Quantitative analysis method for aniline substances in mixture, storage medium and device

Abstract

The invention relates to the technical field of mixture analysis and measurement, in particular to a quantitative analysis method for aniline substances in a mixture, which comprises the following steps: constructing an information library, measuring near infrared spectrums of a mixture X sample without aniline substances, an aniline substance sample and a mixture X sample with known aniline substance concentration, and calculating the concentration of the aniline substances in the mixture X sample. Obtaining a background spectrum database B, an aniline substance spectrum database C and a sample spectrum database M of a mixture X with known aniline substance concentration. Based on the information base, constructing spectral components of the aniline substances in the mixture X sample with known aniline substance concentration, and constructing a standard curve representing the relationship between the concentration and absorbance of the aniline substances in the mixture X sample with known aniline substance concentration. And obtaining the concentration of the aniline substances in the mixture X sample with unknown aniline substance concentration according to the standard curve. The method effectively eliminates the interference of background spectrum and coexisting substances, so that the detection result is more accurate and reliable.

Description

Technical Field

[0001] The present application relates to the technical field of mixture analysis and measurement, and specifically relates to a method for quantitative analysis of aniline substances in a mixture, a storage medium, and a device. Background Art

[0002] In the field of mixture quality control and supervision, it is of great significance to accurately detect the content of aniline substances (such as aniline, N-methylaniline) in mixtures, especially in finished gasoline, diesel, and other oil products. The traditional detection method mainly uses gas chromatography. This method separates different components in the chromatographic column after vaporizing the oil and gas to achieve the determination of the content of aniline substances. Gas chromatography has high accuracy and reliability and is widely used in the laboratory environment. However, its operation process is relatively complex, requiring a series of operations such as sample pretreatment and instrument parameter setting by professional technicians, and the detection process takes a long time, making it difficult to meet the needs of on-site rapid detection. In addition, the gas chromatograph is large in size and inconvenient to carry, and is only suitable for use in a fixed laboratory, unable to adapt to the on-site law enforcement and instant detection scenarios in the field of mixture circulation.

[0003] Mid-infrared spectroscopy, as a means of on-site detection, has the advantages of fast analysis speed and simple operation. For example, the domestic standard method GB / T 33648-2017 uses the standard curve method to detect the concentrations of aniline and N-methylaniline in gasoline, and selects a relatively independent characteristic absorption peak absorbance to establish a standard curve with its concentration for quantification. However, mid-infrared spectroscopy is greatly affected by the changes in gasoline components in practical applications, easily causing spectral baseline drift, thus affecting the accuracy of the measurement of the characteristic peak intensity and further leading to a decrease in the result accuracy.

[0004] Near-infrared spectroscopy combined with multivariate analysis modeling has also been widely used in the detection of various properties of vehicle gasoline, including the detection of aniline substance concentration. Near-infrared spectroscopy has the advantages of rich information and fast analysis speed, but its model establishment cost is high, requiring a large number of calibration samples and complex mathematical processing processes, which to a certain extent limits its popularization in practical applications. Summary of the Invention

[0005] In view of one or more of the problems existing in the prior art, the first aspect of the present application provides a method for quantitative analysis of aniline substances in a mixture, including:

[0006] Construct an information library;

[0007] Based on the information library, construct the spectral components of aniline substances in a mixture X sample with a known concentration of aniline substances;

[0008] Construct a standard curve representing the relationship between the concentration and absorbance of aniline substances in a mixture X sample with a known concentration of aniline substances;

[0009] According to the standard curve, predict the concentration of aniline substances in the mixture X sample with unknown concentration of aniline substances.

[0010] Preferably, constructing the information database specifically includes:

[0011] Obtain a mixture X sample without aniline substances, an aniline substance sample, and a mixture X sample with known concentration of aniline substances;

[0012] Measure the near-infrared spectrum of the mixture X sample without aniline substances to obtain the background spectrum database B;

[0013] Measure the near-infrared spectrum of the aniline substance sample to obtain the aniline substance spectrum database C;

[0014] Measure the near-infrared spectrum of the mixture X sample with known concentration of aniline substances to obtain the mixture X sample spectrum database M with known concentration of aniline substances.

[0015] Preferably, according to the information database, constructing the spectral components of aniline substances in the mixture X sample with known concentration of aniline substances specifically includes:

[0016] Preprocess the data in the background spectrum database B, the aniline substance spectrum database C, and the mixture X sample spectrum database M with known concentration of aniline substances;

[0017] According to the following formula, construct the mixing matrix R and calculate the projection coefficient r of aniline substances in the mixture X sample with known concentration of aniline substances:

[0018] R = [B, C]

[0019] r = (R T R) -1 R T M

[0020] where R T represents the transpose matrix of the mixing matrix R;

[0021] According to the following formula, construct the spectral components H of aniline substances in the mixture X sample with known concentration of aniline substances:

[0022] H = r · M.

[0023] Preferably, use wavelet denoising and first derivative to preprocess the data in the background spectrum database B, the aniline substance spectrum database C, and the mixture X sample spectrum database M with known concentration of aniline substances.

[0024] Preferably, the sliding window method is adopted to dynamically select the background spectral subset that is most relevant to the spectral of mixture X sample with unknown alcohol concentration, and the mixing matrix R is updated accordingly:

[0025] R = [B′, C]

[0026] where B' is the updated background spectral subset.

[0027] Preferably, through the Tikhonov regularization technique, the corrected projection coefficient r is calculated according to the following formula:

[0028] r = (R T R + λI) -1 R T M,

[0029] where λ is the regularization parameter and I is the identity matrix.

[0030] Preferably, constructing the standard curve representing the relationship between the concentration and absorbance of aniline substances in mixture X sample with known aniline substance concentration specifically includes:

[0031] In the spectral component diagram of aniline substances in mixture X samples with different concentrations of known aniline substances, obtain the absorbance at the near-infrared characteristic band of the N—H characteristic peak;

[0032] Make a scatter plot of the absorbance and the concentration value of aniline substances in mixture X sample with the corresponding known aniline substance concentration, and establish a standard curve;

[0033] Preferably, the near-infrared characteristic band of the N—H characteristic peak is specifically at 6000 - 7000 cm -1 in the spectral component diagram.

[0034] Preferably, predicting the concentration of aniline substances in mixture X sample with unknown aniline substance concentration according to the standard curve specifically includes:

[0035] Construct the spectral component of aniline substances in mixture X sample with unknown aniline substance concentration;

[0036] Obtain the absorbance at the near-infrared characteristic band of the N—H characteristic peak and substitute it into the standard curve to obtain the concentration of aniline substances in mixture X sample with unknown aniline substance concentration;

[0037] Preferably, the mixture X is refined oil, and the refined oil includes one of gasoline, diesel, or aviation fuel;

[0038] The aniline substances include one of aniline and N-methylaniline.

[0039] The second aspect of the present application provides a computer-readable storage medium, on which a program that enables a computer to run is stored. After the program is loaded into the memory of the computer, it can preprocess spectral data and construct the spectral components of aniline substances in a mixture X sample with a known concentration of aniline substances.

[0040] The third aspect of the present application provides a quantitative analysis device for aniline substances in a mixture, which is used to implement the above-mentioned quantitative analysis method for aniline substances in a mixture, and includes:

[0041] A near-infrared spectrometer, which is used to collect spectral data of a mixture X sample without aniline substances, an aniline substance sample, and a mixture X sample with a known concentration of aniline substances;

[0042] A data processing unit, which is used to preprocess spectral data and construct the spectral components of aniline substances in a mixture X sample with a known concentration of aniline substances; and

[0043] A result display unit, which is used to output and display the concentration result of aniline substances in a mixture X sample with an unknown concentration of aniline substances.

[0044] One or more of the above embodiments have at least the following beneficial effects:

[0045] The quantitative analysis method for aniline substances in a mixture provided by the present application, by constructing an information library, preprocessing and analyzing the background spectrum, aniline substance spectrum, and spectrum of a mixture with a known concentration, effectively separates the spectral components of aniline substances, eliminates the interference of the background spectrum and coexisting substances, and makes the detection result more accurate and reliable. For example, in the detection of aniline and N-methylaniline in gasoline, the correlation coefficients between the predicted values and the actual values of the verification samples are as high as 0.9996 and 0.9998, the maximum relative deviation is only 3.8%, and the average deviation is 0.17 g / L, significantly improving the detection accuracy.

[0046] The near-infrared spectroscopy analysis technology has the characteristics of fast speed and non-destructiveness. This solution makes full use of this advantage, with simple sample preparation and convenient spectral collection. Combining with the pre-established standard curve, the concentration of aniline substances in an unknown sample can be predicted in a short time, without complex and time-consuming sample pretreatment and instrument operation, which is especially suitable for on-site law enforcement inspections and instant detections of the quality of refined oil in the circulation field, greatly improving the supervision efficiency.

[0047] Compared with the traditional gas chromatography method, this method does not require complex sample pretreatment and instrument parameter settings, is easy to operate, has a fast detection speed, and is especially suitable for on-site rapid detection. Compared with the gas chromatography method, it omits the cumbersome sample pretreatment process and long detection time, can provide detection results in time on-site, and meets the needs of on-site law enforcement and instant detection in the oil product circulation field. Description of the Drawings

[0048] The drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation to the present application. In the drawings:

[0049] Figure 1 It is a flowchart of the quantitative analysis method for aniline substances in the mixture provided by an embodiment of the present application;

[0050] Figure 2 It is a spectral component diagram of aniline extracted from certain samples in the quantitative analysis method for aniline substances in the mixture provided by an embodiment of the present application;

[0051] Figure 3 It is a spectral component diagram of N-methylaniline extracted from certain samples in the quantitative analysis method for aniline substances in the mixture provided by an embodiment of the present application;

[0052] Figure 4 It is a standard curve diagram of aniline mass concentration established for the quantitative analysis method for aniline substances in the mixture provided by an embodiment of the present application;

[0053] Figure 5 It is a standard curve diagram of N-methylaniline mass concentration established for the quantitative analysis method for aniline substances in the mixture provided by an embodiment of the present application;

[0054] Figure 6 It is a relationship diagram of the actual value and predicted value of aniline provided by the quantitative analysis method for aniline substances in the mixture provided by an embodiment of the present application;

[0055] Figure 7 It is a relationship diagram of the actual value and predicted value of N-methylaniline provided by the quantitative analysis method for aniline substances in the mixture provided by an embodiment of the present application. Detailed Description of the Embodiments

[0056] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but merely represents the selected embodiments of the present application.

[0057] Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0058] In the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation to the present application. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0059] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0060] To make the technical problems, technical solutions and advantages to be solved by the present application clearer, the following will be described in detail with reference to the drawings and specific embodiments. Obviously, the embodiments described below are part of the embodiments of the present application, rather than all of the embodiments.

[0061] The first aspect of the present application provides a method for quantitative analysis of aniline substances in a mixture. Figure 1 It is a schematic flow diagram of the method for quantitative analysis of aniline substances in a mixture, as Figure 1 shown, the method includes:

[0062] S100. Construct an information database.

[0063] Specifically, constructing the information database includes:

[0064] S110. Obtain a mixture X sample without aniline substances, an aniline substance sample, and a mixture X sample with a known aniline substance concentration.

[0065] To ensure the reliability and accuracy of the database, we should obtain a sufficient number of mixture X samples without aniline substances. According to statistical principles, the more the number of samples, the more the constructed database can reflect the characteristics of the overall population. In actual operation, the specific number of samples can be determined according to the complexity of mixture X and the expected analysis accuracy.

[0066] In some embodiments, mixture X may be refined oil, which includes one of gasoline, diesel, or aviation fuel, or other mixtures. To ensure the comprehensiveness and representativeness of the background spectral database B, we need to obtain samples of mixture X without aniline substances from multiple different channels. These samples can be sourced from different manufacturers, different batches, or even refined oil from different regions. For example, for gasoline, gasoline samples of different grades (such as 92#, 95#) can be obtained from large refineries such as PetroChina and Sinopec. Additionally, gasoline samples produced by some independent refineries can be collected from the market to increase the diversity of the database.

[0067] According to the actual detection requirements and the possible aniline substances in mixture X, select the corresponding pure aniline substance samples. Common aniline substances include aniline, N-methylaniline, etc. For example, in gasoline, aniline and N-methylaniline are the most common additives, so pure samples of these two aniline substances need to be obtained. For some mixtures with special uses, such as aviation fuel, other types of aniline substances can be considered according to needs.

[0068] The obtained aniline substance samples should have a high purity to ensure the accuracy of spectral measurement. Generally, a purity of more than 99% is required. These samples can be purchased from professional chemical reagent suppliers.

[0069] In some embodiments, samples of mixture X with known aniline substance concentrations can be prepared by precise formulation methods.

[0070] The specific steps are as follows:

[0071] Determine the formulation concentration range: According to the possible concentration range of aniline substances in actual applications, determine the standard concentration sequence for formulation.

[0072] Precisely weigh and mix: Use high-precision liquid mass measurement tools or solid mass measurement tools to weigh a certain amount of aniline substances and mixture X, and mix them in a predetermined ratio. During the mixing process, ensure sufficient stirring or oscillation to make the aniline substances evenly distributed in mixture X.

[0073] In some specific examples, to establish an accurate standard curve, a series of samples of mixture X with known aniline substance concentrations and different concentration gradients need to be prepared. Generally, the concentration gradient should cover the entire range from low concentration to high concentration, and more samples should be set at key concentration points.

[0074] S120. Measure the near-infrared spectrum of the mixture X sample without aniline substances to obtain the background spectrum database B; measure the near-infrared spectrum of the aniline substance sample to obtain the aniline substance spectrum database C; measure the near-infrared spectrum of the mixture X sample with a known aniline substance concentration to obtain the mixture X sample spectrum database M with a known aniline substance concentration.

[0075] In some embodiments, near-infrared spectroscopy analysis is performed using a NIR-501A portable Fourier near-infrared oil analyzer. This instrument has advantages such as a wide spectral range (9100 - 4000 cm - -1), high wavenumber accuracy (6523 ± 1 cm - -1), and a signal-to-noise ratio better than 10000:1, and can meet the requirements for spectral measurement of the mixture X sample. Before measurement, the instrument should be calibrated and preheated to ensure the accuracy of the measurement results.

[0076] In some embodiments, the mixture X sample without aniline substances, the aniline substance sample, and the mixture X sample with a known aniline substance concentration are respectively injected into the sample cell, noting that the optical path of the sample cell should be kept consistent, and an optical path of 10 mm is generally selected. Spectral acquisition is performed on each sample at 3 different orientations, and the average spectrum is taken as the representative spectrum of the sample. Each sample is measured 3 times, and the average value is taken to reduce the measurement error. The spectral data of the above three substances are sorted and stored to form the background spectrum database B, the spectrum database C, and the spectrum database M.

[0077] In near-infrared spectroscopy analysis, each spectrum can be represented as an n×2 matrix, containing two columns of wavenumber and absorbance. For example, in some specific examples, the background spectrum database B is composed of the absorbance column vectors of X mixture X samples without alcohol substances, forming an n×X matrix; the spectrum database C is composed of the absorbance column vectors of Y alcohol substance samples, forming an n×Y matrix; the mixture X sample spectrum database M with a known aniline substance concentration is composed of the absorbance column vectors of Z samples, forming an n×Z matrix. These databases provide data support for subsequent spectral analysis and quantitative analysis.

[0078] S200. Based on the information database, construct the spectral components of aniline substances in the mixture X sample with a known aniline substance concentration, specifically including:

[0079] S210. Preprocess the data in the background spectrum database B, the aniline substance spectrum database C, and the mixture X sample spectrum database M with a known aniline substance concentration.

[0080] In spectral analysis, the original spectral data is often interfered by various factors, such as instrument noise, baseline drift, light intensity fluctuation, etc. These interferences will affect the accuracy and reliability of subsequent analysis. Therefore, preprocessing of spectral data is an essential step. The purpose of data preprocessing is to remove or reduce these interference factors, enhance spectral features, and improve the quality and comparability of data.

[0081] In some embodiments, wavelet denoising and the first derivative are used to preprocess the data in the background spectral database B, the aniline substance spectral database C, and the spectral database M of the mixture X sample with known aniline substance concentration.

[0082] Wavelet denoising is a signal processing technique based on wavelet transform. Wavelet transform can decompose a signal into a linear combination of wavelet basis functions at different scales and positions, thus realizing multi-resolution analysis of the signal. During the denoising process, by selecting an appropriate threshold, the wavelet coefficients of the noise part are set to zero, and then the inverse wavelet transform is performed to obtain the denoised signal.

[0083] In some embodiments, the first derivative is used to preprocess the data in the background spectral database B, the aniline substance spectral database C, and the spectral database M of the mixture X sample with known aniline substance concentration.

[0084] Taking the first derivative of spectral data can enhance the subtle features of the spectrum and reduce the influence of baseline drift. The first derivative reflects the rate of change of spectral intensity with wavelength and can highlight the positions of absorption peaks and reflection peaks.

[0085] During the derivation process, in order to avoid amplifying noise, a smoothing algorithm such as the Savitzky-Golay filter can be combined. This filter performs smoothing and derivation based on local polynomial fitting, which can not only effectively remove noise but also preserve spectral features.

[0086] S220. Construct the mixing matrix R = [B, C], and calculate the projection coefficient r of the aniline substances in the mixture X sample with known aniline substance concentration = (R T R) -1 R T M.

[0087] The mixing matrix R is a matrix composed of the spectral data in the background spectral database B and the aniline substance spectral database C.

[0088] In some embodiments, considering that there may be differences in the background spectra of refined oil from different batches or sources, the sliding window method is introduced. Through this method, it is possible to dynamically select the subset of background spectra that is most relevant to the spectrum of the current sample to be measured, and accordingly update the mixing matrix R = [B', C], where B' is the updated subset of background spectra, ensuring that the mixing matrix can better reflect the characteristics of the sample to be measured.

[0089] The construction of the mixing matrix R is to integrate the information of the background spectra and the spectra of aniline substances, providing a basis for the subsequent calculation of projection coefficients. Through the mixing matrix, the complex mixed spectra can be decomposed into a linear combination of the background spectra and the spectra of aniline substances, thereby realizing the extraction of the spectral components of aniline substances.

[0090] The calculation of the projection coefficients is based on the concept of projection in linear algebra. Assuming that the column vectors of the mixing matrix R form a subspace, then any mixed spectrum can be expressed as a linear combination of the vectors in this subspace. By calculating the projection coefficients, the components of the mixed spectrum in the direction of the spectra of aniline substances can be determined.

[0091] In some embodiments, the specific calculation steps for calculating the projection coefficient r of aniline substances in the mixture X sample with a known concentration of aniline substances are as follows:

[0092] Calculate R T R, where R T is the transpose of the mixing matrix R, and R T R is a square matrix with dimensions (m + k) × (m + k).

[0093] Find the inverse matrix (R T R) -1 : Perform a matrix inversion operation on R T R to obtain its inverse matrix.

[0094] Calculate R T M: M is the spectral matrix in the spectral database of the mixture X sample with a known concentration of aniline substances, and R T M is the product of the transpose of the mixing matrix R and M.

[0095] Solve for the projection coefficient r: Calculate the projection coefficient r through the formula r = (R T R) -1 R T M. Here, r is a vector with the same length as the number of columns of the mixing matrix R, and each element represents the contribution degree of the corresponding spectrum in the mixed spectrum.

[0096] In some embodiments, to solve the ill-conditioned problems that may occur during the calculation process, the Tikhonov regularization technique is introduced. The calculation formula for the projection coefficients is modified to r = (RT (R + λI) -1 R T M, where λ is the regularization parameter and I is the identity matrix. It is determined and optimized through the cross - validation method to achieve the best balance between fitting accuracy and model stability. This correction not only improves the computational stability but also effectively prevents the occurrence of overfitting phenomena.

[0097] S230. Construct the spectral component H = r·M of aniline substances in the mixture X sample with known aniline substance concentration.

[0098] Using the calculated projection coefficient r, the spectral component of aniline substances can be separated from the mixed spectrum. Specifically, the projection coefficient r is applied to the spectral database M of the mixture X sample with known aniline substance concentration, and the spectral component H of aniline substances is obtained through linear combination.

[0099] After completing the construction of the spectral component H of aniline substances in the mixture X sample with known aniline substance concentration, in order to more intuitively display and analyze these spectral components, a spectrogram can be formed.

[0100] The specific operation is as follows: First, organize the data in the spectral component H according to the wavelength and absorbance value to ensure the accuracy and integrity of the data. Then, use professional drawing software (such as Origin) to draw the spectrogram of the spectral component. During the drawing process, with the wavelength (cm - -1) as the horizontal axis and the absorbance as the vertical axis, connect the data points in the spectral component H in the order of wavelength to form a curve, thus forming a spectrogram of the spectral component. Set different colors or line types for the spectrograms of different samples for easy distinction and comparison.

[0101] S300. Construct a standard curve representing the relationship between the concentration and absorbance of aniline substances in the mixture X sample with known aniline substance concentration.

[0102] In the spectrograms of aniline substances in the mixture X samples with different concentrations of known aniline substance concentration, calculate the total absorbance at the N - H near - infrared characteristic band.

[0103] In some specific examples, the N - H stretching vibration of aniline substances in the near - infrared spectrum usually appears in the 6000 - 7000 cm - -1 band. The selection of this band is based on the spectral characteristics of aniline substances, because the N - H stretching vibration has strong absorption characteristics in this band, which can effectively reflect the concentration change of aniline substances.

[0104] In some embodiments, it is possible to pass through the 6000 - 7000 cm in the spectrogram of the spectral component -Integrate or sum the absorbance values in a band to obtain the total absorbance of the band.

[0105] Pair the calculated total absorbance with the corresponding known concentration values of aniline substances to form data points. For example, if there are 4 samples of mixture X with known concentrations of aniline substances, corresponding to 4 total absorbance values respectively, then there will be 4 data points, and a standard curve is established.

[0106] In some specific examples, the absorbance at one or more wavelength points in the O—H near-infrared characteristic band can also be used to replace the total absorbance.

[0107] According to the distribution of the scatter plot, select an appropriate regression model. For the relationship between the concentration of aniline substances and the total absorbance, a linear regression model is usually adopted to obtain the standard curve.

[0108] In some specific examples, the linear regression equation can be expressed as: A = k·c + b

[0109] Where: A represents the total absorbance; c represents the concentration of aniline substances; k represents the slope; b represents the intercept.

[0110] S400. According to the standard curve, predict the concentration of aniline substances in the sample of mixture X with unknown concentration of aniline substances.

[0111] For the sample of mixture X with unknown concentration of aniline substances, referring to the operation of extracting spectral components in the above steps, extract the spectral components of aniline substances in the sample of mixture X with unknown concentration of aniline substances, then calculate the total absorbance in its O—H near-infrared characteristic band, and substitute this total absorbance into the standard curve to obtain the predicted concentration of aniline substances in the sample of mixture X with unknown concentration of aniline substances.

[0112] The second aspect of the present application provides a computer-readable storage medium, on which a program that enables a computer to run is stored. After the program is loaded into the memory of the computer, it can preprocess spectral data and construct the spectral components of aniline substances in the sample of mixture X with known concentration of aniline substances.

[0113] The third aspect of the present application provides a quantitative analysis device for aniline substances in a mixture, which is used to implement the above-mentioned quantitative analysis method for aniline substances in a mixture, including:

[0114] A near-infrared spectrometer, which is used to collect spectral data of the sample of mixture X without aniline substances, aniline substance samples, and the sample of mixture X with known concentration of aniline substances;

[0115] A data processing unit for preprocessing spectral data and constructing spectral components of aniline substances in a mixture X sample with known aniline substance concentration; and

[0116] A result display unit for outputting and displaying the concentration result of aniline substances in a mixture X sample with unknown aniline substance concentration.

[0117] The technical solution of the present application is further demonstrated through the following examples:

[0118] Example 1

[0119] Aniline, N-methylaniline, and petroleum ether (60 - 90 °C) were prepared and purchased from Shanghai Macklin Biochemical Co., Ltd.; 8 finished gasoline products of different brands were collected from the domestic market, including 92# gasoline and 95# gasoline.

[0120] The instruments used are as follows:

[0121] NIR-501A portable Fourier near-infrared oil analyzer (jointly developed by the National Institute of Metrology of China and Beijing Yixingyuan Petrochemical Technology Co., Ltd.). Spectral range: 9100 - 4000 cm -1 ; Wavenumber accuracy: ±1 cm -1 @6523 cm -1 ; Wavenumber precision: ±0.1 cm -1 @6523 cm -1 ; Signal-to-noise ratio better than 10000:1.

[0122] AY200 type analytical balance with an analytical accuracy of one ten-thousandth.

[0123] Six gasoline samples were randomly selected from gasoline products of different brands produced by 8 different manufacturers as the calibration sample set A, and the rest were used as the verification sample solvent. The added aniline compounds were weighed using the AY200 type balance and placed in a 50 ml volumetric flask, then the solvent was added to the scale, shaken well, sealed, and stored in a cool place for later use. 42 calibration samples were prepared, including 21 aniline gasoline solution samples with a concentration range of 2.2 g / L to 150.5 g / L and 21 N-methylaniline gasoline solution samples with a concentration range of 2 g / L to 150 g / L, as shown in Table 1. 10 verification samples were prepared, including 5 aniline gasoline solution samples with a concentration range of 4.5 g / L to 145.5 g / L and 5 N-methylaniline gasoline solution samples with a concentration range of 4.0 g / L to 150.4 g / L, as shown in Table 2.

[0124] Table 1 Calibration sample concentration

[0125]

[0126] Table 2 Verification sample concentration

[0127]

[0128] Use the NIR-501A portable Fourier near-infrared oil analyzer to collect the near-infrared spectra of each sample, pure aniline solution, and pure N-methylaniline sample. Before spectrum collection, turn on the instrument and preheat it for 1 h. Set the ambient temperature to 20 °C and the relative humidity to 18%. Place a clean empty quartz cuvette in the sample holder and collect the reference spectrum; then inject the sample to be measured to a height of 2 / 3 of the cuvette and collect its absorbance spectrum. By rotating the cuvette, collect 3 spectra repeatedly in different orientations, and take the average spectrum as the sample spectrum. All samples completed their near-infrared spectrum collection on the day of preparation.

[0129] After obtaining the spectra of pure aniline, pure N-methylaniline, and each sample in the calibration sample set A, perform the following operations:

[0130] Construct the background spectrum database B: containing the near-infrared spectra of the mixture X samples without aniline substances.

[0131] Construct the aniline substance spectrum database C: containing the near-infrared spectra of aniline solution and N-methylaniline solution samples.

[0132] Measure the near-infrared spectra of each sample in the calibration sample set A to obtain the spectrum database M of the mixture X samples with known alcohol substance concentrations.

[0133] Construct the mixing matrix R: Combine the spectral data in the background spectrum database B and the aniline substance spectrum database C into a matrix R.

[0134] Adopt the sliding window method to dynamically select the background spectrum subset most relevant to the current sample spectrum to be measured, and update the mixing matrix R = [B', C] accordingly, where B' is the updated background spectrum subset.

[0135] Through the Tikhonov regularization technique. According to the formula r = (R T R + λI) -1 R T M, calculate the projection coefficient r.

[0136] Construct the spectral components H of aniline and N-methylaniline in each sample of the calibration sample solvent: H = r·M.

[0137] To more intuitively display and analyze these spectral components, the spectral components can be formed into a spectrogram, as Figure 2 and Figure 3 shown. At the same time, the results of spectral analysis of pure aniline and pure N-methylaniline are also reflected in Figure 2 and Figure 3 .

[0138] After separating the solvent spectrum from the solute spectrum through the above spectral component extraction, the standard curve method can be applied. The spectral components of aniline and N-methylaniline are linearly fitted with their respective concentrations to establish the concentration standard curves of aniline and N-methylaniline respectively. As Figure 4 and Figure 5 shown, the standard curve of aniline is: y1 = 0.002x - 0.00054, and the correlation coefficient R 2 = 0.9997. The standard curve of N-methylaniline is: y2 = 0.001x - 0.0002, and the correlation coefficient R 2 = 0.9998.

[0139] The spectral components of aniline and N-methylaniline are extracted from the spectra of the validation samples respectively, and then the intensity values of these spectral components are used to predict their concentration values according to the standard curves of aniline (y1) and N-methylaniline (y2), as shown in Table 3. It can be seen that the absolute deviation between the actual value and the predicted value of aniline does not exceed 2.3 g / L, and the maximum relative deviation is 3.1% (@4.5 g / L); the absolute deviation between the actual value and the predicted value of N-methylaniline does not exceed 1.5 g / L, and the maximum relative deviation is 4.0% (@4.0 g / L). As Figure 6 and Figure 7 shown as the relationship between the actual value and the predicted value, the prediction correlation coefficients (R 2 ) of aniline and N-methylaniline in the validation samples are 0.9996 and 0.9998 respectively, indicating good correlation.

[0140] Table 3 Comparison of the actual values and predicted values of the concentrations of aniline additives in the validation samples

[0141]

[0142]

[0143] Randomly select aniline gasoline samples with concentrations of 4.5 g / L, 55.1 g / L, and 145.5 g / L and N-methylaniline gasoline samples with concentrations of 4.0 g / L, 55.0 g / L, and 140.4 g / L from the validation samples, and collect 6 spectra for each sample repeatedly. The predicted concentration values of aniline additives in gasoline are obtained as shown in Table 4. It can be seen that the actual values and the predicted values are basically in agreement, the standard deviation of the 6 repeated predicted values is less than 0.38 g / L, and the relative deviation standard is less than 3.8%.

[0144] Table 4 Precision of the near-infrared spectral standard curve method

[0145]

[0146] It can be seen that the present application proposes a method for quantitative analysis of aniline substances in a mixture. A standard curve for determining the concentration of aniline or N-methylaniline in vehicle gasoline was established using near-infrared spectroscopy, with correlation coefficients (R2) of 0.9997 and 0.9998. The correlation coefficients between the actual values and predicted values of aniline and N-methylaniline in the verification samples were 0.9996 and 0.9998 respectively, the absolute value of the maximum deviation was 3.8%, the average deviation was 0.17 g / L, and the maximum relative deviation was 4.0%. This overcomes the problem that the near-infrared spectrum cannot directly establish a standard curve due to spectral band overlap, and there is no need for a multivariate analysis model.

[0147] It should be noted that the technical solutions in the various embodiments of the present application can be combined with each other, but the basis for the combination is that those of ordinary skill in the art can implement it; when the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist, that is, it does not fall within the protection scope of the present application.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A quantitative analysis method for aniline substances in a mixture, characterized in that, Including: Constructing an information database; Based on the information database, constructing spectral components of aniline substances in a mixture X sample with known aniline substance concentration; Constructing a standard curve representing the relationship between the concentration and absorbance of aniline substances in a mixture X sample with known aniline substance concentration; According to the standard curve, predicting the concentration of aniline substances in a mixture X sample with unknown aniline substance concentration.

2. The quantitative analysis method of aniline substances in the mixture according to claim 1, characterized in that, Specifically, constructing the information database includes: Obtaining a mixture X sample without aniline substances, an aniline substance sample, and a mixture X sample with known aniline substance concentration; Measuring the near-infrared spectrum of the mixture X sample without aniline substances to obtain a background spectrum database B; Measuring the near-infrared spectrum of the aniline substance sample to obtain an aniline substance spectrum database C; Measuring the near-infrared spectrum of the mixture X sample with known aniline substance concentration to obtain a mixture X sample spectrum database M of the mixture X sample with known aniline substance concentration.

3. The quantitative analysis method of the alcohol substances in the mixture according to claim 2, characterized in that Specifically, based on the information database, constructing spectral components of aniline substances in a mixture X sample with known aniline substance concentration includes: Preprocessing the data in the background spectrum database B, the aniline substance spectrum database C, and the mixture X sample spectrum database M of the mixture X sample with known aniline substance concentration; According to the following formula, constructing a mixing matrix R and calculating the projection coefficient r of aniline substances in a mixture X sample with known aniline substance concentration: R = [B, C] r=(R T R) -1 R T M Among them, R T represents the transposed matrix of the mixing matrix R; According to the following formula, constructing spectral components H of aniline substances in a mixture X sample with known aniline substance concentration: H = r · M.

4. The quantitative analysis method of aniline substances in the mixture according to claim 3, characterized in that, Using wavelet denoising and first derivative to preprocess the data in the background spectrum database B, the aniline substance spectrum database C, and the mixture X sample spectrum database M of the mixture X sample with known aniline substance concentration.

5. The quantitative analysis method of the alcohol substances in the mixture according to claim 3, characterized in that, Adopting a sliding window method to dynamically select the background spectrum subset most relevant to the spectrum of the mixture X sample with unknown alcohol substance concentration, and updating the mixing matrix R accordingly: R = [B′, C] Where B' is the updated background spectrum subset.

6. The quantitative analysis method of the alcohol substances in the mixture according to claim 3, characterized in that, Through Tikhonov regularization technology, calculating the corrected projection coefficient r according to the following formula: r=(R T R + λI) -1 R T M, Where λ is the regularization parameter and I is the identity matrix.

7. The quantitative analysis method of aniline substances in the mixture according to claim 1, characterized in that, Specifically, constructing a standard curve representing the relationship between the concentration and absorbance of aniline substances in a mixture X sample with known aniline substance concentration includes: In the spectral component diagram of aniline substances in a mixture X sample with known aniline substance concentration at different concentrations, obtaining the absorbance at the near-infrared characteristic band of the N—H characteristic peak; Making a scatter plot of the absorbance and the concentration value of aniline substances in the mixture X sample with known aniline substance concentration corresponding thereto, and establishing a standard curve; Preferably, the N—H characteristic peak is specifically at 6000~7000cm in the spectral component map in the near-infrared characteristic band -1 herein 8. The quantitative analysis method of aniline substances in the mixture according to claim 1, characterized in that, Specifically, according to the standard curve, predicting the concentration of aniline substances in a mixture X sample with unknown aniline substance concentration includes: Constructing spectral components of aniline substances in a mixture X sample with unknown aniline substance concentration; Obtaining the absorbance at the near-infrared characteristic band of the N—H characteristic peak and substituting it into the standard curve to obtain the concentration of aniline substances in the mixture X sample with unknown aniline substance concentration; Preferably, the mixture X is refined oil, and the refined oil includes one of gasoline, diesel oil or aviation fuel; The aniline substance includes one of aniline and N-methylaniline.

9. A computer-readable storage medium, characterized in that, A program enabling the computer to operate is stored thereon, and after the program is loaded into the memory of the computer, it can preprocess spectral data and construct spectral components of aniline substances in a mixture X sample with a known concentration of aniline substances.

10. A quantitative analysis device for aniline substances in a mixture, characterized in that, For implementing the quantitative analysis method of aniline substances in the mixture according to any one of claims 1 to 8, it includes: A near-infrared spectrometer for collecting spectral data of a mixture X sample without aniline substances, an aniline substance sample, and a mixture X sample with a known concentration of aniline substances; A data processing unit for preprocessing spectral data and constructing spectral components of aniline substances in a mixture X sample with a known concentration of aniline substances; and A result display unit for outputting and displaying the concentration result of aniline substances in a mixture X sample with an unknown concentration of aniline substances.

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