Method, apparatus and storage medium for detecting tetrahydrofuran content

Abstract

The present application relates to the field of analysis and detection, and discloses a detection method, equipment and storage medium for tetrahydrofuran content. The detection method for tetrahydrofuran content provided by the present application comprises the following steps: obtaining near-infrared spectrum information of a sample to be detected; obtaining a quantitative analysis model; inputting the near-infrared spectrum information into the quantitative analysis model to calculate the predicted content of tetrahydrofuran in the sample to be detected. The detection method for tetrahydrofuran content provided by the present application can quickly and accurately obtain a detection result and avoid the harm of toxic substances to human health.

Description

Technical Field

[0001] This invention relates to the field of analytical testing, and in particular to a method, apparatus, and storage medium for detecting tetrahydrofuran content. Background Technology

[0002] The titanium-magnesium catalyst for gas-phase ethylene polymerization is a complex formed by titanium-magnesium compounds, tetrahydrofuran, and other components. Tetrahydrofuran acts as an internal electron donor in the titanium-magnesium catalyst, and its content directly affects the stability and particle morphology of the catalyst. It also has a sensitive moderating effect on the catalyst activity.

[0003] Existing methods for detecting tetrahydrofuran content generally employ gas chromatography, which typically uses acetone as the extractant. However, acetone is toxic and can easily harm the health of operators during use. Furthermore, the sample preparation and extraction process with acetone is lengthy, failing to provide timely data support for the catalyst production process. Summary of the Invention

[0004] This invention provides a method, device, and storage medium for detecting tetrahydrofuran content, which can quickly and accurately obtain detection results and avoid the harm of toxic substances to human health.

[0005] In a first aspect, embodiments of the present invention provide a method for detecting tetrahydrofuran content. The method is used to detect the tetrahydrofuran content of a sample to be tested, including: acquiring near-infrared spectral information of the sample to be tested; acquiring a quantitative analysis model; and inputting the near-infrared spectral information into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

[0006] The method for detecting tetrahydrofuran content provided in this invention obtains the near-infrared spectral information of the sample to be tested and inputs the near-infrared spectral information into a quantitative analysis model to obtain the tetrahydrofuran content in the catalyst sample. Compared with the gas chromatography method in the prior art, the method for detecting tetrahydrofuran content provided in this invention does not require pretreatment of the sample to be tested, avoiding damage to the sample, generation of hazardous substances or harm to the health of operators during pretreatment. Furthermore, while ensuring the accuracy of the detection results, the detection time is also greatly shortened.

[0007] Optionally, the steps for obtaining the near-infrared spectral information of the sample to be tested include: obtaining the raw near-infrared spectral information of the sample to be tested in the full band range; and preprocessing the raw near-infrared spectral information to obtain the preprocessed near-infrared spectral information.

[0008] Optionally, the step of obtaining the original near-infrared spectral information of the sample to be tested in the full band range includes: scanning the sample to be tested at least twice based on the solid diffuse reflectance detection method to obtain the original near-infrared spectral information, wherein the scanning spectral wavelength range when detecting the sample to be tested is 400nm~2500nm and the scanning potential is 32.

[0009] Optionally, in the step of acquiring the raw near-infrared spectral information of the sample to be tested in the full band range, the ambient temperature of the sample to be tested is 20℃~25℃ and the ambient humidity is 40%~65%.

[0010] Optionally, the step of preprocessing the original near-infrared spectral information to obtain the preprocessed near-infrared spectral information includes: eliminating baseline drift and background interference of the original near-infrared spectral information based on standard normal variable transformation and second derivative, so as to obtain the preprocessed near-infrared spectral information.

[0011] Optionally, the quantitative analysis model is constructed through the following steps: obtaining multiple samples to be tested; obtaining qualified samples to be tested based on gas chromatography internal standard method and near-infrared spectroscopy; and constructing a quantitative analysis model based on the near-infrared spectral information of the qualified samples to be tested and the actual content of tetrahydrofuran.

[0012] Optionally, the steps for obtaining qualified samples to be tested based on gas chromatography internal standard method and near-infrared spectroscopy include: detecting the actual tetrahydrofuran content of each sample to be tested based on gas chromatography internal standard method to obtain actual results; detecting the predicted tetrahydrofuran content of each sample to be tested based on near-infrared spectroscopy to obtain predicted results; comparing the actual results and predicted results, eliminating samples to be tested where the absolute difference between the predicted results and the actual results is greater than a preset difference, and retaining qualified samples to be tested.

[0013] Optionally, the steps for constructing a quantitative analysis model based on the near-infrared spectral information and the actual content of tetrahydrofuran of a qualified sample to be tested include: calculating the near-infrared spectral information and the actual content of tetrahydrofuran based on partial least squares method to construct a quantitative analysis model.

[0014] Optionally, after the step of constructing the quantitative analysis model, the method further includes: obtaining the modeling set and validation set of the quantitative analysis model; and performing cross-validation on the quantitative analysis model based on the validation set and the modeling set until the coefficient of determination of the validation set is greater than or equal to 0.9.

[0015] Optionally, the validation set includes samples to be tested from multiple modeling sets, and the proportion of the number of samples to be tested in the validation set to that in the modeling set is not less than a preset proportion.

[0016] In a second aspect, embodiments of the present invention provide a tetrahydrofuran content detection device, comprising: a processor and a memory, wherein the memory stores instructions; the processor invokes the instructions in the memory to cause the processor to execute the tetrahydrofuran content detection method of any of the foregoing embodiments of the first aspect of the present invention.

[0017] The method provided in this embodiment of the invention includes: acquiring near-infrared spectral information of the sample to be tested; acquiring a quantitative analysis model; and inputting the near-infrared spectral information into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

[0018] The processor of the tetrahydrofuran content detection device provided in this embodiment of the invention executes the tetrahydrofuran content detection method of any of the foregoing embodiments of the first aspect of the invention by calling instructions in the memory. It does not require pretreatment of the sample to be tested, thus avoiding damage to the sample, generation of hazardous substances or harm to the health of operators during pretreatment. Furthermore, it greatly shortens the detection time while ensuring the accuracy of the detection results.

[0019] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing instructions that, when executed by a computer, implement the method for detecting tetrahydrofuran content according to any of the foregoing embodiments of the first aspect of the present invention.

[0020] The method provided in this embodiment of the invention includes: acquiring near-infrared spectral information of the sample to be tested; acquiring a quantitative analysis model; and inputting the near-infrared spectral information into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

[0021] The instructions stored in the computer-readable storage medium provided in the embodiments of the present invention can be called by a processor and executed by the method for detecting tetrahydrofuran content according to any of the foregoing embodiments of the first aspect of the present invention. There is no need to pre-treat the sample to be tested, which avoids damage to the sample, generation of hazardous substances or harm to the health of operators during pre-treatment. Furthermore, the detection time is greatly shortened while ensuring the accuracy of the detection results. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0023] Figure 1 This is a flowchart of one embodiment of the method for detecting tetrahydrofuran content according to the present invention;

[0024] Figure 2 This is a flowchart of step S110 in one embodiment of the method for detecting tetrahydrofuran content of the present invention;

[0025] Figure 3 This is a flowchart of step S120 in one embodiment of the method for detecting tetrahydrofuran content of the present invention;

[0026] Figure 4 This is a flowchart of step S122 in one embodiment of the method for detecting tetrahydrofuran content of the present invention;

[0027] Figure 5 This is a spectral image of the original near-infrared spectrum of the sample to be tested obtained by scanning in one embodiment of the method for detecting tetrahydrofuran content of the present invention.

[0028] Figure 6 This is a near-infrared spectrum of the sample to be tested after pretreatment, as shown in one embodiment of the method for detecting tetrahydrofuran content of the present invention.

[0029] Figure 7 The principal factor plot of the sample to be tested in one embodiment of the method for detecting tetrahydrofuran content of the present invention is modeled using partial least squares method;

[0030] Figure 8 This is a correlation graph between the predicted and measured values ​​of tetrahydrofuran content in a sample within the range of 25.00% to 29.00% in one embodiment of the method for detecting tetrahydrofuran content of the present invention.

[0031] Figure 9 This is a graph showing the fluctuation range of the measured tetrahydrofuran content of the sample to be tested in the range of 25.00% to 29.00% in one embodiment of the method for detecting tetrahydrofuran content of the present invention.

[0032] Figure 10 This is a graph showing the fluctuation range of the predicted tetrahydrofuran content of the sample to be tested in the range of 25.00% to 29.00% in one embodiment of the method for detecting tetrahydrofuran content of the present invention.

[0033] Figure 11 This is a structural block diagram of one embodiment of the tetrahydrofuran content detection device of the present invention.

[0034] Explanation of icon numbers:

[0035] 110 - Processor; 120 - Memory; 130 - Communication interface; 140 - Bus. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0037] It should be noted that all directional indications in the embodiments of the present invention, such as up, down, left, right, front, back, etc., are only used to explain the relative positional relationship and movement of the components in a specific posture as shown in the attached figure. If the specific posture changes, the directional indication will also change accordingly.

[0038] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0039] For ease of understanding, the method for detecting tetrahydrofuran content according to an embodiment of the present invention is described below. This method is used to detect the tetrahydrofuran content of a sample to be tested, wherein the sample to be tested is a sample of a gas-phase ethylene polymerization titanium-magnesium catalyst. Figure 1 As shown, the method for detecting tetrahydrofuran content in this embodiment of the invention includes steps S110 to S130.

[0040] In step S110, the near-infrared spectral information of the sample to be tested is obtained.

[0041] In some optional embodiments, step S110 includes steps S111 to S112.

[0042] In step S111, the raw near-infrared spectral information of the sample to be tested in the full band range is obtained.

[0043] Further, in step S111, the step of obtaining the original near-infrared spectral information is as follows: based on the solid diffuse reflectance detection method, the sample to be detected is scanned at least twice to obtain the original near-infrared spectral information.

[0044] The scanning spectral wavelength range for detecting the sample is 400 nm to 2500 nm, with 32 scanning potentials. The ambient temperature for the sample is 20°C to 25°C, and the ambient humidity is 40% to 65%. Maintaining stable ambient temperature and humidity helps reduce the influence of the external environment on the spectrum and the sample, thereby improving the accuracy of the model. The near-infrared spectrometer used in this invention can be a grating-type near-infrared spectrometer or a Fourier transform near-infrared spectrometer.

[0045] Specifically, in this embodiment, when acquiring the raw near-infrared spectral information, the powdered sample to be tested is poured into a sample vial with an inner diameter of 15 mm, ensuring that the sample is evenly distributed at the bottom of the vial to a height of approximately 1 cm. The vial is then placed on a porous diffuse reflectance sample plate for near-infrared spectral scanning. The resulting raw near-infrared spectral information is shown in the image below. Figure 5 As shown.

[0046] This invention uses the near-infrared spectrum of the sample to detect the tetrahydrofuran content. Compared with existing technologies, this method not only speeds up the detection process and reduces the amount of sample consumed, but also avoids generating pollutants during the detection process and does not harm the health of operators.

[0047] In step S112, the original near-infrared spectral information is preprocessed to obtain preprocessed near-infrared spectral information. The preprocessed near-infrared spectrum is shown below. Figure 6 As shown.

[0048] In this embodiment of the invention, scattering correction and mathematical processing are used to preprocess the original near-infrared spectral information of the sample to be processed. The scattering correction method can be standard normal transformation, scattering processing, multivariate scattering correction, or a combination of standard normal transformation and scattering processing. The mathematical processing method can include derivative processing, spectral point spacing processing, or smoothing processing.

[0049] Specifically, in this embodiment, baseline drift and background interference of the original near-infrared spectral information are eliminated based on standard normal variable transformation and second derivative to obtain preprocessed near-infrared spectral information.

[0050] Since near-infrared spectroscopy mainly consists of first-order and second-order absorption spectra of hydrogen-containing groups, the absorption intensity is relatively weak, the sensitivity is relatively low, and the absorption bandwidth is relatively wide with significant overlap. In addition to the spectral information of the catalyst sample itself, the near-infrared spectra of the samples to be tested also carry other cluttered information. Therefore, it is necessary to preprocess the spectra to eliminate this cluttered information.

[0051] In this embodiment of the invention, near-infrared spectrometer modeling software is used to implement the above-mentioned scattering correction and mathematical processing. The near-infrared spectral information parameters after standard normal variable transformation and second derivative preprocessing are shown in Table 1.

[0052]

[0053] Table 1

[0054] As shown in Table 1, the most accurate results are obtained by using standard normal variable transformation and second derivative to eliminate baseline drift and background interference in the original near-infrared spectral information.

[0055] Wherein, SNV stands for Standard Normal Transform, NPS for smoothing, MSC for multivariate scattering correction, 1st Der for first derivative, 2nd Der for second derivative, 3rd Der for third derivative, and R... 2 is the coefficient of determination, and RMSEC is the corrected root mean square error.

[0056] In step S120, a quantitative analysis model is obtained.

[0057] like Figure 3 As shown, in some optional embodiments, the quantitative analysis model is constructed by the following steps, including steps S121 to S123.

[0058] In step S121, multiple samples to be tested are obtained.

[0059] In this embodiment, before constructing the quantitative analysis model, multiple samples to be tested and their corresponding near-infrared spectral information are acquired for subsequent construction of the quantitative analysis model. For example, in this embodiment of the invention, 100 samples to be tested with a tetrahydrofuran mass percentage ranging from 25.0% to 29.0% and their corresponding near-infrared spectral information are prepared.

[0060] The optimal near-infrared absorption wavelength range of tetrahydrofuran was determined by scanning the near-infrared spectrum of the sample. Then, scattering correction and mathematical processing were performed on the near-infrared absorption wavelengths of tetrahydrofuran in the sample to eliminate extraneous information and ensure the accuracy of the model obtained when constructing the quantitative analysis model.

[0061] In step S122, qualified samples to be tested are obtained based on gas chromatography internal standard method and near-infrared spectroscopy.

[0062] In this embodiment, the actual tetrahydrofuran content and the predicted tetrahydrofuran content of each sample are detected by gas chromatography with internal standard method and near-infrared spectroscopy, respectively. Abnormal samples are eliminated by comparing the difference between the actual tetrahydrofuran content and the predicted tetrahydrofuran content, so as to improve the accuracy of the quantitative analysis model. The specific steps are as follows.

[0063] like Figure 4 As shown, step S122 further includes steps S1221 to S1223.

[0064] In step S1221, the actual content of tetrahydrofuran in each sample to be tested is detected based on the gas chromatography internal standard method to obtain the actual result.

[0065] In step S1223, the predicted content of tetrahydrofuran in each sample to be tested is detected based on near-infrared spectroscopy to obtain the prediction result.

[0066] In step S1223, the actual results and the predicted results are compared, and the samples to be tested that have an absolute difference between the predicted results and the actual results that is greater than a preset difference are eliminated, while the qualified samples to be tested are retained.

[0067] The preset difference is typically set to 0.3%. The actual result is the actual mass fraction of tetrahydrofuran in the sample to be tested obtained by gas chromatography with internal standard method, and the predicted result is the predicted mass fraction of tetrahydrofuran in the sample to be tested obtained by near-infrared spectroscopy.

[0068] Samples to be tested with an absolute difference between the predicted and actual results greater than 0.3% are identified as abnormal samples and removed. The remaining qualified samples are used to construct a quantitative analysis model to ensure the accuracy of the constructed quantitative analysis model and avoid excessive model error.

[0069] In step S123, a quantitative analysis model is constructed based on the near-infrared spectral information of the qualified sample to be tested and the actual content of tetrahydrofuran.

[0070] Furthermore, in step S123, the near-infrared spectral information and the actual tetrahydrofuran content are calculated based on partial least squares (PLS) to construct a quantitative analysis model. Using PLS to construct the quantitative analysis model effectively eliminates complex background signals and extracts the target signal, thereby improving the efficiency of model construction. Alternatively, a quantitative analysis model can also be constructed using multiple linear regression.

[0071] When using partial least squares, the quantitative analysis model is constructed based on the number of principal factors, i.e. the optimal number of variables. Insufficient principal factors will lead to underfitting, while excessive principal factors will lead to overfitting, both of which will affect the accuracy of the quantitative analysis model.

[0072] like Figure 7 As shown, in this embodiment of the invention, an interactive validation method is used to determine the number of principal factors required for modeling. That is, the predicted residual sum of squares decreases as the number of principal factors increases. When an inflection point is reached, i.e., the minimum value, the predicted residual sum of squares begins to increase. The number of factors corresponding to the minimum value of the predicted residual sum of squares at this point is the optimal number of variables for the quantitative analysis model. If no inflection point exists, the minimum number of factors corresponding to the predicted residual sum of squares is taken; for example, in this embodiment of the invention, the number of principal factors is 10.

[0073] like Figure 3 As shown, in some optional embodiments, after obtaining the quantitative analysis model through step S123, the method for detecting tetrahydrofuran content further includes steps S124 to S125.

[0074] In step S124, the modeling set and validation set of the quantitative analysis model are obtained.

[0075] Furthermore, the validation set includes samples to be tested from multiple modeling sets, and the proportion of samples to be tested in the validation set to the modeling set is not less than a preset proportion. In this embodiment, the preset proportion is 15%, meaning the proportion of samples to be tested in the validation set to the modeling set is not less than 15%. The preset proportion can also be 16%, 19%, or 20%, etc., and this application does not impose any limitation. By limiting the proportion of the validation set in the modeling set, the accuracy of the constructed quantitative analysis model is ensured.

[0076] In step S125, the quantitative analysis model is cross-validated based on the validation set and the modeling set until the coefficient of determination of the validation set is greater than or equal to 0.9.

[0077] By performing cross-validation on the quantitative analysis model through steps S124 to S125 of this embodiment of the invention, when the obtained coefficient of determination is greater than or equal to 0.9, it is proven that the accuracy of the quantitative analysis model meets the requirements. The closer the coefficient of determination is to 1, the higher the accuracy of the quantitative analysis model.

[0078] As shown in Table 2, when using the partial least squares method, the root mean square error of the validation set is close to and the minimum, the coefficient of determination is closest to 1, and the established model prediction is the most accurate and stable.

[0079]

[0080] Table 2

[0081] Among them, MLR is multiple linear regression, PLS is partial least squares regression, and R... 2 is the coefficient of determination, and RMSEC is the corrected root mean square error.

[0082] Since different spectral bands can also affect the accuracy of the model, this embodiment of the invention selects the full band and the sensitive band for tetrahydrofuran for comparison and judgment of the modeling set and the validation set. As shown in Table 3, among the two spectral bands, the root mean square error of the modeling set and the root mean square error of the validation set of the full band are close and the smallest, and the coefficient of determination is closest to 1.

[0083]

[0084] Table 3

[0085] The parameters of the selected optimal quantitative analysis model are shown in Table 4.

[0086]

[0087] Table 4

[0088] Where SNV stands for Standard Normal Transform, 2nd Der is the second derivative, PLS is the partial least squares method, and R... 2 is the coefficient of determination, and RMSEC is the corrected root mean square error.

[0089] After obtaining the optimal quantitative analysis model, it is necessary to test the precision of the quantitative analysis model. In this embodiment, three different mass fractions of gas-phase ethylene polymerization titanium-magnesium catalyst samples, with six bottles of each mass fraction, were used as samples, and each sample was tested twice.

[0090]

[0091] Table 5

[0092]

[0093] Table 6

[0094]

[0095] Table 7

[0096] Table 5 shows the original results of tetrahydrofuran content in the samples detected by the quantitative analysis model; Table 6 shows the average tetrahydrofuran content of each sample after two measurements; and Table 7 shows the precision parameters of each mass fraction of the sample. The average value of the tetrahydrofuran content in the sample, n ij The number of measurements for this sample, S rj The standard deviation is the repeatability.

[0097] By adopting S rj The average value, i.e., choosing S rj With a value of 0.065, the absolute difference in mass fraction can be obtained to be no greater than 0.19%, that is, the absolute difference between two independent detection results obtained by the method for detecting tetrahydrofuran content in this embodiment of the invention is no greater than 0.19%, and the number of results greater than 0.19% does not exceed 5% of the total number, which is better than the detection standard of 0.30% determined by gas chromatography.

[0098] After obtaining the optimal quantitative analysis model, it is also necessary to test the accuracy of the quantitative analysis model. As shown in Table 8, in this embodiment of the invention, 14 samples were selected for testing, and the tetrahydrofuran content was determined by gas chromatography and the tetrahydrofuran content detection method of this embodiment of the invention.

[0099] Sample Name Near-infrared prediction value % Gas chromatography determination % absolute difference % 1 28.95 28.94 0.01 2 28.91 28.94 -0.04 3 27.69 27.72 -0.03 4 27.71 27.72 -0.01 5 27.38 27.36 0.02 6 27.28 27.36 -0.08 7 27.31 27.24 0.07 8 27.17 27.24 -0.07 9 26.38 26.37 0.01 10 26.36 26.37 -0.01 11 26.88 26.94 -0.06 12 26.94 26.94 0.00 13 27.25 27.31 -0.06 14 27.36 27.31 0.05

[0100] Table 8

[0101] from Figures 8 to 10 As shown in Table 8, the tetrahydrofuran content of the sample measured by the method provided in this embodiment of the invention is in good agreement with the tetrahydrofuran content measured by gas chromatography, and the absolute difference between the two methods is no greater than 0.10%. This proves that the method provided by this invention and the quantitative analysis model obtained by the method provided by this invention can be used to accurately quantify the tetrahydrofuran content of the sample, solving the problem that hazardous chemical reagents are required when detecting the tetrahydrofuran content of titanium-magnesium catalysts for gas-phase ethylene polymerization using existing methods. Furthermore, the time required to measure the near-infrared spectral information of a single sample can be shortened to within 1 minute, which is significantly shorter than the time required by existing gas chromatography, thus solving the problem of excessively long analysis and detection time in gas chromatography.

[0102] In step S130, near-infrared spectral information is input into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

[0103] In this embodiment, after obtaining the near-infrared spectral information of the sample to be tested and the quantitative analysis model for calculating the predicted tetrahydrofuran content of the sample to be tested, the parameters of the near-infrared spectral information of the sample to be tested are input into the quantitative analysis model, so that the quantitative analysis model can calculate the predicted tetrahydrofuran content of the sample to be tested based on the received near-infrared spectral information and present the calculation results to the operator.

[0104] The method for detecting tetrahydrofuran content provided in this invention includes: acquiring near-infrared spectral information of the sample to be tested; acquiring a quantitative analysis model; and inputting the near-infrared spectral information into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

[0105] The method for detecting tetrahydrofuran content provided in this invention obtains the near-infrared spectral information of the sample to be tested and inputs the near-infrared spectral information into a quantitative analysis model to obtain the tetrahydrofuran content in the catalyst sample. Compared with the gas chromatography method in the prior art, the method for detecting tetrahydrofuran content provided in this invention does not require pretreatment of the sample to be tested, avoiding damage to the sample, generation of hazardous substances or harm to the health of operators during pretreatment. Furthermore, while ensuring the accuracy of the detection results, the detection time is also greatly shortened.

[0106] In addition to the above method embodiments, the present invention also provides, as follows: Figure 11 The device for detecting tetrahydrofuran content shown includes a processor 110 and a memory 120. The memory 120 stores instructions, and the processor 110 can call the instructions in the memory 120 to execute the tetrahydrofuran content detection method of any of the foregoing embodiments of the present invention.

[0107] The method for detecting tetrahydrofuran content provided in this invention includes: acquiring near-infrared spectral information of the sample to be tested; acquiring a quantitative analysis model; and inputting the near-infrared spectral information into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

[0108] The processor 110 of the tetrahydrofuran content detection device provided in this embodiment of the invention executes the tetrahydrofuran content detection method of any of the foregoing embodiments of the invention by calling the instructions in the memory 120. There is no need to pre-treat the sample to be tested, which avoids damage to the sample, generation of hazardous substances or harm to the health of operators during pre-treatment. Furthermore, the detection time is greatly shortened while ensuring the accuracy of the detection results.

[0109] Furthermore, the tetrahydrofuran content detection device provided in this embodiment of the invention may also include a communication interface 130 and a bus 140, with the processor 110, memory 120 and communication interface 130 electrically connected via the bus 140.

[0110] The memory 120 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 130 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network. The bus 140 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0111] Processor 110 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 110 or by instructions in software form. The processor 110 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 120, and processor 110 reads information from memory 120 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0112] This invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the above-described method for detecting tetrahydrofuran content.

[0113] The method provided in this embodiment of the invention includes: acquiring near-infrared spectral information of the sample to be tested; acquiring a quantitative analysis model; and inputting the near-infrared spectral information into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

[0114] The computer-readable storage medium provided in this embodiment of the invention stores data and computer-executable instructions for the above-mentioned method for detecting tetrahydrofuran content. The method for detecting tetrahydrofuran content includes: acquiring near-infrared spectral information of the sample to be tested; acquiring a quantitative analysis model; and inputting the near-infrared spectral information into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

[0115] The instructions stored in the computer-readable storage medium provided in the embodiments of the present invention can be called by a processor and executed by the method for detecting tetrahydrofuran content according to any of the foregoing embodiments of the present invention. There is no need to pre-treat the sample to be tested, which avoids damage to the sample, generation of hazardous substances or harm to the health of operators during pre-treatment. Furthermore, the detection time is greatly shortened while ensuring the accuracy of the detection results.

[0116] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0117] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting tetrahydrofuran content, characterized in that, The method is used to detect the tetrahydrofuran content of a sample to be tested, and the method includes: Obtain the near-infrared spectral information of the sample to be tested; Obtain a quantitative analysis model; The near-infrared spectral information is input into the quantitative analysis model to calculate the predicted tetrahydrofuran content of the sample to be tested.

2. The method for detecting tetrahydrofuran content according to claim 1, characterized in that, The step of obtaining the near-infrared spectral information of the sample to be tested includes: Obtain the raw near-infrared spectral information of the sample to be tested across the entire wavelength range; The original near-infrared spectral information is preprocessed to obtain preprocessed near-infrared spectral information.

3. The method for detecting tetrahydrofuran content according to claim 2, characterized in that, The step of obtaining the raw near-infrared spectral information of the sample to be tested in the full wavelength range includes: Based on the solid diffuse reflectance detection method, the sample to be tested is scanned at least twice to obtain the original near-infrared spectral information. The scanning spectral wavelength range for detecting the sample is 400nm to 2500nm, and the scanning potential is 32.

4. The method for detecting tetrahydrofuran content according to claim 2, characterized in that, In the step of obtaining the raw near-infrared spectral information of the sample to be tested in the full band range, the ambient temperature of the sample to be tested is 20℃~25℃ and the ambient humidity is 40%~65%.

5. The method for detecting tetrahydrofuran content according to claim 2, characterized in that, The step of preprocessing the original near-infrared spectral information to obtain preprocessed near-infrared spectral information includes: Baseline drift and background interference of the original near-infrared spectral information are eliminated by standard normal variable transformation and second derivative to obtain preprocessed near-infrared spectral information.

6. The method for detecting tetrahydrofuran content according to claim 1, characterized in that, The quantitative analysis model is constructed through the following steps: Obtain multiple samples to be tested; The qualified sample to be tested was obtained based on gas chromatography with internal standard and near-infrared spectroscopy. The quantitative analysis model is constructed based on the near-infrared spectral information and the actual tetrahydrofuran content of the qualified sample to be tested.

7. The method for detecting tetrahydrofuran content according to claim 6, characterized in that, The steps for obtaining qualified samples for testing based on gas chromatography with internal standard and near-infrared spectroscopy include: The actual content of tetrahydrofuran in each of the samples to be tested was determined by gas chromatography with internal standard method to obtain the actual results; The predicted content of tetrahydrofuran in each of the samples to be tested is detected using near-infrared spectroscopy to obtain the prediction results; The actual results and the predicted results are compared, and the samples to be tested that have an absolute difference between the predicted results and the actual results that is greater than a preset difference are eliminated, while the qualified samples to be tested are retained.

8. The method for detecting tetrahydrofuran content according to claim 6, characterized in that, The steps for constructing the quantitative analysis model based on the near-infrared spectral information and the actual tetrahydrofuran content of the qualified sample to be tested include: The near-infrared spectral information and the actual content of tetrahydrofuran are calculated based on the partial least squares method to construct the quantitative analysis model.

9. The method for detecting tetrahydrofuran content according to claim 6, characterized in that, Following the step of constructing the quantitative analysis model, the method further includes: Obtain the modeling set and validation set of the quantitative analysis model; The quantitative analysis model is cross-validated based on the validation set and the modeling set until the coefficient of determination of the validation set is greater than or equal to 0.

9.

10. The method for detecting tetrahydrofuran content according to claim 9, characterized in that, The validation set includes multiple samples to be tested from the modeling set, and the proportion of the number of samples to be tested in the validation set to the proportion in the modeling set is not less than a preset proportion value.

11. A tetrahydrofuran content detection device, characterized in that, The detection device includes a processor and a memory, wherein the memory stores instructions; the processor invokes the instructions in the memory to enable the detection device to implement the method for detecting tetrahydrofuran content as described in any one of claims 1 to 10.

12. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by a computer, they implement the method for detecting tetrahydrofuran content as described in any one of claims 1 to 10.

Images