Volatile component characterization method of agilawood koji

Abstract

The invention provides a volatile component characterization method of agilawood koji, which comprises the following steps: carrying out initial data acquisition on an agilawood sample, obtaining concentration distribution information of heavy metal pollution in the sample and initial signal characteristics of volatile components, and carrying out lossless scanning on the sample to obtain an original data set of heavy metal pollution degree and volatile component signals; classifying the heavy metal pollution degree aggravating characteristics, dividing the sample into different pollution grade intervals, and determining a matrix effect change range corresponding to each grade; based on the matrix effect change range, analyzing the influence of chemical reaction enhancement on complex formation, obtaining a quantitative index of complex formation on volatilization characteristic change, and if the quantitative index exceeds a preset threshold range, dynamically optimizing the pretreatment process to obtain a treatment scheme adapted to the current pollution degree.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for characterizing volatile components of agarwood. Background Art

[0002] As a precious natural fragrance and medicinal material, the study of its volatile components plays a crucial role in fragrance development and quality evaluation. These components not only determine agarwood's unique aroma but are also closely related to its cultural heritage and economic value. However, the interference of environmental pollutants, especially heavy metals, often present in agarwood samples poses a serious challenge to component analysis, and in-depth research is urgently needed to ensure the accuracy and reliability of detection. Although some methods have been developed to address the interference of heavy metals in agarwood samples on the detection of volatile components, these methods often ignore the dynamic impact of changes in contamination levels on the sample matrix. In particular, when heavy metal concentrations increase, the complex chemical environment in the sample changes significantly, and existing technologies have difficulty adapting to such changes, resulting in biased and unstable detection results. This limitation is not simply due to the single nature of the technology itself, but rather due to a lack of systematic understanding of the interplay between contamination levels and matrix effects. Against this backdrop, the core challenges facing research are gradually becoming apparent. First, the chemical reaction between heavy metal ions such as lead and cadmium and volatile organic compounds (VOCs) intensifies as the level of pollution increases. This reaction may form complexes, thereby changing the volatility of the organic compounds and interfering with the accuracy of the detection signal. This is because heavy metal ions bind to organic molecules through coordination bonds, increasing their molecular weight and changing their polarity, significantly reducing the vapor pressure of the original compound. To complicate matters further, this decrease in volatility further exacerbates the difficulty of removing heavy metals during sample pretreatment, making conventional treatment processes unable to effectively meet the special needs of highly polluted samples. This is because the formation of complexes strengthens the binding of heavy metals to the organic matrix, making it difficult for traditional separation methods to break this coordination binding relationship. These two factors are intertwined, making it difficult to simultaneously balance detection sensitivity and pollution monitoring effectiveness, creating a technical bottleneck. Therefore, how to dynamically adjust the sample pretreatment process to balance the sensitivity of volatile component detection and the effectiveness of heavy metal pollution monitoring as the level of heavy metal pollution increases has become a key issue that needs to be addressed urgently. Summary of the Invention

[0003] The present invention provides a method for characterizing the volatile components of agarwood, which mainly includes:

[0004] Obtaining heavy metal pollution concentration distribution data and volatile component signal data in the agarwood sample to generate an original data set of heavy metal pollution degree and volatile component signal;

[0005] According to the original data set, the heavy metal pollution concentration distribution data is classified by a preset pollution concentration classification model to obtain the pollution level range of the agarwood sample and the corresponding matrix effect variation range;

[0006] According to the range of variation of the matrix effect, a chemical reaction model of volatile components and heavy metal pollution is constructed, a quantitative index of complex formation in the chemical reaction model is obtained, and the pretreatment process parameters of the agarwood sample are adjusted according to the quantitative index to generate a pretreatment plan;

[0007] According to the pre-treatment scheme, the agarwood sample is pre-treated using a chemical separation device to extract processed signal data of volatile components in the agarwood sample, verify the accuracy of the processed signal data, and correct the processed signal data that does not meet the standard to obtain a corrected signal characteristic value;

[0008] The signal response intensity and background noise ratio of the agarwood sample at different pollution levels are analyzed using the corrected signal characteristic values ​​to determine the detection parameter configuration and generate a detection result data set.

[0009] Furthermore, the agarwood sample is non-destructively scanned by a spectral analysis device to obtain heavy metal pollution concentration distribution data and volatile component signal data in the agarwood sample, and generate an original data set of heavy metal pollution degree and volatile component signal, including:

[0010] Dividing a scanning area according to the morphological characteristics of the agarwood sample, scanning the scanning area using a near-infrared spectrometer, and obtaining spectral reflectance intensity data of the scanning area within a preset wavelength range;

[0011] Extracting a principal component eigenvector of the spectral reflection intensity data using a principal component analysis algorithm based on the spectral reflection intensity data, matching the principal component eigenvector with a preset heavy metal spectral feature library, and determining heavy metal pollution concentration distribution data of the scanned area;

[0012] The signal intensity data of the volatile component is obtained, and a correlation coefficient between the heavy metal pollution concentration distribution data and the signal intensity data is calculated based on the heavy metal pollution concentration distribution data and the signal intensity data of the volatile component to generate the original data set.

[0013] Furthermore, the calculating of the correlation coefficient between the heavy metal pollution concentration distribution data and the signal intensity data based on the heavy metal pollution concentration distribution data and the signal intensity data of the volatile component to generate the original data set includes:

[0014] Calculating the covariance between the heavy metal pollution concentration distribution data and the signal intensity data according to the heavy metal pollution concentration distribution data and the signal intensity data;

[0015] The correlation coefficient is generated according to the ratio of the covariance to the standard deviation, and the correlation coefficient is integrated with the heavy metal pollution concentration distribution data and the signal intensity data to generate the original data set.

[0016] Furthermore, the heavy metal pollution concentration distribution data is classified according to the original data set by a preset pollution concentration classification model to obtain the pollution level interval of the agarwood sample and the corresponding matrix effect variation range, including:

[0017] Grouping the heavy metal pollution concentration distribution data in the original data set by a clustering algorithm to determine the boundary value of the pollution level interval;

[0018] According to the boundary value of the pollution level interval, the variance of the volatile component signal intensity within the pollution level interval is calculated to generate the matrix effect variation range.

[0019] Furthermore, the heavy metal pollution concentration distribution data is classified according to the original data set by a preset pollution concentration classification model to obtain the pollution level interval of the agarwood sample and the corresponding matrix effect variation range, including:

[0020] Grouping the heavy metal pollution concentration distribution data in the original data set by a clustering algorithm to determine the boundary value of the pollution level interval;

[0021] According to the boundary value of the pollution level interval, the variance of the volatile component signal intensity within the pollution level interval is calculated to generate the matrix effect variation range.

[0022] Furthermore, the agarwood sample is pre-treated using a chemical separation device according to the pre-treatment scheme, and processed signal data of volatile components in the agarwood sample is extracted, and the accuracy of the processed signal data is verified, including:

[0023] performing a complexation precipitation treatment on the agarwood sample to extract processed signal data of volatile components in the agarwood sample;

[0024] The processed signal data is compared with preset standard sample signal data, and the relative deviation of the processed signal data is calculated to determine the accuracy of the processed signal data.

[0025] Furthermore, the signal response intensity and background noise ratio of the agarwood sample at different pollution levels are analyzed by using the corrected signal characteristic value, the detection parameter configuration is determined, and a detection result data set is generated, including:

[0026] Based on the corrected signal characteristic values, gas chromatography-mass spectrometry was used to repeatedly test samples of different pollution levels. By measuring the peak signal response intensity and the corresponding baseline noise intensity at each pollution level, the ratio of signal response intensity to background noise and its variation with pollution degree were obtained.

[0027] Based on the ratio data and its variation pattern, the gas chromatograph column temperature program, carrier gas flow rate, and electron multiplier voltage of the mass spectrometer detector are optimized. If the ratio of the signal response intensity to the background noise is lower than the preset detection requirement threshold, the electron multiplier voltage setting is increased to obtain the optimal parameter configuration that meets the detection sensitivity requirements;

[0028] Based on the optimal parameter configuration, the sample consumption volume under different detection accuracy settings was measured. By comparing the difference in sample usage between the high-precision detection mode and the standard detection mode and the corresponding changes in the heavy metal detection limit, the numerical relationship between the improvement in detection accuracy and the increase in sample loss was obtained;

[0029] Based on the numerical relationship, a weighted average calculation method is used to comprehensively score the detection sensitivity index, pollution monitoring accuracy and sample utilization efficiency. The overall evaluation score is calculated according to the preset evaluation weight distribution ratio to obtain a detection result data set containing the numerical values ​​of various performance indicators and comprehensive evaluation results.

[0030] Furthermore, the method also includes: generating a characteristic distribution map of the volatile components of the agarwood sample based on the detection result data set, identifying the coordinate position and deviation degree of the abnormal data points in the characteristic distribution map, determining the cause of the abnormal data points, and feeding back the cause determination result to the pre-processing process parameter adjustment module to generate an updated process parameter configuration.

[0031] Furthermore, generating a characteristic distribution map of the volatile components of the agarwood sample based on the test result data set, identifying the coordinate positions and deviation degrees of abnormal data points in the characteristic distribution map, and determining the causes of the abnormal data points includes:

[0032] Based on the performance index values ​​and comprehensive evaluation results in the test result data set, a characteristic distribution map of the volatile components of the agarwood sample is constructed. The data points are plotted with the retention time of each compound as the abscissa and the signal intensity as the ordinate to obtain a two-dimensional distribution map reflecting the composition characteristics of the volatile components of the sample;

[0033] Using the two-dimensional distribution map, the deviation of each data point from the reference concentration range of the standard agarwood sample is calculated. If the signal intensity of a data point deviates from the standard range by more than a preset anomaly detection threshold, it is marked as an abnormal data point, and the map coordinate position of the abnormal data point and the corresponding deviation degree value are obtained;

[0034] The cause of the abnormal data point is determined based on the map coordinate position and the deviation degree value of the abnormal data point.

[0035] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0036] The present invention discloses a method for characterizing the volatile components of agarwood. The method acquires raw data through spectral analysis, establishes a pollution classification model, constructs a chemical reaction enhancement model, optimizes the pretreatment process, removes heavy metal interference, and extracts volatile component signals. A support vector machine algorithm is used to correct the signal, analyze the balance between detection sensitivity and pollution monitoring effect, and determine the optimal detection parameters. A characteristic distribution map of volatile components is generated, abnormal data points are verified, the cause of the abnormality is determined, and feedback is provided to the process optimization module. The present invention achieves accurate detection of heavy metal pollution and volatile components in agarwood samples, balances detection accuracy and sample loss, improves pollution monitoring accuracy, and provides a reliable basis for agarwood quality assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention provides a flow chart of a method for characterizing the volatile components of agarwood. DETAILED DESCRIPTION

[0038] To further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and examples. The present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0039] like Figure 1 The method for characterizing the volatile components of agarwood in this embodiment may specifically include:

[0040] Step S101: Initial data collection is performed on the agarwood sample to obtain the concentration distribution information of heavy metal pollution in the sample and the preliminary signal characteristics of the volatile components. The sample is non-destructively scanned to obtain the original data set of heavy metal pollution degree and volatile component signals.

[0041] Agarwood samples were divided into regions, and the scanning range was determined based on the sample's appearance and density. Each region was continuously scanned using a near-infrared spectrometer to obtain spectral reflectance intensity data within a specific wavelength range. The characteristic signals of heavy metal elements were identified by changes in the absorption peaks of the spectral reflectance intensity at different wavelengths. Principal component analysis (PCA) was used to reduce the dimensionality of the identified heavy metal signatures. The input was the reflectance intensity value corresponding to each wavelength, and the output was the reduced principal component eigenvector. The eigenvectors were then matched against a pre-established library of heavy metal spectral signatures. Similarity was determined by calculating the Euclidean distance. If the similarity exceeded a preset threshold, the region was deemed contaminated with the corresponding heavy metal, and the heavy metal species and concentration values ​​were determined for each scanned region. Based on the heavy metal concentration values ​​for each scanned region, volatile components were collected at the corresponding locations using a headspace solid-phase microextraction device. The volatiles were separated and detected using gas chromatography-mass spectrometry. Compound species were identified based on retention times, and signal intensities were determined based on mass spectrometry peak areas. The volatile species and signal intensity data for each region were established. Based on the heavy metal concentration values ​​of each area and the volatile component signal intensity data at the corresponding position, the linear correlation between each heavy metal concentration and each volatile component signal intensity is calculated. The correlation coefficient value is obtained by dividing the covariance by the product of the standard deviation. If the absolute value of the correlation coefficient is greater than the preset correlation threshold, it is determined that there is a significant correlation between this group of data. The heavy metal concentration values, volatile component signal intensity data and their correlation coefficient values ​​of all areas are integrated to obtain the original data set of heavy metal pollution degree and volatile component signal characteristics.

[0042] For example, the regional division of agarwood samples is mainly based on the difference in appearance and density to determine the scanning range.

[0043] For example, due to the uneven resin deposition during the formation process, agarwood has significant density differences in different areas. High-density areas are typically dark brown or black and rich in resin, while low-density areas are light yellow or white and primarily wood. Through visual observation and tactile perception, samples can be divided into high-density, medium-density, and low-density areas. Each area is further subdivided into multiple scanning units using a grid. The working principle of a near-infrared spectrometer is to utilize the selective absorption characteristics of a substance for near-infrared light.

[0044] In one possible implementation, when near-infrared light is irradiated onto the surface of an agarwood sample, different chemical components produce absorption peaks at specific wavelengths. Heavy metal elements such as lead, cadmium, and mercury have unique absorption characteristics in the near-infrared region, with lead exhibiting a distinct absorption peak near 1450nm, cadmium exhibiting characteristic absorption at 1680nm, and mercury showing strong absorption near 2100nm. The presence and intensity of these characteristic signals can be identified by continuously scanning the spectral reflectance intensity data. Principal component analysis (PCA) offers significant advantages in processing spectral data.

[0045] Specifically, spectral data typically contains reflectance intensity values ​​at hundreds of wavelengths, resulting in high dimensionality and redundant information. Principal component analysis projects the original high-dimensional data into a low-dimensional space through linear transformation, retaining the direction of maximum variance as the first principal component, and so on to obtain multiple principal components. When processing agarwood spectral data, the first 3-5 principal components can typically retain more than 95% of the information, greatly simplifying the subsequent matching calculation process. A pre-established heavy metal spectral feature library is constructed by collecting spectral data of heavy metal standard samples of known concentrations.

[0046] In one embodiment, standard heavy metal solutions with varying concentration gradients (10 ppm, 50 ppm, 100 ppm, and 500 ppm) were prepared and evenly coated onto a carrier similar to the agarwood matrix. Standard spectra were acquired using the same spectral acquisition parameters, and principal component analysis was performed to obtain characteristic vectors at each concentration. The Euclidean distance between the characteristic vectors of the actual sample and those in the standard library was calculated; smaller distances indicate greater similarity, thereby determining the type and concentration range of the heavy metal. Headspace solid-phase microextraction (SPME) can effectively enrich the volatile components of agarwood.

[0047] It should be noted that this technology involves exposing fibers coated with an adsorbent material to the headspace above the sample, where volatile compounds are adsorbed and enriched. The main volatile components of agarwood include linalool, linalool furan, and various sesquiterpenoids, which evaporate at different rates at different temperatures. Through gas chromatography, the components elute in order of boiling point, and the mass spectrometer records the molecular ion peak and fragment ion peak of each component, forming a unique mass spectrum for compound identification. Correlation analysis reveals the inherent connection between heavy metal pollution and volatile components.

[0048] Ideally, when heavy metal ions enter agarwood tissue, they affect the resin biosynthesis pathway, inhibiting or promoting the production of certain volatile components. The extent of this effect can be quantified by calculating the correlation coefficient. A positive correlation indicates that as the heavy metal concentration increases, the content of the volatile component also increases, while a negative correlation indicates the opposite. This correlation analysis provides important data support for subsequent quality assessment and pollution source tracing.

[0049] Step S102 , classifying the features of the increased heavy metal pollution level, dividing the samples into different pollution level intervals, and determining the range of matrix effect variation corresponding to each level.

[0050] Heavy metal concentration values ​​and corresponding volatile component signal intensities were extracted from the original data set. The concentration values ​​were grouped using the K-means clustering method. The sum of squares within the group was minimized through iterative calculation. The optimal number of clusters was determined according to the elbow rule, and the center point concentration value and cluster boundary concentration value of each cluster were obtained. Based on the center point concentration value and cluster boundary concentration value of each cluster, a pollution level classification criterion was established. When the heavy metal concentration value of the sample was less than the lowest cluster boundary, it was classified as a light pollution level. When the concentration value was between two adjacent cluster boundaries, it was classified as a moderate or heavy pollution level. The pollution level identification of each sample was obtained. For the sample set with the same pollution level identification, the volatile component signal intensity data were extracted. The difference between the signal intensity of each sample and the average signal intensity of all samples within the level was calculated. The variance was obtained by dividing the sum of the squares of the difference by the number of samples, and the mean and standard deviation range of the matrix effect corresponding to the pollution level was determined. Based on the mean and standard deviation range of the matrix effect, a mapping relationship between contamination level and matrix effect was constructed. The concentration range of each contamination level and its corresponding variation range formed by the mean value of the matrix effect plus or minus the standard deviation were recorded. The mapping relationships of all levels were integrated to form a complete grading model, and the variation range of the matrix effect corresponding to each level was determined.

[0051] For example, the K-means clustering method shows unique advantages in processing agarwood heavy metal concentration data.

[0052] For example, when the heavy metal concentration values ​​of 100 agarwood samples in the original data set are obtained, these values ​​show obvious distribution characteristics. The concentration values ​​range from 0.5ppm to 500ppm, forming a continuous but uneven distribution. K-means clustering randomly selects the initial cluster center, then iteratively assigns each data point to the nearest cluster center, and then recalculates the center position of each cluster. This process continues until the cluster center no longer changes significantly. The application of the elbow rule helps determine the optimal number of clusters.

[0053] In one possible implementation, the data is clustered into 2, 3, 4, and 5 clusters, calculating the within-group sum of squares for each case. When the number of clusters increases from 3 to 4, the decline in the within-group sum of squares decreases significantly, forming an "elbow"-shaped inflection point, indicating that 3 clusters is a more reasonable choice. The resulting three cluster centers represent light, moderate, and heavy pollution levels, respectively. The boundary values ​​of each cluster naturally form the demarcation lines for pollution levels. The criteria for classifying pollution levels are based on the statistical properties of the clustering results.

[0054] Specifically, the first cluster has a central concentration of 5 ppm and a boundary of 15 ppm; the second cluster has a center of 50 ppm, with a boundary between 15 and 150 ppm; and the third cluster has a center of 300 ppm, with a boundary exceeding 150 ppm. This division is not based on fixed thresholds set by humans, but rather is adaptively determined based on the distribution characteristics of the data itself. Therefore, it better reflects the actual distribution of heavy metal contamination in agarwood samples. The concept of matrix effect is crucial in agarwood analysis.

[0055] It should be noted that the matrix effect refers to the influence of the agarwood matrix components on the volatile component detection signal. When the degree of heavy metal contamination varies, the matrix composition of the agarwood will change, which in turn affects the release and detection of volatile components. By calculating the average volatile component signal intensity of all samples within the same contamination level, the baseline signal level for that level can be obtained. The deviation of each sample signal from the baseline level reflects individual differences, and the statistical distribution of these deviations reveals the range of the matrix effect. The variance calculation process reveals the degree of dispersion of the data.

[0056] In one embodiment, for 30 samples of light pollution level, its volatile component signal intensity mean value is 1000 units, and the difference square sum of each sample and mean value is 90000, and obtains variance 3000 divided by sample number 30, and standard deviation is about 54.8. This means that the matrix effect variation range of light pollution sample is roughly between 945.2 to 1054.8 units. The calculation process of moderate and heavy pollution level is similar, but because pollution increases the weight of matrix component and changes larger, its standard deviation also increases accordingly. The construction of mapping relationship realizes the quantitative association of pollution level and matrix effect.

[0057] Optimally, a complete comparison table is created by recording the concentration range for each contamination level and its corresponding matrix effect variation range. This mapping relationship not only provides a basis for determining the degree of contamination but also predicts the possible variation range of volatile components detected under different contamination levels, laying the foundation for subsequent data correction and quality assessment.

[0058] Step S103: Based on the range of matrix effect variation, the effect of chemical reaction enhancement on complex formation is analyzed to obtain a quantitative index of the change in volatility characteristics caused by complex formation. If the quantitative index exceeds a preset threshold range, the pretreatment process is dynamically optimized to obtain a treatment solution that adapts to the current pollution level.

[0059] Based on the data within the range of matrix effect variation, mass spectrometry was used to determine the binding strength between heavy metal ions and volatile component molecules. By calculating the equilibrium constant under different concentration conditions, a functional relationship between the complexation reaction rate and the pollution concentration was established. The functional relationship can be:

[0060]

[0061] , v complex represents the complexation reaction rate, k f represents the reaction rate constant, C pollutant represents the concentration of pollutants, n represents the reaction order, C product represents the product concentration, C max Represents the maximum product concentration, and this functional relationship is used as the core parameter of the chemical reaction enhancement model. Based on the core parameters of the chemical reaction enhancement model, the change in the release rate of volatile components before and after the formation of the complex is calculated, and the ratio of complexed and free volatile components is determined by thermal desorption experiments. The deviation of this ratio value from the preset baseline value is used as a quantitative indicator of the change in volatile characteristics. According to the quantitative indicator value of the change in volatile characteristics, its impact on the sample detection signal is analyzed. If the quantitative indicator exceeds the preset threshold range, the required compensation coefficient is calculated according to the preset chemical reaction enhancement model, and the reaction time and reaction temperature parameters in the pretreatment process are adjusted to obtain a treatment plan that adapts to the current degree of pollution. The compensation coefficient can be calculated using the following formula:

[0062]

[0063] , K c represents the compensation coefficient of the chemical reaction enhancement model, k1 and k2 represent the reaction rate constants, Q th Indicates the preset threshold, Q obs Represents the observed quantitative index value, C cat represents the catalyst concentration, T react Represents the reaction temperature, T ref Indicates the reference temperature.

[0064] For example, mass spectrometry plays a key role in determining the binding strength of heavy metal ions to volatile components.

[0065] For example, when the linalool molecules in agarwood encounter lead ions, the empty orbitals of the lead ions coordinate with the lone pair of electrons of the hydroxyl oxygen atoms in the linalool molecules to form a complex. Using electrospray ionization mass spectrometry, the molecular ion peak of the complex can be directly observed. In the mass spectrum, the molecular ion peak of free linalool appears at m / z 222, while the peak of the complex after complexing with lead ions appears at m / z 429. This mass difference corresponds exactly to the mass of one lead ion. By measuring the intensity ratio of the complex peak to the free molecular peak at different concentrations, the equilibrium constant of the complex reaction can be calculated. The construction of the chemical reaction enhancement model is based on the principle of complex reaction kinetics.

[0066] In one possible implementation, the rate of the complexation reaction exhibits a nonlinear growth as the heavy metal concentration increases from 1 ppm to 100 ppm. Initially, the reaction rate is proportional to the concentration, but after the concentration exceeds a critical value, the rate slows due to saturation of active sites. This relationship can be described mathematically using a similar form to the Michaelis-Menten equation, with the maximum reaction rate and the half-saturation constant as the core parameters of the model. These parameters are obtained by nonlinearly fitting the experimental data, forming a mathematical model that quantitatively describes the chemical reaction enhancement effect. Thermal desorption experiments reveal the specific mechanism by which complexation influences volatility.

[0067] Specifically, a sample of agarwood containing complexes was placed in a programmed temperature device and gradually heated from room temperature to 300 degrees Celsius. Free linalool begins to volatilize in large quantities at around 150 degrees Celsius, while complexed linalool requires higher temperatures to be released. By collecting volatiles at different temperature points and conducting quantitative analysis, a volatile release curve can be plotted. The ratio of the complexed to free state is calculated by the ratio of the areas under the curve, which directly reflects the extent of the impact of heavy metal pollution on volatility. The determination of quantitative indicators involves considerations from multiple dimensions.

[0068] It should be noted that in addition to the ratio of the complexed state to the free state, the change in the release rate of volatiles is also an important indicator. In unpolluted samples, the release of volatiles follows the law of first-order kinetics, while in heavy metal contaminated samples, due to the presence of complexation, the release process becomes a complex multi-stage process. By comparing the release rate constants of contaminated samples and standard samples, the rate change coefficient can be obtained. When this coefficient deviates from 1.0 and exceeds the set threshold, it indicates that the pollution has significantly affected the volatility characteristics of the sample. The calculation of the compensation coefficient is based on the prediction results of the chemical reaction enhancement model.

[0069] In one embodiment, if the quantitative indicator indicates a 50% decrease in the volatile release rate, the model can calculate how much the extraction temperature should be increased or how much the extraction time should be extended to compensate for this loss. This compensation is not a simple linear relationship, but is determined by a combination of the dissociation energy of the complex and the reaction kinetic parameters.

[0070] In step S104, the sample is pre-processed according to the processing scheme, and the volatile component signal after processing is extracted to determine whether the signal accuracy meets the detection requirements. If the signal accuracy does not meet the preset standard, the signal is corrected and the corrected signal characteristic value is obtained. If the signal accuracy meets the preset standard, the original signal characteristic value is directly used for subsequent analysis.

[0071] According to the reaction temperature and reaction time parameters determined by the treatment plan, the sample is pre-treated by the chelating agent complex precipitation method. By adding ethylenediaminetetraacetic acid solution, the heavy metal ions form a stable complex and precipitate and separate, the volatile components in the supernatant are obtained, and the signal intensity, peak area and retention time data of the treated sample are obtained by gas chromatography. The signal intensity data of the treated sample is compared with the signal intensity of the pre-determined non-contaminated standard sample, and the ratio of the difference between the two and the signal intensity of the standard sample is calculated as the relative deviation to determine whether the relative deviation exceeds the preset accuracy threshold. If the relative deviation exceeds the preset accuracy threshold, the support vector machine algorithm is used to construct a correction model, and the signal intensity, peak area, retention time and relative deviation data are input, and the correction coefficient is output. The corrected signal characteristic value is obtained by multiplying the original signal value by the correction coefficient; if the relative deviation does not exceed the threshold, the signal intensity, peak area and retention time data are directly used as the original signal characteristic value for subsequent analysis.

[0072] For example, the chelating agent complex precipitation method has a unique chemical mechanism in removing heavy metal interference.

[0073] For example, ethylenediaminetetraacetic acid molecules contain four carboxyl groups and two amino groups, functional groups that can form stable hexacoordinate complexes with heavy metal ions. When an EDTA solution is added to agarwood samples contaminated with heavy metals, the metal ions are encapsulated in the "cage-like" structure formed by the EDTA molecules. The resulting complex easily precipitates due to its increased molecular weight. After centrifugation, the volatile components in the supernatant are retained, while the heavy metal contaminants are effectively removed. This selective separation is based on differences in the stability constants of the complexes. The complex stability constants of EDTA with heavy metals are typically between 10^15 and 10^25, far higher than its interaction with organic molecules. Gas chromatography provides a precise means of quantitative analysis of volatile components.

[0074] In one possible implementation, the treated sample is vaporized through an inlet and propelled by a carrier gas into the chromatographic column. Different volatile components, due to differences in boiling points and polarity, have different distribution coefficients between the stationary and mobile phases, enabling separation. The detector records the peak elution time (retention time) of each component, as well as the peak height and area. Retention time is a qualitative indicator of a compound and is reproducible under identical chromatographic conditions; peak area, which is proportional to the compound's content, is the basis for quantitative analysis. Signal intensity reflects the detector's response to the compound. Calculating relative deviation reveals the effectiveness of pretreatment.

[0075] Specifically, uncontaminated standard samples undergo the same analytical process to produce a set of baseline signal values. The difference between the treated sample signal and the baseline signal is divided by the baseline signal value and multiplied by 100% to obtain the relative deviation percentage. This metric comprehensively reflects the thoroughness of heavy metal removal and the retention rate of volatile components. A positive relative deviation indicates signal enhancement, possibly due to the removal of inhibitory effects; a negative value indicates signal weakening, possibly due to the loss of volatile components or residual interference. The support vector machine algorithm demonstrates powerful nonlinear mapping capabilities in signal correction.

[0076] It should be noted that this algorithm uses a kernel function to map the original feature space into a high-dimensional space, searching for the optimal classification hyperplane within that high-dimensional space. In signal correction applications, the input variables include original signal intensity, peak area, retention time, and relative deviation. These multidimensional data reflect the comprehensive characteristics of the sample. The algorithm uses training data to learn the complex relationship between signal deviation and actual concentration and establishes a correction model. The correction coefficients output by the model account for multiple factors such as matrix effects, instrument response, and pretreatment losses. The application of these correction coefficients ensures accurate signal restoration.

[0077] In one example, if the original signal intensity of a volatile component is 1000 units and the correction factor is 1.15, the corrected signal characteristic value is 1150 units. This correction is not a simple linear amplification, but rather an intelligent adjustment based on a complex mathematical model. The correction factor may vary for different compounds, reflecting the varying degree of influence during pretreatment. Through this personalized correction, the resulting signal characteristic value more closely resembles the true composition of the sample.

[0078] In step S105, the balance between detection sensitivity and pollution monitoring effect is analyzed through the corrected signal characteristic values. The optimal detection parameter configuration is determined by evaluating the signal response intensity and background noise ratio under different pollution levels. The balance point between detection accuracy and sample loss is analyzed, and the detection sensitivity index and pollution monitoring accuracy are comprehensively evaluated to generate a detection result data set.

[0079] According to the corrected signal characteristic value, a gas chromatograph-mass spectrometer is used to repeatedly detect samples of different pollution levels. By measuring the signal response intensity peak and the corresponding baseline noise intensity under each pollution level, the ratio data of the signal response intensity to the background noise and its variation law with the degree of pollution are obtained. According to the ratio data and its variation law, the column temperature program of the gas chromatograph, the carrier gas flow rate and the electron multiplier voltage of the mass spectrometer are optimized. If the ratio of the signal response intensity to the background noise is lower than the preset detection requirement threshold, the electron multiplier voltage setting is increased to obtain the optimal parameter configuration that meets the detection sensitivity requirements. According to the optimal parameter configuration, the sample consumption volume under different detection accuracy settings is measured. By comparing the sample dosage difference and the corresponding heavy metal detection limit change between the high-precision detection mode and the standard detection mode, the numerical relationship between the detection accuracy improvement and the sample loss increase is obtained. According to the numerical relationship, the weighted average calculation method is used to comprehensively score the detection sensitivity index, pollution monitoring accuracy and sample utilization efficiency. The overall evaluation score is calculated according to the preset evaluation weight distribution ratio to obtain a detection result data set containing the numerical values ​​of each performance indicator and the comprehensive evaluation results.

[0080] For example, the application of gas chromatography-mass spectrometry in repeated detection is based on the principles of instrument response stability and data reproducibility.

[0081] In a possible implementation, by performing at least three parallel tests on the same sample, the statistical distribution characteristics of the signal response intensity and the variation range of the baseline noise can be obtained.

[0082] For example, when the α-pinene signal peaks in a lightly contaminated sample are 125,000, 128,000, and 126,500 counts, respectively, the average signal intensity is 126,500 counts, the corresponding baseline noise intensity is approximately 2,500 counts, and the signal-to-noise ratio reaches 50.6:1. In contrast, due to matrix effects, the signal intensity of the same compound in a heavily contaminated sample drops to 95,000 counts, while the baseline noise increases to 4,200 counts, and the signal-to-noise ratio drops to 22.6:1. This difference in signal quality directly reflects the impact of contamination level on detection performance. Based on the variation in the aforementioned signal-to-noise ratio data, fine-tuning instrument parameters becomes a key step in improving detection performance.

[0083] It is important to note that optimizing the column temperature program can improve chromatographic separation. The initial temperature was set at 60°C and held for 2 minutes, followed by a temperature increase of 10°C / min to 250°C. This gradient temperature ramp ensured complete elution of volatile components while avoiding baseline drift at high temperatures. Adjusting the carrier gas flow rate was also crucial. When the helium flow rate was adjusted from 1.0 mL / min to 1.2 mL / min, the peak resolution increased from 1.8 to 2.3, and peak symmetry was significantly improved. Optimizing the electron multiplier voltage directly impacted detection sensitivity. When the signal-to-noise ratio fell below the threshold of 30:1, increasing the voltage from 1800 V to 2000 V increased signal intensity by approximately 40%, while keeping the noise level essentially unchanged.

[0084] In one embodiment, the application of injection volume control technology reflects the balance between detection accuracy and sample consumption. High-precision detection mode usually requires increasing the injection volume from the standard 1μL to 2μL. Although the sample consumption has doubled, the detection limit can be reduced from 5mg / kg to 2mg / kg, and the detection ability of heavy metal pollutants has been significantly improved. This increase in sample volume needs to be weighed in practical applications, especially for precious agarwood samples. Excessive sample consumption may affect other subsequent detection items. By establishing a relationship curve between sample volume and detection accuracy, it was found that when the injection volume increased from 1μL to 1.5μL, the detection limit improvement effect was most significant, and the marginal benefit of continuing to increase the injection volume gradually decreased. Therefore, the injection volume of 1.5μL became the best choice to balance detection accuracy and sample protection. The weighted average calculation method plays an important role in unifying quantitative standards in comprehensive evaluation.

[0085] Specifically, the detection sensitivity index is standardized by the inverse of the detection limit, the contamination monitoring accuracy is based on the comprehensive calculation of the true positive rate and the false negative rate, and the sample utilization efficiency is measured by the amount of effective information obtained per unit sample volume.

[0086] For example, when the standardized score of detection sensitivity is 85 points, the pollution monitoring accuracy is 92 points, and the sample utilization efficiency is 78 points, the weighted calculation is performed according to the preset weight distribution ratio of 40%, 35%, and 25%, and the comprehensive evaluation score is 85.25 points.

[0087] In step S106, a characteristic distribution map of volatile components is generated based on the test result data set, abnormal data points in the characteristic distribution map of volatile components that deviate from the normal volatile component concentration range are verified, the location and degree of deviation of the abnormal data points are identified, the cause of the abnormal points is determined, and the abnormal point determination results and processing experience are fed back to the pre-processing process optimization module to obtain the updated process parameter configuration.

[0088] Based on the performance index values ​​and comprehensive evaluation results in the test result data set, a characteristic distribution map of volatile components is constructed. By plotting the data points using the retention time of each compound as the horizontal axis and the signal intensity as the vertical axis, a two-dimensional distribution map reflecting the composition characteristics of the sample's volatile components is obtained. Through the two-dimensional distribution map, the deviation of each data point from the reference concentration range of the standard agarwood sample is calculated. If the signal intensity of the data point deviates from the standard range by more than the preset abnormality detection threshold, it is marked as an abnormal data point, and the map coordinate position of the abnormal data point and the corresponding deviation degree value are obtained. Based on the map coordinate position and deviation degree value of the abnormal data point, a decision tree algorithm is used to automatically classify and identify the cause of the abnormality. By analyzing the size of the deviation value, the distribution pattern and the compound type characteristics, it is determined whether the abnormal point is caused by heavy metal pollution interference, the quality difference of the sample itself or the fluctuation of the detection equipment, and the abnormal point determination result marked with the cause category is obtained. Based on the abnormal point determination results, a parameter feedback regulator is used to transmit the cause analysis results and the corresponding deviation degree information to the pre-processing process control unit, and the corresponding process parameter adjustment plan is automatically matched according to the abnormal cause category to obtain an updated process parameter configuration for the current abnormality type.

[0089] Exemplarily, the application of the data visualization processor in constructing the feature distribution map is based on the principle of two-dimensional projection of multidimensional data.

[0090] In one possible implementation, the processor converts the complex data matrix obtained by gas chromatography-mass spectrometry detection into an intuitive graphical representation, where the horizontal axis represents the retention time of the compound in the chromatographic column, reflecting the polarity and boiling point characteristics of the molecule, and the vertical axis represents the signal intensity of the detector response, corresponding to the relative content of the compound in the sample.

[0091] For example, α-pinene shows a signal peak at 8.5 minutes with an intensity of 125,000 counts, while β-pinene appears at 9.2 minutes with an intensity of 98,000 counts. These data points form a specific distribution pattern in the spectrum. Agarwood samples from different origins and quality grades exhibit unique distribution characteristics, providing a visual data foundation for subsequent anomaly identification. Based on the establishment of this two-dimensional distribution spectrum, the precise analysis function of the numerical comparator becomes crucial.

[0092] It should be noted that the reference concentration range of standard agarwood samples is a benchmark database established through statistical analysis of a large number of high-quality samples, and each major volatile component has a corresponding normal concentration range.

[0093] In one example, the standard signal intensity range for α-pinene is set at 120,000-140,000 counts. When the signal intensity of this compound in a sample reaches 95,000 counts, the deviation from the lower limit reaches 20.8%, exceeding the preset 10% anomaly detection threshold. The system automatically marks this data point as an anomaly. This automated anomaly identification mechanism can quickly screen out suspicious data requiring further analysis, significantly improving the efficiency and accuracy of quality control.

[0094] In one possible implementation, a decision tree algorithm demonstrates powerful classification capabilities in anomaly cause analysis. This algorithm uses a series of if-then rules to perform logical reasoning, making a comprehensive judgment based on the magnitude of the deviation, the distribution of the anomaly data point in the graph, and the chemical properties of the affected compound.

[0095] For example, when multiple oxygen-containing compounds exhibit simultaneous signal suppression, and the degree of suppression is positively correlated with the compound's coordination capacity, the decision tree will determine that the anomaly is caused by interference from heavy metal contamination. Conversely, if the anomaly manifests as random fluctuations in individual compounds, and the amplitude of the fluctuation is small, it is determined to be within the normal fluctuation range of the detection equipment. Through this structured analysis method, the system can accurately distinguish between different types of anomaly causes, providing a scientific basis for subsequent targeted processing. As the core component of the entire quality control system, the parameter feedback regulator achieves closed-loop control from anomaly identification to process optimization.

[0096] Specifically, when the system identifies a signal anomaly caused by heavy metal contamination, the feedback regulator automatically adjusts the solid-phase extraction elution conditions, increasing the amount of elution solvent or extending the elution time to more thoroughly remove the heavy metal interference. If the anomaly is attributed to differences in sample quality, the detector sensitivity setting is adjusted to enhance the detection of weak signals by increasing the electron multiplier voltage.

[0097] For example, to address the abnormal situation of 20% low signal intensity, the pre-treatment extraction time was extended from the original 30 minutes to 45 minutes, and the detector voltage was increased from 1800V to 1950V. This precise parameter adjustment ensures the reliability and consistency of the test results, enabling the entire detection process to adapt to changes in different sample characteristics and contamination conditions.

[0098] 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 in the scope of protection of the present invention.

Claims

1. A method for characterizing the volatile components of agarwood, characterized in that: The method comprises: Obtaining heavy metal pollution concentration distribution data and volatile component signal data in the agarwood sample to generate an original data set of heavy metal pollution degree and volatile component signal; According to the original data set, the heavy metal pollution concentration distribution data is classified by a preset pollution concentration classification model to obtain the pollution level range of the agarwood sample and the corresponding matrix effect variation range; According to the range of variation of the matrix effect, a chemical reaction model of volatile components and heavy metal pollution is constructed, a quantitative index of complex formation in the chemical reaction model is obtained, and the pretreatment process parameters of the agarwood sample are adjusted according to the quantitative index to generate a pretreatment plan; Pre-treating the agarwood sample using a chemical separation device according to the pre-treatment scheme, extracting processed signal data of volatile components in the agarwood sample, verifying the accuracy of the processed signal data, and correcting the processed signal data that does not meet the standards to obtain corrected signal characteristic values; The signal response intensity and background noise ratio of the agarwood sample at different pollution levels are analyzed using the corrected signal characteristic values ​​to determine the detection parameter configuration and generate a detection result data set.

2. The method for characterizing the volatile components of agarwood according to claim 1, wherein: The agarwood sample is non-destructively scanned by a spectral analysis device to obtain heavy metal pollution concentration distribution data and volatile component signal data in the agarwood sample, and generate an original data set of heavy metal pollution degree and volatile component signal, including: Dividing a scanning area according to the morphological characteristics of the agarwood sample, scanning the scanning area using a near-infrared spectroscopy device, and obtaining spectral reflectance intensity data of the scanning area within a preset wavelength range; Extracting a principal component eigenvector of the spectral reflection intensity data using a principal component analysis algorithm based on the spectral reflection intensity data, matching the principal component eigenvector with a preset heavy metal spectral feature library, and determining heavy metal pollution concentration distribution data of the scanned area; The signal intensity data of the volatile component is obtained, and a correlation coefficient between the heavy metal pollution concentration distribution data and the signal intensity data is calculated based on the heavy metal pollution concentration distribution data and the signal intensity data of the volatile component to generate the original data set.

3. The method for characterizing the volatile components of agarwood according to claim 2, wherein: The step of calculating the correlation coefficient between the heavy metal pollution concentration distribution data and the signal intensity data of the volatile component according to the heavy metal pollution concentration distribution data and the signal intensity data to generate the original data set includes: Calculating the covariance between the heavy metal pollution concentration distribution data and the signal intensity data according to the heavy metal pollution concentration distribution data and the signal intensity data; The correlation coefficient is generated according to the ratio of the covariance to the standard deviation, and the correlation coefficient is integrated with the heavy metal pollution concentration distribution data and the signal intensity data to generate the original data set.

4. The method for characterizing the volatile components of agarwood according to claim 1, wherein: According to the original data set, the heavy metal pollution concentration distribution data is classified by a preset pollution concentration classification model to obtain the pollution level interval of the agarwood sample and the corresponding matrix effect variation range, including: Grouping the heavy metal pollution concentration distribution data in the original data set by a clustering algorithm to determine the boundary value of the pollution level interval; According to the boundary value of the pollution level interval, the variance of the volatile component signal intensity within the pollution level interval is calculated to generate the matrix effect variation range.

5. The method for characterizing the volatile components of agarwood according to claim 1, wherein: According to the original data set, the heavy metal pollution concentration distribution data is classified by a preset pollution concentration classification model to obtain the pollution level interval of the agarwood sample and the corresponding matrix effect variation range, including: Grouping the heavy metal pollution concentration distribution data in the original data set by a clustering algorithm to determine the boundary value of the pollution level interval; According to the boundary value of the pollution level interval, the variance of the volatile component signal intensity within the pollution level interval is calculated to generate the matrix effect variation range.

6. The method for characterizing the volatile components of agarwood according to claim 1, wherein: The method of pre-treating the agarwood sample using a chemical separation device according to the pre-treatment scheme, extracting processed signal data of volatile components in the agarwood sample, and verifying the accuracy of the processed signal data includes: performing a complexation precipitation treatment on the agarwood sample to extract processed signal data of volatile components in the agarwood sample; The processed signal data is compared with preset standard sample signal data, and the relative deviation of the processed signal data is calculated to determine the accuracy of the processed signal data.

7. The method for characterizing volatile components of agarwood according to claim 1, wherein: The method of analyzing the signal response intensity and background noise ratio of the agarwood sample at different pollution levels using the corrected signal characteristic value, determining the detection parameter configuration, and generating a detection result data set includes: Based on the corrected signal characteristic values, gas chromatography-mass spectrometry was used to repeatedly test samples of different pollution levels. By measuring the peak signal response intensity and the corresponding baseline noise intensity at each pollution level, the ratio of signal response intensity to background noise and its variation with pollution degree were obtained. Based on the ratio data and its variation pattern, the gas chromatograph column temperature program, carrier gas flow rate, and electron multiplier voltage of the mass spectrometer detector are optimized. If the ratio of the signal response intensity to the background noise is lower than the preset detection requirement threshold, the electron multiplier voltage setting is increased to obtain the optimal parameter configuration that meets the detection sensitivity requirements; Based on the optimal parameter configuration, the sample consumption volume under different detection accuracy settings was measured. By comparing the difference in sample usage between the high-precision detection mode and the standard detection mode and the corresponding changes in the heavy metal detection limit, the numerical relationship between the improvement in detection accuracy and the increase in sample loss was obtained; Based on the numerical relationship, a weighted average calculation method is used to comprehensively score the detection sensitivity index, pollution monitoring accuracy and sample utilization efficiency. The overall evaluation score is calculated according to the preset evaluation weight distribution ratio to obtain a detection result data set containing the numerical values ​​of various performance indicators and comprehensive evaluation results.

8. The method for characterizing volatile components of agarwood according to claim 1, wherein: The method also includes: generating a characteristic distribution map of the volatile components of the agarwood sample based on the test result data set, identifying the coordinate positions and deviation degrees of abnormal data points in the characteristic distribution map, determining the causes of the abnormal data points, and feeding back the cause determination results to the pre-processing process parameter adjustment module to generate an updated process parameter configuration.

9. The method for characterizing volatile components of agarwood according to claim 1, wherein: Generating a characteristic distribution map of the volatile components of the agarwood sample based on the test result data set, identifying the coordinate positions and deviation degrees of abnormal data points in the characteristic distribution map, and determining the causes of the abnormal data points includes: Based on the performance index values ​​and comprehensive evaluation results in the test result data set, a characteristic distribution map of the volatile components of the agarwood sample is constructed. The data points are plotted with the retention time of each compound as the abscissa and the signal intensity as the ordinate to obtain a two-dimensional distribution map reflecting the composition characteristics of the volatile components of the sample; Using the two-dimensional distribution map, the deviation of each data point from the reference concentration range of the standard agarwood sample is calculated. If the signal intensity of a data point deviates from the standard range by more than a preset anomaly detection threshold, it is marked as an abnormal data point, and the map coordinate position of the abnormal data point and the corresponding deviation degree value are obtained; The cause of the abnormal data point is determined based on the map coordinate position and the deviation degree value of the abnormal data point.

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