Gasoline near infrared spectrum conversion method

Abstract

The invention relates to a near infrared spectrum conversion method for gasoline, which comprises the following steps of: respectively collecting spectrums on two spectrometers by using a hydrocarbon pure compound as a standard substance sample, and obtaining a fitting coefficient vector of a standard substance spectrum matrix on a first spectrometer and a typical gasoline sample spectrum by using a fitting algorithm; and carrying out mathematical transformation on a gasoline fitting spectrum matrix obtained by multiplying the fitting coefficient vector by standard substance spectrum matrixes obtained by the two spectrometers, so as to obtain gasoline near infrared spectrum conversion coefficients of the first spectrograph and the second spectrograph. The method can eliminate tiny spectrum difference generated between near infrared spectrums of gasoline samples measured by different spectrometers, gasoline standard samples do not need to be collected in the actual application process, manpower and material resources are saved, meanwhile, the accuracy of spectrum conversion is improved, and the method is suitable for rapid analysis of composition, physical properties and the like of the gasoline samples.

Description

Technical Field

[0001] The present invention relates to a gasoline near-infrared spectrum conversion method. Background Art

[0002] Existing research on crude oil detection is based on a variety of modern instrumental analysis methods, including chromatography, mass spectrometry, nuclear magnetic resonance and infrared / near infrared spectroscopy, among which near infrared spectroscopy analysis technology has the advantages of rapid and non-destructive analysis. In practical applications, if the near infrared spectral database established on one model of near infrared spectroscopy instrument is used on another model of instrument, due to the differences between the spectra, it will lead to wrong identification results. For example, the same crude oil may be misjudged as two different crude oils. At present, the commonly used methods to solve the problem of inconsistent spectra collected by different instruments are DS algorithm (Direct Standardization) and Mean Spectra Subtraction Correction Method (MSSC). However, these methods all have some problems, such as the time-consuming and labor-intensive collection of actual device standard samples, the lack of representativeness of samples, etc., which leads to wrong identification results. Summary of the invention

[0003] The purpose of the present invention is to provide a gasoline near-infrared spectrum conversion method, which can eliminate the tiny spectral differences between the near-infrared spectra of gasoline samples measured by different spectrometers and improve the accuracy of spectral conversion.

[0004] In order to achieve the above object, the present disclosure provides a gasoline near-infrared spectrum conversion method, which comprises the following steps:

[0005] Acquire multiple standard substance samples, use a first spectrometer to collect spectra of the standard substance samples to obtain a first spectrum matrix, and use a second spectrometer to collect spectra of the standard substance samples to obtain a second spectrum matrix; wherein the standard substance samples are pure hydrocarbon compounds;

[0006] Acquire typical spectra of a plurality of typical gasoline samples on the first spectrometer;

[0007] Fitting each of the typical spectra through the first spectrum matrix to obtain a set of fitting coefficient vectors;

[0008] Calculating the product of the fitting coefficient vector and the first spectrum matrix to obtain a first gasoline fitting spectrum matrix;

[0009] Calculating the product of the fitting coefficient vector and the second spectrum matrix to obtain a second gasoline fitting spectrum matrix;

[0010] The conversion coefficients of the first gasoline fitting spectrum matrix and the second gasoline fitting spectrum matrix are calculated as the gasoline near-infrared spectrum conversion coefficients of the first spectrometer and the second spectrometer.

[0011] Optionally, the standard substance samples include alkane samples, olefin samples and aromatic hydrocarbon samples.

[0012] Optionally, the alkane sample is a pure hydrocarbon compound of alkane monomers selected from C5 to C12, the olefin sample is a pure hydrocarbon compound of olefin monomers selected from C5 to C12, and the aromatic sample is a pure hydrocarbon compound of aromatic monomers selected from C6 to C8.

[0013] Optionally, fitting each of the typical spectra through the first spectrum matrix to obtain a set of fitting coefficient vectors includes:

[0014] The non-zero fitting coefficients are determined by using a non-negative constrained least squares method, and the non-zero fitting coefficients are normalized to obtain the fitting coefficient vector.

[0015] Optionally, fitting each of the typical spectra through the first spectrum matrix to obtain a set of fitting coefficient vectors includes:

[0016] Using the first spectral matrix X A All spectra in the above example are fitted according to the following formula (1):

[0017]

[0018] In formula (1), y A represents the typical spectrum, v i is the spectrum of the i-th standard substance sample in the first spectrum matrix, k is the number of spectra in the first spectrum matrix, a i is the fitting coefficient corresponding to the spectrum of the i-th standard substance sample in the first spectrum matrix and satisfies the objective function shown in the following formula (2):

[0019]

[0020] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (3):

[0021]

[0022] In formula (3), b i is the normalized fitting coefficient, g is the number of spectra in the first spectral matrix corresponding to the non-zero fitting coefficient;

[0023] All the obtained normalized fitting coefficients are combined into the fitting coefficient vector.

[0024] Optionally, the calculating the conversion coefficients of the first gasoline fitting spectrum matrix and the second gasoline fitting spectrum matrix includes:

[0025] The conversion coefficient is calculated according to the following formula (4):

[0026] E A ×F=E B Formula (4),

[0027] In formula (4), E A represents the first gasoline fitting spectrum matrix, E B represents the second gasoline fitting spectrum matrix, and F is the conversion coefficient.

[0028] Optionally, the first spectral matrix and the second spectral matrix each select a characteristic spectral range of 6500 to 8900 cm -1 The spectral matrix obtained by the absorbance.

[0029] Optionally, the first spectrometer and the second spectrometer are near-infrared spectrometers of different models.

[0030] Optionally, the first spectrometer and the second spectrometer are Fourier transform near-infrared spectrometers respectively.

[0031] Optionally, the detection conditions include: a resolution of 2 to 16 cm -1 , the wave number range is 4000~10000cm -1 , the number of scans is 16 to 128 times.

[0032] Through the above technical scheme, the present disclosure uses pure hydrocarbon compounds as standard material samples to collect spectra on two spectrometers respectively, and then uses a fitting algorithm to obtain a fitting coefficient vector of the standard material spectrum matrix on the first spectrometer and the typical gasoline sample spectrum. The gasoline fitting spectrum matrix obtained by multiplying the fitting coefficient vector with the standard material spectrum matrix obtained by the two spectrometers is mathematically converted to obtain the gasoline near-infrared spectrum conversion coefficient of the first spectrometer and the second spectrometer. This method can eliminate the tiny spectral differences between the near-infrared spectra of gasoline samples measured by different spectrometers. In the actual application process, it is not necessary to collect gasoline standard samples, saving manpower and material resources, while improving the accuracy of spectral conversion, and is suitable for rapid analysis of gasoline sample composition, physical properties, etc.

[0033] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0035] Figure 1 It is a schematic flow chart of a gasoline near-infrared spectrum conversion method provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0037] The present invention provides a gasoline near-infrared spectrum conversion method, such as Figure 1 As shown, the following steps S101 to S106 are included:

[0038] S101, obtaining a plurality of standard substance samples, using a first spectrometer to collect spectra of the standard substance samples to obtain a first spectrum matrix, and using a second spectrometer to collect spectra of the standard substance samples to obtain a second spectrum matrix; wherein the standard substance samples are pure hydrocarbon compounds;

[0039] S102, obtaining typical spectra of a plurality of typical gasoline samples on the first spectrometer;

[0040] S103, fitting each of the typical spectra using the first spectrum matrix to obtain a set of fitting coefficient vectors;

[0041] S104, calculating the product of the fitting coefficient vector and the first spectrum matrix to obtain a first gasoline fitting spectrum matrix;

[0042] S105, calculating the product of the fitting coefficient vector and the second spectrum matrix to obtain a second gasoline fitting spectrum matrix;

[0043] S106. Calculate conversion coefficients of the first gasoline fitting spectrum matrix and the second gasoline fitting spectrum matrix as gasoline near-infrared spectrum conversion coefficients of the first spectrometer and the second spectrometer.

[0044] The present invention aims at the wavelength difference between the near-infrared spectra of gasoline samples measured by different spectrometers, adopts hydrocarbon pure compounds as standard material samples, performs spectrum fitting and model transfer, the method is simple, and the spectrum conversion accuracy is high.

[0045] In step S101, the first spectrometer is a source machine (or host), and the second spectrometer is a target machine (or slave). The method disclosed in the present invention is applicable to two near-infrared spectrometers of different models, that is, the first spectrometer and the second spectrometer are near-infrared spectrometers of different models. Specifically, the first spectrometer and the second spectrometer can be near-infrared spectrometers of different models under the same type, or the first spectrometer and the second spectrometer can be near-infrared spectrometers of different models under different types. Among them, the types of near-infrared spectrometers include Fourier transform near-infrared spectrometers, grating spectrometers, etc., and the differences between near-infrared spectrometers of different models include, for example, structural differences, wavelength differences, absorbance differences, etc. of key components.

[0046] In a specific embodiment, the first spectrometer and the second spectrometer are Fourier transform near-infrared spectrometers respectively. The conditions for detecting samples using a Fourier transform near-infrared spectrometer may be well known to those skilled in the art. Detection of the standard substance sample using the first spectrometer and detection of the standard substance sample using the second spectrometer are preferably performed under the same detection conditions to improve the accuracy of spectral conversion. Specifically, the detection may be a transmission detection, using a cuvette injection method, with a transmission light path of 0.5 mm; the detection conditions may include: a resolution of 2 to 16 cm -1 , the wave number range is 4000~10000cm -1 , scanning times 16 to 128 times.

[0047] In one embodiment, the standard material sample is a hydrocarbon monomer hydrocarbon pure compound, including alkane samples, olefin samples and aromatic hydrocarbon samples. Further, the alkane sample is a C5-C12 alkane monomer hydrocarbon pure compound, including normal alkanes and / or isoalkanes; the olefin sample is a C5-C12 olefin monomer hydrocarbon pure compound, including monoolefins and / or polyolefins; the aromatic hydrocarbon sample is a C6-C8 aromatic hydrocarbon pure compound.

[0048] The first spectrum matrix and the second spectrum matrix are spectrum matrices obtained through optional spectrum preprocessing. Specifically, the spectrum preprocessing may include but is not limited to one or more of differential processing, standardization processing, normalization processing and wavelet transformation processing.

[0049] In one embodiment, the first spectral matrix and the second spectral matrix each select a characteristic spectral range of 6500 to 8900 cm -1 By selecting the absorbance within the appropriate characteristic spectral range to construct the spectral matrix, it is helpful to further improve the accuracy of spectral conversion.

[0050] In step S102, the typical gasoline samples refer to gasoline samples with known composition and / or properties, which may be typical gasoline samples in the gasoline database established on the first spectrometer or actual gasoline samples collected on site. The number of the typical gasoline samples may be 10 to 30.

[0051] In step S103, the fitting coefficient vector is preferably a normalized non-zero fitting coefficient vector, for example, the fitting coefficient vector is determined according to the non-negative constrained least squares method. Specifically, the non-zero fitting coefficients are determined using the non-negative constrained least squares method, and the non-zero fitting coefficients are normalized to obtain the fitting coefficient vector.

[0052] For example, the first spectral matrix X is used A All spectra in the above example are fitted according to the following formula (1):

[0053]

[0054] In formula (1), y A represents the typical spectrum, v i is the spectrum of the i-th standard substance sample in the first spectrum matrix, k is the number of spectra in the first spectrum matrix, a i is the fitting coefficient corresponding to the spectrum of the i-th standard substance sample in the first spectrum matrix and satisfies the objective function shown in the following formula (2):

[0055]

[0056] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (3):

[0057]

[0058] In formula (3), b i is the normalized fitting coefficient, g is the number of spectra in the first spectral matrix corresponding to the non-zero fitting coefficient;

[0059] All the obtained normalized fitting coefficients are combined into the fitting coefficient vector.

[0060] In step S104 and step S105, by calculating the product of the fitting coefficient vector and the standard material spectrum matrix on the first spectrometer and the second spectrometer, the gasoline fitting spectrum matrix on the two spectrometers can be obtained respectively.

[0061] In step S106, the gasoline fitting spectrum matrices on the first spectrometer and the second spectrometer are mathematically transformed to obtain the data relationship between the gasoline fitting spectrum matrices on the two spectrometers (i.e., the gasoline near-infrared spectrum conversion coefficient), which can be calculated and solved by, for example, matrix analysis method, principal component regression algorithm, etc.

[0062] For example, the conversion coefficient is calculated according to the following formula (4):

[0063] E A ×F=E B Formula (4),

[0064] In formula (4), E A represents the first gasoline fitting spectrum matrix, E B represents the second gasoline fitting spectrum matrix, and F is the conversion coefficient.

[0065] The obtained gasoline near-infrared spectrum conversion coefficient can realize the spectrum database conversion from the source machine (first spectrometer) to the target machine (second spectrometer), that is, the product of the gasoline spectrum matrix A established on the first spectrometer and the gasoline near-infrared spectrum conversion coefficient is calculated to obtain the gasoline spectrum matrix B on the second spectrometer.

[0066] The disclosed method is applicable to near-infrared spectrum conversion between different spectrometers in gasoline sample detection. The gasoline sample is not particularly limited, for example, it may include one or more of straight-run gasoline, alkylated gasoline, coker gasoline, catalytic cracking gasoline, catalytic reforming gasoline s-zorb gasoline, etc. In practical applications, a spectrum correction model can be further established on the basis of spectrum conversion for rapid analysis of gasoline sample composition, physical properties, etc. The disclosure has no particular restrictions on the subsequent steps of spectrum conversion (such as spectrum correction, sample analysis, etc.), and can be carried out using conventional methods in the art.

[0067] The disclosed method firstly synchronously measures the near-infrared spectra of the standard material samples on the source machine and the target machine, then fits the typical gasoline sample spectra collected on the source machine to obtain a fitting coefficient vector, and multiplies the fitting coefficient vector with the standard material spectrum matrix collected on the target machine to obtain a virtual typical gasoline spectrum matrix on the target machine, and then mathematically transforms the virtual typical gasoline spectrum matrices on the source machine and the target machine to obtain a conversion coefficient. The model established on the source machine can obtain the model on the target machine through the conversion coefficient, thereby realizing the conversion between the near-infrared spectra measured by spectrometers of different models.

[0068] The present disclosure is further described in detail below through examples, but the present disclosure is not limited thereto.

[0069] In the example, the instrument a for collecting gasoline near-infrared spectra is a Fourier transform near-infrared spectrometer produced by Thermo, model Antaris II, and the instrument b is a Fourier transform near-infrared spectrometer produced by ABB, model MB 3600. The spectrum collection conditions of both instruments a and b are: resolution 8 cm -1 , wave number range 4000~10000cm -1 , cumulative scanning times 64 times, transmission measurement method.

[0070] Example 1

[0071] In this embodiment, it is known that a gasoline near-infrared spectrum database is established on instrument a, and the database contains 200 gasoline samples and corresponding research octane number data.

[0072] (1) Collect spectra of pure compounds of normal C5, C6, C7, C8, C9, C10, C11, C12 alkanes, normal C5, C6, C7, C8, C9, C10, C11, C12 olefins and C6, C7, C8 aromatics on instruments a and b, respectively, and select 6500-8900 cm -1 The absorbance of the spectral region is used to establish the near-infrared spectral absorbance matrix of the pure compound standard substance, namely the first spectral matrix A pure and the second spectral matrix B pure .

[0073] In an actual device application of a petrochemical refinery, 25 gasoline samples processed daily were collected, and their near-infrared spectra were collected on instrument b and their octane numbers were determined using the national standard method GB / T 5487, which were used as unknown samples to verify the spectral conversion effect.

[0074] (2) Select 20 typical gasoline samples from the gasoline database established on instrument a and use the first spectral matrix A pure Each typical spectrum is fitted to obtain 20 sets of normalized non-zero fitting coefficient vectors. Specifically,

[0075] Using all the spectra in the first spectrum matrix, the typical spectrum is fitted according to the following formula (1):

[0076]

[0077] In formula (1), y A represents the typical spectrum, v i is the spectrum of the i-th standard substance sample in the first spectrum matrix, k is the number of spectra in the first spectrum matrix, a i is the fitting coefficient corresponding to the spectrum of the i-th standard substance sample in the first spectrum matrix and satisfies the objective function shown in the following formula (2):

[0078]

[0079] The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (3):

[0080]

[0081] In formula (3), b i is the normalized fitting coefficient, g is the number of spectra in the first spectral matrix corresponding to the non-zero fitting coefficient;

[0082] All the obtained normalized fitting coefficients are combined into the fitting coefficient vector.

[0083] (3) The standard material spectrum matrix A collected by instrument a and instrument b pure and B pure Multiplying by the above 20 groups of fitting coefficient vectors respectively, we can obtain the gasoline fitting spectrum matrix of instrument a and instrument b, namely the first gasoline fitting spectrum matrix EA and the second gasoline fitting spectrum matrix EB.

[0084] (4) Calculate the conversion coefficient F of EA×F=EB based on matrix analysis.

[0085] (5) The gasoline spectrum matrix A established on instrument a is multiplied by the conversion coefficient F, that is, A×F=B a We get the matrix B a That is, the spectrum conversion from instrument a to instrument b is completed.

[0086] (6) Using gasoline near infrared database B a A gasoline octane number prediction model was established using the partial least squares (PLS) method. The 25 unknown gasoline samples collected by instrument b were predicted, and the results are shown in Table 1.

[0087] (7) Establish a partial least squares (PLS) spectral correction model on instrument a and select the optimal principal factor f best The model was used to predict the above 25 gasoline samples collected by instrument a under the condition of , and the results are shown in Table 2.

[0088] The calculation formula of relevant statistical parameters is as follows:

[0089]

[0090] in, is the predicted value of the i-th sample, y i (i=1,2,…,n) is the true value of the i-th sample, and n is the number of samples in the test set.

[0091] Deviation = measured value - predicted value.

[0092] Table 1

[0093]

[0094] Table 2

[0095]

[0096] By comparing Table 1 and Table 2, it can be seen that the prediction accuracy of the PLS model established on the second spectrometer after spectral conversion by the method disclosed in the present invention is basically equivalent to that before spectral conversion (first spectrometer), indicating that the method disclosed in the present invention can eliminate the tiny spectral differences between the near-infrared spectra of gasoline samples measured by different spectrometers and realize spectral conversion between different spectrometers.

[0097] Comparative Example 1

[0098] The conventional DS algorithm is used to convert the spectra of instrument a and instrument b, specifically:

[0099] (1) In an actual device application of a petrochemical refinery, 20 typical gasoline sample spectra were collected on instrument a and instrument b respectively as standard samples for model transfer. The 6500-8900 cm -1 The absorbance of the spectral region is used to establish the near-infrared spectral absorbance matrix of the gasoline standard sample, namely the first spectral matrix A s and the second spectral matrix B s .

[0100] Twenty-five gasoline samples that are processed daily were collected, their near-infrared spectra were acquired on instrument b, and their octane numbers were determined using the national standard method GB / T5487, which were used as unknown samples to verify the spectral conversion effect.

[0101] (2) The first spectral matrix A s and the second spectral matrix B s Perform eigenvalue decomposition respectively, A s =U1S1V1,B s =U2S2V2,A s Represents host B s Represents the slave, and the spectrum preprocessing process is the same as in Example 1.

[0102] (3) Let Z1=U1S1, Z2=U2S2.

[0103] (4) Solve for the regression coefficient F and find the conversion coefficient F of Z1×F=Z2 based on matrix analysis.

[0104] (5) The gasoline spectrum library matrix A established on instrument a is multiplied by the conversion coefficient F, that is, A×F=B a 'Then we get the matrix B a'The spectrum conversion from instrument a to instrument b is completed.

[0105] (6) Using gasoline near infrared database B a 'A gasoline octane number prediction model was established. The modeling method was partial least squares (PLS). The 25 unknown gasoline samples collected by instrument b were predicted. The results are shown in Table 3.

[0106] Table 3

[0107]

[0108]

[0109] By comparing Table 1 and Table 3, it can be seen that compared with the traditional method for spectral conversion, the prediction accuracy of the PLS model established in the second spectrometer after spectral conversion by the method disclosed in the present invention is better than the transfer algorithm effect of the traditional DS model, and there is no need to collect gasoline standard samples in the actual application process, saving manpower and material resources.

[0110] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0111] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0112] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A gasoline near-infrared spectrum conversion method, characterized in that: The method comprises the following steps: Acquire multiple standard substance samples, use a first spectrometer to collect spectra of the standard substance samples to obtain a first spectrum matrix, and use a second spectrometer to collect spectra of the standard substance samples to obtain a second spectrum matrix; wherein the standard substance samples are pure hydrocarbon compounds; Acquire typical spectra of a plurality of typical gasoline samples on the first spectrometer; Fitting each of the typical spectra through the first spectrum matrix to obtain a set of fitting coefficient vectors; Calculating the product of the fitting coefficient vector and the first spectrum matrix to obtain a first gasoline fitting spectrum matrix; Calculating the product of the fitting coefficient vector and the second spectrum matrix to obtain a second gasoline fitting spectrum matrix; The conversion coefficients of the first gasoline fitting spectrum matrix and the second gasoline fitting spectrum matrix are calculated as the gasoline near-infrared spectrum conversion coefficients of the first spectrometer and the second spectrometer.

2. The method according to claim 1, wherein: The standard substance samples include alkane samples, olefin samples and aromatic hydrocarbon samples.

3. The method according to claim 2, wherein: The alkane sample is a pure hydrocarbon compound selected from C5 to C12 alkane monomers, the olefin sample is a pure hydrocarbon compound selected from C5 to C12 olefin monomers, and the aromatic sample is a pure hydrocarbon compound selected from C6 to C8 aromatic monomers.

4. The method according to claim 1, wherein: The step of fitting each of the typical spectra by using the first spectrum matrix to obtain a set of fitting coefficient vectors includes: The non-zero fitting coefficients are determined by using a non-negative constrained least squares method, and the non-zero fitting coefficients are normalized to obtain the fitting coefficient vector.

5. The method according to claim 4, wherein: The step of fitting each of the typical spectra by using the first spectrum matrix to obtain a set of fitting coefficient vectors includes: Using the first spectral matrix X A All spectra in the above example are fitted according to the following formula (1): In formula (1), y A represents the typical spectrum, v i is the spectrum of the i-th standard substance sample in the first spectrum matrix, k is the number of spectra in the first spectrum matrix, a i is the fitting coefficient corresponding to the spectrum of the i-th standard substance sample in the first spectrum matrix and satisfies the objective function shown in the following formula (2): The non-zero fitting coefficients among all the obtained fitting coefficients are extracted and normalized according to the following formula (3): In formula (3), b i is the normalized fitting coefficient, g is the number of spectra in the first spectral matrix corresponding to the non-zero fitting coefficient; All the obtained normalized fitting coefficients are combined into the fitting coefficient vector.

6. The method according to claim 1, wherein: The calculating of the conversion coefficients of the first gasoline fitting spectrum matrix and the second gasoline fitting spectrum matrix includes: The conversion coefficient is calculated according to the following formula (4): E A ×F=E B Equation (4), In formula (4), E A represents the first gasoline fitting spectrum matrix, E B represents the second gasoline fitting spectrum matrix, and F is the conversion coefficient.

7. The method according to claim 1, wherein: The first spectral matrix and the second spectral matrix each select a characteristic spectral range of 6500 to 8900 cm -1 The spectral matrix obtained by the absorbance.

8. The method according to claim 1, wherein: The first spectrometer and the second spectrometer are near-infrared spectrometers of different models.

9. The method according to claim 8, wherein: The first spectrometer and the second spectrometer are respectively Fourier transform near-infrared spectrometers.

10. The method according to claim 9, wherein: The detection conditions include: resolution of 2 to 16 cm -1 , the wave number range is 4000~10000cm -1 , the number of scans is 16 to 128 times.

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