Fragrance base module blending method and device, storage medium and program product
By optimizing the blending process of the fragrance base module using GC-MS and LASSO algorithms, the problem of reliance on perfumer experience was solved, and objective and accurate modulation of the fragrance base module was achieved, reducing the difficulty and cost of modulation.
Patent Information
- Application Number
- CN202512021914.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-02-13
AI Technical Summary
In existing technologies, the process of preparing the fragrance base module relies on the perfumer's experience, making it difficult to achieve objective and accurate selection and proportioning of fragrances, resulting in high preparation difficulty and cost.
The aroma component data of the target aroma base module were analyzed using GC-MS technology, and aroma raw materials were screened from the single fragrance database using the LASSO algorithm. The compound formulation was optimized by chromatogram and sensory identification results to generate the aroma base module formulation.
An objective and accurate method for modulating aroma-based modules is provided, which reduces the difficulty and cost of modulation and improves modulation efficiency.
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Figure CN121518218A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the technical field of flavor modulation, and in particular to a flavor base module modulation method and device, a storage medium and a program product. BACKGROUND
[0002] A flavor base module or essence is a mixture of flavorings (such as synthetic monomers, essential oils, extracts, and balsams) and certain adjuvants, etc. prepared according to a certain ratio and modulation process, and is an important additive in cigarette production. The formula has been one of the core technologies of essence companies and cigarette enterprises.
[0003] Flavor base module modulation generally includes two processes: flavor identification and modulation. Flavor identification is to find out the types of flavorings used in the target product, and modulation is to mix the flavorings according to a certain ratio to obtain a flavor base module consistent with the function of the target product. SUMMARY
[0004] The inventors have noticed that the screening of flavor raw materials and the determination of modulation ratios have been a research difficulty in flavor base module modulation, due to the complexity of ingredients, the variety of products, and the numerous sources of flavorings. Due to the influence of factors such as the difficulty of analyzing the whole ingredients of essence, the rarity of ingredients, and the cost, the analysis results of the whole or part of the ingredients of essence cannot be completely used for essence re-compounding in actual operation. Currently, the modulation of flavor base modules mainly relies on the sense of smell of a flavorist to identify the types of flavorings in essence, and the use ratio is determined through repeated modulation. The modulation process requires high ability and experience of the flavorist, thereby significantly increasing the difficulty and cost of flavor base module modulation.
[0005] Accordingly, the present disclosure provides a flavor base module modulation method. The method includes analyzing flavor ingredient data of a target flavor base module by using GC-MS technology, screening a plurality of flavor raw materials corresponding to the flavor ingredient data from a monomer flavor database by using a LASSO algorithm, generating a candidate flavor raw material set according to the flavor ingredient data, determining the content of each candidate flavor raw material in the candidate flavor raw material set to generate a re-compounding formula, modulating a re-compounded flavor base module according to the re-compounding formula, optimizing the re-compounding formula according to the deviation of the chromatograms of the target flavor base module and the re-compounded flavor base module, obtaining a flavor base module formula, and modulating a flavor base module by using the flavor base module formula. Thus, an objective and accurate means is provided for flavor base module modulation, and the difficulty and cost of flavor base module modulation are effectively reduced.
[0006] In a first aspect of the present disclosure, a fragrance base module formulation method is provided, which is performed by a fragrance base module formulation device and includes: analyzing fragrance component data of a target fragrance base module by using a GC-MS technology; screening a plurality of fragrance raw materials corresponding to the fragrance component data from a monomer fragrance database by using a LASSO algorithm; screening part of the fragrance raw materials from the plurality of fragrance raw materials to generate a first candidate fragrance raw material set according to a preset rule; screening a candidate fragrance raw material from the first candidate fragrance raw material set to generate a second candidate fragrance raw material set according to a chromatogram and a sensory smell recognition result of each candidate fragrance raw material in the first candidate fragrance raw material set, and a target chromatogram and a sensory smell recognition result of the target fragrance base module; determining a content of each candidate fragrance raw material in the second candidate fragrance raw material set by using a LASSO algorithm to generate a compounding formula; formulating a compounded fragrance base module according to the compounding formula; optimizing the compounding formula according to a deviation of the target chromatogram and a chromatogram of the compounded fragrance base module to obtain a fragrance base module formula; and formulating a fragrance base module by using the fragrance base module formula.
[0007] In some embodiments, the optimizing the compounding formula according to the deviation of the target chromatogram and the chromatogram of the compounded fragrance base module includes: if a first fragrance component is included in both the target chromatogram and the chromatogram of the compounded fragrance base module, obtaining a first peak area of the first fragrance component in the target chromatogram; obtaining a second peak area of the first fragrance component in a chromatogram of a current compounded fragrance base module; if a deviation of the first peak area and the second peak area is greater than a peak area difference threshold, adjusting a content of a fragrance raw material corresponding to the first fragrance component in a current compounding formula according to the deviation of the first peak area and the second peak area, so as to optimize the current compounding formula; and determining the fragrance base module formula according to the current compounding formula.
[0008] In some embodiments, the determining the fragrance base module formula according to the current compounding formula includes: taking the compounded fragrance base module formulated by the current compounding formula as a current compounded fragrance base module; repeatedly performing the obtaining of the second peak area of the first fragrance component in the chromatogram of the current compounded fragrance base module until the deviation of the first peak area and the second peak area is not greater than the peak area difference threshold; and taking the current compounding formula as the fragrance base module formula.
[0009] In some embodiments, the optimizing the compounding formula according to the deviation between the target chromatogram and the chromatogram of the compounding base module comprises: if a second flavor ingredient is included in the target chromatogram but not included in the chromatogram of the compounding base module, screening a first target fragrance material corresponding to the second flavor ingredient from the monomer fragrance database; adding the first target fragrance material to the current compounding formula, and determining the content of the first target fragrance material in the current compounding formula according to a third peak area of the second flavor ingredient in the target chromatogram, so as to optimize the current compounding formula; determining the base module formula according to the current compounding formula.
[0010] In some embodiments, the determining the base module formula according to the current compounding formula comprises: taking the compounding base module compounded from the current compounding formula as a current compounding base module; obtaining a fourth peak area of the second flavor ingredient in a chromatogram of the current compounding base module; if a deviation between the third peak area and the fourth peak area is greater than a peak area difference threshold, adjusting the content of the first target fragrance material in the current compounding formula according to the deviation between the third peak area and the fourth peak area, so as to optimize the current compounding formula; repeatedly taking the compounding base module compounded from the current compounding formula as the current compounding base module until the deviation between the third peak area and the fourth peak area is not greater than the peak area difference threshold; and taking the current compounding formula as the base module formula.
[0011] In some embodiments, the optimizing the compounding formula according to the deviation between the target chromatogram and the chromatogram of the compounding base module comprises: if a third flavor ingredient is not included in the target chromatogram but included in the chromatogram of the compounding base module, screening a second target fragrance material corresponding to the third flavor ingredient from the monomer fragrance database; screening fragrance materials having similar functions as the second target fragrance material from the monomer fragrance database to obtain a set of replacement fragrance materials; selecting a fragrance material not including the third flavor ingredient from the set of replacement fragrance materials as a third target fragrance material; replacing the second target fragrance material in the current compounding formula with the third target fragrance material, so as to optimize the current compounding formula; and determining the base module formula according to the current compounding formula.
[0012] In some embodiments, the screening a candidate fragrance material from the first set of candidate fragrance materials to generate a second set of candidate fragrance materials comprises: detecting a difference degree between a chromatogram of an nth candidate fragrance material in the first set of candidate fragrance materials and the target chromatogram, N is the total number of candidate fragrance raw materials in the first candidate fragrance raw material set; if the difference between the chromatogram of the nth candidate fragrance raw material and the target chromatogram is greater than a predetermined difference threshold, and the deviation between the sensory olfactory result of the nth candidate fragrance raw material and the sensory olfactory result of the target fragrance base module is greater than a predetermined sensory olfactory result deviation threshold, the nth candidate fragrance raw material is deleted to optimize the first candidate fragrance raw material set; and the optimized first candidate fragrance raw material set is taken as the second candidate fragrance raw material set.
[0013] In some embodiments, the screening of the partial fragrance raw materials from the plurality of fragrance raw materials comprises: arranging the plurality of fragrance raw materials in descending order of the preset fragrance importance information to obtain a fragrance raw material queue; and extracting a plurality of fragrance raw material groups from the fragrance raw material queue, wherein the mth fragrance raw material group comprises the first m fragrance raw materials in the fragrance raw material queue, M is the total number of fragrance raw material groups; calculating a cross-validation error value of each fragrance raw material group; and selecting one fragrance raw material group with a cross-validation error value less than an error threshold as the first candidate fragrance raw material set.
[0014] In some embodiments, the selecting one fragrance raw material group with a cross-validation error value less than an error threshold as the first candidate fragrance raw material set comprises: if the cross-validation error value of the kth fragrance raw material group is greater than the error threshold, and the cross-validation error value of the k+1th fragrance raw material group is less than the error threshold, the k+1th fragrance raw material group is taken as the first candidate fragrance raw material set, wherein k is a positive integer less than M.
[0015] In some embodiments, the determining the content of each candidate fragrance raw material in the second candidate fragrance raw material set by using the LASSO algorithm comprises: taking the vector data matrix of each candidate fragrance raw material in the second candidate fragrance raw material set in the monomer fragrance database as an independent variable of a regression model; taking the fragrance component data of the target fragrance base module as a dependent variable of the regression model; performing regression analysis on the regression model by using the LASSO algorithm to obtain the coefficient of each independent variable in the regression model; and determining the content of the candidate fragrance raw material corresponding to each independent variable according to the coefficient of each independent variable.
[0016] In a second aspect of the present disclosure, a fragrance base module deployment device is provided, comprising: a memory; a processor coupled to the memory, the processor being configured to perform a fragrance base module deployment method according to any of the above embodiments based on instructions stored in the memory.
[0017] In a third aspect of the present disclosure, a computer readable storage medium is provided, wherein the computer readable storage medium stores computer instructions, and the instructions are executed by a processor to implement a fragrance base module deployment method according to any of the above embodiments.
[0018] In a fourth aspect of the present disclosure, a computer program product is provided, comprising computer instructions, wherein the computer instructions, when executed by a processor, implement the base note module blending method according to any one of the above embodiments.
[0019] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the following drawings. BRIEF DESCRIPTION OF DRAWINGS
[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure or the prior art, the drawings required to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.
[0021] Figure 1 Flowchart of the base note module blending method according to an embodiment of the present disclosure;
[0022] Figure 2 Cross-validation error diagram according to an embodiment of the present disclosure;
[0023] Figure 3 Comparison diagram of chromatograms according to an embodiment of the present disclosure;
[0024] Figure 4 Comparison diagram of chromatograms according to another embodiment of the present disclosure;
[0025] Figure 5 Structure diagram of the base note module blending device according to an embodiment of the present disclosure;
[0026] Figure 6 Comparison diagram of chromatograms according to yet another embodiment of the present disclosure;
[0027] Figure 7 Comparison diagram of chromatograms according to yet another embodiment of the present disclosure;
[0028] Figure 8 Comparison diagram of chromatograms according to yet another embodiment of the present disclosure;
[0029] Figure 9 Odor radar chart of the base note module according to an embodiment of the present disclosure;
[0030] Figure 10 Evaluation of the aroma style radar chart of the base note module according to an embodiment of the present disclosure. DETAILED DESCRIPTION
[0031] The technical solutions in the embodiments of the present disclosure will be described clearly and completely in the embodiments of the present disclosure in combination with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The description of the at least one exemplary embodiment is actually only illustrative, but not as any limitation on the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present disclosure.
[0032] Unless specifically stated, the relative arrangement of the components and steps, numerical expressions, and numerical values set forth in the examples herein are not meant to limit the scope of the present disclosure.
[0033] It should be understood, however, that the sizes of the various components shown in the drawings are not necessarily drawn to scale.
[0034] Techniques, methods, and apparatus known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered as part of the description of the application.
[0035] In all the examples shown and discussed herein, any specific value should be interpreted as merely an example, and not as a limitation. Thus, other examples of the example embodiments can have different values.
[0036] It should be noted that like reference numerals and letters refer to like items in the drawings, and thus, once an item is defined in one drawing, it need not be discussed further in subsequent drawings.
[0037] Figure 1 A flowchart of a fragrance module formulation method for an embodiment of the present disclosure is shown. In some embodiments, the fragrance module formulation method is performed by a fragrance module formulation device, including steps 11-18.
[0038] In step 11, the fragrance component data of the target fragrance module is analyzed using GC-MS technology.
[0039] It should be noted that GC-MS (Gas Chromatography- Ass spectrometry) is an analytical instrument that combines gas chromatography and mass spectrometry, mainly used for qualitative and quantitative analysis of volatile organic small molecule compounds.
[0040] In step 12, the LASSO algorithm is used to screen a plurality of fragrance raw materials corresponding to the fragrance component data from the monomer fragrance database.
[0041] It should be noted that the LASSO (Least Absolute Shrinkage and Selection Operator) is a regression analysis method proposed by Tibshirani in 1996, which minimizes the sum of squared residuals so that the sum of the absolute values of the coefficients is less than a constant (penalty coefficient), and automatically compresses the coefficients with smaller absolute values to 0, thereby achieving significance variable selection and corresponding parameter estimation.
[0042] In step 13, a part of the plurality of fragrance raw materials is screened out according to a preset rule to generate a first candidate fragrance raw material set.
[0043] In some embodiments, the step of screening out a part of the plurality of fragrance raw materials includes steps S101-S104.
[0044] S101, arrange the plurality of fragrance raw materials in descending order according to the preset fragrance importance information to obtain a fragrance raw material queue.
[0045] It should be noted that, since the importance of different fragrance raw materials in the fragrance base module is not the same, the fragrance raw materials can be sorted according to their importance, so as to preferentially select the fragrance raw materials that are more important to the fragrance base module.
[0046] For example, among the plurality of fragrance raw materials, there are sweet orange oil and lavender oil. Since the importance of sweet orange oil is greater than that of lavender oil, in the fragrance raw material queue, sweet orange oil is located in front of lavender oil.
[0047] S102, extract a plurality of fragrance raw material groups from the fragrance raw material queue, wherein the mth fragrance raw material group includes the first m fragrance raw materials in the fragrance raw material queue, M is the total number of fragrance raw material groups.
[0048] S103, calculate the cross-validation error value of each fragrance raw material group.
[0049] It should be noted that cross-validation is a statistical analysis method used to verify the performance of a classifier.
[0050] S104, select one fragrance raw material group with a cross-validation error value less than an error threshold value as the first candidate fragrance raw material set.
[0051] In some embodiments, if the cross-validation error value of the kth fragrance raw material group is greater than the error threshold value, and the cross-validation error value of the k+1th fragrance raw material group is less than the error threshold value, the k+1th fragrance raw material group is selected as the first candidate fragrance raw material set, where k is a positive integer less than M.
[0052] It should be noted that, as shown in Figure 2 the cross-validation error value first decreases rapidly and then enters a smooth state. For example, the cross-validation error value of the 54th fragrance material group is greater than the error threshold, but the cross-validation error value of the 55th fragrance material group is less than the error threshold, and the cross-validation error values of other fragrance material groups after the 55th fragrance material group are also less than the error threshold, so the 55th fragrance material group is selected as the first candidate fragrance material set.
[0053] Of course, other fragrance material groups after the 55th fragrance material group, such as the 56th fragrance material group, the 57th fragrance material group, etc., can also be selected. However, since the number of fragrance materials in the 55th fragrance material group is the least, it can be beneficial to improve the processing efficiency.
[0054] In step 14, according to the chromatogram of each candidate fragrance material in the first candidate fragrance material set and the sensory smell recognition result, and the target chromatogram and the sensory smell recognition result of the target fragrance base module, the candidate fragrance material is screened from the first candidate fragrance material set to generate a second candidate fragrance material set.
[0055] In some embodiments, the step of screening the candidate fragrance material from the first candidate fragrance material set to generate the second candidate fragrance material set includes steps S201-S202.
[0056] S201, detecting the difference degree of the chromatogram of the nth candidate fragrance material in the first candidate fragrance material set and the target chromatogram, N is the total number of candidate fragrance materials in the first candidate fragrance material set.
[0057] S202, if the difference degree of the chromatogram of the nth candidate fragrance material and the target chromatogram is greater than a predetermined difference degree threshold, and the deviation of the sensory smell recognition result of the nth candidate fragrance material and the sensory smell recognition result of the target fragrance base module is greater than a predetermined sensory smell recognition result deviation threshold, the nth candidate fragrance material is deleted to optimize the first candidate fragrance material set.
[0058] S203, taking the optimized first candidate fragrance material set as the second candidate fragrance material set.
[0059] For example, the first candidate fragrance material set includes lavender oil. As shown in Figure 3 the difference between the chromatogram of the lavender oil and the target chromatogram of the target fragrance base module is large, indicating that the lavender oil is not included in the target fragrance base module, and the deviation of the sensory smell recognition result of the lavender oil and the sensory smell recognition result of the target fragrance base module is also large, so the lavender oil is excluded.
[0060] The sensory smell recognition process is briefly described below.
[0061] The aroma profile evaluation method is used to obtain the aroma characteristics of the tobacco flavor. The aroma of the tobacco flavor includes 18 common aroma notes (resin, hay, fresh, fruit, spice, wood, green, flower, herb, bean, cocoa, milk, cream, roasting, burnt, wine, sweet, and sour), and the quantification method is 0-5 points, with a score unit of 0.5 points (as shown in Table 1). After removing outliers, the evaluation results are determined by calculating the average value of each aroma note of the tobacco flavor.
[0062] Table 1: Evaluation table of aroma characteristics of tobacco flavor
[0063]
[0064] Each index of the cigarette sensory evaluation is scored according to the requirements in Table 2, and the evaluation score of each index ranges from 0 to 5, with a minimum score unit of 0.5. The final evaluation result is the average value of the scores given by the sensory evaluation group.
[0065] Table 2: Evaluation index of aroma characteristics of cigarette smoke
[0066]
[0067] In step 15, the content of each candidate flavor raw material in the second candidate flavor raw material set is determined using the LASSO algorithm to generate a complex formula.
[0068] In some embodiments, the step of determining the content of each candidate flavor raw material in the second candidate flavor raw material set using the LASSO algorithm includes steps S301-S304.
[0069] S301, the vector data matrix of each candidate flavor raw material in the second candidate flavor raw material set in the monomer flavor database is used as the independent variable of the regression model.
[0070] S302, the aroma component data of the target flavor base module is used as the dependent variable of the regression model.
[0071] S303, the LASSO algorithm is used to perform regression analysis on the regression model to obtain the coefficient of each independent variable in the regression model.
[0072] S304, according to the coefficient of each independent variable, the content of the candidate flavor raw material corresponding to each independent variable is determined.
[0073] For example, the second candidate flavor raw material set includes sweet orange oil, Peruvian balsam, Zimbabwe tobacco essential oil, Virginia tobacco essential oil, benzyl benzoate, bergamot oil, benzoic acid, ethyl valerate, cassia leaf oil, 2,3,5-trimethylpyrazine, and guaiacwood acetate. The regression model obtained by using the LASSO algorithm is shown in formula (1).
[0074]
[0075] From formula (1), the content ratio of sweet orange oil is 0.57, the content ratio of Peruvian balsam is 2.097, and so on.
[0076] In step 16, the compound fragrance base module is prepared according to the compound formula.
[0077] In step 17, the compound formula is optimized according to the deviation between the target chromatogram and the chromatogram of the compound fragrance base module, to obtain the fragrance base module formula.
[0078] In some embodiments, if the first fragrance ingredient is included in both the target chromatogram and the chromatogram of the compound fragrance base module, the step of optimizing the compound formula includes steps S401-S404.
[0079] S401, obtaining a first peak area of the first fragrance ingredient in the target chromatogram.
[0080] S402, obtaining a second peak area of the first fragrance ingredient in the chromatogram of the current compound fragrance base module.
[0081] S403, if the deviation between the first peak area and the second peak area is greater than the peak area difference threshold, adjusting the content of the fragrance raw material corresponding to the first fragrance ingredient in the current compound formula according to the deviation between the first peak area and the second peak area, so as to optimize the current compound formula.
[0082] S404, determining the fragrance base module formula according to the current compound formula.
[0083] It should be noted that the current compound formula can be repeated multiple times on the basis of steps S401-S404 to obtain an ideal compound formula.
[0084] In some embodiments, if the first fragrance ingredient is included in both the target chromatogram and the chromatogram of the compound fragrance base module, the step of optimizing the compound formula includes steps S501-S506.
[0085] S501, obtaining a first peak area of the first fragrance ingredient in the target chromatogram.
[0086] S502, obtaining a second peak area of the first fragrance ingredient in the chromatogram of the current compound fragrance base module.
[0087] S503, determining whether the deviation between the first peak area and the second peak area is greater than the peak area difference threshold.
[0088] If the deviation between the first peak area and the second peak area is greater than the peak area difference threshold, step S504 is executed; otherwise, step S506 is executed.
[0089] S504, adjusting the content of the fragrance material corresponding to the first flavor ingredient in the current compounding formula according to the deviation of the first peak area and the second peak area, so as to optimize the current compounding formula.
[0090] S505, taking the compounding fragrance base module compounded by the current compounding formula as the current compounding fragrance base module, and returning to step S502.
[0091] S506, taking the current compounding formula as the fragrance base module formula.
[0092] For example, as shown in Figure 4 , comparing the target chromatogram of the target fragrance base module with the compounding chromatogram of the compounding fragrance base module, the flavor ingredient has a first peak area in the target chromatogram, and the flavor ingredient has a second peak area in the compounding chromatogram, and the first peak area and the second peak area have a large deviation. In this case, the content of the fragrance material corresponding to the flavor ingredient can be adjusted multiple times according to the deviation of the first peak area and the second peak area, so that the deviation of the first peak area and the second peak area is controlled within a predetermined range.
[0093] In some embodiments, if the flavor ingredient is included in the target chromatogram but not included in the chromatogram of the compounding fragrance base module, the step of optimizing the compounding formula includes steps S601-S603.
[0094] S601, screening a first target fragrance material corresponding to the flavor ingredient from the monomer fragrance database.
[0095] S602, adding the first target fragrance material to the current compounding formula, and determining the content of the first target fragrance material in the current compounding formula according to a third peak area of the flavor ingredient in the target chromatogram, so as to optimize the current compounding formula.
[0096] S603, determining the fragrance base module formula according to the current compounding formula.
[0097] It should be noted here that the current compounding formula can be repeated multiple times on the basis of steps S601-S603 to obtain an ideal compounding formula.
[0098] In some embodiments, if the flavor ingredient is included in the target chromatogram but not included in the chromatogram of the compounding fragrance base module, the step of optimizing the compounding formula includes steps S701-S707.
[0099] S701, screening a first target fragrance material corresponding to the flavor ingredient The corresponding first target fragrance raw material.
[0100] S702. Add the first target fragrance ingredient to the current compound formula, and according to the fragrance components... The area of the third peak in the target chromatogram is used to determine the content of the first target fragrance ingredient in the current compound formulation in order to optimize the current compound formulation.
[0101] S703. Use the compound fragrance base module prepared by the current compound formula as the current compound fragrance base module.
[0102] S704, Obtaining Fragrance Components The area of the fourth peak in the chromatogram of the current compound fragrance module.
[0103] S705. Determine whether the deviation between the area of the third peak and the area of the fourth peak is greater than the peak area difference threshold.
[0104] If the deviation between the area of the third peak and the area of the fourth peak is greater than the peak area difference threshold, then proceed to step S706; otherwise, proceed to step S707.
[0105] S706. Based on the deviation between the areas of the third and fourth peaks, adjust the content of the first target fragrance ingredient in the current compound formula to optimize the current compound formula, and return to step S703.
[0106] S707, Use the current compound formula as the fragrance base module formula.
[0107] For example, such as Figure 4 As shown, comparing the target chromatogram of the target fragrance base module with the compound chromatogram of the compound fragrance base module, the target chromatogram contains fragrance components. The chromatographic peak was 43, but no aroma component was found in the chromatogram of the compound preparation. The chromatographic peaks. In this case, screening for aroma components from a single fragrance database. The corresponding fragrance raw material is selected and added to the current compound formula. The content of the fragrance raw material is adjusted multiple times to obtain the ideal compound formula.
[0108] In some embodiments, if the fragrance component The steps for optimizing the compound formulation, which are not included in the target chromatogram but are included in the chromatogram of the compound fragrance module, include steps S801-S805.
[0109] S801, Screening fragrance components from a single fragrance database The corresponding second target fragrance raw material.
[0110] S802, screening out a fragrance raw material with similar function from the monomer fragrance database to obtain a set of replacement fragrance raw materials.
[0111] S803, selecting a fragrance raw material without the fragrance component from the set of replacement fragrance raw materials as the third target fragrance raw material.
[0112] S804, replacing the second target fragrance raw material in the current compounding formula with the third target fragrance raw material to optimize the current compounding formula.
[0113] S805, determining the fragrance base module formula according to the current compounding formula.
[0114] For example, as shown in FIG. 4, the target chromatogram of the target fragrance base module and the compounding chromatogram of the compounding fragrance base module are compared, and the compounding chromatogram has a chromatographic peak 44 with a fragrance component, while the target chromatogram does not have a chromatographic peak with the fragrance component. In this case, the fragrance raw material corresponding to the fragrance component is screened from the monomer fragrance database, and the fragrance raw material with similar function is screened from the monomer fragrance database. For example, the fragrance raw material corresponding to the fragrance component is bay leaf oil-1, and other bay leaf oils are screened from the monomer fragrance database, such as bay leaf oil-2 and bay leaf oil-3. Since different manufacturers use different raw materials when producing bay leaf oil, the fragrance components are also different. Through the query, it is found that the bay leaf oil-2 does not include the fragrance component, and therefore the bay leaf oil-2 is used to replace the bay leaf oil-1 to ensure that the compounding chromatogram no longer includes the fragrance component. Figure 4
[0115] In step 18, the fragrance base module is prepared by using the fragrance base module formula.
[0116] In the fragrance base module preparation method provided by the above embodiments of the present disclosure, the fragrance component data of the target fragrance base module is analyzed by using the GC-MS technology, a plurality of fragrance raw materials corresponding to the fragrance component data are screened from the monomer fragrance database by using the LASSO algorithm, a set of candidate fragrance raw materials is generated according to the fragrance component data, the content of each candidate fragrance raw material in the set of candidate fragrance raw materials is determined to generate a compounding formula, the compounding fragrance base module is prepared according to the compounding formula, the compounding formula is optimized according to the deviation of the chromatograms of the target fragrance base module and the compounding fragrance base module to obtain a fragrance base module formula, and then the fragrance base module is prepared by using the fragrance base module formula. Thus, an objective and accurate means is provided for the preparation of the fragrance base module, and the difficulty and cost of the preparation of the fragrance base module are effectively reduced.
[0117] Figure 5 A structural schematic diagram of a base module deployment device for an embodiment of the present disclosure.
[0118] As shown in Figure 5 , the base module deployment device 50 can be in the form of a general computing device. The base module deployment device 50 includes a memory 51, a processor 52, and a bus 53 connecting different system components.
[0119] The memory 51 can include, for example, system memory, non-volatile storage media, etc. The system memory stores, for example, an operating system, application programs, a Boot Loader, and other programs, etc. The system memory can include volatile storage media such as random access memory (RAM) and / or cache memory. The non-volatile storage media stores, for example, instructions of at least one base module deployment method being executed.
[0120] The processor 52 can be implemented in the form of a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete hardware component, or the like. Accordingly, each module such as the acquisition module, the calculation module, and the adjustment module can be implemented by a central processing unit (CPU) running instructions of corresponding steps stored in the memory, or by a dedicated circuit performing corresponding steps.
[0121] For example, the processor 52 is configured to perform the method as described in any of the embodiments of Figure 1 .
[0122] The bus 53 can use any of a variety of bus structures. For example, the bus structure includes, but is not limited to, an industry standard architecture (ISA) bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus.
[0123] These interfaces 54, 55, 56 of the base module deployment device 50, as well as the memory 51 and the processor 52, can be connected through the bus 53. The input / output interface 54 can provide a connection interface for input / output devices such as a display, a mouse, a keyboard, etc. The network interface 55 provides a connection interface for various networking devices. The storage interface 56 provides a connection interface for external storage devices such as a floppy disk, a U disk, an SD card, etc.
[0124] The computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks.
[0125] These computer readable program instructions can be supplied to a computer, or other programmable data processing apparatus, to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks.
[0126] These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the instructions stored in the computer readable storage medium produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks.
[0127] The computer program instructions can also be loaded onto a computer, or other programmable instruction execution apparatus, to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce computer implemented processes such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart and / or block diagram block or blocks.
[0128] The computer program instructions can also be loaded onto a computer, or other programmable instruction execution apparatus, to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce computer implemented processes such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, or other programmable instruction execution apparatus, to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce computer implemented processes such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart and / or block diagram block or blocks.
[0129] The computer program instructions can also be loaded onto a computer, or other programmable instruction execution apparatus, to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce computer implemented processes such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer, or other programmable instruction execution apparatus, to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce computer implemented processes such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart and / or block diagram block or blocks.
[0130] The computer program instructions can also be loaded onto a computer, or other programmable instruction execution apparatus, to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce computer implemented processes such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart and / or block diagram block or blocks.
[0131] Embodiment One
[0132] 1. Materials, reagents and instruments
[0133] Materials: single flavor materials collected by Fujian China Tobacco. Self-made flavor base module: 7 kinds of flavor materials were randomly selected from the single flavor material library to obtain the flavor base module A. The flavor base module A was obtained by mixing ethyl valerate, 2,3,5-trimethylpyrazine, 2-acetylpyridine, ethyl hexanoate, sweet orange oil, Zimbabwe tobacco essential oil, and Peru extract according to the ratio of 0.02:0.6:0.3:0.003:0.65:1.0:0.5:0.012 (provided by Fujian China Tobacco Sugar Flavor Center).
[0134] Reagents: heptadecane, naphthalene (purity > 99%, J&K).
[0135] Data processing software: DPS18.10 data processing system (Hangzhou Rui Feng).
[0136] Instruments: 7890A-5975C gas chromatograph-mass spectrometer (Agilent Technologies, USA), ML204 electronic balance (sensitivity 0.1 mg, Mettler Toledo, USA), piston pipettes 200~1000 (Brand, Germany).
[0137] 2. Analytical Methods
[0138] Gas chromatography conditions:
[0139] Inlet temperature: 250 Split ratio: 15:1; Injection volume: 1 Column: DB-5MS (60m) 0.25mm 0.25 Carrier gas: helium; Column flow rate: 1 mL / min; Temperature program: Initial temperature 50 °C Maintain for 2 minutes, at 8 Heat up to 280 / min Hold for 25 minutes; Transmission line temperature: 280°C .
[0140] Mass spectrometry conditions:
[0141] Ion source temperature: 230 Ionization voltage: 70 eV; Solvent delay: 4.8 min; Scan range: 33–400.
[0142] 3. Specific processing procedure
[0143] 3.1 Fragrance component analysis of fragrance base module A
[0144] A total of 44 aroma components were detected from aroma base module A (Table 3). The content of the detected aroma components was matched with the components in the single fragrance database according to their Chinese names for subsequent data processing.
[0145] Table 3. Results of aroma component analysis for aroma base module A
[0146]
[0147] 3.2 LASSO Regression Analysis
[0148] (1) Parameter setting
[0149] According to the principles of LASSO regression analysis , The larger the value of , the better. The larger the value, the larger the value. The number of factors selected will be relatively small. Therefore, in general... The value is 0.01~0.05. To select more spices for optimal model selection, the project sets... The value is 0.01. This sets the coefficients of all individual fragrances in the database to be in a penalized state, and all coefficients to be greater than zero.
[0150] (2) Screening of fragrance raw materials
[0151] Multiple fragrance ingredients selected using the LASSO algorithm are arranged in descending order of their pre-defined importance, forming a fragrance ingredient queue. Next, multiple fragrance ingredient groups are extracted from the queue, and the cross-validation error (CPE) value for each group is calculated. If the CPE value of the k-th fragrance ingredient group is greater than an error threshold, and the CPE value of the (k+1)-th fragrance ingredient group is less than the error threshold, then the (k+1)-th fragrance ingredient group is selected as the first candidate fragrance ingredient set. This yields 16 fragrance ingredients: sweet orange oil, Peruvian balm, Zimbabwean tobacco essential oil, Virginia tobacco essential oil, benzyl benzoate, bergamot oil, benzoic acid, ethyl valerate, laurel leaf oil, menthone, broom flower absolute, 2,3,5-trimethylpyrazine, beeswax absolute, lavender oil, mimosa absolute, and cinnamon acetate.
[0152] (3) Manual confirmation of spice identification results
[0153] The chromatograms of the selected fragrance raw materials were compared with those of the fragrance base module A to confirm the screening results. Simultaneously, each raw material was subjected to empirical scent testing. For example... Figure 3 As shown, the chromatogram of lavender oil differs significantly from that of fragrance base module A, indicating that it is not in fragrance base module A. Furthermore, its aroma also differs considerably from that of fragrance base module A, so it is excluded. Following the same method, five other fragrance ingredients—menthol, broom flower absolute, lavender oil, mimosa absolute, and beeswax absolute—were also excluded.
[0154] Thus, the obtained fragrance raw materials include sweet orange oil, Peruvian balsam, Zimbabwean tobacco essential oil, Virginia tobacco essential oil, benzyl benzoate, bergamot oil, benzoic acid, ethyl valerate, laurel leaf oil, 2,3,5-trimethylpyrazine, and laurate acetate. The regression model obtained using the LASSO algorithm is shown in the above formula (1). The resulting simulated formulation and the formulation of fragrance base module A are shown in Table 4.
[0155] Table 4. Fragrance Base Module A Formulation and Simulated Formulation
[0156]
[0157] (4) Simulate the formulation substances and adjust the proportions
[0158] The calculated formula was proportionally modulated, and flavor component analysis was performed. From Figure 6 It can be seen that the flavor base module A has high similarity with the modulation module chromatogram, and 2-acetylpyridine exists in the flavor base module A, and there is a certain difference in the amount of some components. Through several chromatographic analysis and formula adjustment, the module formula can be better resolved.
[0159] Example two
[0160] The commercial flavor base module W was provided by Fujian China Tobacco Sugar Flavor Center. The monomer flavor raw materials, reagents and instruments, flavor component analysis method, sensory evaluation method, data processing method were the same as in Example 1.
[0161] 1. Flavor component analysis of flavor base module W
[0162] A total of 32 flavor components were detected from the flavor base module W (as shown in Table 5), and the detected flavor component contents were respectively corresponding to the components in the monomer flavor database according to the Chinese name, so as to facilitate subsequent data processing.
[0163] Table 5 Analysis results of flavor components of flavor base module W
[0164]
[0165] 2. LASSO regression analysis
[0166] (1) Parameter setting
[0167] Project setting 0.01. Set the coefficients of all monomer flavors in the database to be in the penalty state, and the coefficients are all greater than zero.
[0168] (2) Flavor screening
[0169] The multiple flavor raw materials screened by the LASSO algorithm were arranged in descending order according to the preset flavor importance information, and a flavor raw material queue was obtained. Next, multiple flavor raw material groups were extracted from the flavor raw material queue, and the cross-validation error value of each flavor raw material group was calculated. If the cross-validation error value of the kth flavor raw material group is greater than the error threshold value, and the cross-validation error value of the k+1th flavor raw material group is less than the error threshold value, then the k+1th flavor raw material group is taken as the first candidate flavor raw material set. Thus, 18 flavor raw materials were obtained, including menthol, isoamyl isovalerate, clove oil, eugenol, benzyl alcohol, ethyl butyrate, maltol, citronellol, ethyl vanillin, dihydrocoumarin, borneol acetate, p-methoxyacetophenone, oak moss extract, geranium oil, Zimbabwe tobacco extract, Turkish flavor tobacco extract, burley tobacco extract, and nerolidol.
[0170] (3) Artificial confirmation of flavor raw material identification results
[0171] The chromatogram of the screened flavor raw material is compared with the chromatogram of the flavor base module W, and the screening result is confirmed, as shown in the following table. Figure 7 As shown in the table, the chromatogram of the oak extract is less different from the chromatogram of the flavor base module W, and the smell of the oak extract is also less different from the flavor base module W, indicating that it is included in the flavor base module W. On the contrary, the chromatogram of the Turkish tobacco extract and the white burley extract is quite different from the chromatogram of the flavor base module W, and the smell of the oak extract is also quite different from the flavor base module W, so it is excluded.
[0172] Thus, the obtained flavor raw materials include menthol, isoamyl isovalerate, clove oil, eugenol, benzyl alcohol, ethyl butyrate, maltol, citronellol, ethyl vanillin, dihydrocoumarin, borneol acetate, p-methoxyacetophenone, oak extract, geranium oil, Zimbabwe tobacco extract, nerolidol, and the regression model obtained by using the LASSO algorithm is shown in formula (2).
[0173]
[0174] According to formula (2), the obtained simulation formula and the formula of the flavor base module W are shown in Table 6.
[0175] Table 6 Fitting formula of flavor base module W
[0176]
[0177] By chromatographic analysis of the fitting formula, it is found that the content of some flavor ingredients is different from that in the target object, and the amount of some flavor raw materials is adjusted. At the same time, it is found that some chromatographic peaks are missing in the target object, and after empirical judgment, Peruvian extract, sweet orange oil and other flavor raw materials are added to the formula. After multiple rounds of adjustment, the chromatographic analysis of the obtained formula is carried out, and the results are shown in Table 5. Figure 8
[0178] The sensory evaluation of the flavor base module W and its fitting formula is carried out, and the evaluation results are shown in Table 3 and Table 4. Figure 9 Figure 10 As shown in Table 3 and Table 4, the smell of the target sample and the simulation sample is consistent in terms of the type of flavor notes, the intensity of flavor notes, and the style of smoking aroma, which achieves the target of flavor base module modulation.
[0179] It should be noted here that Figure 9 is the radar chart of the smell of the flavor base module W, Figure 10 is the radar chart of the style of smoking aroma of the flavor base module W.
[0180] In some embodiments, the functional units described above can be implemented as a general processor, a Programmable Logic Controller (PLC), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any appropriate combination thereof, for performing the functions described in the present disclosure.
[0181] Those of ordinary skill in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.
[0182] The description of the present disclosure is given for the purpose of illustration and description, and is not intended to be exhaustive or to limit the present disclosure to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are chosen and described in order to best explain the principles of the present disclosure and its practical application, and to enable others skilled in the art to understand the present disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
Claims
1. A method for blending a fragrance base module, executed by a fragrance base module blending device, comprising: The aroma component data of the target aroma module were analyzed using GC-MS technology; Using the LASSO algorithm, multiple fragrance raw materials corresponding to the fragrance component data are screened from the single fragrance database; Using preset rules, a subset of fragrance ingredients are selected from the plurality of fragrance ingredients to generate a first candidate fragrance ingredient set; Based on the chromatogram and sensory olfaction results of each candidate fragrance ingredient in the first candidate fragrance ingredient set, and the target chromatogram and sensory olfaction results of the target fragrance base module, candidate fragrance ingredients are screened from the first candidate fragrance ingredient set to generate a second candidate fragrance ingredient set; The LASSO algorithm is used to determine the content of each candidate fragrance ingredient in the second candidate fragrance ingredient set in order to generate a compound formulation; The compound fragrance base module was prepared according to the compound formula; Based on the deviation between the target chromatogram and the chromatogram of the compound fragrance module, the compound formulation is optimized to obtain the fragrance module formulation; The fragrance base module is formulated using the aforementioned fragrance base module formula.
2. The method for blending fragrance base modules according to claim 1, wherein, The step of optimizing the compound formulation based on the deviation between the target chromatogram and the chromatogram of the compound fragrance module includes: If the first aroma component is included in both the target chromatogram and the chromatogram of the compound fragrance module, obtain the first peak area of the first aroma component in the target chromatogram; Obtain the second peak area of the first aroma component in the chromatogram of the current compound fragrance module; If the deviation between the first peak area and the second peak area is greater than the peak area difference threshold, the content of the fragrance raw material corresponding to the first fragrance component in the current compound formula is adjusted according to the deviation between the first peak area and the second peak area in order to optimize the current compound formula. The fragrance base module formulation is determined based on the current compound formulation.
3. The method for blending fragrance base modules according to claim 2, wherein, Determining the flavor base module formula based on the current compound formula includes: The compound fragrance base module formulated with the current compound formula is used as the current compound fragrance base module; Repeat the process of obtaining the second peak area of the first aroma component in the chromatogram of the current compound aroma module until the deviation between the first peak area and the second peak area is not greater than the peak area difference threshold. The current compound formulation is used as the fragrance base module formulation.
4. The method for blending fragrance base modules according to claim 1, wherein, The step of optimizing the compound formulation based on the deviation between the target chromatogram and the chromatogram of the compound fragrance module includes: If the second aroma component is included in the target chromatogram but not in the chromatogram of the compound aroma base module, the first target aroma ingredient corresponding to the second aroma component is screened from the monomer aroma database; The first target fragrance ingredient is added to the current compound formulation, and the content of the first target fragrance ingredient in the current compound formulation is determined based on the third peak area of the second fragrance component in the target chromatogram, so as to optimize the current compound formulation. The fragrance base module formulation is determined based on the current compound formulation.
5. The method for blending fragrance base modules according to claim 4, wherein, Determining the flavor base module formula based on the current compound formula includes: The compound fragrance base module formulated with the current compound formula is used as the current compound fragrance base module; Obtain the fourth peak area of the second fragrance component in the chromatogram of the current compound fragrance module; If the deviation between the area of the third peak and the area of the fourth peak is greater than the peak area difference threshold, the content of the first target fragrance raw material in the current compound formula is adjusted according to the deviation between the area of the third peak and the area of the fourth peak in order to optimize the current compound formula. Repeat the process of using the compound fragrance base module formulated with the current compound formula as the current compound fragrance base module until the deviation between the area of the third peak and the area of the fourth peak is not greater than the peak area difference threshold. The current compound formulation is used as the fragrance base module formulation.
6. The method for blending fragrance base modules according to claim 1, wherein, The step of optimizing the compound formulation based on the deviation between the target chromatogram and the chromatogram of the compound fragrance module includes: If the third aroma component is not included in the target chromatogram but is included in the chromatogram of the compound aroma base module, a second target aroma ingredient corresponding to the third aroma component is screened from the single aroma database; From the single fragrance database, fragrance ingredients with similar functions to the second target fragrance ingredient are selected to obtain a set of alternative fragrance ingredients; Select fragrance raw materials that do not include the third fragrance component from the set of alternative fragrance raw materials, and use them as the third target fragrance raw materials; The second target fragrance ingredient in the current compound formula is replaced with the third target fragrance ingredient in order to optimize the current compound formula; The fragrance base module formulation is determined based on the current compound formulation.
7. The method for blending fragrance base modules according to claim 1, wherein, Candidate fragrance ingredients are selected from the first candidate fragrance ingredient set to generate a second candidate fragrance ingredient set, including: The difference between the chromatogram of the nth candidate fragrance ingredient in the first candidate fragrance ingredient set and the target chromatogram is detected. N is the total number of candidate fragrance raw materials in the first candidate fragrance raw material set; If the difference between the chromatogram of the nth candidate fragrance ingredient and the target chromatogram is greater than a predetermined difference threshold, and the deviation between the sensory olfaction result of the nth candidate fragrance ingredient and the sensory olfaction result of the target fragrance base module is greater than a predetermined sensory olfaction result deviation threshold, then the nth candidate fragrance ingredient will be deleted in order to optimize the first candidate fragrance ingredient set. The optimized first candidate fragrance ingredient set is used as the second candidate fragrance ingredient set.
8. The method for blending a fragrance base module according to any one of claims 1-7, wherein, The process of selecting a subset of fragrance ingredients from the plurality of fragrance ingredients includes: The multiple fragrance raw materials are arranged in descending order of their pre-defined importance information to obtain a fragrance raw material queue; Multiple fragrance ingredient groups are extracted from the fragrance ingredient queue, wherein the m-th fragrance ingredient group includes the first m fragrance ingredients in the fragrance ingredient queue. M represents the total number of fragrance raw material groups; Calculate the cross-validation error for each fragrance ingredient group; Select a fragrance ingredient group whose cross-validation error value is less than the error threshold as the first candidate fragrance ingredient set.
9. The method for blending fragrance base modules according to claim 8, wherein, The step of selecting a group of fragrance ingredients whose cross-validation error value is less than the error threshold as the first candidate fragrance ingredient set includes: If the cross-validation error of the kth fragrance ingredient group is greater than the error threshold, and the cross-validation error of the (k+1)th fragrance ingredient group is less than the error threshold, then the (k+1)th fragrance ingredient group is taken as the first candidate fragrance ingredient set, where k is a positive integer less than M.
10. The method for blending a fragrance base module according to any one of claims 1-7, wherein, The process of determining the content of each candidate fragrance ingredient in the second candidate fragrance ingredient set using the LASSO algorithm includes: The vector data matrix of each candidate fragrance ingredient in the second candidate fragrance ingredient set in the single fragrance database is used as the independent variable of the regression model; The fragrance component data of the target fragrance module are used as the dependent variable of the regression model; The regression model is analyzed using the LASSO algorithm to obtain the coefficient of each independent variable in the regression model. Based on the coefficient of each independent variable, the content of the candidate fragrance raw material corresponding to each independent variable is determined.
11. A fragrance base module blending device, comprising: Memory; A processor, coupled to a memory, is configured to implement the spice module blending method as described in any one of claims 1-10, based on the memory-stored instruction execution.
12. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the aroma-based module blending method as described in any one of claims 1-10.
13. A computer program product comprising computer instructions, wherein the computer instructions, when executed by a processor, implement the aroma-based module blending method as described in any one of claims 1-10.