Tobacco extract product smoking score prediction method and device and storage medium

Abstract

The invention provides a tobacco extract product smoking score prediction method and device and a storage medium. The tobacco extract product smoking score prediction method comprises the steps that S1, tobacco extract is obtained; s2, carrying out characterization analysis treatment on the tobacco extract to obtain tobacco characteristic aroma data of the tobacco extract; and S3, based on the tobacco extract multi-module component model, processing the tobacco characteristic aroma data to obtain the smoking fraction, physicochemical separation treatment and stabilizing additive of the tobacco extract. According to the method, a tobacco extract multi-module component model based on principal component and neural network analysis is constructed, and the physical and chemical separation method, the stabilizing additive and the product smoking fraction are output by utilizing the association of the physical and chemical characteristics and the physical and chemical separation method of the tobacco extract and the association of the stabilizing additive and the product smoking fraction. According to the software, the efficiency of the stabilization process of the tobacco essence is greatly improved, and technical support is provided for stably storing the tobacco essence in batches, modularly developing the smoking function of the tobacco essence and the like.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of tobacco testing, and particularly relates to a method for predicting a smoking score of a tobacco essence, a device for predicting a smoking score of a tobacco essence, and a storage medium. BACKGROUND

[0002] The tobacco essence is a new type of tobacco product. Because the tobacco essence releases strong flavor substances during use and has relatively low harmful components, and because the tobacco essence improves the smoking taste compared to traditional cigarettes, the tobacco essence has a broad market prospect. However, the tobacco essence has complex components, high water content, and large polarity difference, which causes side reactions among the components, product aging, and easy separation after long storage, and other unstable problems, which hinders the stable storage and use of the tobacco essence and reduces the smoking taste of the tobacco essence.

[0003] Currently, in the prior art, the flavor and aroma of a product are evaluated according to the characteristics of the product itself, and the sensory evaluation effect is predicted based on the physical and chemical characteristics of the product and using a neural network technology. For example, in a method for predicting the sensory quality of a cigar raw material based on a BP neural network, Henan University of Technology proposed a BP neural network model for predicting the sensory evaluation of a treated sample of a cigar leaf. That is, the treated sample of the cigar leaf is subjected to simultaneous conventional chemical component content detection and sensory evaluation, and the data are input into the BP neural network model for training. The conventional chemical component content of a tested treated sample of the cigar leaf is input into the trained BP neural network model to obtain the sensory quality of the cigar raw material for verification and optimization.

[0004] However, there is no technology that relates the chemical components of a tobacco product and changes thereof to smoking results and aging stabilization methods. Currently, there is no method for predicting a smoking score based on the chemical components of a tobacco essence and using a neural network to establish a model to simultaneously predict a smoking score, a physical and chemical separation method, and a stable additive. SUMMARY

[0005] Therefore, the present application aims to provide a method for predicting a smoking score of a tobacco essence, a device for predicting a smoking score of a tobacco essence, and a storage medium to solve the above problems.

[0006] To solve the above technical problems, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for predicting a smoking score of a tobacco essence. The method comprises the following steps: S1, obtaining a tobacco essence; S2, performing characterization analysis and processing on the tobacco essence to obtain tobacco characteristic aroma data of the tobacco essence; and S3, processing the tobacco characteristic aroma data based on a multi-module component model of the tobacco essence to obtain a smoking score of the tobacco essence, a physical and chemical separation processing, and a stabilization additive.

[0008] Further, the tobacco characteristic aroma data includes first tobacco characteristic aroma data and second tobacco characteristic aroma data, and step S2 includes: step S21, detecting the tobacco essence by ultraviolet fluorescence spectroscopy to obtain the first tobacco characteristic aroma data; and step S22, detecting the tobacco essence by gas chromatography mass spectrometry to obtain the second tobacco characteristic aroma data.

[0009] Further, the first tobacco characteristic aroma data includes aroma data of monocyclic aromatic compounds, bicyclic aromatic compounds and polycyclic aromatic compounds.

[0010] Further, the second tobacco characteristic aroma data includes aroma data of acids, ketones, aldehydes, alcohols, olefins, nicotine and pyrroles.

[0011] Further, the tobacco essence multi-module component model is constructed by the following steps: step M1, obtaining a test tobacco essence; step M2, performing characterization analysis processing on the test tobacco essence to obtain test tobacco characteristic aroma data; step M3, performing sensory evaluation processing on the test tobacco essence to obtain sensory score data; and step M4, correlating the test tobacco characteristic aroma data and the sensory score data and inputting them into the tobacco essence multi-module component model based on principal component and neural network analysis.

[0012] Further, the test tobacco characteristic aroma data includes first tobacco characteristic aroma data, second tobacco characteristic aroma data and third tobacco characteristic aroma data, and step M2 includes: step M21, performing an aging experiment on the test tobacco essence based on a test stabilizing additive to obtain the first tobacco characteristic aroma data; step M22, performing multi-module component processing on the test tobacco essence based on test physicochemical separation processing to obtain the second tobacco characteristic aroma data; and step M23, directly performing characterization analysis processing on the test tobacco essence to obtain the third tobacco characteristic aroma data.

[0013] Further, the second tobacco characteristic aroma data includes multi-module aroma data and aged multi-module aroma data, and step M22 includes: step M221, performing multi-module component processing on the test tobacco essence to obtain the multi-module aroma data; and step M222, performing multi-module component processing and an aging experiment on the test tobacco essence to obtain the aged multi-module aroma data.

[0014] In a second aspect, the application provides a tobacco essence product score prediction device, which comprises an acquisition module, an analysis module and a processing module, the acquisition module is used for acquiring the tobacco essence product; the analysis module is used for performing characterization analysis processing on the tobacco essence product to obtain tobacco characteristic aroma data of the tobacco essence product; and the processing module is used for processing the tobacco characteristic aroma data based on a tobacco essence product multi-module component model to obtain a product score of the tobacco essence product, physical and chemical separation processing and a stabilizing additive.

[0015] In a third aspect, a computer system comprises a memory and a processor and a computer program stored in the memory, the processor executes the computer program to implement the steps of the tobacco essence product score prediction method described above.

[0016] In a fourth aspect, a computer program product comprises computer programs / instructions which, when executed by a processor, implement the steps of the tobacco essence product score prediction method described above.

[0017] From the above technical solutions, the tobacco essence product score prediction method, device and storage medium provided by the application have the following advantages and positive effects:

[0018] (1) The application adopts multiple physical and chemical separation methods for the tobacco essence product, which can fully separate the tobacco essence product to obtain multi-module components and concentrate flavor substances. The multi-module components are added with a stabilizing additive and subjected to aging test, which can inhibit the aging and interaction between components of the multi-module components, thereby ensuring the stability of the functions such as flavor and strength during the storage of the tobacco essence product.

[0019] (2) The application uses the tobacco essence product to replace the traditional tobacco raw material, and cooperates with the physical and chemical separation method and the stabilizing additive of the application, which can further stimulate the flavor of the tobacco essence product and make the flavor more concentrated during the smoking process.

[0020] (3) The application constructs a tobacco essence product multi-module component model based on principal components and neural network analysis, which associates the physical and chemical properties of the tobacco essence product with the physical and chemical separation method, the stabilizing additive and the product score, and outputs the physical and chemical separation method, the stabilizing additive and the final product score. This software greatly improves the efficiency of the stabilization process of the tobacco essence product, and provides technical support for batch stabilization and preservation of the tobacco essence product and modular development of the product score of the tobacco essence product. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above content of the application and the following detailed embodiments can be better understood when read in conjunction with the accompanying drawings. It should be noted that the drawings are only examples of the claimed technical solutions.

[0022] Figure 1is a flow chart of the method for predicting the smoking score of the essence of tobacco according to the present application;

[0023] Figure 2 is an inventive concept diagram of the method for predicting the smoking score of the essence of tobacco according to the present application;

[0024] Figure 3 is an interface diagram of the multi-module component model of the essence of tobacco according to the present application;

[0025] Figure 4 is a construction framework diagram of the multi-module component model of the essence of tobacco according to the present application;

[0026] Figure 5 is a fluorescence intensity curve diagram of the aromatic component of the essence of tobacco according to the present application;

[0027] Figure 6 is a peak area diagram of various light components of the essence of tobacco according to the present application. DETAILED DESCRIPTION

[0028] The detailed features and advantages of the present application will be described in detail in the detailed description, and the skilled in the art can easily understand the related purposes and advantages of the present application according to the description, claims and drawings disclosed in the specification.

[0029] The present application will now be described with reference to the drawings, in which like reference numerals refer to like elements throughout. While specific structures and arrangements are discussed, it should be understood that these are used by way of example only. Those skilled in the art will recognize that other structures and arrangements can be used without departing from the spirit and scope of the present application. It will be apparent to those skilled in the art that the present application can be employed in a variety of other applications.

[0030] In this specification and in the claims, reference will be made to a number of terms, which will be defined to have the following meanings unless otherwise indicated:

[0031] The singular forms "a," "an," and "the" include the corresponding plural referents unless the context clearly dictates otherwise. "At least one" means one or more. "Plural" means two or more. "At least one of the following" or similar phrases means any of the items in the list that follows, including single items or combinations of multiple items. For example, at least one of a, b, and c, or the like, can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single items or multiple items.

[0032] All numbers used herein to express quantities, properties, etc. should be considered in all cases to be modified by the term "in or about" or "approximately" the indicated number, unless otherwise specifically indicated. Accordingly, the numerical values set forth herein are approximations that can vary depending upon the desired properties sought to be obtained by the present application. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical value should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

[0033] It should be understood that the term "and / or", merely describes an association between associated objects, it means that there can be three kinds of relationships, for example, A and / or B, can mean: A alone, A and B exist at the same time, B alone, three cases, where A, B can be singular or plural. In addition, the character " / " herein generally indicates that the associated objects before and after are an "or" relationship, but can also mean an "and / or" relationship, which can be understood in the context.

[0034] In the description of the present embodiment, it should be noted that the terms "upper", "lower", "inner", "bottom" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship when the product is usually placed, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0035] Unless otherwise indicated, the following abbreviations have the following meanings and any other abbreviations not defined herein have their generally accepted standard meanings as used in the art:

[0036] All other terms used herein are intended to have their ordinary meanings to those of ordinary skill in the art, particularly as understood by those of ordinary skill in the art after reading the claims, specification, and drawings of the present patent, and are intended to have meanings that can be directly and unambiguously determined by those of ordinary skill in the art after reading the claims, specification, and drawings of the present patent.

[0037] Even if there are incomplete descriptions, omissions or ambiguities in the grammar, words, punctuation, graphics, symbols, etc. in the claims, specification and drawings of the present patent, those of ordinary skill in the art can still arrive at a unique and correct understanding without much reasoning or testing by reading the claims, specification and drawings as a whole, and effectively exclude all kinds of incorrect understanding methods not aimed at achieving the purpose of the present patent.

[0038] The person of ordinary skill in the art will give priority to reading the patent claims, the specification and the drawings to reasonably explain the terms, secondly to reading the relevant definitions in other documents disclosed by the applicant before the application date to reasonably explain the terms, thirdly to reasonably explaining the terms by referring to the cited references in the patent, and finally to reasonably explaining the terms by combining the technical dictionaries, technical manuals, reference books, textbooks, national or industry technical standards commonly used by the person of ordinary skill in the art.

[0039] In order to make the purpose, technical scheme and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0040] Please refer to Figure 1 and Figure 2 The present application provides a tobacco essence product smoking score prediction method, which comprises the following steps:

[0041] Step S1: obtaining a tobacco essence.

[0042] The tobacco essence is a substance with specific functions or components obtained by extraction, separation or purification from tobacco raw materials through physical, chemical or biological techniques.

[0043] Because the tobacco raw material is pretreated, the flavor substances released during use are strong, the harmful components are relatively low, and the taste and smell of the product are improved compared with traditional cigarettes.

[0044] Step S2: performing characterization analysis and processing on the tobacco essence to obtain tobacco characteristic aroma data of the tobacco essence.

[0045] Specifically, the tobacco characteristic aroma data includes first tobacco characteristic aroma data and second tobacco characteristic aroma data.

[0046] Step S2 includes:

[0047] Step S21: performing ultraviolet fluorescence spectrum detection on the tobacco essence to obtain the first tobacco characteristic aroma data.

[0048] The first tobacco characteristic aroma data includes aroma data of monocyclic aromatic substances, bicyclic aromatic substances and polycyclic aromatic substances.

[0049] Step S22: performing gas chromatography mass spectrometry detection on the tobacco essence to obtain the second tobacco characteristic aroma data.

[0050] The second tobacco characteristic aroma data includes aroma data of acids, ketones, aldehydes, alcohols, olefins, nicotine and pyrroles.

[0051] Step S3: Based on the tobacco essence multi-module component model, the tobacco characteristic aroma data is processed to obtain the product absorption score of the tobacco essence, the physical and chemical separation treatment and the stabilizing additive.

[0052] Please refer to Figure 3 The above recorded characterization data is input into the tobacco essence multi-module component model based on principal component and neural network analysis, which will output the final product absorption score, physical and chemical separation method (liquid-liquid extraction, supercritical extraction, membrane separation, vacuum distillation and molecular distillation) or stabilizing additive.

[0053] Please refer to Figure 4 The tobacco essence multi-module component model is constructed by the following steps:

[0054] Step M1: Obtain the test tobacco essence.

[0055] Step M2: Perform characterization analysis on the test tobacco essence to obtain test tobacco characteristic aroma data.

[0056] The test tobacco characteristic aroma data includes first tobacco characteristic aroma data, second tobacco characteristic aroma data and third tobacco characteristic aroma data.

[0057] Step M2 includes:

[0058] Step M21: Based on the test stabilizing additive, perform aging experiment on the test tobacco essence to obtain the first tobacco characteristic aroma data.

[0059] Specifically, the tobacco essence is mixed with different stabilizing additives, and aging experiments are performed on the tobacco essence and the tobacco essence mixed liquid.

[0060] The tobacco essence after aging experiment is characterized by UV (ultraviolet fluorescence spectrum detection) and GC-MS (gas chromatography mass spectrometry).

[0061] For the fluorescence curve of the tobacco essence sample obtained after UV characterization, the single ring aromatic substance (250-290nm), double ring aromatic substance (290-325nm) and multi ring aromatic substance (325-500nm) are segmented and integrated respectively, and the data of the tobacco essence after aging experiment is recorded.

[0062] For the peak area data of each substance in the tobacco essence sample obtained after GC-MS characterization, the peak areas of acids, ketones, aldehydes, alcohols, olefins, nicotine and pyrroles are counted respectively, and the data of the tobacco essence after aging experiment is recorded.

[0063] The recorded characterization data, the index product puff score, the final product puff score, and the stabilization additives are integrated (Table 1) for the tobacco essence samples mixed with different stabilization additives and aged for different times.

[0064] Table 1: Tobacco essence sample characterization data and product puff total score integration data

[0065]

[0066] And, the UV, GC-MS, product puff score CSV format files (Table 2) corresponding to the formed are input into the above model.

[0067] Table 2: Tobacco essence multi-module component model input CSV file based on principal component and neural network analysis

[0068]

[0069] Step M22: Based on the test physicochemical separation processing, the test tobacco essence is subjected to multi-module component processing to obtain second tobacco characteristic aroma data.

[0070] The second tobacco characteristic aroma data includes: multi-module aroma data and aged multi-module aroma data.

[0071] Step M22 includes:

[0072] Step M221: The test tobacco essence is subjected to multi-module component processing to obtain multi-module aroma data.

[0073] Specifically, the multi-module component processing includes: liquid-liquid extraction, supercritical extraction, membrane separation, vacuum distillation, and molecular distillation. The tobacco essence is subjected to physicochemical separation by liquid-liquid extraction, supercritical extraction, membrane separation, vacuum distillation, and molecular distillation, respectively, to prepare tobacco essence multi-module components.

[0074] The tobacco essence after multi-module component processing is subjected to UV (ultraviolet fluorescence spectrum detection) and GC-MS (gas chromatography mass spectrometry) characterization analysis.

[0075] For the fluorescence curve of the tobacco essence sample obtained after UV characterization, the single-ring aromatic substances (250-290 nm), double-ring aromatic substances (290-325 nm), and multi-ring aromatic substances (325-500 nm) are segmented and integrated, respectively, and the data of the tobacco essence after multi-module component processing are recorded.

[0076] For the peak area data of each substance in the tobacco essence sample obtained after GC-MS characterization, the peak areas of acids, ketones, aldehydes, alcohols, olefins, nicotine, and pyrroles are counted, respectively, and the data of the tobacco essence after multi-module component processing are recorded.

[0077] Specifically, referring to the format of Table 2, the recorded tobacco extract characterization data after multi-module component processing, each index smoking score, final smoking score and physicochemical separation method can be input into the above model.

[0078] Step M222: Perform multi-module component processing and aging experiments on the test tobacco extract to obtain aged multi-module aroma data.

[0079] An aging experiment was performed on the tobacco extract after the multi-module component treatment. The specific steps can be referred to the above multi-module component treatment and aging experiment section and will not be repeated here.

[0080] The characterization data of the aged multi-module aroma data, the inhalation scores of each indicator, the final inhalation score and the physicochemical separation method are then input into the above model.

[0081] Step M23: directly performing characterization analysis on the test tobacco extract to obtain third tobacco characteristic aroma data.

[0082] The tobacco extract was characterized and analyzed by UV (ultraviolet fluorescence spectroscopy) and GC-MS (gas chromatography-mass spectrometry).

[0083] Please refer to Figure 5 For the fluorescence curve of the tobacco extract sample obtained after UV characterization, the monocyclic aromatic compounds (250-290nm), bicyclic aromatic compounds (290-325nm) and polycyclic aromatic compounds (325-500nm) were segmented and integrated, and the data were recorded.

[0084] Please refer to Figure 6 , for the peak area data of each substance in the tobacco extract sample obtained after GC-MS characterization, the peak areas of acids, ketones, aldehydes, alcohols, olefins, nicotine and pyrroles were counted and the data were recorded.

[0085] Step M3: Perform a smoking evaluation process on the test tobacco extract to obtain smoking score data.

[0086] Specifically, tobacco extract was tasted using five evaluation criteria: aroma, smoke flavor, mouthfeel, strength, and stimulation. Each criterion was scored out of 10 and accounted for 20% of the total score. The average of these five taste evaluation criteria was calculated to obtain a final taste evaluation score (out of 10). The taste evaluation scores for each criterion and the final taste evaluation score were recorded. These recorded characterization data, taste evaluation scores for each criterion, and the final taste evaluation score were then input into a multi-module component model for tobacco extract based on principal component analysis and neural network analysis.

[0087] Step M4: Correlate the test tobacco characteristic aroma data and smoking score data and input into the tobacco essence multi-module component model based on principal component and neural network analysis.

[0088] Specifically, based on the SPSS software, the peak area of acids, ketones, aldehydes, alcohols, olefins, nicotine and pyrrole in all samples was processed by principal component analysis, 2 principal components were screened out, and the corresponding characteristic matrix was calculated by SPSS (Table 3).

[0089] Table 3: Characteristic matrix

[0090] Ingredients 1 2 Alcohols 0.943 -0.048 Acids 0.914 0.171 Olefins 0.899 -0.209 Monocyclic aromatics 0.734 -0.265 Pyrroles 0.634 0.581 Aldehydes -0.097 0.907 Ketones -0.24 0.846 Nicotine -0.29 0.912 Bicyclic aromatics -0.293 0.825 Polycyclic aromatics -0.302 0.878

[0091] At the same time, the segmented integral area of monocyclic aromatic compounds (250-290 nm), bicyclic aromatic compounds (290-325 nm) and polycyclic aromatic compounds (325-500 nm) in all samples was processed by dimension reduction, and 0-1 principal components were extracted. The method is the same as Table 3. The principal components of the samples are associated with the physical and chemical separation method and the stabilizing additive, and are associated with each smoking index score (aroma, smoke, taste, strength, irritation), total smoking score. Based on the matlab software, a sensory evaluation total score prediction model is constructed by using BP neural network, and a tobacco essence multi-module component model based on principal component and neural network analysis is constructed.

[0092] Exemplarily, please refer to the following specific embodiments:

[0093] (1) The tobacco essence (pyrolysis of tobacco waste at 400°C) is denoted as sample A. 0.4000 g of sample A is taken and diluted with methanol to 10 g. The diluted tobacco essence sample is subjected to UV (ultraviolet fluorescence spectrum detection) test, and the fluorescence intensity curve of the tobacco essence sample is obtained (Fig. 1). Figure 5 ).

[0094] According to the segmented integration of monocyclic aromatic compounds (250-290 nm), bicyclic aromatic compounds (290-325 nm) and polycyclic aromatic compounds (325-500 nm), the data is recorded. The fluorescence intensity integral of monocyclic aromatic compounds of sample A is 2647.689; the fluorescence intensity integral of bicyclic aromatic compounds is 2068.717; and the fluorescence intensity integral of polycyclic aromatic compounds is 2152.947.

[0095] (2) Take 0.1000 g of sample A and add methanol to dilute to 10 g. Perform GC-MS characterization on the diluted sample A, and arrange the peak areas of the sample according to the types of substances. The peak area of acids is 7446961991, the peak area of ketones is 4056454320, the peak area of aldehydes is 1550058853, the peak area of alcohols is 3816387128, the peak area of olefins is 1156907609, the peak area of nicotine is 40826214646, and the peak area of pyrroles is 6219714758.

[0096] (3) Input the peak areas of each type of substance of sample A (acids, ketones, aldehydes, alcohols, olefins, nicotine, and pyrroles) and aromatic substance curve integration (monocyclic aromatic substances, bicyclic aromatic substances, and polycyclic aromatic substances) into the tobacco essence multi-module component model based on principal component and neural network analysis (http: / / www.tobaccoessence.com / page / main.aspx), and click "predict". The prediction result is: aroma score 7.5, smoke 8, taste 7, strength 7.5, stimulation 7, final score 7.4, and result "unqualified for smoking". The recommended use method is: methyl acetate liquid-liquid extraction method to obtain the extraction phase. Figure 3

[0097] (4) Take 100 mL of sample A, mix with methyl acetate at a mass ratio of 1:1, mechanically shake the mixture on a shaking table at 40 rpm for 30 minutes, and separate the mixture in a separatory funnel to obtain the upper liquid phase-extraction phase. Use a rotary evaporator (normal pressure, 70°C) to rotary evaporate the extraction phase for 30 minutes to obtain the extraction tobacco essence based on the stabilization method, which is denoted as sample B.

[0098] (5) Take 0.4000 g of sample B and add methanol to dilute to 10 g. Perform UV (ultraviolet fluorescence spectrum detection) test on the diluted tobacco essence sample, and perform segmented integration on monocyclic aromatic substances (250-290 nm), bicyclic aromatic substances (290-325 nm), and polycyclic aromatic substances (325-500 nm) respectively. Record the data-monocyclic aromatic substance fluorescence intensity integral of sample A is 2812.113; bicyclic aromatic substance fluorescence intensity integral is 2009.134; polycyclic aromatic substance fluorescence intensity integral is 1989.542.

[0099] (6) Take 0.1000 g of sample B and add methanol to dilute to 10 g. Perform GC-MS characterization on the diluted sample A, and obtain the peak area of acids is 2142012708, the peak area of ketones is 2637556184, the peak area of aldehydes is 481542896, the peak area of alcohols is 896093905, the peak area of olefins is 367424695, the peak area of nicotine is 1077611546, and the peak area of pyrroles is 1943707925.

[0100] ​(7) input the peak area of each type of substance (acid, ketone, aldehyde, alcohol, olefin, nicotine and pyrrole) of sample B and the aromatic substance curve integral (monocyclic aromatic substance, bicyclic aromatic substance and polycyclic aromatic substance) into the tobacco essence multi-module component model based on principal component and neural network analysis, and click "predict". The prediction result is: aroma score is 8.5, smoke is 9, taste is 8.5, strength is 8, irritation is 8.5, and the final score is 8.5, and the result is "qualified for smoking; it is suggested to add 0.5% lycopene".

[0101] (8) take 10g of sample B, add 0.05g of lycopene (0.5% relative to the concentration of tobacco essence), and seal and store at room temperature. The sample is a stabilized tobacco essence that meets the smoking evaluation standard.

[0102] Based on the same inventive concept, the present application also provides a tobacco essence smoking score prediction device, which comprises an acquisition module, an analysis module and a processing module.

[0103] The acquisition module is used to acquire the tobacco essence. The analysis module is used to perform characterization analysis on the tobacco essence to obtain tobacco characteristic aroma data of the tobacco essence. The processing module is used to process the tobacco characteristic aroma data based on a tobacco essence multi-module component model to obtain a smoking score, a physical and chemical separation treatment and a stabilizing additive of the tobacco essence.

[0104] The analysis module is also used to perform ultraviolet fluorescence spectrum detection on the tobacco essence to obtain first tobacco characteristic aroma data, and perform gas chromatography mass spectrometry detection on the tobacco essence to obtain second tobacco characteristic aroma data.

[0105] It can be understood that the tobacco essence smoking score prediction device provided by the present application corresponds to the tobacco essence smoking score prediction method provided by the present application. In order to make the specification brief, the same or similar parts can refer to the content of the tobacco essence smoking score prediction method part, and will not be repeated here.

[0106] Each module in the above smoking score prediction device can be realized by software, hardware and their combination.

[0107] The above modules can be embedded in or independent of the processor in the server in hardware form, or can be stored in the memory in the server in software form, so as to be called and executed by the processor. The processor can be a central processing unit (CPU), a microprocessor, a single-chip microcomputer, etc.

[0108] The above smoking score prediction method and / or smoking score prediction device can be realized in the form of computer readable instructions, which can run on a computer system.

[0109] The embodiment of the present application also provides a computer system, which comprises a memory, a processor and computer readable instructions stored in the memory and executable on the processor, and the processor implements the product suction score prediction device and method when executing the program.

[0110] The computer system can be a server. The computer system comprises a processor, a non-volatile storage medium, an internal memory, an input device, a display screen and a network interface connected through a system bus. The non-volatile storage medium of the computer system can store an operating system and computer readable instructions, and the computer readable instructions can make the processor execute the product suction score prediction method of the embodiments of the present application when executed. The specific implementation process of the method can refer to the specific content of the product suction score prediction method, which will not be described here. Figure 1

[0111] The processor of the computer system is used to provide computing and control capabilities to support the operation of the entire computer system. The internal memory can store computer readable instructions, and the computer readable instructions can make the processor execute a product suction score prediction method when executed by the processor. The input device of the computer system is used for input of various parameters, the display screen of the computer system is used for display, and the network interface of the computer system is used for network communication.

[0112] Based on the same inventive concept, the embodiment of the present application provides a computer readable storage medium, which stores computer readable instructions, and the program is executed by the processor to implement the steps of the product suction score prediction method.

[0113] ​The memory in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0114] The above embodiments can be implemented, in whole or in part, by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented by software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs.

[0115] When the computer instructions or computer programs are loaded or executed on the computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as infrared, wireless, microwave, etc.) or wireless means.

[0116] The computer-readable storage medium can be any available media or a collection of one or more of the available media accessible by a computer. The available media can be a magnetic media (e.g., a floppy diskette, a hard disk, a magnetic tape), an optical media (e.g., a DVD), or a semiconductor media. The semiconductor media can be a solid-state hard drive.

[0117] It should be understood that the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0118] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the devices, apparatuses and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0120] In several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are only schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0121] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0122] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0123] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product in essence or in the form of a part of the prior art or a part of the technical solutions of the present application. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various storage medium capable of storing program codes.

[0124] In this specification, the reference to "one embodiment", "one implementation" means that the particular feature, structure or characteristic described in connection with this embodiment / implementation is included in at least one embodiment / implementation of the present application. Therefore, the phrase "in one embodiment / implementation" appearing in various places in the specification does not necessarily all refer to the same embodiment / implementation, but can be. In addition, a particular feature, structure or characteristic can be combined in one or more embodiments / implementation in any suitable manner, as those skilled in the art can understand from the present disclosure.

[0125] Similarly, it should be understood that in the above description of the exemplary embodiments / implementation of the present application, various features of the present application are sometimes grouped together in a single embodiment / implementation or its figures and description, for the purpose of simplifying the disclosure and helping to understand one or more of the various inventive aspects. However, the description method of the present patent should not be interpreted as reflecting an intention that the claimed application requires more features than those explicitly stated in each claim. On the contrary, the inventive aspect reflected by the claims is in the single aforementioned disclosed embodiment / implementation. Therefore, the claims following the detailed description are expressly incorporated into the present detailed description, and each claim exists independently as a separate embodiment / implementation of the present application.

[0126] Furthermore, the combination of features of different embodiments / embodiments is intended to be within the scope of the application, and forms different embodiments / embodiments as will be apparent to those skilled in the art from the teachings and guidance presented herein. For example, in the claims below any of the embodiments / embodiments can be used in any combination.

[0127] The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention that in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the application claimed.

[0128] It is therefore to be understood that, while the application has been disclosed with specific reference to the preferred embodiments, exemplary embodiments and optional features set forth, variations in the preferred embodiments, exemplary embodiments and optional features can be made by those skilled in the art without departing from the scope of the application as claimed.

[0129] The specific embodiments given above are illustrative of the general principles of the application and are not to be taken as limiting thereof. Numerous modifications can be made by those skilled in the art without departing from the general principles of the application.

[0130] The foregoing description of specific embodiments will so fully reveal the general nature of the application that others can, by applying knowledge of the present art, adapt it for various applications or modify it to

[0131] Accordingly, all such modifications are intended to be within the scope of the application alone. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Thus, the terminology or phraseology of the present specification is not to be considered limiting. Rather, any term or phraseology should be considered in the context of its usage in the present specification.

[0132] Furthermore, the scope of the application should not be limited by any of the specific exemplary embodiments, but should be defined solely by the appended claims and equivalents thereof.

Claims

1. A method for predicting the smoking score of tobacco extract, characterized in that: The method for predicting the product absorption score includes: Step S1: obtaining tobacco extract; Step S2: performing characterization analysis on the tobacco extract to obtain tobacco characteristic aroma data of the tobacco extract; Step S3: Based on the multi-module component model of tobacco extract, the tobacco characteristic aroma data is processed to obtain the smoking score, physicochemical separation treatment and stabilization additives of the tobacco extract.

2. The method for predicting the taste score of a product according to claim 1, wherein: The tobacco characteristic aroma data includes: first tobacco characteristic aroma data and second tobacco characteristic aroma data, and step S2 includes: Step S21: performing ultraviolet fluorescence spectrum detection on the tobacco extract to obtain the first tobacco characteristic aroma data; Step S22: performing gas chromatography-mass spectrometry detection on the tobacco extract to obtain the second tobacco characteristic aroma data.

3. The method for predicting the taste score of a product according to claim 2, wherein: The first tobacco characteristic aroma data includes aroma data of monocyclic aromatic substances, bicyclic aromatic substances and polycyclic aromatic substances.

4. The method for predicting the taste score of a product according to claim 2, wherein: The second tobacco characteristic aroma data includes aroma data of acids, ketones, aldehydes, alcohols, olefins, nicotine and pyrroles.

5. The method for predicting the taste score of a product according to claim 1, wherein: The multi-module component model of tobacco extract is constructed by the following steps: Step M1: obtaining a test tobacco extract; Step M2: performing characterization analysis on the test tobacco extract to obtain characteristic aroma data of the test tobacco; Step M3: performing a smoking evaluation process on the test tobacco extract to obtain smoking score data; Step M4: Correlate the test tobacco characteristic aroma data and the inhalation score data, and input them into a tobacco extract multi-module component model based on principal component analysis and neural network analysis.

6. The method for predicting the taste score of a product according to claim 5, wherein: The test tobacco characteristic aroma data includes first tobacco characteristic aroma data, second tobacco characteristic aroma data, and third tobacco characteristic aroma data, and the step M2 includes: Step M21: performing an aging experiment on the test tobacco extract based on the test stabilizing additive to obtain the first tobacco characteristic aroma data; Step M22: Based on the test physicochemical separation process, perform multi-module component processing on the test tobacco extract to obtain the second tobacco characteristic aroma data; Step M23: directly performing characterization analysis on the test tobacco extract to obtain the third tobacco characteristic aroma data.

7. The method for predicting the taste score of a product according to claim 6, wherein: The second tobacco characteristic aroma data includes: multi-module aroma data and aged multi-module aroma data, and the step M22 includes: Step M221: performing multi-module component processing on the test tobacco extract to obtain the multi-module aroma data; Step M222: performing multi-module component processing and aging experiments on the test tobacco extract to obtain the aged multi-module aroma data.

8. A device for predicting the smoking score of tobacco extract, characterized in that: The device for predicting the score of taste absorption includes: an acquisition module, an analysis module and a processing module. The acquisition module is used to obtain tobacco extract; The analysis module is used to perform characterization analysis on the tobacco extract to obtain tobacco characteristic aroma data of the tobacco extract; The processing module is used to process the tobacco characteristic aroma data based on the tobacco extract multi-module component model to obtain the smoking score, physicochemical separation treatment and stabilization additives of the tobacco extract.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the method for predicting the taste score of any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method for predicting the taste score of any one of claims 1 to 7 are implemented.

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