Qualitative and quantitative detection method for surfactant

Abstract

The invention discloses a qualitative and quantitative detection method for a surfactant, which relates to the technical field of active agent detection and comprises the following steps: pretreating a sample to be detected to remove impurities and adjust the pH value so as to extract a surfactant component; determining key characteristic parameters of the extracted components to identify specific characteristic signals of the surfactant; on the basis of the recognized feature signals, a feature event report containing signal types, strength degrees and preliminary judgment results is generated, the extraction purity of the surfactant is improved through preprocessing, reference substances are screened according to the feature signals and a special database, a calibration model is adjusted in combination with environmental parameters, and therefore the detection result is more accurate and reliable.

Description

Technical Field

[0001] This invention belongs to the field of surfactant testing technology, specifically a qualitative and quantitative detection method for surfactants. Background Technology

[0002] In the fields of chemical engineering, environmental protection, and daily chemicals, the qualitative and quantitative detection of surfactants is crucial. They are widely used in detergents, cosmetics, and industrial auxiliaries. Excessive use or abnormal composition can lead to environmental pollution and substandard product quality, thus necessitating accurate detection.

[0003] Currently, commonly used detection methods include high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). However, existing technologies have significant limitations: the pretreatment process is complex and can easily lead to the loss of target components, affecting detection accuracy; different surfactants have similar characteristic signals, making qualitative analysis susceptible to interference and resulting in misjudgments. In quantitative analysis, the fluctuations in environmental parameters (such as temperature and pH) are not fully considered, which often leads to result deviations. Furthermore, the poor coordination between modules in the detection process results in low data transmission and analysis efficiency, making it difficult to meet the requirements for rapid and accurate detection.

[0004] Furthermore, existing methods lack a systematic approach to screening reference materials, resulting in low matching accuracy and further reducing detection precision. Therefore, a qualitative and quantitative detection method for surfactants is urgently needed to address these technical challenges. Summary of the Invention

[0005] The purpose of this invention is to provide a qualitative and quantitative detection method for surfactants to solve the problems mentioned in the background art.

[0006] A method for qualitative and quantitative detection of surfactants, comprising: The sample to be tested is pretreated to remove impurities and adjust the pH, thereby extracting the surfactant components; Determine the key characteristic parameters of the extracted components to identify the characteristic signals unique to surfactants; Based on the identified feature signals, a feature event report is generated that includes the signal type, strength, and preliminary judgment results. Retrieve a dedicated relational database that stores the chemical properties, standard spectra, and corresponding reference substances of surfactants. Combine this with feature event reports to filter out matching reference substance groups. Based on the type and intensity of the characteristic signals, the detection condition range is set for the reference material group, and instructions are sent to the detection module; The detection module performs detection under set conditions and outputs raw detection data and real-time environmental parameters; The system receives raw test data and environmental parameters, adjusts the calibration model using the environmental parameters, and compares and analyzes the raw test data based on the adjusted model to generate a test report.

[0007] Furthermore, the preprocessing step includes: Obtain the initial state information of the sample; If the impurity content in the sample is high, a multi-stage impurity removal procedure is performed to remove impurities step by step according to the required precision; if the impurity content is low, a single method is used for impurity removal. After removing impurities, the pH of the sample is adjusted, and its changes are monitored in real time. Once the pH level stabilizes within the target range, stop adjusting and proceed with component extraction.

[0008] Furthermore, the step of determining the key characteristic parameters includes: Multi-dimensional signal acquisition was performed on the extracted components to obtain their fluorescence, ultraviolet, infrared and mass spectrometry characteristic signals, and the time-domain curves and peak time series of each signal were recorded. Analyze the peak synchronicity and peak shape matching between the signals, establish a correlation matrix, and select the signal combination with the highest correlation. The signal with the largest sum of peak intensities in the signal combination is used as the core parameter, and the others are used as auxiliary parameters. Based on the characteristic peak positions of the core parameters, locate the response interval in the auxiliary parameters and calculate the signal energy ratio of the auxiliary parameters within that interval; If the proportion is higher than the preset threshold, then based on the feature markings of the auxiliary parameters, the matching signal fluctuation pattern is searched in the core parameters. If a strong signal appears in the auxiliary parameter within the non-response range of the core parameter, its conflict index is calculated. If it exceeds the critical value, it is considered an interference and is removed. After processing, the overall consistency between the core parameters and the remaining auxiliary parameters is calculated. If the consistency meets the requirements, the feature signal is confirmed to be valid; otherwise, the resolution is increased and the data is re-acquired. If the consistency still does not meet the requirements, a secondary extraction using gradient elution is initiated.

[0009] Furthermore, the step of generating a feature event report includes: Analyze the characteristic signals to determine whether there is signal overlap interference; If there is overlap, calculate the ratio of the relevant parameters of each signal peak to determine whether it is an overlap of signals from different substances or multiple peaks of the same substance. If signals from different substances overlap, they are marked; if the same substance has multiple peak signals, the main signal peak is identified by the peak area ratio. The signal strength is calculated based on the main signal peak, and then a characteristic event report is generated.

[0010] Compared with the prior art, the beneficial effects of the present invention are: This invention's detection method improves the purity of surfactant extraction through pretreatment, selects reference substances based on characteristic signals and a dedicated database, and adjusts the calibration model in conjunction with environmental parameters, making the detection results more accurate and reliable. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the method framework structure of the present invention. Detailed Implementation

[0012] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0013] Please see Figure 1 This application provides a method for the qualitative and quantitative detection of surfactants, comprising: The sample to be tested is pretreated to remove impurities and adjust the pH to extract surfactant components. It should be noted that the pretreatment is to remove any interfering impurities that may be present in the sample, and at the same time adjust its pH to a suitable range.

[0014] In practice, particulate impurities can be removed by centrifugation, and then a buffer solution can be added to adjust the pH value, making it easier for surfactants to separate from the sample matrix. After this treatment, impurities will not interfere with the surfactant signal during subsequent detection, resulting in purer extracted components and laying the foundation for accurate detection.

[0015] The key characteristic parameters of the extracted components are determined to identify the unique characteristic signals of surfactants. It should be noted that these key characteristic parameters refer to specific indicators reflecting the molecular structure or chemical properties of surfactants, such as the position of ultraviolet absorption peaks and the mass-to-charge ratio of characteristic ions in mass spectrometry. During measurement, a spectrometer can be used to scan the extract within a specific wavelength range and record absorbance changes, or a mass spectrometer can be used to analyze ion fragmentation information. This allows for the capture of the unique characteristic signals of surfactants, thereby distinguishing the target surfactant from other substances and avoiding misidentification.

[0016] Based on the identified characteristic signals, a characteristic event report is generated, including the signal type, intensity, and preliminary judgment result. It should be noted that the signal type indicates the physical or chemical signal, the intensity reflects the approximate concentration range of the surfactant, and the preliminary judgment result is a preliminary inference about the surfactant type based on the signal characteristics. This report reduces the blind spots in screening reference materials.

[0017] A dedicated correlation database was retrieved, which stores the chemical properties, standard spectra, and corresponding reference substances of surfactants. Matching reference substance groups were then selected based on characteristic event reports. It should be noted that this dedicated correlation database is specifically designed to store surfactant-related information, and its construction requires extensive experimental data and literature review. Specifically, pure samples of common surfactants, such as sodium dodecyl sulfate and nonylphenol polyoxyethylene ether, were first collected. Their chemical properties under different conditions, such as molecular weight, hydrophilic-lipophilic balance, and standard spectra, including infrared spectroscopy and nuclear magnetic resonance spectroscopy, were obtained using standardized detection methods.

[0018] Furthermore, reference substances with known components are selected, such as standard solutions containing specific surfactants. The matching relationship between these reference substances and the corresponding surfactants is recorded. This information is then categorized, organized, and stored in a database to form basic data.

[0019] Furthermore, the database will be continuously updated and improved based on newly discovered surfactant types to ensure its comprehensiveness and timeliness. This database allows for the rapid identification of comparable reference materials, providing a clear basis for testing and improving the accuracy of qualitative analysis.

[0020] Based on the type and intensity of the characteristic signals, the detection condition range for the reference material group is set, and instructions are sent to the detection module. It should be noted that the detection module is a hardware assembly that performs specific detection operations, typically including a detection instrument and a control unit. For example, when detecting anionic surfactants, the high-performance liquid chromatograph in the detection module will operate at the set mobile phase ratio and column temperature, while the control unit simultaneously records relevant data. Targeted setting of detection conditions allows the instrument to operate in its most optimal state, reducing interference from irrelevant signals and making detection more efficient.

[0021] It should be further explained that the detection conditions should be set in combination with the type, intensity and material properties of the characteristic signal. For example, when the spectral signal is weak, the scanning time should be extended and the wavelength interval should be reduced to improve the ability to capture weak signals, etc., which will not be elaborated on in detail.

[0022] The detection module performs detection under set conditions and outputs raw detection data and real-time environmental parameters. It should be noted that environmental parameters include temperature and humidity during detection. Simultaneous acquisition of raw data and environmental parameters avoids result deviations due to neglecting environmental factors.

[0023] The system receives raw test data and environmental parameters, adjusts the calibration model using the environmental parameters, and then compares and analyzes the raw test data based on the adjusted model to generate a test report. It should be noted that the calibration model is a mathematical model used to correct deviations in the test data; by substituting environmental parameters into the model, it can be made to better reflect the actual test environment.

[0024] For example, a calibration model is a mathematical model used to correct deviations in detection data. Its construction requires first selecting a series of standard samples with known concentrations, conducting tests under different environmental conditions, recording the corresponding detection values ​​and environmental parameters, and then establishing a correlation formula between the detection values, environmental parameters, and the true concentration through mathematical methods.

[0025] Taking the detection of the absorbance of a certain surfactant as an example, if temperature has a significant effect on absorbance, the following relationship can be established: by using the detection data of standard samples of known concentration at different temperatures, the correlation between the absorbance correction value and the amount of temperature change can be obtained.

[0026] For example, when the temperature rises, the absorbance will show a certain regular change. After data fitting, a relationship reflecting this change can be obtained. This relationship can be used to correct the measured absorbance based on the difference between the actual temperature and the standard temperature, so as to get closer to the absorbance corresponding to the true concentration.

[0027] By incorporating environmental parameters into the model, it becomes more closely aligned with the actual testing environment. Based on the adjusted model, the original test data is compared and analyzed, ultimately generating a test report that clearly identifies the types and concentrations of surfactants. The calibrated model, adjusted for environmental parameters, effectively mitigates the impact of environmental changes on testing, making the analytical results closer to reality and ensuring quantitative accuracy.

[0028] Practical results: This detection method improves the purity of surfactant extraction through pretreatment, selects reference substances based on characteristic signals and a dedicated database, and adjusts the calibration model in conjunction with environmental parameters, making the detection results more accurate and reliable.

[0029] As a further embodiment, the preprocessing step includes: Obtain the initial state information of the sample; specifically, this refers to the sample's physical properties, approximate impurity content range, and other basic information. This can be obtained by visually observing the sample's turbidity or using rapid test strips to preliminarily determine the impurity content. Having this information provides a basis for selecting subsequent impurity removal methods and avoids blind operation.

[0030] If the sample contains a high level of impurities, a multi-stage impurity removal process is performed, removing impurities step by step according to the required precision. If the impurity content is low, a single impurity removal method is used. It should be noted that, for example, if the sample is turbid and contains a large number of particles and colloidal impurities, larger particles are first removed by filtration through filter paper, then colloidal impurities are treated by centrifugation, and finally, trace residual impurities are adsorbed using a solid-phase extraction column. Each step achieves a higher precision in impurity removal than the previous one. This staged processing ensures more thorough removal of different types of impurities and avoids interference caused by incomplete impurity removal in a single step. If the impurity content is low, such as if the sample is relatively clear and contains only a small number of fine particles, a single impurity removal method is used, such as direct filtration through a 0.45-micron filter membrane, which satisfies the impurity removal requirements while saving operation time.

[0031] After impurity removal, the pH of the sample is adjusted and its changes are monitored in real time. Specifically, this can be done by adding acid or alkali solutions dropwise, while a probe-type monitoring device continuously tracks the pH value. This allows for timely monitoring of the adjustment progress and avoids over- or under-adjustment.

[0032] Once the pH stabilizes within the target range, adjustment is stopped, and component extraction proceeds. Specifically, at this point, surfactant molecules maintain a stable state, are easily separated from the sample matrix, and the extracted components have higher purity, providing a more reliable basis for subsequent determination of characteristic parameters.

[0033] It should be understood that traditional detection methods have many insurmountable limitations when dealing with samples with complex compositions. For example, when a sample contains both surfactants and structurally similar impurities, relying solely on a single signal (such as ultraviolet absorption) is often insufficient for differentiation, as impurities may produce similar absorption at the same wavelength, leading to the misjudgment of impurity signals as characteristics of the target analyte. Even when multiple signals are used for detection, the conventional approach is simply to compare whether the peak values ​​of each signal appear, ignoring the implicit correlations between signals. When impurity signals and target signals accidentally overlap in peak time, misjudgment is easily caused. Therefore, as a further embodiment, the step of determining key characteristic parameters includes: Multi-dimensional signal acquisition was performed on the extracted components to obtain their fluorescence, ultraviolet, infrared, and mass spectrometry characteristic signals, and the time-domain curves and peak sequences of each signal were recorded. For example, when detecting surfactants in washing wastewater, a fluorescence spectrophotometer was used to capture the intensity changes of their excited light, while an infrared spectrometer recorded the characteristic absorption of molecular vibrations. Each signal corresponds to different molecular structural characteristics. When the ultraviolet absorption of impurities and target analytes is similar, the difference in fluorescence signals can help distinguish them. The recorded time-domain curves and peak sequences can also capture instantaneous signal changes, avoiding the omission of key features due to the brief appearance of signals.

[0034] Analyze the peak synchronicity and peak shape matching between various signals, establish a correlation matrix, and select the signal combination with the highest correlation. It should be understood that during the analysis, the peak occurrence time of different signals is compared first. If the peak occurs at the same time point or within a very short time difference, it is considered to have peak synchronicity. Peak shape matching is obtained by comparing characteristic parameters such as the rising slope, falling slope, and half-width of the peak. If the difference of these parameters is within the set range, it indicates that the peak shape matches.

[0035] For example, when establishing a correlation matrix, signal types are used as rows and columns. The values ​​in the matrix represent the correlation between two corresponding signals. The correlation is calculated by peak synchronicity and peak shape matching in a certain proportion. For instance, the synchronicity of fluorescence and ultraviolet signals is 0.8, the peak shape matching is 0.7, and the correlation can be calculated as 0.75. When detecting surfactants in a mixed system, if a signal exhibits an abnormal peak due to impurity interference, while other signals do not respond synchronously, this isolated signal can be identified by analyzing synchronicity and matching. For example, if the ultraviolet absorption peak of a surfactant appears synchronously with the mass spectrometry characteristic peak, and their rising slope and half-width are similar, their values ​​in the correlation matrix will be high. Such a combination can corroborate each other, reducing misjudgments caused by interference with a single signal and making feature identification more reliable.

[0036] The signal with the largest sum of peak intensities in the signal combination is used as the core parameter, and the others are used as auxiliary parameters. For example, when detecting high concentrations of surfactants in industrial wastewater, mass spectrometry signals often exhibit more obvious characteristic peaks, and using these as the core parameter can comprehensively reflect the main characteristics of the substance. When the core parameter fluctuates due to slight interference, the stable response of auxiliary parameters, such as the ultraviolet signal, can play a verification role, avoiding the influence of fluctuations in a single parameter on the judgment and improving the stability of feature recognition.

[0037] Based on the characteristic peak positions of the core parameters, the response interval is located within the auxiliary parameters, and the signal energy proportion of the auxiliary parameters within this interval is calculated. The specific process involves first determining the start and end times of the core parameter's characteristic peak to define the response interval, then calculating the total signal energy of the auxiliary parameters within that interval and comparing it to the total signal energy of the auxiliary parameters to obtain the proportion. For example, when detecting a certain anionic surfactant, if the mass spectrometry characteristic peak of the core parameter appears within a specific time period, and the signal energy proportion of the UV auxiliary parameters is high within the corresponding interval, it indicates that the two parameters respond consistently within that interval. When trace amounts of interfering substances are present, their signals are mostly distributed in other intervals. This method of localization allows for focusing the effective signal and reducing interference.

[0038] If the proportion exceeds a preset threshold, the matching signal fluctuation pattern is searched for in the core parameters based on the characteristic markers of the auxiliary parameters. It should be understood that characteristic markers refer to signal features in the auxiliary parameters that reflect the specific structure of a substance, such as the characteristic absorption peak of hydroxyl groups in infrared signals or the specific emission wavelength of fluorescence signals. When detecting hydroxyl-containing surfactants, the characteristic absorption of hydroxyl groups in infrared signals can be used as a marker. If its proportion in the corresponding range of the core parameters meets the standard, the fluctuation pattern of hydroxyl fragment ions is searched in the mass spectrometry signal. If the intensity change trends of the two are consistent, it indicates that the signal truly reflects the properties of the substance. Even if a small amount of impurities carry similar functional groups, the difference in their signal fluctuation patterns can be identified, further ensuring the accuracy of the characteristics.

[0039] If an auxiliary parameter exhibits a strong signal within the non-response range of the core parameter, its conflict index is calculated. If the index exceeds a critical value, it is considered interference and removed. It should be understood that the conflict index is calculated as the ratio of the intensity of the strong signal to the average intensity of the core parameter within the corresponding range. When detecting a sample, if the core parameter shows no significant peak during a certain time period, but the fluorescence auxiliary parameter exhibits a strong signal, it may be due to impurities being excited under specific conditions. By calculating the conflict index, it is possible to determine whether this signal is related to the target analyte. Removing such signals avoids misinterpreting impurity signals as surfactant characteristics, ensuring signal purity.

[0040] After processing, the overall consistency between the core parameters and the remaining auxiliary parameters is calculated. If it meets the requirements, the feature signal is confirmed to be valid; otherwise, the resolution is increased and the data is re-acquired. If it still does not meet the requirements, a secondary extraction using gradient elution is initiated. It should be understood that the overall consistency is calculated by weighting multiple indicators such as peak synchronicity, peak shape consistency, and signal energy percentage. For example, peak synchronicity has a weight of 0.3, peak shape consistency has a weight of 0.4, and signal energy percentage has a weight of 0.3. The overall consistency is the sum of the scores multiplied by their weights. When the overall consistency meets the standard, it indicates that each signal confirms the presence and characteristics of the surfactant from different perspectives. For example, when detecting unknown surfactants, this multi-parameter consistency can enhance the confidence in the judgment. If the consistency is insufficient, there may still be interference in the extracted components. By increasing the resolution or performing secondary extraction, interference can be eliminated, and an accurate feature signal can be obtained, laying a reliable foundation for subsequent qualitative and quantitative analysis.

[0041] This invention establishes a correlation matrix, transforming the originally ambiguous peak synchronicity and peak shape matching into quantifiable correlation values, thus clearly revealing the intrinsic connections between different signals through in-depth mining of signal correlations. After distinguishing between core and auxiliary parameters, it accurately identifies effective signals related to the target characteristics by locating the response interval and calculating the signal energy ratio, avoiding interference from impurity signals. For conflicting signals, objective elimination is achieved through conflict index calculation, overcoming the limitations of human experience. Furthermore, when a signal fails to meet the standards, it is not simply abandoned, but optimized by increasing the resolution for re-acquisition or initiating a secondary extraction. This solves the core problem of the susceptibility and difficulty in identifying surfactant characteristic signals in complex systems.

[0042] As a further embodiment, the step of screening the reference material group includes: Based on the signal type, intensity, and preliminary judgment results from the characteristic event reports, the database is searched, and surfactants are classified according to their chemical bond and functional group characteristics to form a primary set. It should be understood that the database is a dedicated collection of data storing information such as the chemical properties of various surfactants, including the chemical bond composition, functional group types, and corresponding characteristic signals of different surfactants. For example, if the characteristic event report shows that the signal is related to surfactants containing ether bonds or sulfonic acid groups, the substances with these characteristics will be grouped into one category when searching the database.

[0043] Furthermore, when actually testing surfactants in dyeing and printing wastewater, if it is initially determined that anionic substances are present, classification by chemical bonds and functional groups can quickly eliminate irrelevant substances from cationic and nonionic surfactants, avoiding an excessively broad range of subsequent analysis. Traditional methods often classify surfactants roughly by type, which easily leads to the inclusion of substances with large structural differences. However, subdividing by chemical bonds and functional groups can narrow down the range based on the essential molecular structure.

[0044] The primary set is stratified based on signal intensity, and the deviation from the standard signal is calculated. Substances with low deviation are selected to form the secondary set. It should be noted that the standard signal refers to the characteristic signals of various surfactants under standard conditions stored in the database, which can be used as a reference. Signal intensity reflects the approximate range of substance content. After stratification, the deviation can be calculated for different intensity ranges. For example, the signal intensity in the primary set can be divided into high, medium, and low layers, and compared with the corresponding standard signals. The deviation is calculated as the ratio of the difference between the peak values ​​of the two signals to the peak value of the standard signal. For instance, when detecting a low-concentration surfactant sample, substances with high-intensity signals in the primary set will have a large deviation from the actual signal and will be excluded, leaving substances with low deviation to form the secondary set. This solves the problem in traditional screening where high-concentration reference substances interfere with low-concentration detection because they do not distinguish between signal intensities. Through deviation-based quantitative screening, the secondary set better reflects the actual situation of the sample.

[0045] The initial assessment is broken down into molecular structure and substituent sub-items, which are then compared item by item with the secondary set of substances to calculate the matching degree. It should be noted that molecular structure and substituent sub-items refer to the basic structural units and substituent groups that make up the surfactant molecule, such as carbon chain length, hydroxyl position, and number of sulfonic acid groups. The matching degree is calculated by first counting the total number of sub-items derived from the initial assessment, then counting the number of sub-items that match the structure of a certain substance in the secondary set, and dividing the number of matching sub-items by the total number of sub-items. The resulting value is the matching degree of that substance.

[0046] It should be understood that, when dealing with surfactants with complex structures, such as substances with multiple substituents, this disassembly and comparison can avoid misselecting reference substances due to similar overall structures but different key sub-items. It avoids the problem of traditional methods that often perform overall structure comparisons and easily overlook local differences, and makes the comparison more accurate by disassembling sub-items.

[0047] The reference materials are sorted by matching degree, and high-matching reference materials are selected, while low-frequency materials with similar structures are removed, ultimately forming a reference material group. It should be noted that after sorting, the top few materials are selected, while simultaneously checking for highly similar structures but low frequency of occurrence in the database. These materials may be rare impurities or recording errors and are therefore removed. When detecting a novel surfactant, if two highly matching materials have similar structures, one of which appears only in a few records, removing it avoids interference with the detection. This helps to address the problem of existing technologies retaining all highly matching materials while ignoring the potential bias caused by low-frequency materials with similar structures, and the removal step further ensures the reliability of the reference material group.

[0048] The innovation of this application lies in the fact that, through multi-dimensional detailed analysis and quantitative calculation, this invention solves the problems of structural mismatch between reference material and target material, excessive range, or mixing of interfering substances, so that the reference material group can accurately match the surfactant to be detected.

[0049] As a further embodiment, the step of setting the detection condition range includes: The intensity level of the characteristic signal is determined. It should be noted that the intensity level of the characteristic signal is usually divided into three levels: high, medium, and low. This can be determined by comparing the signal peak value with a preset threshold. For example, a certain peak value can be set as the high-level threshold. When the characteristic signal peak value exceeds this threshold, it is determined to be of high intensity; when it is below another preset threshold, it is determined to be of low intensity; and when it is in between, it is of medium intensity. This method of determining the intensity level by clearly defining thresholds makes the condition setting more objective, helps to avoid the interference of subjective factors, and lays a reliable foundation for subsequent steps. The detection temperature and flow rate ranges are determined based on the intensity level. Higher intensity levels result in a narrower range, while lower intensity levels result in a wider range, with corresponding adjustments to the detection sensitivity. Specifically, at high intensity levels, the detection temperature range can be set to 30-32 degrees Celsius, and the flow rate range to 1.0-1.2 ml / min, while simultaneously reducing the detection sensitivity to avoid signal saturation. At low intensity levels, the temperature range can be widened to 25-35 degrees Celsius, and the flow rate range to 0.8-1.5 ml / min, while simultaneously increasing the detection sensitivity to capture weak signals. In particular, when detecting low concentrations of surfactants, a wider temperature and flow rate range reduces signal loss due to minor fluctuations, while increased sensitivity enhances the ability to capture weak signals. This method of dynamically adjusting the range and sensitivity based on signal intensity flexibly adapts to signals of varying intensities, ensuring clear and effective detection signals under various conditions, thus improving the adaptability and accuracy of the detection.

[0050] Verify that the set conditions match the stability parameters of the reference material group. Specifically, the stability parameters of the reference material group refer to the range within which it remains stable at different temperatures and flow rates. For example, a reference material may have a stable signal at 28 to 32 degrees Celsius and a flow rate of 1.0 to 1.4 ml / min. Compare the set temperature and flow rate ranges with these parameters. If the set ranges are entirely within the stability parameter range, the system is considered a match; if they partially exceed the ranges, the system is considered a mismatch. This step ensures that the set conditions do not exceed the stability range of the reference material, providing strong support for the stability of the detection and reducing detection errors caused by the instability of the reference material.

[0051] If a mismatch occurs, the fluctuation range of the conditions is reduced until a match is achieved before sending the command. It should be noted that reducing the fluctuation range can be achieved by gradually adjusting the upper and lower limits of temperature and flow rate. For example, in the case of the aforementioned mismatch, the upper limit of the temperature range can be gradually lowered from 33 degrees Celsius to 30 degrees Celsius, ensuring that the set range falls entirely within the stable temperature range of the reference material group. For instance, when testing a complex sample, by repeatedly reducing the fluctuation range, the set condition range can eventually match the stability parameters of the reference material group, avoiding abnormal reference material signals due to unsuitable conditions, ensuring the reliability of the test results, and improving the flexibility of the test without needing to change the reference material due to condition mismatch. This technical solution, based on the dynamic adjustment of signal strength and reference material characteristics, effectively solves the problem of mismatch between the condition range and detection requirements, allowing the detection conditions to better adapt to the detection needs of actual samples, and improving the accuracy, stability and flexibility of detection.

[0052] As a further embodiment, the steps of outputting raw detection data and environmental parameters include: After the test is completed, the signal response value and test condition parameters are extracted. It should be noted that the signal response value refers to the signal intensity value related to the surfactant captured by the testing instrument, and the test condition parameters include the set values ​​of temperature, flow rate, sensitivity and other parameters during the test.

[0053] It should be understood that by fully collecting the core data during the testing process, comprehensive original evidence can be provided for subsequent analysis and verification, ensuring the integrity and traceability of the data.

[0054] The process involves determining whether the signal response value exceeds the measurement range and analyzing whether the signal morphology of the out-of-range portion conforms to the target characteristics. It's important to note that the measurement range is the range of signal intensity that the measuring instrument can accurately measure; exceeding the range means the signal response value exceeds this range. Signal morphology includes the shape of the peak value, the trend of the rising and falling edges, etc. This meticulous assessment of out-of-range signals avoids simply discarding valid data due to a simplistic assessment of exceeding the range, providing direction for subsequent processing.

[0055] If the signal exceeds the range but the morphology matches the target characteristics, dilution is initiated and the signal is tested again, with the dilution ratio estimated based on the degree of exceedance. It should be noted that the degree of exceedance can be determined by how many times the signal response value exceeds the upper limit of the range. For example, if the signal value is 5 times the upper limit, a dilution of 5 to 10 times can be estimated. When detecting surfactants in industrial wastewater, if the out-of-range signal morphology matches the target characteristics, dilution is estimated at 8 times before testing. This ensures the signal falls within the range and reduces errors from the dilution operation. This method of reasonably estimating the dilution ratio based on the degree of exceedance improves the efficiency of retesting and ensures the validity of the signal.

[0056] Record the dilution factor, the signal value detected again, and the changes in condition parameters, and calculate the linear correlation. It's important to note that linear correlation refers to whether there is a linear relationship between the diluted signal value and the dilution factor. This can be determined by calculating the correlation coefficient; the closer the correlation coefficient is to 1, the better the linearity. For example, diluting the sample 5 times and 10 times and then detecting the corresponding signal values ​​yielded a correlation coefficient of 0.99, indicating good linear correlation. Recording these data and calculating the correlation verifies the accuracy of the dilution operation and helps ensure that the diluted signal accurately reflects the concentration of surfactant in the sample.

[0057] If the correlation meets the requirements, the content is calculated based on the results of the retest, taking into account the stability of the condition parameters. It should be noted that the stability of the condition parameters refers to whether the parameters such as temperature and flow rate remain stable compared with the first test. If the fluctuation is within the allowable range, it is considered stable.

[0058] It should be understood that the specific calculation method for content is as follows: First, determine the signal response value at the time of re-detection. Then, combine this with the corresponding dilution factor to calculate the theoretical signal value of the undiluted sample. Finally, based on the standard curve of this signal value versus surfactant concentration, the surfactant content in the sample can be deduced. The standard curve is a curve plotted by detecting a series of surfactant standard solutions of known concentrations, with concentration on the x-axis and signal response value on the y-axis. It can be used to establish the correspondence between signal and concentration. For example, if a sample is diluted 8 times and re-detected, obtaining a signal response value of 200, then the theoretical signal value of the undiluted sample is 200 × 8 = 1600. If the standard curve of this surfactant shows that a signal response value of 1600 corresponds to a concentration of 0.5 g / L, then the surfactant content in the sample is 0.5 g / L.

[0059] This calculation method uses the dilution factor to restore the detection signal to the original state of the sample, and then uses a standard curve to convert the signal into concentration. It takes into account the impact of dilution on the signal and relies on the standard curve to ensure the accuracy of quantification, so that the calculated content is closer to the actual situation of the sample, providing reliable data support for subsequent analysis and application.

[0060] If the results do not meet the requirements, analyze the cause, optimize the dilution ratio or stabilize the conditions, and then test again until the results meet the requirements. It should be noted that if the linear correlation is poor, the dilution ratio may be unreasonable; the dilution factor can be adjusted and the test repeated. If the condition parameters are unstable, check the instrument status and test again after the parameters stabilize. For example, if the correlation is poor after the first dilution, analysis may reveal that the dilution ratio is too high, resulting in a weak signal. Adjusting to a lower dilution ratio and testing again will yield satisfactory results. This continuous optimization and adjustment process ensures the accuracy of the final test results and avoids data invalidation due to a single failed test.

[0061] If multiple adjustments still fail to meet the requirements, record the details of the over-range test and the retesting process, and output the original test data containing this information. It should be noted that over-range details include the over-range signal value and signal morphology, while the retesting process includes the dilution ratio and changes in condition parameters. For example, if the correlation still does not meet the requirements after multiple adjustments, recording this information in detail and outputting it along with the original data provides complete process data for subsequent in-depth analysis and demonstrates the rigor of the testing. This invention ensures that the output raw detection data is accurate, reliable, and complete by carefully processing the over-range signal, reasonably estimating the dilution ratio, and considering the stability of the condition parameters. This provides a solid data foundation for the quantitative analysis of surfactants and improves the flexibility and rigor of the detection process.

[0062] As a further embodiment, the comparative analysis steps include: The interference level and correction coefficient are calculated based on environmental parameters. Specifically, environmental parameters include temperature and humidity during detection, and changes in these parameters may interfere with the detection signal. The interference level can be calculated by combining the deviation of the actual environmental parameters from the standard environmental parameters with a preset interference influence coefficient. For example, for every 1 degree Celsius the temperature deviates from the standard value, the interference level increases by 5%; if the actual temperature deviates by 2 degrees Celsius, the interference level is 10%. The correction coefficient is a coefficient determined based on the interference level and used to correct the detection data. When the interference level is 10%, the correction coefficient can be set to 0.9. This method of quantifying interference based on environmental parameters reduces the impact of environmental factors on the detection results.

[0063] If interference is significant, an interfering substance database is retrieved to compare the difference in characteristic signals. Specifically, this database stores information such as the characteristic signals and molecular structures of common interfering substances, and can be used to identify potential interfering substances. The difference in characteristic signals is calculated by comparing the peak position, peak shape, and intensity of the detected signal with those in the interfering substance database. The smaller the difference, the more similar the two signals are. For example, if significant interference occurs when detecting a sample, retrieving the database reveals that the peak position of a certain interfering substance differs from the detected signal by only 2 nm, with an 80% peak shape similarity, the calculated difference is 20%. By comparing the difference, potential interfering substances can be quickly identified, providing direction for subsequent data correction.

[0064] If the difference reaches a predetermined threshold, the interference subtraction algorithm is used to correct the data; if the interference level is low, the original data is used directly. Specifically, the predetermined threshold can be set according to the detection accuracy requirements. When the calculated difference is 20%, the interference subtraction algorithm is activated when the threshold is reached.

[0065] Interference subtraction algorithms remove the portion of the detected signal that is similar to the characteristic signal of the interfering substance, while retaining the characteristic signal of the target surfactant. For example, subtracting a portion of the detected signal intensity comparable to that of the interfering substance. In the example above, after subtracting the interference signal using the algorithm, the signal data obtained only reflects the target surfactant. This targeted correction method effectively removes the influence of interference signals, ensuring the authenticity of the data; and when the interference level is low, the original data can be used directly, improving analysis efficiency.

[0066] The corrected data is matched with standard spectra and further confirmed using other parameters to form an analytical conclusion. Specifically, standard spectra are characteristic signal spectra of different surfactants under standard conditions, serving as a benchmark for identifying the target substance. Matching involves comparing the peak position, peak shape, and intensity of the corrected data with the standard spectra, while also considering other parameters such as the surfactant's molecular structure and functional groups for a comprehensive assessment. For example, if the corrected data shows a peak position completely consistent with the standard spectra of a certain surfactant, with a peak shape similarity of 95%, and the functional groups in the molecular structure also match, then the surfactant is ultimately confirmed as the target substance, leading to the analytical conclusion. This multi-dimensional matching and confirmation makes the analytical conclusion more convincing and ensures the accuracy of the detection results. By precisely handling interference, correcting data, and comprehensively confirming multiple parameters, this invention effectively ensures the accuracy and reliability of analytical conclusions, providing strong support for the qualitative and quantitative analysis of surfactants, while also enhancing the scientific rigor and precision of the detection process.

[0067] As a further embodiment, the dedicated association database is a dynamically optimized database, which also stores historical detection data and its corresponding final verification results; The method also includes the following steps: The feature signal recognition algorithm and the reference material matching algorithm are trained regularly based on historical data. It should be noted that the historical data includes information such as the feature signals of different samples under various detection conditions, the reference material matching status, and the final verification results.

[0068] For example, during training, these historical data are proportionally divided into training and validation sets. The parameters in the algorithm are adjusted using the training set data, making the algorithm's recognition of feature signals and matching of reference materials more closely resemble actual detection conditions. The training effect is then verified using the validation set data. For instance, historical data from the past three months are selected each quarter to adjust the threshold for judging signal peaks in the feature signal recognition algorithm, allowing the algorithm to more accurately distinguish between target signals and interference signals. This periodic training enables the algorithm to adapt to the detection needs of different periods and different types of samples, maintaining good recognition and matching performance.

[0069] The preliminary judgment results of the feature event reports generated in each detection are compared with the final analysis conclusions for algorithm optimization. It should be noted that the preliminary judgment results of the feature event reports are based on initial signal analysis, while the final analysis conclusions are the results after a complete detection process and verification. During the comparison, the degree of agreement between the preliminary judgment and the final conclusion is statistically analyzed, and cases where discrepancies exist and their reasons are recorded. For example, in one detection, the preliminary judgment might identify a certain type of nonionic surfactant, but the final analysis conclusion confirms a different type. Comparison reveals that this was a misjudgment of a certain functional group signal during feature signal identification. This information provides specific directions for algorithm optimization. Through this real-time comparison, shortcomings in the algorithm's practical application can be identified promptly, ensuring more targeted optimization.

[0070] The feature signal recognition algorithm and the reference substance matching algorithm are iteratively optimized using the comparison results. Specifically, for discrepancies discovered during the comparison, problems in the algorithm's signal recognition and substance matching stages are analyzed, and the algorithm's judgment logic or parameters are adjusted. For example, if feature signal recognition deviations are repeatedly caused by a specific interference signal, the filtering mechanism for that interference signal in the algorithm is optimized; if the weighting of a certain type of substituent is unreasonable during reference substance matching, the weighting parameters in the matching algorithm are adjusted. This iterative optimization allows the algorithm to continuously absorb new detection experience and gradually improve its performance.

[0071] The optimized algorithm was applied to the feature signal recognition and reference material screening steps in subsequent detection.

[0072] Specifically, the optimized algorithm can more accurately identify the characteristic signals of surfactants and reduce false positives. When screening reference materials, it can also more efficiently match groups of reference materials highly correlated with the target substance. For example, the optimized feature signal recognition algorithm can more sensitively capture the weak signals of low-concentration surfactants, and the optimized reference material matching algorithm can more quickly screen the most suitable reference materials among structurally similar substances. This not only improves the efficiency and accuracy of subsequent detection but also allows the entire detection method to continuously evolve with changing application scenarios, better adapting to complex and diverse detection needs. As a further embodiment, the steps for generating a test report include: When the analysis results indicate that the content of interfering substances is high, add standard substances and perform retesting; Calculate the recovery rate, and accordingly indicate the interference, reliability, and recommendations in the report; The detection accuracy value is adjusted according to the type of interfering substance, and a report is finally generated that includes the type, content, accuracy and interference status.

[0073] Specifically, standards are pure surfactants with known purity and concentration. The amount added must be determined based on the sample volume and the content of interfering substances. Typically, the amount added is such that the concentration of the standard in the sample is comparable to the estimated concentration of the target surfactant. Adding standards verifies the accuracy of the detection method's response to the target substance in the presence of interfering substances, providing data for subsequent recovery rate calculations. Furthermore, the formula for calculating the recovery rate is: For example, if the original sample concentration is 0.08 g / L, and after adding 0.1 g / L of standard, the total concentration is 0.17 g / L, then the recovery rate is (0.17 - 0.08) ÷ 0.1 × 100% = 90%. If the recovery rate is within a reasonable range of 80%-120%, it indicates that the interference has a small impact on the test results, and the reliability is high. The report can recommend directly using the test results. If the recovery rate is below 80% or above 120%, it indicates that the interference has a large impact, and the reliability is low. It is recommended to perform purification treatment before retesting. This recovery rate-based labeling method allows report users to clearly understand the degree of interference's impact on the results, providing a reference for subsequent decision-making. Furthermore, different interfering substances have different effects on detection accuracy, and correction coefficients need to be determined based on the chemical properties of the interfering substances and their similarity to the target substance.

[0074] The corrected accuracy value can more accurately reflect the reliability of the test results. The final report clearly lists the types of surfactants, the calculated content, the corrected accuracy, and the types and extent of interference substances, making the report comprehensive and accurate, and meeting the application needs of test results in different scenarios.

[0075] 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.

Claims

1. A method for the qualitative and quantitative detection of surfactants, characterized in that, include: The sample to be tested is pretreated to remove impurities and adjust the pH, thereby extracting the surfactant components; Determine the key characteristic parameters of the extracted components to identify the characteristic signals unique to surfactants; Based on the identified feature signals, a feature event report is generated that includes the signal type, strength, and preliminary judgment results. Retrieve a dedicated relational database that stores the chemical properties, standard spectra, and corresponding reference substances of surfactants. Combine this with feature event reports to filter out matching reference substance groups. Based on the type and intensity of the characteristic signals, the detection condition range is set for the reference material group, and instructions are sent to the detection module; The detection module performs detection under set conditions and outputs raw detection data and real-time environmental parameters; The system receives raw test data and environmental parameters, adjusts the calibration model using the environmental parameters, and compares and analyzes the raw test data based on the adjusted model to generate a test report.

2. The qualitative and quantitative detection method for surfactants according to claim 1, characterized in that, The preprocessing steps include: Obtain the initial state information of the sample; If the impurity content in the sample is high, a multi-stage impurity removal procedure is performed to remove impurities step by step according to the required precision; if the impurity content is low, a single method is used for impurity removal. After removing impurities, the pH of the sample is adjusted, and its changes are monitored in real time. Once the pH level stabilizes within the target range, stop adjusting and proceed with component extraction.

3. The qualitative and quantitative detection method for surfactants according to claim 1, characterized in that, The steps for determining the key characteristic parameters include: Multi-dimensional signal acquisition was performed on the extracted components to obtain their fluorescence, ultraviolet, infrared and mass spectrometry characteristic signals, and the time-domain curves and peak time series of each signal were recorded. Analyze the peak synchronicity and peak shape matching between the signals, establish a correlation matrix, and select the signal combination with the highest correlation. The signal with the largest sum of peak intensities in the signal combination is used as the core parameter, and the others are used as auxiliary parameters. Based on the characteristic peak positions of the core parameters, locate the response interval in the auxiliary parameters and calculate the signal energy ratio of the auxiliary parameters within that interval; If the proportion is higher than the preset threshold, then based on the feature markings of the auxiliary parameters, the matching signal fluctuation pattern is searched in the core parameters. If a strong signal appears in the auxiliary parameter within the non-response range of the core parameter, its conflict index is calculated. If it exceeds the critical value, it is considered an interference and is removed. After processing, the overall consistency between the core parameters and the remaining auxiliary parameters is calculated. If the consistency meets the requirements, the feature signal is confirmed to be valid; otherwise, the resolution is increased and the data is re-acquired. If the consistency still does not meet the requirements, a secondary extraction using gradient elution is initiated.

4. The qualitative and quantitative detection method for surfactants according to claim 1, characterized in that, The steps for generating feature event reports include: Analyze the characteristic signals to determine whether there is signal overlap interference; If there is overlap, calculate the ratio of the relevant parameters of each signal peak to determine whether it is an overlap of signals from different substances or multiple peaks of the same substance. If signals from different substances overlap, they are marked; if the same substance has multiple peak signals, the main signal peak is identified by the peak area ratio. The signal strength is calculated based on the main signal peak, and then a characteristic event report is generated.

5. The qualitative and quantitative detection method for surfactants according to claim 1, characterized in that, The steps for screening the reference material group include: Based on the signal type, intensity, and preliminary judgment results in the characteristic event reports, the database is searched, and surfactants are classified according to chemical bond and functional group characteristics to form a primary set; The primary set is stratified based on signal strength, the deviation from the standard signal is calculated, and substances with low deviation are selected to form a secondary set. The initial judgment is broken down into molecular structure and substituent items, and compared with the secondary aggregate of substances item by item to calculate the matching degree. The reference materials were sorted by matching degree, and reference materials with high matching degree were selected. Low-frequency materials with similar structures were removed, and finally a reference material group was formed.

6. The qualitative and quantitative detection method for surfactants according to claim 1, characterized in that, The step of setting the detection condition range includes: Determine the intensity level of the characteristic signal; The detection temperature range and flow rate range are determined according to the intensity level. The range is narrowed for a higher intensity level and widened for a lower intensity level, and the detection sensitivity is adjusted accordingly. Verify whether the set conditions match the stability parameters of the reference material group; If there is no match, the fluctuation range of the condition will be reduced until a match is found before sending the instruction.

7. The qualitative and quantitative detection method for surfactants according to claim 1, characterized in that, The steps for outputting raw detection data and environmental parameters include: After the detection is completed, extract the signal response value and detection condition parameters; Determine whether the signal response value exceeds the range, and analyze whether the signal morphology of the out-of-range portion conforms to the target characteristics; If the range is exceeded and the morphology is correct, dilution should be initiated and the test repeated, with the dilution ratio estimated based on the extent of the exceedance. Record the dilution factor, the signal value detected again, and the changes in condition parameters, and calculate the linear correlation; If the correlation meets the requirements, the content is calculated based on the results of the retest, taking into account the stability of the condition parameters. If the results do not meet the requirements, analyze the reasons, optimize the dilution ratio or stabilization conditions, and test again until the results meet the requirements. If multiple adjustments still do not meet the requirements, record the details of the over-range test and the retest process, and output the original test data containing this information.

8. The qualitative and quantitative detection method for surfactants according to claim 1, characterized in that, The steps of the comparative analysis include: The degree of interference and correction coefficients are calculated based on environmental parameters; If the interference is significant, retrieve the database of interfering substances and compare the differences in characteristic signals; If the difference reaches a predetermined threshold, the data is corrected using an interference subtraction algorithm; if the interference level is low, the original data is used directly. The corrected data is matched with the standard spectrum, and combined with other parameters for final confirmation to form an analytical conclusion.

9. The qualitative and quantitative detection method for surfactants according to claim 1, characterized in that, The dedicated association database is a dynamically optimized database, which also stores historical detection data and its corresponding final verification results; The method further includes the following steps: The feature signal recognition algorithm and the reference material matching algorithm are trained regularly based on historical data; The preliminary judgment results of the feature event reports generated in each detection are compared with the final analysis conclusions for algorithm optimization. The feature signal recognition algorithm and the reference substance matching algorithm are iteratively optimized using the comparison results; The optimized algorithm was applied to the feature signal recognition and reference material screening steps in subsequent detection.

10. A method for qualitative and quantitative detection of surfactants according to claim 9, characterized in that, The steps for generating the test report include: When the analysis results indicate that the content of interfering substances is high, add standard substances and perform retesting; Calculate the recovery rate, and accordingly indicate the interference, reliability, and recommendations in the report; The detection accuracy value is adjusted according to the type of interfering substance, and a report is finally generated that includes the type, content, accuracy and interference status.

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