Construction method for identifying molecular fragrance threshold value model of sweet fragrance spice

Abstract

The invention discloses a method for constructing a model for identifying a molecular fragrance threshold value of a sweet fragrance spice, and the method comprises the steps: grasping a fragrance generation mechanism of fragrance molecules on an atomic scale through the combination of quantum chemical calculation and machine learning, constructing a fragrance intensity fitting model between a molecular structure and fragrance intensity, and carrying out the recognition of the fragrance threshold value of the fragrance molecules. And predicting threshold values of different sweet fragrance spice molecules by using the fragrance intensity fitting model. According to the method, accurate prediction of the molecular fragrance threshold value is creatively realized based on the characteristics of the functional groups in the molecular structure, a reliable tool is provided for smell optimization in cigarette production, and the sensory quality of sweet fragrance characteristics in the Chinese style cigarettes is effectively improved.

Description

Technical Field

[0001] The invention relates to a method for constructing an aroma threshold model for identifying burnt-sweet aroma spice molecules. Background Art

[0002] Sweet and caramel aroma is one of the main notes of Chinese cigarettes. Improving the sweet and caramel aroma characteristics of smoke can help improve the sensory quality of Chinese cigarettes. There are many compounds in smoke, and there will be certain interactions between them, which will affect the flavor of cigarettes. The aroma of the mixture is not equal to the superposition of the original aroma, but produces a "mutual promotion and mutual elimination" effect. Therefore, understanding the aroma mechanism of these characteristic compounds themselves, and then studying the influence of the aroma groups and molecular charge distribution changes caused by the interaction between aroma molecules in the solvent on the aroma results, is of great significance for regulating the aroma of cigarettes.

[0003] With the improvement of the accuracy of quantum chemistry and density functional theory methods and the improvement of computing power, people can use theoretical calculations to obtain accurate molecular structure information, charge density distribution, electronic structure information, etc., and can also obtain changes in geometric structure, electron density distribution, and energy level structure under interactions with other molecules, solvents, etc., and can also obtain some spectral information that can be compared with experimental observations through simulation, such as infrared, Raman and other vibrations. Artificial intelligence machine learning models can analyze and process new data much faster than humans, allowing people to associate information at the atomic scale with observable and perceptible information. Help people extract the key factors that determine a certain property from the complex information, and then predict the properties of molecules or materials. At present, there are no reports on the use of quantitative computing combined with machine learning to build a model to predict the aroma threshold of sweet and fragrant molecules. Summary of the invention

[0004] The present invention aims to avoid the deficiencies of the above-mentioned prior art and provide a method for constructing an aroma threshold model for identifying caramel-sweet fragrance molecules, in order to reveal the relationship between changes in molecular structure and changes in aroma intensity, and to provide a theoretical basis for the accurate prediction of molecular aroma thresholds.

[0005] The present invention adopts the following technical solutions to solve the technical problems:

[0006] The method for constructing the aroma threshold model for identifying caramel-sweet aroma flavor molecules of the present invention is to grasp the aroma presentation mechanism of aroma molecules at the atomic scale by combining quantum chemical calculations and machine learning, construct an aroma intensity fitting model between molecular structure and aroma intensity, and use the aroma intensity fitting model to predict the thresholds of different caramel-sweet aroma flavor molecules.

[0007] The method for constructing a flavor threshold model for identifying burnt sweet fragrance molecules of the present invention comprises the following steps: firstly, obtaining structural information of burnt sweet fragrance molecules by using a CAS number or a SMILES character string through a CASSciFinder database or an RDKit software package; optimizing the molecular structure by using quantum computing software Gaussian for the structural information to obtain an optimized molecular structure, and obtaining an infrared spectrum of the optimized molecular structure; then, obtaining structure-related descriptors by performing piecewise integration processing on the infrared spectrum; then, extracting key feature descriptors for the structure-related descriptors by using a machine learning tool SISSO, constructing a model of the relationship between functional groups and threshold values ​​by using the key feature descriptors as a flavor intensity fitting model; and predicting the threshold values ​​of different burnt sweet fragrance molecules by using the flavor intensity fitting model.

[0008] The method for constructing a flavor threshold model for identifying burnt sweet flavor molecules of the present invention comprises the following steps:

[0009] Step 1: Collecting the molecular information of caramel-sweet aroma flavor with an existing threshold; including: obtaining the CAS number or SMIELS string of the molecule by searching; obtaining the structure file by downloading from the CAS-SciFinder database according to the CAS number, or converting the SMILES to the structure file through the RDKit software package in Python according to the SMILES string to obtain the structure file; completing the collection of the molecular information of caramel-sweet aroma flavor with an existing threshold;

[0010] Step 2: Optimize the molecular structure and predict its infrared spectrum through the quantum computing software Gaussian; including: obtain the experimental infrared spectrum of the caramel sweet flavor molecule from the CAS-SciFinder database; in Gaussian, use different functionals and basis sets to optimize the structure and calculate the infrared spectrum and complete the frequency correction to obtain the infrared spectrum of the optimized molecular structure; compare the infrared spectrum of the optimized molecular structure with the experimental infrared spectrum, and select the functional and basis set with the smallest error with the characteristic peak for subsequent calculation of infrared spectrum information;

[0011] Step 3: Use Python to analyze the infrared spectrum from 500-4000cm -1 The frequency is processed by 50 segment integration, each segment of the integral corresponds to a descriptor, and a total of 50 descriptor features are obtained, denoted as Feature (N) (N=1-50);

[0012] Step 4: Screen the 50 descriptor features and extract key features through the machine learning tool SISSO, and build a model of the relationship between functional groups and thresholds through the key features as a fragrance intensity fitting model;

[0013] Step 5: Use the aroma intensity fitting model to predict the thresholds of different caramel-sweet aroma molecules.

[0014] Step 4 of the method for constructing a flavor threshold model for identifying caramel-sweet flavor molecules of the present invention is performed as follows:

[0015] Step 4.1: Perform negative logarithm processing on the thresholds of the collected caramel sweet flavor molecules to obtain the negative logarithm of the threshold, and input the negative logarithm of the threshold and the 50 descriptor Features of the infrared spectrum of each molecule obtained in step 3 into SISSO; the operators allowed in the model are: (+)(-)(*)( / )(exp)(exp-)(^-1)(^2)(^3)(sqrt)(cbrt)(log); the size of the descriptor / model is 2; finally, the key Features are Feature {7,8,10,19,20,38,46} ;

[0016] Feature {7,8,10,19,20,38,46} The corresponding frequencies and characteristic functional groups are:

[0017] Feature 7,8 -904cm-1: CH bending vibration on olefins or aromatic rings;

[0018] Feature 10 -1084cm-1: CO stretching vibration on alcohol, ether and ester groups;

[0019] Feature 19,20 -1768cm-1: C=O stretching vibration on ester, ketone and anhydride groups;

[0020] Feature 38 -3100cm-1: sp on olefins and aromatic rings 2 Hybridized CH stretching vibration;

[0021] Feature 46 -3676cm-1: OH stretching vibration on hydroxyl group;

[0022] Next, two new descriptors are obtained through operator calculation, namely, descriptor D1 represented by formula (1) and descriptor D2 represented by formula (2):

[0023]

[0024] D2= [(feature 36 -feature7)+(feature 38 +feature 46 )] (2)

[0025] Step 4.2: Establish a model of the relationship between the frequency characteristics of the caramel sweet aroma molecule and the threshold value represented by formula (3):

[0026] P=0.00035×D1+0.00082×D2-0.91087 (3)

[0027] Where P is the negative natural logarithm predicted value.

[0028] Step 5 of the method for constructing a threshold value model for identifying the aroma of caramel-sweet aroma flavor molecules of the present invention is performed as follows:

[0029] Step 5.1: Obtain the molecular structure file through the database;

[0030] Step 5.2: Use Gaussian to optimize the molecular structure and obtain the infrared spectrum; the spectrum 500-4000cm -1 The frequency is processed by 50 segments of segment integration, using the Feature {7,8,10,19,20,38,46} Descriptors D1 and D2 are calculated by equation (1) and equation (2), and then the negative natural logarithm prediction value P of the threshold is calculated by equation (3) using the descriptors D1 and D2. The threshold prediction value T is obtained by performing exponential calculation on P.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] The present invention combines quantum chemical calculation with machine learning to deeply understand the aroma mechanism of aroma molecules from the atomic and molecular scale, and construct the structure-activity relationship between molecular structure and aroma intensity. Quantum chemical calculation is used to obtain the structure, vibration frequency and other information of compounds related to caramel sweet aroma, and the aroma threshold is used as a standard. The compound aroma intensity fitting model is constructed through machine learning methods, and the molecular aroma threshold is accurately predicted based on the characteristics of the functional groups in the molecular structure, providing a theoretical basis for odor optimization and flavoring in cigarette production. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the modeling scheme of the present invention;

[0034] Figure 2 This is the optimized structure file of hydroxyacetone in the model test;

[0035] Figure 3This is the optimized structure file of ethyl vanillin in the model test;

[0036] Figure 4 Calculated infrared spectrum of hydroxyacetone in the model test;

[0037] Figure 5 Calculated infrared spectrum of ethyl vanillin in the model test. DETAILED DESCRIPTION

[0038] The method for constructing the aroma threshold model for identifying caramel-sweet aroma molecules in this embodiment is to grasp the aroma presentation mechanism of aroma molecules at the atomic scale by combining quantum chemical calculations and machine learning, construct an aroma intensity fitting model between molecular structure and aroma intensity, and use the aroma intensity fitting model to predict the thresholds of different caramel-sweet aroma molecules.

[0039] In this embodiment, the construction of the aroma threshold model for identifying the molecules of the caramel-sweet aroma flavor is carried out according to the following process:

[0040] Firstly, the structural information of the sweet and caramel aroma molecule is obtained by using the CAS number or SMILES string through the CAS SciFinder database or RDKit software package; the molecular structure is optimized by using the quantum computing software Gaussian according to the structural information to obtain the optimized molecular structure, and the infrared spectrum of the optimized molecular structure is obtained;

[0041] Then, the structure-related descriptors were obtained by performing segmented integration processing on the infrared spectra;

[0042] Then, for the structure-related descriptors, key feature descriptors were extracted using the machine learning tool SISSO, and a model of the relationship between functional groups and thresholds was constructed using the key feature descriptors as a flavor intensity fitting model; the flavor intensity fitting model was used to predict the thresholds of different caramel-sweet flavor molecules.

[0043] In this embodiment, the steps for constructing the aroma threshold model for identifying the molecules of the burnt sweet aroma flavor are as follows:

[0044] Step 1: Collect the information of caramel sweet aroma molecules with existing thresholds;

[0045] The method includes: obtaining the names and thresholds of 32 molecules with existing thresholds and burnt-sweet aroma through literature and databases; obtaining the CAS number or SMIELS string of the molecule through the Chinese and English names of the molecule through various database websites such as PubChem, CAS-SciFinder, Good Scents and Leffingwell & Associates; obtaining the structure file by downloading it from the CAS-SciFinder database based on the CAS number, or converting SMILES to structure file (.mol) through the RDKit software package in Python based on the SMILES string to obtain the structure file; at the same time, obtaining the experimental infrared spectrum corresponding to the molecule in the CAS-SciFinder database; and completing the collection of the molecular information of the burnt-sweet aroma with existing thresholds.

[0046] Step 2: Optimize the molecular structure and predict its infrared spectrum using the quantum computing software Gaussian;

[0047] Including: obtaining the experimental infrared spectrum of the caramel-sweet flavor molecule from the CAS-SciFinder database; in Gaussian, using different functionals and basis sets: B3LYP / 6-31g**, B3LYP / 6-311++g**, B3LYP / aug-cc-pvtz, WB97XD / 6-311++g**, M06 / 6-31+g**, M06 / 6-311++g**, M06-2X / 6-311++g** to optimize the structure and calculate the infrared spectrum and complete the frequency correction to obtain the infrared spectrum of the optimized molecular structure, comparing the infrared spectrum of the optimized molecular structure with the experimental infrared spectrum, and selecting the functional and basis set B3LYP / 6-311++g** with the smallest error with the characteristic peak for subsequent calculation of infrared spectrum information;

[0048] Step 3: Use Python to analyze the infrared spectrum from 500-4000cm -1 The frequency is processed by 50 segment integration, each segment of the integral corresponds to a descriptor, and a total of 50 descriptor features are obtained, denoted as Feature (N) (N=1-50);

[0049] Step 4: 50 descriptor features are screened and key features are extracted through the machine learning tool SISSO. A model of the relationship between functional groups and thresholds is constructed through the key features as a fragrance intensity fitting model;

[0050] Step 4.1: Due to the large difference in threshold values, the difference between the larger value and the smaller value is usually 4-5 orders of magnitude, and the molecules with smaller thresholds (i.e., low concentration and strong odor) are the desired ones. Therefore, the thresholds of the collected sweet and fragrant flavor molecules are negatively logarithmized to obtain the negative logarithm of the threshold. The negative logarithm of the threshold and the 50 descriptor Features of the infrared spectrum of each molecule obtained in step 3 are input into SISSO accordingly; the operators allowed in the model are: (+)(-)(*)( / )(exp)(exp-)(^-1)(^2)(^3)(sqrt)(cbrt)(log); the size of the descriptor / model is 2; the key features finally obtained are Feature {7,8,10,19,20,38,46} ;

[0051] Next, two new descriptors are obtained through operator calculation, namely, descriptor D1 represented by formula (1) and descriptor D2 represented by formula (2):

[0052]

[0053] D2=[(feature 36 -feature7)+(feature 38 +feature 46 )] (2)

[0054] Step 4.2: The model of the relationship between the frequency characteristics and threshold value of the caramel sweet flavor molecule represented by formula (3) is obtained by calculating two new descriptors:

[0055] P=0.00035×D1+0.00082×D2-0.91087 (3)

[0056] Among them, P is the negative natural logarithm prediction value of the threshold, and the determination coefficient R of the model is obtained 2 is 84.16%, R 2 The closer the value is to 1, the better the fitting effect is.

[0057] Feature {7,8,10,19,20,38,46} The corresponding frequencies and characteristic functional groups are:

[0058] Feature 7,8 -904cm-1: CH bending vibration on olefins or aromatic rings;

[0059] Feature 10 -1084cm-1: CO stretching vibration on alcohol, ether and ester groups;

[0060] Feature 19,20-1768cm-1: C=O stretching vibration on ester, ketone and anhydride groups;

[0061] Feature 38 -3100cm-1: sp on olefins and aromatic rings 2 Hybridized CH stretching vibration;

[0062] Feature 46 -3676cm-1: OH stretching vibration on hydroxyl group;

[0063] The functional groups that mainly affect the threshold of caramel-sweet flavor molecules are analyzed as follows: olefin, ketone, hydroxyl, ether and aromatic ring. Among them, furanone compounds with caramel-sweet flavor generally have: olefin, ketone and ether groups at the same time; while ethyl maltol caramel-sweet flavor compounds commonly used in tobacco have aromatic rings, ketone and hydroxyl groups at the same time, which matches the predicted results.

[0064] Step 5: Use the aroma intensity fitting model to predict the thresholds of different caramel-sweet aroma molecules.

[0065] Step 5.1: Obtain the molecular structure file through the database;

[0066] Step 5.2: Use Gaussian to optimize the molecular structure and obtain the infrared spectrum; the spectrum 500-4000cm -1 The frequency is processed by 50-segment segment integration, using Feature {7,8,10,19,20,38,46} Descriptors D1 and D2 are calculated by equations (1) and (2), and then the negative natural logarithm prediction value P of the threshold is calculated by equation (3) using descriptors D1 and D2. The threshold prediction value T is obtained by performing exponential calculation on P.

[0067] Model testing: Hydroxyacetone and ethyl vanillin, which are known to have a sweet and burnt aroma but have no threshold value, were used as examples to test the model.

[0068] Hydroxyacetone is an organic compound with the molecular formula C3H6O2. Its structure consists of an acetone molecule (C3H6O) and a hydroxyl group (-OH). It is an α-hydroxy ketone compound and is generally considered to be an organic synthesis intermediate or a by-product of a chemical reaction. In aroma chemistry, it is generally considered to have a caramel-like, sweet and slightly smoky aroma, so it is often used in spices related to caramel, baking, and smoky flavors. Because it can produce a caramel-like sweet aroma when heated, this property makes it potentially useful in spice blending. Ethyl vanillin is a flavor compound with the molecular formula C9H 10O3 is an ethylated derivative of vanillin. Its chemical structure is similar to vanillin, but there is an ethoxy group (-OCH2CH3) on the aromatic ring instead of the methoxy group (-OCH3) of vanillin. Compared with ordinary vanillin, ethyl vanillin has a stronger aroma. Its aroma is rich and sweet, similar to vanilla, but more intense and lasting. Due to its caramel-like sweetness and warm flavor, it is often used in the food and spice industry, especially in chocolate, desserts and beverages, giving the product a caramel-sweet flavor.

[0069] (1) Obtain molecular structure files through CAS-SciFinde retrieval, optimize the structures through Gaussian's B3LYP / 6-311++g** basis set and functional, and obtain their infrared spectra. Figure 2 The optimized structure file of hydroxyacetone is shown; Figure 3 The optimized structure file of ethyl vanillin is shown; Figure 4 Shown is the calculated infrared spectrum of hydroxyacetone; Figure 5 Shown is the calculated infrared spectrum of ethyl vanillin.

[0070] (2) Perform 50-segment integration on the infrared spectrum and obtain the integral value of each segment as the basic descriptor Feature 1-50 . Select the Feature {7,8,10,19,20,38,46} Substituting (1) and (2) into the calculation, we can obtain D1 and D2 respectively.

[0071]

[0072] D2=[(feature 36 -feature7)+(feature 38 +feature 46 )] (2)

[0073] P=0.00035×D1+0.00082×D2-0.91087 (3)

[0074] Table 1: Feature display of some descriptors of two molecules:

[0075] Feature 7 8 10 19 20 38 46 Hydroxyacetone 127.51 2119.93 3224.51 7598.86 309.38 8.35 624.02 Ethyl vanillin 2347.93 897.55 5904.01 5926.12 117.12 117.79 3245.22

[0076] Substituting D1 and D2 into formula (3), we can obtain the negative natural logarithm prediction value P of the threshold of the two molecules:

[0077] P Hydroxyacetone =0.00035×2128.37+0.00082×1036.67=6.184;

[0078] PEthyl vanillin =0.00035×138.63+0.00082×2346.29=1.9725;

[0079] Then perform exponential processing to obtain the threshold prediction value:

[0080] T Hydroxyacetone =EXP(-6.184)=0.00206ppm;

[0081] T Ethyl vanillin =EXP(-1.9725)=0.1391ppm.

[0082] In summary, molecules with lower odor thresholds can release stronger aromas at the same concentration. Obviously, hydroxyacetone is superior in this regard and has a better threshold effect. Therefore, hydroxyacetone is more suitable for experimental verification to more accurately evaluate its aroma intensity and its potential for application in flavors and cigarette products.

Claims

1. A method for constructing a threshold model for identifying aroma molecules of caramel-sweet aroma, characterized in that By combining quantum chemical calculations and machine learning, we grasp the aroma mechanism of aroma molecules at the atomic scale, construct an aroma intensity fitting model between molecular structure and aroma intensity, and use the aroma intensity fitting model to predict the thresholds of different caramel-sweet aroma molecules.

2. The method for constructing a threshold value model for identifying the aroma of burnt sweet fragrance molecules according to claim 1, characterized in that: First, the structural information of the sweet and caramel aroma molecule is obtained by using the CAS number or SMILES string through the CAS SciFinder database or the RDKit software package; the molecular structure is optimized by using the quantum computing software Gaussian according to the structural information to obtain the optimized molecular structure, and the infrared spectrum of the optimized molecular structure is obtained; Then, the structure-related descriptors are obtained by performing segmented integration processing on the infrared spectrum; Then, for the structure-related descriptors, key feature descriptors were extracted using the machine learning tool SISSO, and a model of the relationship between functional groups and thresholds was constructed using the key feature descriptors as a flavor intensity fitting model; the flavor intensity fitting model was used to predict the thresholds of different caramel-sweet flavor molecules.

3. The method for constructing a threshold value model for identifying the aroma of burnt sweet fragrance molecules according to claim 2, characterized in that The steps include: Step 1: Collect the information of caramel sweet aroma molecules with existing thresholds; The method comprises: obtaining the CAS number or SMIELS string of the molecule by searching; obtaining the structure file by downloading from the CAS-SciFinder database according to the CAS number, or converting the SMILES to the structure file by using the RDKit software package in Python according to the SMILES string to obtain the structure file; and completing the collection of the molecular information of the caramel sweet fragrance with an existing threshold; Step 2: Optimize the molecular structure and predict its infrared spectrum using the quantum computing software Gaussian; Including: obtaining the experimental infrared spectrum of the caramel-sweet flavor molecule from the CAS-SciFinder database; using different functionals and basis sets in Gaussian to optimize the structure and calculate the infrared spectrum and complete the frequency correction to obtain the infrared spectrum of the optimized molecular structure; comparing the infrared spectrum of the optimized molecular structure with the experimental infrared spectrum, and selecting the functional and basis set with the smallest error with the characteristic peak for subsequent calculation of infrared spectrum information; Step 3: Use Python to analyze the infrared spectrum from 500-4000cm -1 The frequency is processed by 50 segment integration, each segment of the integral corresponds to a descriptor, and a total of 50 descriptor features are obtained, denoted as Feature (N) (N=1-50); Step 4: Screen the 50 descriptor features and extract key features through the machine learning tool SISSO, and build a model of the relationship between functional groups and thresholds through the key features as a fragrance intensity fitting model; Step 5: Use the aroma intensity fitting model to predict the thresholds of different caramel-sweet aroma molecules.

4. The method for constructing a threshold value model for identifying the aroma of burnt sweet aroma spices according to claim 3, characterized in that The step 4 is performed as follows: Step 4.1: Perform negative logarithm processing on the thresholds of the collected caramel sweet flavor molecules to obtain the negative logarithm of the threshold, and input the negative logarithm of the threshold and the 50 descriptor Features of the infrared spectrum of each molecule obtained in step 3 into SISSO; the operators allowed in the model are: (+)(-)(*)( / )(exp)(exp-)(^-1)(^2)(^3)(sqrt)(cbrt)(log); the size of the descriptor / model is 2; finally, the key Features are Feature {7,8,10,19,20,38,46} ; Feature {7,8,10,19,20,38,46} The corresponding frequencies and characteristic functional groups are: Feature 7,8 -904cm-1: CH bending vibration on olefins or aromatic rings; Feature 10 -1084cm-1: CO stretching vibration on alcohol, ether and ester groups; Feature 19,20 -1768cm-1: C=O stretching vibration on ester, ketone and anhydride groups; Feature 38 -3100cm-1: sp on olefins and aromatic rings 2 Hybridized CH stretching vibration; Feature 46 -3676cm-1: OH stretching vibration on hydroxyl group; Next, two new descriptors are obtained through operator operation, namely, descriptor D1 represented by formula (1) and descriptor D2 represented by formula (2): D2=[(feature 36 -feature7)+(feature 38 +feature 46 )] (2) Step 4.2: Establish a model of the relationship between the frequency characteristics of the caramel sweet aroma molecule and the threshold value represented by formula (3): P=0.00035×D1+0.00082×D2-0.91087 (3) Where P is the negative natural logarithm predicted value.

5. The method for constructing a threshold value model for identifying the aroma of burnt sweet aroma spices according to claim 3, characterized in that The step 5 is performed as follows Step 5.1: Obtain the molecular structure file through the database; Step 5.2: Use Gaussian to optimize the molecular structure and obtain the infrared spectrum; the spectrum 500-4000cm -1 The frequency is processed by 50 segments of segment integration, using the Feature {7,8,10,19,20,38,46} Descriptors D1 and D2 are calculated by equation (1) and equation (2), and then the negative natural logarithm prediction value P of the threshold is calculated by equation (3) using the descriptors D1 and D2. The threshold prediction value T is obtained by performing exponential calculation on P.

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