Method for identifying aliphatic amine with similar carbon chain structure and composition for sensor array

Abstract

The invention belongs to the technical field of analysis and detection, and particularly relates to a method for identifying aliphatic amine with similar carbon chain structures and a composition for a sensor array. The method for identifying the aliphatic amine with the similar carbon chain structure comprises the following steps: 1) adding the aliphatic amine into a supramolecular aggregate sensing solution for reaction, and testing an ultraviolet-visible spectrum; (2) respectively calculating ultraviolet-visible spectrum absorbance difference values of the sensing solution before and after different fatty amines are added, and carrying out principal component analysis by taking the ultraviolet-visible spectrum absorbance difference values as a principal component analysis input matrix; and 3) determining a characteristic spectrum for a modeling sample set in the discriminant model according to a principal component analysis result, and identifying the fatty amine by using the discriminant model. The carbon chain structure of the aliphatic amine generates an effect similar to that of an aggregate side chain, the change of the aggregation form or the aggregation degree enables the spectral information to change, and the characteristic spectrums corresponding to different aliphatic amines are found out by utilizing a statistical analysis method, so that the aliphatic amines are accurately identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of analytical detection technology, and in particular to a method for identifying fatty amines with similar carbon chain structures and a composition for a sensor array. BACKGROUND

[0002] In the chemical and fragrance industry, organic amine compounds occupy an important position due to their unique chemical properties and wide range of applications. In particular, fatty amines, as an important class of organic amines, are usually formed by replacing one or more hydrogen atoms in an ammonia molecule with an alkyl group. Such compounds are not only widely used in pharmaceuticals, pesticides, rubber processing, textiles and other industries, but also play a unique role in odor regulation and fragrance fields.

[0003] The odor characteristics of fatty amines are diverse, ranging from a slight ammonia smell to more complex fishy smell or other special smells, depending on the length of their side chains and their branching degree. Although some fatty amines are limited in direct use as fragrances due to their strong irritating odor, they show an undeniable value in modulating specific fragrances. For example, certain lower fatty amines can be used to simulate the smell of the ocean or fresh fish, adding depth to perfumes, personal care products, and cleaning supplies; and by precisely controlling the type and concentration of fatty amines, the fragrance profile of the final product can be effectively adjusted.

[0004] Currently, how to determine the type of fatty amines contained in an unknown fragrance still relies on chromatographic methods, which are time-consuming and require sophisticated and expensive instruments. At the same time, electronic nose, as an intelligent bionic olfactory system combining sensor technology and pattern recognition algorithm, plays an increasingly important role in the analysis of fragrances and flavors. Electronic nose is a system that combines multiple chemical sensors, uses sensors based on metal oxide semiconductors, and produces optical and electrical physical responses to odor molecules. Further signal processing and statistical analysis by computer complete odor recognition.

[0005] Although electronic nose can classify gases, its resolution for fatty amine molecules with similar carbon chain structures (e.g., close carbon atom numbers, isomers with the same number of carbon atoms) is low. This is mainly due to the fact that the current sensors of electronic nose are still mainly metal oxide (MOS) sensors, whose sensing process relies on the interaction between polar odor molecules and the metal oxide surface, which changes the energy level structure of the metal oxide and thus its electrical properties. This process results in the fact that the sensor can only respond to the difference in the polar end of the odor molecule, and the difference in the non-polar end is difficult to capture. SUMMARY

[0006] The application aims to provide a method for identifying fatty amines with similar carbon chain structures, and solve the problem of low resolution of similar fatty amines in existing identification methods.

[0007] The second object of the application is to provide a composition for a sensor array, and solve the problem of low resolution of similar fatty amines in existing sensor arrays.

[0008] To solve the above technical problems, the technical scheme of the method for identifying fatty amines with similar carbon chain structures of the application is as follows:

[0009] A method for identifying fatty amines with similar carbon chain structures, comprising the following steps:

[0010] 1) Different fatty amines are added into different types of sensing solutions for reaction, and the ultraviolet-visible spectra of the sensing solutions before and after the addition of the fatty amines are tested respectively, wherein the sensing solutions comprise supramolecular aggregates with imide structures;

[0011] 2) The absorbance difference of the ultraviolet-visible spectra of the sensing solutions before and after the addition of different fatty amines is calculated respectively, and is used as a principal component analysis input matrix to perform principal component analysis;

[0012] 3) The characteristic spectrum of the modeling sample set in the discriminant model is determined according to the principal component analysis result, and the fatty amines are identified by using the discriminant model.

[0013] The application provides a method for identifying fatty amines with similar carbon chain structures, which utilizes the conformational sensitivity of supramolecular aggregates, the lone pair electrons of the amino groups of the fatty amines, the strong Lewis acidity of the imide structures of the supramolecular aggregates, and the hydrogen bond accepting ability of the supramolecular aggregates, so that the amine molecules can approach the imide molecules in the solution and be attracted by the imide molecules. When the alkyl amines interact with the imide aggregates, the amino groups will mainly concentrate around the imide due to the strongest electron deficiency of the imide fragments, at this time, the carbon chain structure of the alkyl amines will affect the structure of the imide aggregates, produce an effect similar to the side chain of the aggregates, and show changes in the aggregation form or degree, and then the spectral information changes. The ultraviolet-visible spectrum is used to obtain rich full-band spectral information of the supramolecular aggregates, and a statistical analysis method is used to realize efficient, rapid and accurate identification of fatty amine molecules with similar carbon chain structures.

[0014] To further improve the interaction between the fatty amines and the supramolecular aggregates, and then improve the conformational changes of the aggregates and the changes in the ultraviolet-visible spectral information, preferably, the supramolecular aggregates comprise naphthalene diimide derivatives and / or perylene diimide derivatives.

[0015] In order to further improve the change of ultraviolet-visible spectrum information, preferably, the naphthalene diimide derivative includes one or more of the compounds having the structural formula shown in formula I:

[0016] Formula I;

[0017] In formula I, X1, X2 are independently selected from Br, Cl, CN and H;

[0018] The perylene diimide derivative includes one or more of the compounds having the structural formula shown in formula II-III:

[0019] Formula II;

[0020] Formula III;

[0021] In formula III, X3, X4 are independently selected from Br and H; n=1-3.

[0022] In order to further improve the modeling accuracy and efficiency, preferably, one kind of supramolecular aggregate is added in each sensing solution, and the supramolecular aggregate is selected from more than 7 of the following 10 compounds: in formula I, X1 is Br, and X2 is Br; in formula I, X1 is Cl, and X2 is Br; in formula I, X1 is CN, and X2 is CN; in formula I, X1 is H, and X2 is H; in formula I, X1 is Cl, and X2 is Cl; formula II; in formula III, X3 is Br, X4 is Br, and n is 2; in formula III, X3 is H, X4 is H, and n is 1; in formula III, X3 is H, X4 is H, and n is 2; in formula III, X3 is H, X4 is H, and n is 3.

[0023] In order to further improve the recognition accuracy of the recognition model, preferably, in step 3), the characteristic spectrum of the modeling sample set in the discriminant model is determined according to the principal component score of the principal component with a cumulative contribution rate of more than 99% in the principal component analysis result.

[0024] In order to further improve the change of ultraviolet-visible spectrum, and further improve the accuracy of statistical calculation of characteristic spectrum, preferably, in step 1), before the reaction, the ultraviolet-visible spectrum absorbance difference of the sensing solution before and after the addition of different proportions of fatty amines and different reaction times is determined, and the addition proportion and reaction time of the fatty amines during the reaction are determined according to the addition proportion and reaction time corresponding to the larger absorbance difference.

[0025] In order to reduce the influence of system error on the prediction result, and further improve the prediction accuracy of the model, preferably, in step 2), before calculation, the ultraviolet-visible spectrum of the sensing solution before and after the addition of fatty amines is corrected, and the correction formula is:

[0026] λ i前 =k i· M前 ;

[0027] λ i后 =k i· M 后 ;

[0028] ;

[0029] wherein λ i前 is the wavelength of the ultraviolet-visible spectrum of each sensing solution before the addition of the fatty amine after correction;

[0030] λ i后 is the wavelength of the ultraviolet-visible spectrum of each sensing solution after the addition of the fatty amine after correction;

[0031] M 前 is the wavelength of the ultraviolet-visible spectrum of each sensing solution before the addition of the fatty amine before correction;

[0032] M 后 is the wavelength of the ultraviolet-visible spectrum of each sensing solution after the addition of the fatty amine before correction;

[0033] k i is the correction factor of each sensing solution; i is the number of sensing solutions;

[0034] A ref is the wavelength of the main absorption peak before the addition of the fatty amine or the average value of the wavelength of the main absorption peak;

[0035] A 前 is the wavelength of the main absorption peak of each sensing solution before the addition of the fatty amine.

[0036] In order to further improve the recognition ability, preferably, the fatty amine comprises n-butylamine, isobutylamine, tert-butylamine, 2-methylbutylamine, n-pentylamine, isopentylamine, neopentylamine.

[0037] In order to further improve the prediction accuracy of the model, preferably, the discriminant model is selected from one or both of a partial least squares discriminant analysis model and a k-nearest neighbor identification method model.

[0038] The technical solution of the composition for the sensor array of the present application is:

[0039] A composition for a sensor array, comprising a plurality of compounds having structural formulae shown in formula I~formula III:

[0040] Formula I;

[0041] Formula II;

[0042] Formula III;

[0043] In formula I, X1, X2 are independently selected from Br, Cl, CN and H; in formula III, X3, X4 are independently selected from Br and H; n = 1-3.

[0044] More preferably, 7 or more of the compounds having the structural formula shown in formula I-III are included.

[0045] The composition for the sensor array provided by the present application is a supramolecular aggregate composition, the imide structure of the supramolecular aggregate has strong Lewis acidity, and has hydrogen bond accepting ability, in solution, the fatty amine molecules interact with it, the amino end will mainly concentrate in the vicinity of the imide of the supramolecular aggregate, the carbon chain structure in the alkyl amine produces an effect similar to the side chain of the aggregate, showing changes in the aggregation form or degree of aggregation, and then causing changes in its spectral information, which is conducive to finding the characteristic spectrum corresponding to different fatty amines by combining statistical analysis methods, and then realizing efficient, rapid and accurate recognition of fatty amine molecules with similar carbon chain structures. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 Flow chart of the method for recognizing fatty amines with similar carbon chain structures according to example 1 of the present application;

[0047] Figure 2 UV-visible spectra of the sensing solution and n-butylamine before and after reaction according to example 1 of the present application;

[0048] Figure 3 UV-visible spectra of the sensing solution 2Cl2Br and different fatty amines before and after response according to example 1 of the present application;

[0049] Figure 4 UV-visible spectra of the sensing solution 4Br and different fatty amines before and after response according to example 1 of the present application;

[0050] Figure 5 UV-visible spectra of the sensing solution 4Cl and different fatty amines before and after response according to example 1 of the present application;

[0051] Figure 6 UV-visible spectra of the sensing solution 4CN and different fatty amines before and after response according to example 1 of the present application;

[0052] Figure 7 UV-visible spectra of the sensing solution 4H and different fatty amines before and after response according to example 1 of the present application;

[0053] Figure 8 UV-visible spectra of the sensing solution BrPEPEPBr and different fatty amines before and after response according to example 1 of the present application;

[0054] Figure 9The UV-visible spectra of the sensing solution PBTP of the embodiment 1 of the present application before and after response to different fatty amines;

[0055] Figure 10 The UV-visible spectra of the sensing solution PEP of the embodiment 1 of the present application before and after response to different fatty amines;

[0056] Figure 11 The UV-visible spectra of the sensing solution PEPEP of the embodiment 1 of the present application before and after response to different fatty amines;

[0057] Figure 12 The UV-visible spectra of the sensing solution PEPEPEP of the embodiment 1 of the present application before and after response to different fatty amines. DETAILED DESCRIPTION

[0058] The technical concept of the method for identifying the fatty amines with similar carbon chain structures provided by the present application is as follows:

[0059] The supramolecular aggregate refers to the supramolecular group generated by the monomer molecules through intermolecular interaction and mutual stacking in a certain rule. Due to the close distance between the monomers, the arrangement of the front orbital energy levels is affected in some systems, so the aggregate and the monomer have obvious differences in the absorption or emission spectrum in these systems.

[0060] The supramolecular aggregates are connected with each other by non-covalent interaction inside, so that the aggregation process of the molecules is easily disturbed by other types of molecules, thereby breaking the aggregation or changing the aggregation mode, further causing the arrangement mode of the aggregate to change, thereby causing the change of the spectral signal.

[0061] The electronic nose with metal oxide sensors in the prior art relies on the interaction between the polar odor molecules and the metal oxide surface, so that it can only respond to the difference of the polar end of the odor molecules, and it is difficult to capture the difference of the non-polar end.

[0062] The present application utilizes the interaction between the fatty amines and the supramolecular aggregates with imide structure, the carbon chain structure of the fatty amines produces the effect similar to the side chain of the aggregate, so that the change of the aggregation form or the aggregation degree, and further the change of the spectral information, and the statistical analysis method is used to find out the characteristic spectrum corresponding to different fatty amines, and then the efficient, rapid and accurate identification of the fatty amine molecules with similar carbon chain structure is realized.

[0063] The method for identifying the fatty amines with similar carbon chain structures provided by the present application comprises the following steps:

[0064] 1) Optimization of response conditions: before the reaction, the absorbance difference of the UV-visible spectrum of the sensing solution before and after the addition of different proportions of fatty amines and different reaction times is determined, and the proportion of the fatty amine added and the reaction time corresponding to the larger absorbance difference are determined as the proportion of the fatty amine added and the reaction time during the reaction.

[0065] 2) Different fatty amines are added to different types of sensing solutions for reaction, and the UV-visible spectra of the sensing solutions before and after the addition of the fatty amines are tested, respectively. The sensing solutions include supramolecular aggregates with imide structures. The supramolecular aggregates include naphthalene diimide derivatives and / or perylene diimide derivatives.

[0066] The sensing solution in step 2) is obtained by dissolving the supramolecular aggregates in a solvent; the solvent is selected from one or more of n-hexane, chloroform, dichloromethane, and trichloromethane. Specifically, the solvent is selected from n-hexane and trichloromethane, and the sensing solution is obtained by mixing the supramolecular aggregate solution obtained by dissolving the supramolecular aggregates in trichloromethane with n-hexane, the concentration of the supramolecular aggregate solution is 0.1-0.3 mg / mL, and the volume ratio of the supramolecular aggregate solution to n-hexane is (1-2):(200-250).

[0067] The scanning range of the UV-visible spectrum in step 2) is 300-1000 nm.

[0068] 3) Principal component analysis: the absorbance difference of the UV-visible spectrum of the sensing solution before and after the addition of different fatty amines is calculated, respectively, and is used as the input matrix of principal component analysis for principal component analysis.

[0069] Before the calculation in step 3), the UV-visible spectrum of the sensing solution before and after the addition of the fatty amines is corrected, and the correction formula is:

[0070] λ i前 =k i· M 前 ;

[0071] λ i后 =k i· M 后 ;

[0072] ;

[0073] wherein λ i前 is the wavelength of the UV-visible spectrum of each sensing solution before the addition of the fatty amines after correction;

[0074] λ i后 is the wavelength of the UV-visible spectrum of each sensing solution after the addition of the fatty amines after correction;

[0075] M 前λi is the wavelength of the main absorption peak of each sensing solution before the addition of the aliphatic amine;

[0076] M 后 λi is the wavelength of the main absorption peak of each sensing solution before the addition of the aliphatic amine;

[0077] k i Ci is the correction factor of each sensing solution; i is the number of sensing solutions;

[0078] A ref λ0 is the wavelength of the main absorption peak of each sensing solution before the addition of the aliphatic amine or the average value of the main absorption peak wavelength;

[0079] A 前 λi is the wavelength of the main absorption peak of each sensing solution before the addition of the aliphatic amine.

[0080] 4) According to the principal component analysis result, the characteristic spectrum of the modeling sample set in the partial least squares discriminant analysis model and the k nearest neighbor identification method model is determined, and the discriminant formula model is used for identifying the aliphatic amine.

[0081] The composition for the sensor array of the application comprises a plurality of compounds having structural formulae I-III:

[0082] Formula I;

[0083] Formula II;

[0084] Formula III;

[0085] In formula I, X1 and X2 are independently selected from Br, Cl, CN and H; in formula III, X3 and X4 are independently selected from Br and H; n = 1-3.

[0086] More preferably, the composition comprises more than 7 compounds having structural formulae I-III.

[0087] It can be understood that when the composition is used for the sensor array, a single compound is used as a sensing unit, and multiple compounds form multiple sensing units, and finally form a sensor array.

[0088] Specifically, each compound in the composition is dissolved in a solvent to form a sensing solution, and each sensing solution is placed on a carrier to form a sensor array.

[0089] The mass concentration of the sensing solution is 0.0008-0.003 mg / mL, and the solvent of the sensing solution is selected from one or more of n-hexane, chloroform, dichloromethane and trichloromethane. More preferably, the solvent of the sensing solution is selected from trichloromethane and n-hexane.

[0090] Specifically, the sensing solution is prepared by mixing 0.1-0.3 mg / mL supramolecular aggregate solution with a second solvent at a volume ratio of 1: (100-200).

[0091] It should be noted that the first solvent is used to dissolve the supramolecular aggregate solid powder to obtain a precursor solution dispersed at a molecular level, and the second solvent is used as a poor solvent or anti-solvent to controllably induce molecular self-assembly to form supramolecular aggregates with specific structures, sizes and functions.

[0092] Specifically, the first solvent is chloroform, and the second solvent is n-hexane. The use of chloroform to dissolve the supramolecular aggregate is to obtain a precursor solution dispersed at a molecular level; the addition of n-hexane (poor solvent / anti-solvent) is to controllably induce molecular self-assembly to form supramolecular aggregates with specific structures, sizes and functions. Such structures determine the unique optical / electronic properties of the supramolecular aggregates, which are the basis for achieving high sensitivity and high selectivity sensing. Direct use of a single solvent (either chloroform or n-hexane) or one-step mixing of solvents cannot achieve such precise control over the aggregation process and the final structure, making it difficult to obtain ideal sensing performance.

[0093] Specifically, the carrier is a well plate. The well plate is selected from one of a 6-well plate, a 12-well plate, a 24-well plate, a 48-well plate, and a 96-well plate.

[0094] The embodiments of the application will be further described below with reference to specific examples. The chemical reagents involved in the following examples are commercially available conventional goods unless otherwise specified.

[0095] I. Specific embodiments of the method for identifying fat amines with similar carbon chain structures and the composition for sensor array of the application

[0096] Example 1

[0097] The flow chart of the method for identifying fat amines with similar carbon chain structures of this example is shown in Figure 1 The specific method is as follows:

[0098] 1) Preparation of supramolecular aggregate sensor array

[0099] Ten kinds of supramolecular aggregates were respectively weighed and ultrasonically dissolved in chloroform to prepare a supramolecular aggregate solution with a concentration of 0.1 mg / mL. The supramolecular aggregate solution was ultrasonically mixed with n-hexane at a volume ratio of 1:200 to obtain a sensing solution. 200 μL of the sensing solution was taken into a micro-well of a 96-well plate to obtain an aggregate sensing array.

[0100] The structural formulas of the 10 kinds of supramolecular aggregates are shown in Tables 1-2.

[0101] Table 1 Summary of structural formula of supramolecular aggregates

[0102]

[0103] "-" means no data

[0104] Table 2 Summary of structural formula of supramolecular aggregates

[0105]

[0106] The above 10 supramolecular aggregates are compositions for sensor array provided by the present application.

[0107] 2) Optimization of response conditions of aggregate sensor array

[0108] Response ratio optimization: 10 kinds of sensing solutions were respectively placed in the first row of microwells of a 96-well plate (8 12), and repeated for 5 rows, among which 4 rows were added with fatty amine (such as n-butylamine) at a volume ratio of 200:1, 100:1, 50:1, and 25:1 between the sensing solution and the fatty amine for 2 min, and the remaining row was not added with fatty amine as a blank control group.

[0109] The ultraviolet-visible spectrometer was used to test the ultraviolet-visible spectrum of the sensing solution in each microwell, and the scanning range of the ultraviolet-visible spectrum was 300~1000 nm. The difference in absorbance of the ultraviolet-visible spectrum of the sensing solution under different addition ratios of fatty amine and the blank control group can reflect the difference in response ability under different addition ratios. The addition ratio corresponding to the higher absorbance difference was selected as the best response ratio, and the best ratio of the sensing solution and the fatty amine was 100:1. The ultraviolet-visible spectra of the sensing solution formed by the 2# supramolecular aggregate before and after the addition of n-butylamine (n-Bu) at a volume ratio of 100:1 between the sensing solution and the fatty amine for 2 min are shown in Figure 2 , the abscissa is wavelength (nm), and the ordinate is absorbance (Abs).

[0110] The above response ratio optimization process was repeated using different fatty amines (such as isobutylamine) to obtain the best response ratio of the sensing solution and different fatty amines.

[0111] Response time optimization: 10 kinds of sensing solutions were respectively placed in the first row of microwells of a 96-well plate (8 12), and repeated for 6 rows. After adding fatty amine (such as n-butylamine) at a volume ratio of 100:1 between the sensing solution and the fatty amine, the 6 rows were reacted for 0, 2, 4, 6, 8, and 10 min, respectively.

[0112] The UV-Vis spectrometer was used to test the UV-Vis spectrum of the sensing solution in each microwell, and the scanning range of the UV-Vis spectrum was 300-1000 nm. The change trend of the UV-Vis spectrum absorbance value at different response times was compared, and the time when the absorbance value reached stability was taken as the optimal response time. The optimal response time of the sensing solution and the aliphatic amine was 2 min.

[0113] The above-mentioned response time optimization process was repeated using different aliphatic amines (such as isobutylamine), and the optimal response time of the sensing solution and different aliphatic amines was obtained.

[0114] 3) Principal component analysis

[0115] Ten sensing solutions were respectively placed in the first row of microwells of a 96-well plate (8 12), and the process was repeated for 7 rows. Under the optimal response ratio and optimal response time obtained in step 2), different aliphatic amines were respectively added to the 7 rows of microwells for reaction: n-butylamine (n-Bu), isobutylamine (iso-Bu), tert-butylamine (tert-Bu), 2-methylbutylamine (mebu), n-pentylamine (amy), isoamylamine (isoam), and neopentylamine (dim). Three groups of repeated experimental groups were set for the reaction.

[0116] Correction factor calculation: The UV-Vis spectrum of the sensing solution in each microwell before adding the aliphatic amine was tested by the UV-Vis spectrometer, and the scanning range of the UV-Vis spectrum was 300-1000 nm. The main absorption peak wavelength of each column of sensing solutions (i.e. each sensing solution) was selected. The absorbance at this wavelength should be stable. If there is a theoretical main absorption peak wavelength, the wavelength is selected (for example, the theoretical main absorption peak wavelength of 1# sensing solution in Table 1 is 300 nm; the theoretical main absorption peak wavelength of 2# sensing solution is 900 nm). If there is no theoretical main absorption peak wavelength, the average value of the main absorption peak wavelength of the column of sensing solutions is calculated.

[0117] The calculation formula of the correction factor is:

[0118]

[0119] A ref : Theoretical main absorption peak wavelength or average value of main absorption peak wavelength; A 前 : Main absorption peak wavelength of each column of sensing solutions before adding the aliphatic amine; k i : Correction factor of each column of sensing solutions, i = 1-10.

[0120] The UV-Vis spectrum of each column of sensing solutions before and after adding the aliphatic amine was multiplied by the corresponding correction factor, so that all the UV-Vis spectra before adding the amine were aligned at the main absorption peak wavelength. Specifically:

[0121] λ i前 =ki· M 前 ;

[0122] λ i后 =k i· M 后 ;

[0123] Where, λ i前 The wavelength of the UV-Vis spectrum of each sensing solution before the addition of the fatty amine after correction;

[0124] λ i后 The wavelength of the UV-Vis spectrum of each sensing solution after the addition of the corrected fatty amine;

[0125] M 前 The wavelength of the UV-Vis spectrum of each sensing solution before the addition of the fatty amine was corrected;

[0126] M 后 The wavelengths of the UV-Vis spectra of each sensing solution after the addition of fatty amines were used for correction.

[0127] UV-Vis spectra of different sensing solutions before and after the addition of fatty amines are as follows: Figures 3-12 As shown, the difference between the absorbance of the UV-Vis spectrum of the sensing solution after the addition of different types of fatty amines and before the addition of fatty amines was calculated, completely eliminating interferences such as concentration error of the sensing solution before the addition of amines, cuvette residue, and instrument drift.

[0128] 4) Construction of a fatty amine prediction model

[0129] Using all absorbance differences as the input matrix for principal component analysis (PCA), PCA was performed, and the results are shown in Table 3. The first five principal components represent 99.78% of the cumulative contribution. To improve the recognition accuracy of the model, the scores of the first five principal components were used to construct the feature spectra of the modeling sample set for Partial Least Squares Discriminant Analysis (PLS-DA) and k-Nearest Neighbor (KNN) models to build predictive models.

[0130] Table 3. Principal Component Analysis Results

[0131]

[0132] The results of different fatty amine discrimination models are shown in Table 4. The two models have good modeling performance, with a correct recognition rate of 100% for the modeling set samples and 95% and 99% for the prediction set, respectively.

[0133] It should be noted that the identification of the prediction set refers to reselecting seven known fatty amines: n-butylamine, isobutylamine, tert-butylamine, 2-methylbutylamine, n-pentylamine, iso-pentylamine, neopentylamine, obtaining the principal component analysis result by using the principal component analysis of step 3), obtaining the characteristic spectrum of the prediction set, and inputting into the above determined partial least squares discriminant analysis model (PLS-DA) and k-nearest neighbor identification method model (KNN) to identify the fatty amines, so as to verify the identification rate of the prediction model.

[0134] Table 4 Different fatty amine discriminant model results

[0135]

[0136] It should be noted that when identifying unknown fatty amines, the method of the embodiment uses the characteristic spectrum corresponding to the unknown fatty amines as the reference, and inputs into the fatty amine discriminant model to identify the unknown fatty amines.

[0137] The unknown fatty amine can be one fatty amine, or a mixture of two or more fatty amines.

[0138] In other embodiments, more than 7 supermolecular aggregates can be used to identify fatty amines with similar carbon chain structures according to the method of Embodiment 1, for example, any 7 or more supermolecular aggregates in Tables 1-2 can be used.

[0139] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent replacements to some technical features, as long as they are within the spirit and principles of the present application. Any modification, equivalent replacement, improvement, etc. made within the scope of the present application should be included in the protection scope of the present application.

Claims

1. A method for identifying aliphatic amines with similar carbon chain structures, characterized in that, Includes the following steps: 1) Different fatty amines were added to different types of sensing solutions to carry out the reaction, and the ultraviolet-visible spectra of the sensing solutions before and after the addition of the fatty amines were measured. The sensing solutions included supramolecular aggregates with imide structures. 2) Calculate the difference in UV-Vis absorbance of the sensing solution before and after the addition of different fatty amines, and use it as the input matrix for principal component analysis. 3) Based on the principal component analysis results, determine the characteristic spectra of the sample set used for modeling in the discriminant model, and use the discriminant model to identify fatty amines.

2. The method for identifying aliphatic amines with similar carbon chain structures as described in claim 1, characterized in that, Supramolecular aggregates include naphthalimide derivatives and / or perylene imide derivatives.

3. The method for identifying aliphatic amines with similar carbon chain structures as described in claim 2, characterized in that, Naphthalimide derivatives include one or more compounds having the structural formula shown in Formula I: Formula I; In Formula I, X1 and X2 are independently selected from Br, Cl, CN, and H, respectively; Perylene diimide derivatives include one or more compounds having the structural formulas shown in Formulas II to III: Formula II; Formula III; In Formula III, X3 and X4 are independently selected from Br and H, respectively; n = 1-3.

4. The method for identifying aliphatic amines with similar carbon chain structures as described in claim 3, characterized in that, Each sensing solution contains a corresponding supramolecular aggregate, which is selected from at least 7 of the following 10 compounds: In Formula I, X1 is Br and X2 is Br; In Formula I, X1 is Cl and X2 is Br; In Formula I, X1 is CN and X2 is CN; In Formula I, X1 is H and X2 is H; In Formula I, X1 is Cl and X2 is Cl; In Formula II; In Formula III, X3 is Br and X4 is Br, and n is 2; In Formula III, X3 is H and X4 is H, and n is 1; In Formula III, X3 is H and X4 is H, and n is 2; In Formula III, X3 is H and X4 is H, and n is 3.

5. The method for identifying aliphatic amines with similar carbon chain structures as described in claim 1, characterized in that, In step 3), the characteristic spectrum of the sample set used for modeling in the discriminative model is determined based on the scores of principal components with a cumulative contribution rate of over 99% in the principal component analysis results.

6. The method for identifying aliphatic amines with similar carbon chain structures as described in claim 1, characterized in that, In step 1), before the reaction, the difference in UV-Vis absorbance of the sensing solution before and after different proportions of fatty amine and different reaction times is used to determine the proportion of fatty amine to be added and the reaction time during the reaction based on the addition ratio and reaction time corresponding to the larger absorbance difference.

7. The method for identifying aliphatic amines with similar carbon chain structures as described in claim 1, characterized in that, Before performing the calculation in step 2), the UV-Vis spectra of the sensing solution before and after the addition of the fatty amine are corrected. The correction formula is as follows: l i前 =k i· M 前 ; l i后 =k i· M 后 ; ； Where, λ i前 The wavelength of the UV-Vis spectrum of each sensing solution before the addition of the fatty amine after correction; λ i后 The wavelength of the UV-Vis spectrum of each sensing solution after the addition of the corrected fatty amine; M 前 The wavelength of the UV-Vis spectrum of each sensing solution before the addition of the fatty amine was corrected; M 后 The wavelength of the UV-Vis spectrum of each sensing solution after the addition of the fatty amine was corrected; k i is the correction factor for each sensing solution; i is the number of sensing solutions; A ref The wavelength of the theoretical main absorption peak or the average value of the main absorption peak wavelength before the addition of the fatty amine; A 前 The wavelengths of the main absorption peaks of each sensing solution before the addition of fatty amines are shown.

8. The method for identifying aliphatic amines with similar carbon chain structures as described in claim 1, characterized in that, The fatty amines include n-butylamine, isobutylamine, tert-butylamine, 2-methylbutylamine, n-pentylamine, isopentylamine, and neopentylamine.

9. The method for identifying aliphatic amines with similar carbon chain structures as described in claim 1 or 8, characterized in that, The discriminant model is selected from one or both of the partial least squares discriminant analysis model and the k nearest neighbor identification model.

10. A composition for a sensor array, characterized in that, This includes many compounds having the structural formulas shown in Formulas I to III: Formula I; Formula II; Formula III; In Formula I, X1 and X2 are independently selected from Br, Cl, CN and H, respectively; in Formula III, X3 and X4 are independently selected from Br and H, respectively; n=1-3.

Images