Spectrum-based method for measuring content of poly-alpha-olefin

Abstract

The invention relates to the technical field of spectrums, and discloses a method for measuring the content of poly-alpha-olefin based on spectrums. The method comprises the following steps: collecting a first spectrum and a second spectrum of a sample to obtain a spectrum matrix; constructing a function model P1 of the spectrum matrix of the first spectrum and the poly-alpha-olefin content, and substituting the spectrum matrix of the first spectrum into P1 to obtain Ypred, T1 and yr1; enabling the spectrum matrix of the second spectrum to be orthogonal with T1 to obtain Xorth; a model P2 of the Xorth and the yr1 is constructed; taking a to-be-detected liquid to obtain a spectrum matrix of the first spectrum and a spectrum matrix of the second spectrum of the to-be-detected liquid, and substituting the spectrum matrix of the first spectrum of the to-be-detected liquid into P1 to obtain T1M and Q1M; and substituting the spectrum matrix of the second spectrum of the to-be-detected liquid into P2 to obtain a model score and a load, and substituting the model score and the load into a calculation model to obtain the poly-alpha-olefin content. According to the scheme, operation is easy, the speed is high, and the result is accurate.

Description

Technical Field

[0001] The invention relates to the field of spectral technology, and in particular to a method for measuring poly-alpha-olefin content based on spectroscopy. Background Art

[0002] Polyalphaolefin synthetic oil (PAO) is an olefin oligomer obtained by polymerizing monomer alpha olefin under the action of a catalyst and then hydrogenating and refining. Its molecular structure is regular and uniform and is comb-shaped or tree-shaped. Among PAOs, the PAO base oil produced with 1-decene as raw material has the best performance. The higher the polyalphaolefin content in PAO, the better the product performance including thermal stability, thermal oxidation stability, color and other properties.

[0003] At present, the methods for determining the poly-alpha-olefin content in PAO mainly include high performance liquid chromatography, nuclear magnetic resonance analysis and gel chromatography. However, the above methods are cumbersome in manual operation, have high requirements for analysts, are greatly affected by manual operation, and have slow analysis speeds. The impact of the reagents used on the environment cannot be ignored, and are not the best choice in industrial production. Therefore, there is still a need to develop a method that can quickly, simply and accurately predict the poly-alpha-olefin content in PAO. Summary of the invention

[0004] The purpose of the present invention is to overcome the above problems existing in the prior art and to provide a method for determining the content of poly-alpha-olefin based on spectroscopy, which has the advantages of simple operation, fast speed, accurate results and good repeatability.

[0005] In order to achieve the above object, the present invention provides a method for measuring poly-alpha-olefin content based on spectroscopy, the method comprising:

[0006] (1) obtaining a plurality of standard poly-α-olefin samples with known and different poly-α-olefin contents;

[0007] (2) collecting a first spectrum and a second spectrum for the standard poly-α-olefin sample;

[0008] Select characteristic spectral bands of the above spectra respectively, and obtain corresponding spectral matrices;

[0009] (3) By partial least squares method, a function model P1 of the spectral matrix of the first spectrum and the poly-α-olefin content of the standard poly-α-olefin sample is constructed, and the spectral matrix of the first spectrum is substituted into the function model P1 to obtain the first poly-α-olefin content calculation value Y pred , model score T1, true value of poly-α-olefin content and Y pred The model residual matrix y r1 ;

[0010] (4) Orthogonalize the spectral matrix of the second spectrum with the model score T1 to obtain the orthogonalized matrix X orth ;

[0011] (5) Construct the orthogonalized matrix X through partial least squares method orth With y r1 Function model P2;

[0012] (6) A poly-α-olefin sample with an unknown poly-α-olefin content is taken as a test liquid, and a spectrum is collected in the manner of step (2) to obtain a spectrum matrix of a first spectrum of the test liquid and a spectrum matrix of a second spectrum of the test liquid, and the spectrum matrix of the first spectrum of the test liquid is substituted into the function model P1 to obtain a model score T1 M and load Q1 M ;

[0013] (7) Substitute the spectral matrix of the second spectrum of the test liquid into the function model P2 to obtain the model score T2 M and load Q2 M , substitute into the calculation model Y = T1 M ×Q1 M +T2 M ×Q2 M The poly-α-olefin content Y in the test liquid is obtained.

[0014] The technical solution of the present invention is simple to operate, fast, accurate and has good repeatability. Different spectra are integrated, the information between the spectra can be complementary, the results are comprehensive and accurate, and it is suitable for actual industrial production. DETAILED DESCRIPTION

[0015] The endpoints and any values ​​of the ranges disclosed in this article are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of each range, the endpoint values ​​of each range and the individual point values, and the individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed in this article.

[0016] The present invention provides a method for measuring poly-alpha-olefin content based on spectroscopy, the method comprising:

[0017] (1) obtaining a plurality of standard poly-α-olefin samples with known and different poly-α-olefin contents;

[0018] (2) for the standard poly-α-olefin sample, respectively collecting a first spectrum and a second spectrum (the first spectrum and the second spectrum are different);

[0019] Select characteristic spectral bands of the above spectra respectively, and obtain corresponding spectral matrices;

[0020] (3) By partial least squares method, a function model P1 of the spectral matrix of the first spectrum and the poly-α-olefin content of the standard poly-α-olefin sample is constructed, and the spectral matrix of the first spectrum is substituted into the function model P1 to obtain the first poly-α-olefin content calculation value Y pred , model score T1, true value of poly-α-olefin content and Y pred The model residual matrix y r1 ;

[0021] (4) Orthogonalize the spectral matrix of the second spectrum with the model score T1 to obtain the orthogonalized matrix X orth ;

[0022] (5) Construct the orthogonalized matrix X through partial least squares method orth With y r1 Function model P2;

[0023] (6) A poly-α-olefin sample with an unknown poly-α-olefin content is taken as a test liquid, and a spectrum is collected in the manner of step (2) to obtain a spectrum matrix of a first spectrum of the test liquid and a spectrum matrix of a second spectrum of the test liquid, and the spectrum matrix of the first spectrum of the test liquid is substituted into the function model P1 to obtain a model score T1 M and load Q1 M ;

[0024] (7) Substitute the spectral matrix of the second spectrum of the test liquid into the function model P2 to obtain the model score T2 M and load Q2 M , substitute into the calculation model Y = T1 M ×Q1 M +T2 M ×Q2 M The poly-α-olefin content Y in the test liquid is obtained.

[0025] It can be understood that the vertical coordinates of the obtained spectra may be different for different spectra. For example, the vertical coordinates of infrared spectra and ultraviolet spectra are generally absorbance, and the spectrum matrix at this time is the absorbance matrix. The vertical coordinate of Raman spectra is generally intensity, and the spectrum matrix at this time is the spectrum intensity matrix. Taking the first spectrum as an example, one sample corresponds to a "1×N spectrum matrix", where N is the wavelength; multiple samples correspond to an "M×N spectrum matrix", where M is the number of samples and N is the wavelength.

[0026] The inventors of the present invention have found in their research that, according to the above method, for standard poly-alpha-olefins, by first using the first spectrum as above, and then fusing the data obtained by the first spectrum with the second spectrum, the two spectral information are processed in series, and the final result is given by the combination of the two spectra, which can further ensure the accuracy of the result. The method of the present invention can fully fuse the complementary information in different spectra, realize the complementary advantages between spectral analysis technologies, and make the results more accurate. By using the method of the present invention, for poly-alpha-olefin samples with unknown poly-alpha-olefin content, the poly-alpha-olefin content therein can be quickly and accurately known. Compared with the existing commonly used high performance liquid chromatography, nuclear magnetic resonance analysis and gel chromatography for determining the poly-alpha-olefin content in poly-alpha-olefin samples, the method of the present invention has accurate results, can avoid complicated sample processing processes, is simpler to operate, and is fast.

[0027] According to the present invention, preferably, in step (1), the number of standard poly-α-olefin samples is ≥50, preferably 60-200.

[0028] Generally, the samples are collected so that the samples are evenly distributed within a possible range, and the present invention has no particular restrictions on the poly-α-olefin content in the standard poly-α-olefin sample. However, preferably, the poly-α-olefin content in the standard poly-α-olefin sample satisfies: the mass concentration is greater than 1wt%, preferably 50-99.8wt% (for example, it can be 50wt%, 55wt%, 60wt%, 65wt%, 70wt%, 75wt%, 80wt%, 85wt%, 90wt%, 95wt%, 98wt%, 99wt%, 99.8wt% and the range formed by any two of the above values ​​and the value within the range).

[0029] The present invention has no particular limitation on the conditions for collecting the spectrum, which may be the common conditions in the art. Preferably, the collecting temperature is 15-50°C, preferably 23-27°C.

[0030] According to the present invention, preferably, the resolution of the acquisition is 2-16 cm -1 , more preferably 5-12cm -1 (For example, it can be 5cm -1 、6cm -1 、7cm -1 、8cm -1 、9cm -1 , 10cm -1 、11cm -1 、12cm -1 and the range formed by any two of the above values ​​and the values ​​within the range).

[0031] According to the present invention, preferably, the first spectrum and the second spectrum are each independently selected from one of a mid-infrared spectrum, a Raman spectrum, a near-infrared spectrum, a laser-induced breakdown spectrum and an ultraviolet spectrum, and the first spectrum and the second spectrum are different. It can be understood that in order to fuse complementary information from different spectra and make the result of the calculation model more accurate, the first spectrum and the second spectrum are different.

[0032] According to a preferred embodiment of the present invention, the first spectrum and the second spectrum are selected from mid-infrared spectrum and Raman spectrum. The first spectrum can be mid-infrared spectrum, and the second spectrum can be Raman spectrum; or, the first spectrum can be Raman spectrum, and the second spectrum can be mid-infrared spectrum. However, according to a particularly preferred embodiment of the present invention, the first spectrum is mid-infrared spectrum, and the second spectrum is Raman spectrum. The inventors of the present invention further found in the study that for poly-alpha-olefin samples, when mid-infrared spectrum and Raman spectrum are used, the results can be more accurate than other spectra.

[0033] According to the present invention, preferably, in step (2), the mid-infrared spectrum is collected in a wave number interval of 650-4000 cm -1 When collecting mid-infrared spectra, a Fourier transform infrared spectrometer of model Agilent 4500tFTIR can be used. A drop of crude oil sample is taken with a pipette and added to the sample pool. The spectrum is scanned with air as a reference. The number of sample scans can be 16-128 times.

[0034] According to the present invention, preferably, the Raman spectrum is collected in a wave number interval of 100-2500 cm -1 When collecting Raman spectra, a handheld Raman spectrometer (ThermoFisher, model TruScanRM) can be used, the emission wavelength can be 100-3000nm, the sample is placed in a vial for measurement, and the laser power can be 1-450mw. The number of sample scans can be 16-128 times.

[0035] According to the present invention, preferably, before selecting the characteristic spectral band, the method further includes: preprocessing the spectrum to eliminate redundant information and / or noise in the spectrum. It can be understood that by preprocessing, baseline drift, spectral overlap, noise, etc. in the spectrum can be eliminated to further improve the accuracy of the result.

[0036] According to the present invention, preferably, the preprocessing method is selected from at least one of second-order differential, multivariate scattering correction, first-order differential and standard normal variable transformation; more preferably, the mid-infrared spectrum is preprocessed by second-order differential, and the Raman spectrum is preprocessed by multivariate scattering correction.

[0037] The window width of the second-order differential may be 20-32 (for example, 20, 21, 24, 25, 27, 30, 32).

[0038] According to the present invention, preferably, the wave number interval of the characteristic spectral band of the mid-infrared spectrum is 650-4000cm -1 , more preferably 1603-1663cm -1 and 650-980cm -1 .

[0039] Preferably, the wave number range of the characteristic spectral band of the Raman spectrum is 100-2500cm -1 , more preferably 1018-1755cm -1 .

[0040] The inventors of the present invention further discovered in their research that by selecting the above wavebands, the information of carbon molecules related to poly-α-olefins in the spectrum can be fully obtained, which can further ensure the accuracy of the results.

[0041] It can be understood that in step (3), taking the spectral matrix corresponding to the characteristic spectral band of the first spectrum as an example, multiple standard poly-α-olefin samples form an "M×N matrix" correspondingly, where M is the number of samples and N is the wavelength.

[0042] Preferably, after obtaining the corresponding spectral matrix, the method further comprises: normalizing the spectral matrix. It can be understood that the purpose of normalization is to scale the spectral intensity (normalize the highest value, and other intensities are expressed as decimals less than 1) to avoid loss of details of spectral information due to differences in absorbance or spectral intensity.

[0043] Among them, the model scores and loads in steps (3), (6) and (7) are parameters brought by the partial least squares method. It can be understood that in step (3), the load can also be obtained; in step (6), after substituting the spectrum matrix into the function model P1, the calculated value of model P1 can also be obtained at the same time; in step (7), after substituting the spectrum matrix into the function model P2, the calculated value of model P2 can also be obtained at the same time.

[0044] According to the present invention, preferably, in step (4), the orthogonal method is selected from Schmidt orthogonalization and / or canonical orthogonalization, and more preferably Schmidt orthogonalization. Through orthogonalization, the repeated information between different spectra can be eliminated, and the complementary information can be fully extracted, and the different information of different spectra can be integrated, so that the result can be more accurate.

[0045] For example, in step (6), a poly-α-olefin sample with an unknown poly-α-olefin content is taken, and a spectral matrix corresponding to the characteristic spectral band of the first spectrum and a spectral matrix corresponding to the characteristic spectral band of the second spectrum are formed; the above matrices are respectively a "1×N matrix", where N is the wavelength.

[0046] The function model P2 is related to the orthogonalized matrix X orth With y r1 Wherein, in step (7), when the spectrum matrix of the second spectrum of the liquid to be tested is substituted into the function model P2, the spectrum matrix of the second spectrum corresponds to X orth Substitution.

[0047] According to the present invention, preferably, the sample is poly-α-olefin synthetic oil. After obtaining the poly-α-olefin synthetic oil, the poly-α-olefin content therein can be determined by chromatography, for example, by using the ASTM D6352 method, and the poly-α-olefin content is confirmed according to the boiling point. The poly-α-olefin has a high boiling point, and components with a boiling point exceeding 300° C. are considered to be poly-α-olefins.

[0048] It is understood that, generally, the poly-alpha-olefin synthetic oil produced with 1-decene as raw material has the best performance. Therefore, the poly-alpha-olefin in the poly-alpha-olefin synthetic oil preferably refers to hydrogenated poly-1-decene. The poly-alpha-olefin synthetic oil generally also contains impurities such as 1-dodecene, some linear alpha olefins, beta olefins, and a small amount of aromatics and alkanes.

[0049] The present invention will be described in detail below by way of examples. In the following examples,

[0050] The mid-infrared spectrum of the collected samples was obtained using a Fourier transform infrared spectrometer (model: Agilent 4500t FTIR);

[0051] A handheld Raman spectrometer (purchased from ThermoFisher, model TruScan RM) was used to collect the Raman spectra of the samples;

[0052] In step (1) of the following embodiments,

[0053] The content of poly-α-olefin is confirmed by the boiling point using the gas chromatography ASTM D6352 method. The components with a relatively high boiling point, i.e., those exceeding 300° C., are considered to be poly-α-olefins.

[0054] The operations after spectrum acquisition were performed in MATLAB.

[0055] Example 1

[0056] Used to illustrate the method for determining the content of poly-alpha-olefin based on spectroscopy provided by the present invention

[0057] (1) 100 poly-α-olefin synthetic oil (PAO) samples were obtained, and the poly-α-olefin content therein was determined by gas chromatography (distributed in the range of 69.23-99.13 wt %).

[0058] (2) For the above PAO samples, collect mid-infrared spectra: use a pipette to take a drop of crude oil sample and add it to the sample pool. Use air as a reference to perform spectral scanning in the range of 650-4000cm -1 The range is 8cm -1 The spectra were collected at a resolution of 1.5 Å, the sample scan times were 64, and the collection temperature was 24 °C.

[0059] For the PAO sample, Raman spectra were collected: the emission laser wavelength was 785 nm; the PAO sample was measured through a vial, the laser power was 250 mW, and the range was 100-2500 cm -1 The range is 8cm -1 The spectra were collected at a resolution of 1.5 Å, the sample scan times were 64, and the collection temperature was 24 °C.

[0060] The collected mid-infrared spectrum was processed by second-order differential with a window width of 27 points and a wave number interval of 1603-1663 cm -1 and 650-980cm -1 The region is taken as the characteristic spectral band, and the corresponding absorbance matrix X is obtained. MIR ;

[0061] The collected Raman spectra were subjected to multivariate scattering correction, and the wave number range was 1018-1755cm -1 The area is taken as the characteristic spectral band, and the corresponding spectral intensity matrix X is obtained. Raman ;

[0062] The absorbance and spectral intensity of the above characteristic spectral bands are normalized respectively (the highest value of the absorbance is normalized to unity, and the other values ​​are correspondingly decimals less than 1; or the highest value of the spectral intensity is normalized to unity, and the other values ​​are correspondingly decimals less than 1).

[0063] (3) Construct the absorbance matrix X through partial least squares method MIR The function model P1 of the poly-α-olefin content of the PAO sample and the absorbance matrix X of the mid-infrared spectrum MIR Substitute into P1 and get the calculated value Y pred , and at the same time obtain the model score T1, the true value of the poly-α-olefin content and Y pred The model residual matrix y r1 .

[0064] (4) The spectral intensity matrix XRaman Perform Schmidt orthogonal with the model score T1 to obtain the orthogonalized matrix X orth ;

[0065] (5) Construct the orthogonalized matrix X through partial least squares method orth With y r1 Function model P2;

[0066] (6) 24 poly-α-olefin synthetic oil samples with unknown poly-α-olefin content were taken as a validation set, and the poly-α-olefin content thereof was determined by gas chromatography (see the measured values ​​in Table 1, which are distributed in the range of 63.98-98.76 wt %);

[0067] For each sample to be tested, the following steps to step (7) are performed respectively:

[0068] According to step (2), the mid-infrared spectrum, Raman spectrum, their corresponding characteristic spectral bands, and the corresponding absorbance matrix and spectral intensity matrix (also normalized in the same way) are obtained.

[0069] Substitute the absorbance matrix of the test liquid into the function model P1 to obtain the calculated value Y pred M , and get the model score T1 at the same time M and load Q1 M ;

[0070] (7) Substitute the spectral intensity matrix of the test liquid into the function model P2 to obtain the calculated value Y rpred M , model score T2 M and load Q2 M , then the poly-α-olefin content Y in the test solution is Y = T1 M ×Q1 M +T2 M ×Q2 M .

[0071] Table 1 shows the results of the measured values ​​obtained by chromatography, the Y values ​​calculated by the method of the present invention (i.e. the calculated values ​​in Table 1), and the error between the two (calculated values ​​minus measured values) for each test liquid in the validation set.

[0072] The performance of the method of the present invention is evaluated by using SEP (standard deviation of prediction) and RPD (ratio of standard deviation to root mean square error), wherein:

[0073]

[0074]

[0075] In the above formula, i represents the i-th sample, i = 1, 2, ... m, m is the total number of samples in the validation set 24, y i,actual is the gas chromatography measured value of the i-th sample, y i,predicted is the calculated value of the i-th sample obtained by the method of the present invention, SD V is the standard deviation of the polyalphaolefin content of all samples in the validation set, SD V The calculation formula is as follows:

[0076]

[0077] Among them, i = 1, 2, ..., N, N is the number of samples in the validation set 24, X i represents the poly-α-olefin content of the i-th sample (value measured by gas chromatography), is the average value of the poly-α-olefin content of all samples in the validation set (measured by gas chromatography).

[0078] The SEP is 0.34 and the RPD is 24.71, indicating that the method of the present invention has a high accuracy. In addition, the inventors of the present invention also found that the number of principal factors of the function model P1 is 4 and the number of principal factors of the function model P2 is 5. It can be understood that according to the partial least squares method, the number of principal factors represents the complexity of the model. The fewer the number of principal factors, the closer the model is to linearity. The more linear, the stronger the generalization ability, that is, the better the universality, which can also explain that the method of the present invention has a high accuracy.

[0079] Table 1

[0080]

[0081]

[0082] It can be seen that the method provided by the present invention has a smaller error and a higher accuracy.

[0083] Example 2

[0084] Used to illustrate the repeatability of the method provided by the present invention

[0085] Take a sample with unknown poly-α-olefin content and determine the poly-α-olefin content therein according to the chromatography method in step (1) in Example 1 (ie, the measured value in Table 2).

[0086] Six parallel spectrum acquisitions were performed on the sample. After each parallel spectrum acquisition, the mid-infrared spectrum and Raman spectrum were acquired according to step (2) in Example 1, and the characteristic spectrum band was selected according to step (2), and the corresponding absorbance matrix and spectral intensity matrix were obtained (normalized in the same way). The absorbance matrix of the liquid to be tested was substituted into the function model P1 to obtain the calculated value Y predM ', and get the model score T1 at the same time M ' and load Q1 M '; Substitute the spectral intensity matrix of the test liquid into the function model P2 to obtain the calculated value Y rpred M ', model score T2 M’ and load Q2 M’ , then the poly-α-olefin content Y in the test solution is Y = T1 M '×Q1 M '+T2 M '×Q2 M '.

[0087] For the sample to be tested, six parallel results (calculated values) were obtained, and the error was obtained by subtracting the measured value by gas chromatography from the calculated value each time. RSD was used to evaluate the repeatability of the model. The smaller the value, the better the repeatability. The calculation formula of RSD is as follows:

[0088]

[0089] Where S represents the standard deviation of the calculated values, represents the average value of the sample calculation, i = 1, 2, ..., n, n represents the number of samples, x i Represents the calculated value of the i-th sample.

[0090] The results are shown in Table 2.

[0091] Table 2

[0092]

[0093]

[0094] It can be seen that the method provided by the present invention has good repeatability.

[0095] Comparative Example 1

[0096] For the 100 samples in step 1 of Example 1, the steps (1)-(2) in Example 1 are followed, and then the spectral intensity matrix X is constructed by partial least squares method. Raman , Model P3 as a function of the poly-α-olefin content of the PAO samples.

[0097] Then take the verification set of Example 1 (24 samples), and substitute each absorbance matrix of the test liquid into the function model P1 in Example 1 (obtain the calculated value of using P1 alone), and substitute each spectral intensity matrix of the test liquid into the function model P3 (obtain the calculated value of using P3 alone).

[0098] Among them, the SEP and RPD of using P1 or P3 alone are shown in Table 3, and the calculation formulas of SEP and RPD are as follows.

[0099]

[0100]

[0101] Where i represents the i-th sample, i = 1, 2, ... m, m is the total number of samples in the validation set 24, y i,actual is the gas chromatography measured value of the i-th sample, y i,predicted is the calculated value of the ith sample obtained using P1 or P3 alone, SD V is the standard deviation of the polyalphaolefin content of all samples in the validation set, SD V The calculation formula is as follows:

[0102]

[0103] Among them, i = 1, 2, ..., N, N is the number of samples in the validation set 24, X i represents the poly-alpha-olefin content of the i-th sample (value measured by gas chromatography), is the average value of the poly-α-olefin content of all samples in the validation set (measured by gas chromatography).

[0104] Table 3

[0105] SEP RPD <![CDATA[P1]]> 0.54 15.78 <![CDATA[P3]]> 0.60 14.18 Embodiment 1 0.34 24.71

[0106] Among them, the smaller the SEP, the larger the RPD, indicating that the model accuracy is better. It can be seen that the error of the solution provided by the present invention is significantly smaller than the error of the model directly constructed using mid-infrared spectroscopy and Raman spectroscopy alone.

[0107] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited thereto. Within the technical concept of the present invention, the technical solution of the present invention can be subjected to a variety of simple modifications, including the combination of various technical features in any other suitable manner, and these simple modifications and combinations should also be regarded as the contents disclosed by the present invention and belong to the protection scope of the present invention.

Claims

1. A method for determining the content of poly-alpha-olefins based on spectroscopy, It is characterized in that The method includes: (1) obtaining a plurality of standard poly-α-olefin samples with known and different poly-α-olefin contents; (2) collecting a first spectrum and a second spectrum for the standard poly-α-olefin sample; Select characteristic spectral bands of the above spectra respectively, and obtain corresponding spectral matrices; (3) By partial least squares method, a function model P of the spectral matrix of the first spectrum and the poly-α-olefin content of the standard poly-α-olefin sample is constructed. 1 , and substitute the spectral matrix of the first spectrum into the function model P 1 , and obtain the first poly-α-olefin content calculated value Y pred , model score T 1 , the true value of poly-α-olefin content and Y pred The model residual matrix y r1 ; (4) Compare the spectral matrix of the second spectrum with the model score T 1 Orthogonalize and get the orthogonalized matrix X orth ; (5) Construct the orthogonalized matrix X through partial least squares method orth With y r1 Function model P 2 ; (6) Taking a poly-α-olefin sample with an unknown poly-α-olefin content as a test liquid, collecting spectra in the manner of step (2), obtaining a spectral matrix of a first spectrum of the test liquid and a spectral matrix of a second spectrum of the test liquid, and substituting the spectral matrix of the first spectrum of the test liquid into the function model P 1 , get the model score T 1 M and load Q 1 M ; (7) Substitute the spectral matrix of the second spectrum of the test liquid into the function model P 2 , get the model score T 2 M and load Q 2 M , substitute into the calculation model Y = T 1 M ×Q 1 M +T 2 M ×Q 2 M The poly-α-olefin content Y in the test liquid is obtained.

2. The method according to claim 1, in, In step (1), the number of standard poly-α-olefin samples is ≥50, preferably 60-200.

3. The method according to claim 1 or 2, in, In step (1), the poly-α-olefin content in the standard poly-α-olefin sample satisfies: the mass concentration is greater than 1 wt%, preferably 50-99.8 wt%.

4. The method according to claim 1, in, In step (2), the spectrum collection conditions include: the collection temperature is 15-50°C, preferably 23-27°C; and / or, the acquisition resolution is 2-16cm -1 , preferably 5-12cm -1 .

5. The method according to claim 1 or 4, in, The first spectrum and the second spectrum are each independently selected from one of a mid-infrared spectrum, a Raman spectrum, a near-infrared spectrum, a laser-induced breakdown spectrum, and an ultraviolet spectrum, and the first spectrum and the second spectrum are different.

6. The method according to claim 5, in, The first spectrum is a mid-infrared spectrum, and the second spectrum is a Raman spectrum.

7. The method according to claim 6, in, In step (2), the mid-infrared spectrum is collected in the wavenumber range of 650-4000cm -1 ; Preferably, the Raman spectrum is collected in a wavenumber range of 100-2500 cm -1 .

8. The method according to claim 6, in, Before selecting the characteristic spectral band, the method further includes: preprocessing the spectrum to eliminate redundant information and / or noise in the spectrum; Preferably, the preprocessing method is selected from at least one of second-order differential, multivariate scattering correction, first-order differential and standard normal variable transformation; more preferably, the mid-infrared spectrum is preprocessed by second-order differential, and the Raman spectrum is preprocessed by multivariate scattering correction.

9. The method according to claim 6, in, The characteristic spectral band of mid-infrared spectrum has a wave number range of 650-4000cm -1 , preferably 1603-1663cm -1 and 650-980cm -1 ; And / or, the wave number interval of the characteristic spectral band of the Raman spectrum is 100-2500cm -1 , preferably 1018-1755cm -1 .

10. The method according to claim 1 or 8, in, In step (4), the orthogonal method is selected from Schmidt orthogonalization and / or canonical orthogonalization, more preferably Schmidt orthogonalization.

11. The method according to claim 1, in, The sample is a poly-alpha-olefin synthetic oil.

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