interference is present, three-dimensional fluorescence spectral data of biogas residue samples are acquired, and the fluorescence intensity ratio between the humic acid-like region and the fulvic acid-like region in the three-dimensional fluorescence spectrum is calculated, including:

[0027] When it is initially determined that extracellular polymeric interference exists, the dried sample is placed in the fluorescence spectroscopy sample chamber;

[0028] Set the excitation wavelength and emission wavelength scanning range, perform three-dimensional fluorescence spectroscopy scanning, and obtain three-dimensional fluorescence spectral data;

[0029] Identify humic acid-like and fulvic acid-like regions in three-dimensional fluorescence spectral data;

[0030] The fluorescence intensity in the humic acid-like region and the fulvic acid-like region were integrated to obtain the integrated fluorescence intensity in the humic acid-like region and the integrated fluorescence intensity in the fulvic acid-like region, respectively.

[0031] Calculate the ratio of the integrated fluorescence intensity of the humic acid-like region to the integrated fluorescence intensity of the fulvic acid-like region to obtain the fluorescence intensity ratio.

[0032] Furthermore, phasor space mapping is performed on the three-dimensional fluorescence spectral data to generate phasor point cluster dispersion. The intensity of extracellular polymeric interference on the fluorescence signal is evaluated through a comprehensive analysis of the fluorescence intensity ratio and the phasor point cluster dispersion, including:

[0033] Phasor space mapping is performed on the three-dimensional fluorescence spectral data to obtain the phasor point distribution;

[0034] Calculate the average distance between all phasor points and their centroids in the phasor point distribution to obtain the phasor point cluster dispersion.

[0035] Set normal range thresholds for fluorescence intensity ratio and normal range thresholds for phasor point cluster dispersion, respectively;

[0036] The obtained fluorescence intensity ratio is compared with the normal range threshold of the fluorescence intensity ratio, and the obtained phasor point cluster dispersion is compared with the normal range threshold of the phasor point cluster dispersion.

[0037] The intensity level of extracellular polymeric interference with fluorescence signals is determined by combining the degree to which the fluorescence intensity ratio deviates from its normal range threshold and the degree to which the phasor point cluster dispersion deviates from its normal range threshold.

[0038] Furthermore, phasor space mapping of the three-dimensional fluorescence spectral data to obtain the phasor point distribution includes: performing Fourier transform on each group of fluorescence attenuation signals in the three-dimensional fluorescence spectral data to obtain its frequency domain characterization; extracting the sine and cosine components at a specified frequency from the frequency domain characterization; plotting the mapping points in the phasor space with the sine component as the ordinate and the cosine component as the abscissa; and all mapping points constitute the phasor point distribution.

[0039] Furthermore, the biogas residue sample was chemically extracted to obtain a humic component extract, and the ultraviolet-visible spectrum of the humic component extract was measured to calculate the humification index, including:

[0040] The dried sample was mixed with the alkaline extraction solution in a predetermined ratio, and after shaking extraction, it was allowed to stand to obtain a mixed extract.

[0041] Centrifuge the mixed extract, collect the supernatant and filter it through a filter membrane to obtain the humic component extract;

[0042] The humic component extract was placed in a cuvette of optical path length, and the absorption spectrum in the ultraviolet-visible light range was collected.

[0043] Extract the absorbance at the first characteristic wavelength and the absorbance at the second characteristic wavelength from the absorption spectrum;

[0044] The humification index is obtained by calculating the ratio of absorbance at the first characteristic wavelength to absorbance at the second characteristic wavelength.

[0045] Furthermore, a correlation analysis was conducted between the humification index and spectral data, as well as the intensity of shading interference, to obtain a corrected relationship for rapidly assessing the degree of humification of biogas residue, including:

[0046] The humification index is used as a reference benchmark value.

[0047] The absorbance value at the target wavelength determined by the correlation analysis between the humification index and absorbance in the spectral data and the intensity of the shading interference are used as modeling variables.

[0048] The mathematical relationship between the reference baseline and the modeling variables was established using the multiple linear regression method.

[0049] Significance tests and error analyses were performed on the mathematical relationships to verify their statistical validity.

[0050] The validated mathematical relationships were determined as correction relationships for rapid assessment of the degree of humification of biogas residue.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. By combining multi-dimensional spectral analysis with chemical indicators, the accuracy and reliability of detecting the degree of humification of biogas residue are effectively improved. First, the signal decomposition technology of diffuse reflectance spectroscopy is used to identify and quantify the light scattering interference caused by extracellular polymers. By analyzing the morphological characteristics of the scattering background component, the intensity of the interference is objectively assessed. Then, three-dimensional fluorescence spectroscopy is activated for samples with interference. Combining the fluorescence intensity ratio of humic acid-like and fulvic acid-like regions with the clustering dispersion index generated by phasor space mapping, the intensity of the masking effect is comprehensively evaluated from two dimensions: fluorescence signal attenuation and component heterogeneity. This hierarchical judgment mechanism effectively overcomes the limitation of single optical measurement being susceptible to matrix interference and provides accurate interference intensity parameters for the establishment of subsequent correction relationships.

[0053] 2. The humification index was obtained by chemical extraction as a benchmark value. A multiple linear regression model was established with the interference-corrected spectral data. This approach, which combines rapid optical detection with chemical benchmark verification, maintains the high efficiency of spectral detection while ensuring the accuracy of the data. The established correction relationship fully considers the interference effect of extracellular polymers in actual samples. By introducing the shielding intensity as a correction variable, the applicability and prediction accuracy in samples with different levels of interference are significantly improved. Attached Figure Description

[0054] Figure 1 This is a flowchart of the rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to the present invention. Detailed Implementation

[0055] 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 embodiments of the present invention, and not all embodiments. 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.

[0056] Example: Figure 1 This invention presents a rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators, including:

[0057] S1. Collect spectral data of biogas residue samples, including diffuse reflectance spectral information in the near-infrared and visible light bands;

[0058] S2. By decomposing the spectral data into signals and analyzing the morphological characteristics of the resulting scattering background components, the extracellular polymer shielding intensity is assessed, and the existence of extracellular polymer interference is preliminarily determined.

[0059] S3. When extracellular polymer interference is present, collect three-dimensional fluorescence spectral data of biogas residue samples and calculate the fluorescence intensity ratio between the humic acid-like region and the fulvic acid-like region in the three-dimensional fluorescence spectrum.

[0060] S4. Phasor space mapping is performed on the three-dimensional fluorescence spectral data to generate phasor point cluster dispersion. The intensity of extracellular polymeric interference on fluorescence signals is evaluated by comprehensive analysis of fluorescence intensity ratio and phasor point cluster dispersion.

[0061] S5. Chemically extract the humic component from the biogas residue sample to obtain an extract, and measure the ultraviolet-visible spectrum of the humic component extract to calculate the humification index.

[0062] S6. Correlation analysis was performed between the humification index and spectral data and the intensity of shading interference to obtain a correction relationship for rapid assessment of the humification degree of biogas residue.

[0063] S1. Collect spectral data of biogas residue samples. The spectral data includes diffuse reflectance spectral information in the near-infrared and visible light bands. The specific implementation is as follows:

[0064] First, the obtained raw biogas residue sample is thoroughly ground in a grinding device such as an agate mortar or ball mill. Then, it is sieved through a standard analytical sieve with a mesh size between 100 and 200. This process aims to obtain solid particles with a uniform particle size distribution between 0.074 mm and 0.15 mm. This step ensures that the sample has consistent physical properties and surface morphology during subsequent spectral acquisition, effectively reducing light scattering measurement errors caused by particle size differences. Next, the sieved solid particles are evenly spread on a glass petri dish or ceramic sample tray and transferred to a forced-air constant-temperature drying oven for drying. The drying temperature is maintained between 40°C and 60°C, for example, 45°C or 50°C, for a duration of 12 to 24 hours. This process aims to completely remove free water and some adsorbed water from the sample, thus obtaining a dry sample and avoiding strong absorption peaks in the near-infrared band caused by moisture, which could interfere with subsequent spectral measurements.

[0065] Next, the dried sample is evenly filled into a dedicated circular sample cup using a non-metallic spatula. The sample cup is made of polytetrafluoroethylene or stainless steel, with an inner diameter typically ranging from 10 mm to 30 mm. The filling amount is controlled to achieve a sample thickness of 5 mm to 10 mm. The surface is carefully smoothed using a scraper or glass slide to ensure a flat sample surface that is precisely flush with the edge of the sample cup. This operation ensures that the interaction distance and angle between the laser beam and the sample surface remain consistent during spectral scanning, which is a crucial prerequisite for obtaining highly repeatable diffuse reflectance data. After sample preparation, measurements are performed using a diffuse reflectance spectrometer equipped with an integrating sphere attachment. The instrument model can be a PerkinElmer Lambda 1050+ or ​​equivalent. The sample cup is precisely placed on the sample holder. The spectrometer's scanning range is set to cover the visible light band (400 nm to 780 nm) and the near-infrared band (780 nm to 2500 nm). The scanning interval is set to 1 nm or 2 nm, and the integration time is set to 100 ms to 500 ms. The scanning program is then started to obtain the raw diffuse reflectance spectral voltage signal data.

[0066] Finally, the acquired diffuse reflectance spectral data underwent necessary preprocessing for subsequent analysis. The preprocessing process mainly includes two core steps. The first step is diffuse reflectance conversion, which converts the raw voltage signal value recorded by the instrument into a standard diffuse reflectance value. This conversion is based on baseline data obtained through prior calibration using a standard white plate. Specifically, this is achieved by dividing the sample signal intensity by the signal intensity of the standard white plate at the same wavelength and then multiplying by 100%. The standard white plate is made of barium sulfate or polytetrafluoroethylene, and its reflectance value is consistently greater than 99% in the wavelength range of 250 nm to 2500 nm. The second step is to process the transformed diffuse reflectance spectral data using the standard normal variable transformation algorithm. This algorithm is implemented through the following mathematical process: First, the arithmetic mean of the diffuse reflectance values ​​at all wavelengths of the entire spectrum is calculated. Then, the standard deviation of these diffuse reflectance values ​​is calculated. Finally, the original diffuse reflectance value at each wavelength is subtracted from the average value and then divided by the standard deviation to obtain the transformed spectral data. This processing effectively eliminates baseline drift and random noise caused by differences in sample particle size distribution, surface scattering effects, and changes in the optical path. The final output is preprocessed spectral data for subsequent analysis. The processed data typically falls between -3 and +3.

[0067] S2. By decomposing the spectral data and analyzing the morphological characteristics of the resulting scattering background components, the extracellular polymer shielding intensity is assessed to preliminarily determine whether extracellular polymer interference exists. The specific implementation is as follows:

[0068] First, the spectral data obtained in the preprocessing step is decomposed using either Empirical Mode Decomposition (EMD) or Variational Mode Decomposition (VMD). When EMD is selected, it iteratively decomposes the spectral data into a series of intrinsic mode function (EMF) components arranged from high to low frequency, along with a residual trend term. Each EMF component must satisfy the following mathematical conditions: the mean of the upper and lower envelopes must be zero across the entire data segment, and the number of extrema must not differ from the number of zero-crossing points by more than one. When VMD is selected, it decomposes the input spectral signal into a predetermined number of mode components using a variational framework. The mode component with the lowest center frequency is used as the residual trend term. In this decomposition process, the penalty factor is typically set to 2000, and the convergence threshold is set to 1 × 10⁻⁶. -7 This step ultimately yields the eigenmode function components and residual trend terms containing different frequency elements.

[0069] Next, components representing the scattering background are selected from all the decomposed components. In practice, the process starts with the intrinsic mode function component with the lowest frequency distribution and proceeds sequentially to components with higher frequencies. For each component being evaluated, two key parameters need to be calculated: envelope smoothness and fluctuation amplitude. Envelope smoothness is quantified by calculating the standard deviation of the average values ​​of the upper and lower envelopes of the component signal at each data point, and fluctuation amplitude is quantified by calculating the difference between the maximum and minimum values ​​of the component signal. The first component that simultaneously satisfies an envelope smoothness higher than a preset smoothness threshold and a fluctuation amplitude lower than a preset amplitude threshold is identified as the scattering background component representing the scattering background. The preset smoothness threshold is set by analyzing a large amount of known interference-free sample spectral data and taking the 95th percentile value of the envelope smoothness distribution of the scattering background component, with a specific value range, for example, between 0.05 and 0.15. The preset amplitude threshold is set by analyzing the same dataset and taking the 5th percentile value of the fluctuation amplitude distribution of the scattering background component, with a specific value range, for example, between 0.01 and 0.03.

[0070] Then, three morphological features of the determined scattering background component are extracted for subsequent calculations. The extracted morphological features include envelope smoothness, fluctuation amplitude, and fluctuation frequency. The fluctuation frequency is obtained by calculating the average number of zero-crossing points of the scattering background component signal per unit time, with the unit being Hertz. These three morphological features together characterize the time-domain and frequency-domain characteristics of the scattering background signal, providing a data basis for subsequent evaluation.

[0071] The shading intensity evaluation value is calculated based on the extracted morphological features. First, preset weight coefficients are assigned to the three morphological features: envelope smoothness, fluctuation amplitude, and fluctuation frequency. The weight coefficients are set based on the sensitivity of each feature to extracellular polymer interference, and are determined by principal component analysis or expert experience. For example, the weight coefficient for envelope smoothness can be set to 0.5, the weight coefficient for fluctuation amplitude can be set to 0.3, and the weight coefficient for fluctuation frequency can be set to 0.2, ensuring that the sum of all weight coefficients is 1. Then, the three morphological features are normalized to eliminate the influence of dimensions. The normalization adopts the minimum-maximum scaling method, scaling each feature value to between 0 and 1. Finally, the weighted sum of the three normalized morphological features is calculated, that is, each normalized feature value is multiplied by its corresponding weight coefficient and then summed. The calculation result is used as the shading intensity evaluation value.

[0072] Finally, the calculated shading intensity evaluation value is compared with the preset evaluation threshold. The preset evaluation threshold is determined by analyzing the evaluation values ​​of a large number of known samples using receiver operating characteristic curves. This threshold corresponds to the point where the sum of sensitivity and specificity is the greatest, with a specific value range, for example, between 0.6 and 0.8. If the shading intensity evaluation value exceeds the preset evaluation threshold, it is preliminarily determined that the biogas residue sample has extracellular polymer interference. This determination provides a basis for decision-making on whether three-dimensional fluorescence spectroscopy analysis is needed in subsequent steps, ensuring the integrity and reliability of the entire detection process.

[0073] S3. When extracellular polymeric interference is present, collect three-dimensional fluorescence spectral data of the biogas residue sample and calculate the fluorescence intensity ratio between the humic acid-like region and the fulvic acid-like region in the three-dimensional fluorescence spectrum. Specifically, the implementation is as follows:

[0074] First, take out an appropriate amount of the prepared dried sample, usually between 0.1 g and 0.5 g, such as 0.2 g or 0.3 g, and place it in a standard quartz sample dish or a dedicated fluorescence spectroscopy sample chamber. The optical path length of the sample dish is usually 1 cm, and the sample thickness is controlled between 1 mm and 5 mm, such as 2 mm or 3 mm. Ensure that the sample surface is flat and perpendicular to the optical path to avoid optical path obstruction or enhanced scattering effect caused by improper sample placement. Maintain the sample chamber temperature between 20 degrees Celsius and 25 degrees Celsius to keep the measurement conditions stable.

[0075] Next, set the scanning parameters of the fluorescence spectrometer. Set the excitation wavelength scanning range to 220 nm to 400 nm, the emission wavelength scanning range to 300 nm to 550 nm, the excitation wavelength interval to 5 nm, the emission wavelength interval to 2 nm, the scanning speed to 1200 nm per minute, and the photomultiplier tube voltage to a specific value between 600 V and 800 V, such as 650 V or 700 V. The specific value should be adjusted appropriately according to the instrument model and the fluorescence intensity of the sample. The instrument model can be Hitachi F-7000 or a fluorescence spectrophotometer with equivalent performance. After completing the parameter settings, start the three-dimensional fluorescence spectroscopy scanning program to obtain a three-dimensional fluorescence spectral data matrix containing three dimensions: excitation wavelength, emission wavelength, and fluorescence intensity. This data matrix is ​​stored in the form of a two-dimensional array, where the row index corresponds to the excitation wavelength, the column index corresponds to the emission wavelength, and the array element value is the fluorescence intensity value under the corresponding wavelength combination.

[0076] After obtaining the three-dimensional fluorescence spectral data, it is necessary to identify the humic acid-like region and the fulvic acid-like region. The humic acid-like region is defined as the rectangular region enclosed by the excitation wavelength of 300 nm to 400 nm and the emission wavelength of 400 nm to 550 nm, and the fulvic acid-like region is defined as the rectangular region enclosed by the excitation wavelength of 220 nm to 250 nm and the emission wavelength of 380 nm to 450 nm. Region identification is completed by setting the corresponding wavelength boundary conditions and extracting the corresponding data points in the data matrix. In specific implementation, the excitation wavelength index range is first determined, then the emission wavelength index range is determined, and finally all data points in the intersection area are extracted.

[0077] Then, the fluorescence intensity values ​​in the identified humic acid-like and fulvic acid-like regions were integrated. The integration calculation adopted a numerical integration method, such as the trapezoidal method or Simpson's method, which summed the fluorescence intensity values ​​of all data points in the region and multiplied them by the wavelength interval. The specific calculation formula is to multiply the fluorescence intensity value of each data point by the corresponding excitation wavelength interval and emission wavelength interval and then sum them up. Two numerical results were obtained by the integration calculation: the integrated fluorescence intensity of the humic acid-like region and the integrated fluorescence intensity of the fulvic acid-like region. The unit of integrated intensity is any fluorescence intensity unit multiplied by the square of nanometers. This result reflects the total amount of fluorescent material in each region.

[0078] Finally, the ratio of the integrated fluorescence intensity of the humic acid-like region to that of the fulvic acid-like region was calculated. The ratio was obtained by dividing the humic acid-like region's integrated fluorescence intensity by the denominator. This ratio reflects the degree of humification in the sample; a higher ratio indicates a higher degree of humification, while a lower ratio indicates a lower degree of humification. The calculated fluorescence intensity ratio will serve as one of the important input parameters for evaluating the interference of extracellular polymers on the fluorescence signal in subsequent steps, providing a data basis for subsequent comprehensive analysis.

[0079] Throughout the implementation process, attention must be paid to handling abnormal situations. When the integrated intensity value is zero, the accuracy of region identification needs to be rechecked. When the signal-to-noise ratio is lower than 3:1, the instrument parameters need to be readjusted or the sample needs to be re-prepared to ensure the reliability and accuracy of the measurement results. All data processing is completed automatically by computer programs without manual intervention, ensuring the consistency and repeatability of the results.

[0080] S4. Phasor space mapping is performed on the three-dimensional fluorescence spectral data to generate phasor point cluster dispersion. The intensity of extracellular polymeric interference on the fluorescence signal is evaluated by comprehensive analysis of fluorescence intensity ratio and phasor point cluster dispersion. The specific implementation is as follows:

[0081] First, the obtained three-dimensional fluorescence spectral data undergoes phasor space mapping processing. This processing is performed on each group of fluorescence decay signals in the three-dimensional data matrix, with each signal corresponding to a specific excitation wavelength-emission wavelength combination. A Fast Fourier Transform (FFT) is then performed on each group of fluorescence decay signals to transform them from the time domain to the frequency domain, obtaining a frequency domain characterization containing amplitude and phase information. The sampling frequency during the Fourier transform is set to 100 MHz, and the number of transform points is set to 1024. Sine and cosine components at a specified frequency are extracted from the frequency domain characterization. The specified frequency is typically chosen as the laser modulation frequency or the instrument's inherent operating frequency, such as 80 MHz. A mapping point is plotted in two-dimensional phasor space for each wavelength combination, with the sine component as the ordinate and the cosine component as the abscissa. The coordinate range of the phasor space is normalized to between -1 and +1. All mapping points corresponding to the excitation wavelength-emission wavelength combinations collectively constitute a phasor point distribution, which reflects the differences in relaxation characteristics of different fluorescent components in the sample and typically contains 1000 to 5000 mapping points.

[0082] Next, the average distance between all phasor points and their centroids in the phasor point distribution is calculated to obtain the phasor point cluster dispersion. First, the centroid coordinates of the phasor point distribution are calculated. The x-coordinate of the centroid is the arithmetic mean of the x-coordinates of all phasor points, and the y-coordinate is the arithmetic mean of the y-coordinates of all phasor points. Then, the Euclidean distance from each phasor point to the centroid is calculated, which is the square root of the sum of the square of the difference between the x-coordinate of each point and the x-coordinate of the centroid, plus the square of the difference between the y-coordinate of each point and the y-coordinate of the centroid. Finally, the arithmetic mean of the distances from all phasor points to the centroid is calculated to obtain the phasor point cluster dispersion. The larger this value, the more dispersed the phasor point distribution is, reflecting the higher the heterogeneity of the sample's fluorescent components. Its value range is usually between 0.05 and 0.25.

[0083] The normal range thresholds for fluorescence intensity ratio and phasor point cluster dispersion are set separately. The normal range threshold for fluorescence intensity ratio is determined by statistically analyzing the fluorescence intensity ratio data of a large number of known, interference-free biogas residue samples. Fluorescence intensity ratio data from at least 50 interference-free samples are collected, and the mean and standard deviation of these data are calculated. The mean plus or minus twice the standard deviation is taken as the normal range, for example, between 1.5 and 2.5. The normal range threshold for phasor point cluster dispersion is determined using the same method. Phasor point cluster dispersion data from the same number of samples are collected, and the mean and standard deviation are calculated. The mean plus or minus twice the standard deviation is taken as the normal range, for example, between 0.05 and 0.15. These thresholds need to be appropriately calibrated according to the specific instrument model and sample type, with a calibration cycle of every six months.

[0084] The obtained fluorescence intensity ratio is compared with the normal range threshold of the fluorescence intensity ratio, and the obtained phasor point cluster dispersion is compared with the normal range threshold of the phasor point cluster dispersion. The comparison operation is implemented by numerical comparison to determine whether the measured value falls within the corresponding normal range threshold. If it falls within the range, it is normal; if it falls outside the range, the degree of deviation is recorded. The degree of deviation is quantified by calculating the percentage of the difference between the measured value and the nearest threshold boundary to the width of the threshold range. For example, if the threshold is 1.5 to 2.5 and the measured value is 2.8, then the degree of deviation is (2.8-2.5) / (2.5-1.5)×100%=30%.

[0085] The masking interference level of extracellular polymers on fluorescence signals is determined based on the combination of the degree to which the fluorescence intensity ratio deviates from its normal range threshold and the degree to which the phasor point cluster dispersion deviates from its normal range threshold. The degree of deviation is divided into three levels: 0% to 20% is mild deviation, 20% to 50% is moderate deviation, and above 50% is severe deviation. The final masking interference level is then determined based on the combination of the deviation levels of the two parameters. Specifically, if both parameters deviate slightly, it is considered mild interference; if one parameter deviates moderately and the other slightly, it is considered moderate interference; and if both parameters deviate moderately or either parameter deviates severely, it is considered severe interference. This grading method comprehensively reflects the overall impact of extracellular polymers on fluorescence signals, providing an accurate basis for subsequent humification correction.

[0086] During implementation, it is also necessary to consider handling abnormal situations. When the phasor point distribution shows obvious multi-peak characteristics, the data quality needs to be rechecked. When the fluorescence intensity ratio or the dispersion of phasor point clustering exceeds the instrument's measurement range, the detection parameters need to be adjusted and remeasured to ensure the accuracy and reliability of the analysis results. All calculation processes are completed automatically by dedicated software. The software has a built-in quality control module that can automatically identify abnormal data and prompt for remeasurement, ensuring the consistency and repeatability of the analysis process.

[0087] S5. Chemically extract the humic components from the biogas residue sample to obtain an extract, and measure the UV-Vis spectrum of the humic components extract to calculate the humification index. The specific implementation is as follows:

[0088] First, accurately weigh 1.0 g to 5.0 g (e.g., 2.0 g or 3.0 g) of the prepared dried sample and mix it with an alkaline extraction solution at a predetermined mass-volume ratio of 1:10 to 1:20. The alkaline extraction solution is a mixture of 0.1 mol / L sodium hydroxide and 0.1 mol / L sodium pyrophosphate. Use a pH meter to precisely adjust the pH of the solution to between 13.0 and 13.5. Place the mixture in a constant temperature shaker and extract at a temperature of 25°C to 30°C at a speed of 150 rpm to 200 rpm for 4 to 6 hours (e.g., 5 hours). After shaking, let it stand for 12 to 24 hours to allow the solid and liquid to separate completely and obtain a mixed extract. The entire extraction process must be carried out under an inert gas atmosphere to prevent oxidation.

[0089] Transfer the mixed extract into a 50 mL centrifuge tube and centrifuge at 4000 to 5000 rpm for 15 to 20 minutes at a temperature of 4 to 10 degrees Celsius. The centrifugation acceleration is set to 3000 × g to 4000 × g. Collect the supernatant. Vacuum filter the supernatant through an aqueous filter membrane with a pore size of 0.22 to 0.45 micrometers. The filter membrane material is a mixed cellulose ester or polyethersulfone. Connect the filter device to a vacuum pump to maintain a negative pressure of 0.05 to 0.08 MPa to obtain a clear humic component extract. The extract should be immediately transferred to a brown sample bottle, protected from light, and subsequent tests should be completed within 4 hours to prevent photodegradation or oxidation of the humic component.

[0090] The humic component extract was transferred into a standard quartz cuvette with a path length of 1 cm. Both transparent surfaces of the cuvette were wiped clean with lens paper to ensure they were free of streaks. A UV-Vis spectrophotometer was used to perform a full-band scan in the wavelength range of 200 nm to 800 nm, with a scan interval of 1 nm, a scan speed of medium speed, and a slit width of 2 nm. The absorption spectrum in the UV-Vis band was collected. Baseline calibration was performed with a blank alkaline extraction solution before each measurement to ensure measurement accuracy. Each sample was scanned at least three times and the average value was taken.

[0091] The absorbance at the first characteristic wavelength and the absorbance at the second characteristic wavelength were extracted from the absorption spectrum. The first characteristic wavelength was selected as 465 nm, which corresponds to the absorption characteristics of chromophores such as carboxyl groups and phenolic hydroxyl groups in humic substances. The second characteristic wavelength was selected as 665 nm, which corresponds to the absorption characteristics of aromatic ring structures in humic substances. The absorbance value was read directly from the spectral data, with the reading accurate to 0.001 absorbance units. If the absorbance value exceeded 2.0, the sample needed to be appropriately diluted and measured again.

[0092] The ratio of absorbance at the first characteristic wavelength to absorbance at the second characteristic wavelength is calculated. This is done by dividing the absorbance at 465 nm (numerator) by the absorbance at 665 nm (denominator) to obtain the humification index. This index reflects the aromatic condensation degree and molecular weight of humic substances in the biogas residue sample. A higher index value indicates a higher degree of humification, while a lower index value indicates a lower degree of humification. The calculated humification index will serve as a reference value for establishing correction relationships in subsequent steps.

[0093] Strict quality control procedures need to be established during the implementation process. When the difference in absorbance values ​​at two characteristic wavelengths is too small, the extraction efficiency needs to be checked and optimized by adjusting the extraction time or temperature. All operations should be carried out under light-protected conditions to avoid degradation of photosensitive components. Each sample should be measured at least three times and the average value should be taken. The relative standard deviation should be controlled within 5% to ensure the reproducibility and reliability of the data. Blank tests and standard sample measurements should be performed simultaneously during the experiment to ensure the accuracy and comparability of the experimental process.

[0094] S6. Correlation analysis is performed between the humification index and spectral data and the intensity of shading interference to obtain a correction relationship for rapidly assessing the degree of humification of biogas residue. The specific implementation is as follows:

[0095] First, the calculated humification index is used as a reference baseline value. This value is an accurate measurement obtained through chemical extraction methods and represents the true degree of humification of the biogas residue sample. The numerical range of the reference baseline value is usually between 0.5 and 5.0, depending on the source and processing technology of the sample. For example, the humification index of municipal solid waste biogas residue is usually between 1.2 and 3.5, while that of agricultural waste biogas residue is usually between 2.0 and 4.5. When establishing the calibration relationship, a sufficient number of sample data needs to be collected, usually at least 30 to 50 representative biogas residue samples, to ensure that the samples cover different ranges of humification degree, while taking into account the influence of different seasons, different raw material sources, and different process conditions.

[0096] Next, the absorbance value at the target wavelength determined by the correlation analysis between the humification index and absorbance in the spectral data, and the shading interference intensity are used as modeling variables. The target wavelength is determined by the following method: First, the Pearson correlation coefficient between the humification index and the absorbance value across the entire wavelength range is calculated, and wavelengths with an absolute correlation coefficient greater than 0.6 are selected as candidate wavelengths. Then, stepwise regression analysis is used to finally determine 3 to 5 target wavelengths, which are usually distributed between 400 nm and 800 nm, such as 465 nm, 550 nm, and 665 nm. The shading interference intensity comes from the determined shading interference intensity level and needs to be converted into a numerical variable. For example, mild interference is assigned a value of 1, moderate interference is assigned a value of 2, and severe interference is assigned a value of 3. The ratio of the number of modeling variables to the number of samples should be controlled at more than 1:10 to avoid overfitting.

[0097] A multiple linear regression method was used to establish a mathematical relationship between the reference baseline value and the modeling variables. The general form of the mathematical relationship is that the reference baseline value equals the constant term plus the sum of the product of each modeling variable and its corresponding regression coefficient. The regression coefficients were calculated using the least squares method, which determines the optimal coefficients by minimizing the sum of squared residuals between the predicted and actual values. During the calculation process, the problem of multicollinearity among variables needs to be considered. This can be achieved by detecting the variance inflation factor. When the variance inflation factor is greater than 10, highly correlated variables need to be removed, or principal component regression or other methods can be used to eliminate the effects of multicollinearity. The final mathematical relationship includes a constant term and several regression coefficients, each of which has a clear physical meaning and reflects the contribution of the corresponding variable to the degree of humification.

[0098] Significance tests and error analyses are performed on the mathematical relationships to verify their statistical validity. Significance tests include F-tests on the overall model and t-tests on each regression coefficient. The significance level for both F-tests and t-tests should be less than 0.05. Error analysis includes calculating the coefficient of determination, adjusted coefficient of determination, root mean square error, and mean absolute percentage error. The coefficient of determination should be greater than 0.8, the adjusted coefficient of determination should be greater than 0.75, and the root mean square error should be less than 0.5 times the standard deviation of the reference value. Residual analysis is also required to check whether the residuals satisfy the assumptions of normal distribution and homogeneity of variance, which can be verified using the Shapiro-Wilke test and the Brosch-Pagan test.

[0099] The validated mathematical relationships are established as calibration relationships for rapidly assessing the degree of humification of biogas residue. These calibration relationships require regular validation and updates, typically every six months or when sample characteristics change significantly. Validation methods include internal cross-validation and external validation. Internal cross-validation can employ leave-one-out or k-fold cross-validation, while external validation requires data from new samples not involved in the modeling. In practical applications, inputting the spectral data of new samples into the calibration relationships allows for rapid prediction of their humification degree. The deviation between the predicted results and the values ​​determined by chemical methods should be controlled within 15%. The final established calibration relationships can be integrated into dedicated software to achieve rapid, non-destructive testing of the degree of humification in biogas residue, providing technical support for the resource utilization of biogas residue.

[0100] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0101] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0102] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0103] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0105] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0107] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0109] In conclusion, 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, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators, characterized in that, include: S1. Collect spectral data of biogas residue samples, including diffuse reflectance spectral information in the near-infrared and visible light bands; S2. By decomposing the spectral data into signals and analyzing the morphological characteristics of the resulting scattering background components, the extracellular polymer shielding intensity is assessed, and the existence of extracellular polymer interference is preliminarily determined. S3. When extracellular polymer interference is present, collect three-dimensional fluorescence spectral data of biogas residue samples and calculate the fluorescence intensity ratio between the humic acid-like region and the fulvic acid-like region in the three-dimensional fluorescence spectrum. S4. Phasor space mapping is performed on the three-dimensional fluorescence spectral data to generate phasor point cluster dispersion. The intensity of extracellular polymeric interference on fluorescence signals is evaluated by comprehensive analysis of fluorescence intensity ratio and phasor point cluster dispersion. S5. Chemically extract the humic component from the biogas residue sample to obtain an extract, and measure the ultraviolet-visible spectrum of the humic component extract to calculate the humification index. S6. Correlation analysis was performed between the humification index and spectral data and the intensity of shading interference to obtain a correction relationship for rapid assessment of the degree of humification of biogas residue.

2. The rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to claim 1, characterized in that, Spectral data of biogas residue samples were collected, including: The biogas residue sample was ground and sieved to obtain solid particles with uniform particle size. The solid particles were placed in a constant temperature drying oven to remove free moisture and obtain a dried sample. Fill the sample cup with the dried sample evenly and smooth the surface to ensure that the sample surface is flat and flush with the edge of the sample cup. A diffuse reflectance spectrometer equipped with an integrating sphere was used to scan the dry sample in the visible and near-infrared bands to obtain raw diffuse reflectance spectral data. The raw diffuse reflectance spectral data were preprocessed by diffuse reflectance conversion and standard normal variable transformation to obtain spectral data.

3. The rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to claim 1, characterized in that, The extracellular polymeric shielding intensity is assessed by decomposing the spectral data and analyzing the morphological characteristics of the resulting scattering background components, thus providing a preliminary assessment of the presence of extracellular polymeric interference, including: Empirical mode decomposition or variational mode decomposition is performed on spectral data to obtain eigenmode function components and residual trend terms containing different frequency components; Starting from the intrinsic mode function component with the lowest frequency distribution, the components are judged sequentially towards higher frequency components. The component with the first envelope smoothness higher than the preset smoothness threshold and the fluctuation amplitude lower than the preset amplitude threshold is taken as the scattering background component representing the scattering background. The smoothness, fluctuation amplitude, and fluctuation frequency of the envelope of the scattering background component are extracted as morphological features. Calculate the shading intensity evaluation value based on morphological features; The shading intensity evaluation value is compared with the preset evaluation threshold. If the shading intensity evaluation value exceeds the preset evaluation threshold, it is preliminarily determined that there is extracellular polymer interference.

4. The rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to claim 3, characterized in that, The occlusion intensity evaluation value is calculated based on morphological features by: assigning preset weight coefficients to envelope smoothness, fluctuation amplitude, and fluctuation frequency respectively; calculating the weighted sum of the three morphological features after normalizing envelope smoothness, fluctuation amplitude, and fluctuation frequency, and using the calculation result as the occlusion intensity evaluation value.

5. The rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to claim 1, characterized in that, When extracellular polymeric interference is present, three-dimensional fluorescence spectral data of biogas residue samples are acquired, and the fluorescence intensity ratio between the humic acid-like region and the fulvic acid-like region in the three-dimensional fluorescence spectrum is calculated, including: When it is initially determined that extracellular polymeric interference exists, the dried sample is placed in the fluorescence spectroscopy sample chamber; Set the excitation wavelength and emission wavelength scanning range, perform three-dimensional fluorescence spectroscopy scanning, and obtain three-dimensional fluorescence spectral data; Identify humic acid-like and fulvic acid-like regions in three-dimensional fluorescence spectral data; The fluorescence intensity in the humic acid-like region and the fulvic acid-like region were integrated to obtain the integrated fluorescence intensity in the humic acid-like region and the integrated fluorescence intensity in the fulvic acid-like region, respectively. Calculate the ratio of the integrated fluorescence intensity of the humic acid-like region to the integrated fluorescence intensity of the fulvic acid-like region to obtain the fluorescence intensity ratio.

6. The rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to claim 1, characterized in that, Phasor space mapping was performed on three-dimensional fluorescence spectral data to generate phasor point cluster dispersion. The intensity of extracellular polymeric interference on the fluorescence signal was evaluated through a comprehensive analysis of the fluorescence intensity ratio and the phasor point cluster dispersion, including: Phasor space mapping is performed on the three-dimensional fluorescence spectral data to obtain the phasor point distribution; Calculate the average distance between all phasor points and their centroids in the phasor point distribution to obtain the phasor point cluster dispersion. Set normal range thresholds for fluorescence intensity ratio and normal range thresholds for phasor point cluster dispersion, respectively; The obtained fluorescence intensity ratio is compared with the normal range threshold of the fluorescence intensity ratio, and the obtained phasor point cluster dispersion is compared with the normal range threshold of the phasor point cluster dispersion. The intensity level of extracellular polymeric interference with fluorescence signals is determined by combining the degree to which the fluorescence intensity ratio deviates from its normal range threshold and the degree to which the phasor point cluster dispersion deviates from its normal range threshold.

7. The rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to claim 6, characterized in that, Phasor space mapping of three-dimensional fluorescence spectral data to obtain phasor point distribution includes: performing Fourier transform on each group of fluorescence decay signals in the three-dimensional fluorescence spectral data to obtain its frequency domain characterization; extracting the sine and cosine components at a specified frequency from the frequency domain characterization; plotting the mapping points in phasor space with the sine component as the ordinate and the cosine component as the abscissa; and all mapping points constitute the phasor point distribution.

8. The rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to claim 1, characterized in that, Chemical extraction was performed on biogas residue samples to obtain humic component extracts, and the ultraviolet-visible spectra of the humic component extracts were measured to calculate the humification index, including: The dried sample was mixed with the alkaline extraction solution in a predetermined ratio, and after shaking extraction, it was allowed to stand to obtain a mixed extract. Centrifuge the mixed extract, collect the supernatant and filter it through a filter membrane to obtain the humic component extract; The humic component extract was placed in a cuvette of optical path length, and the absorption spectrum in the ultraviolet-visible light range was collected. Extract the absorbance at the first characteristic wavelength and the absorbance at the second characteristic wavelength from the absorption spectrum; The humification index is obtained by calculating the ratio of absorbance at the first characteristic wavelength to absorbance at the second characteristic wavelength.

9. The rapid characterization method for the degree of humification of biogas residue based on spectral and chemical indicators according to claim 1, characterized in that, Correlation analysis was performed between the humification index and spectral data, as well as the intensity of shading interference, to obtain a corrected relationship for rapidly assessing the degree of humification in biogas residue, including: The humification index is used as a reference benchmark value. The absorbance value at the target wavelength determined by the correlation analysis between the humification index and absorbance in the spectral data and the intensity of the shading interference are used as modeling variables. The mathematical relationship between the reference baseline and the modeling variables was established using the multiple linear regression method. Significance tests and error analyses were performed on the mathematical relationships to verify their statistical validity. The validated mathematical relationships were determined as correction relationships for rapid assessment of the degree of humification of biogas residue.

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