Chemometric and sensory omics analysis of finished cigarette paper after combustion

Abstract

The present invention discloses a chemometrics and sensory omics analysis method for finished cigarette paper after burning, which includes: detecting the smoke of finished cigarette paper samples of the same brand processed in a different place after burning, and obtaining GC-IMS data of the finished cigarette paper samples of the brand after burning; using an unsupervised combined with a supervised pattern recognition method to perform characteristic analysis and pattern recognition of sensory omics data on the GC-IMS data of finished cigarette paper samples produced by different cigarette factories after burning; and evaluating the sensory consistency of cigarette products of different production points of multi-point processing varieties when ignited and smoked according to the pattern recognition results. The chemometrics and sensory omics analysis method for finished cigarette paper after burning provided by the present invention can identify the differential components of smoke after ignition through a sensory component analysis method, and effectively distinguish finished cigarette paper samples of the same brand processed in a different place after burning, which is helpful to achieve accurate evaluation of the overall quality of the product.

Description

Technical Field

[0001] The present invention relates to the technical field of tobacco product quality evaluation, and in particular to a chemometrics and sensory omics analysis method for finished cigarette paper after combustion. Background Art

[0002] Flavor and fragrance system refers to special cigarette paper made by adding flavors, fragrances, extracts and their materials with the purpose of enhancing flavor, sweetening and coloring during the manufacturing process of cigarette paper. When the cigarette burns, the flavoring additives on the cigarette paper release the flavor components through volatilization, cracking and other methods to achieve the purpose of imparting a certain characteristic flavor. In recent years, cigarette paper flavoring technology has been widely used in the production of high-end cigarettes to improve the smoking quality of cigarettes. It has the advantages of effectively masking the impurities of cigarettes, giving the smoke a sweet and smooth feeling, reducing the irritation of cigarettes, and increasing the softness and delicacy of smoke. Due to the large number of volatile components, complex components and low content of aroma components contained in the flavor and fragrance system, it is difficult to trace the raw materials of the flavor and fragrance system, and there is a lack of effective stability monitoring methods. Therefore, there is no mature and reliable quality control system for the quality of the flavor and fragrance system, which affects the sensory quality stability of the cigarette brand.

[0003] At present, the main method for quality control of flavors and fragrances in my country is still physical evaluation indicators such as acidity, miscibility, refractive index, and density. In addition, the existing GC / MS method in the national standard has the disadvantages of insufficient specificity and sensitivity for the detection of trace aroma compounds, and mainly relies on qualitative and quantitative analysis methods of targeted compounds, lacking the overall quality evaluation method for the complex system of flavors and fragrances. The above problems constitute the shortcomings of the current quality control of flavors and fragrances.

[0004] How to effectively conduct chemometrics and sensory omics analysis on finished cigarette paper after combustion, and then analyze the complex system of flavors and fragrances to achieve accurate evaluation of the overall quality of the product has become a key technical bottleneck that needs to be urgently resolved in the tobacco industry.

[0005] Therefore, there is an urgent need for a chemometric and sensory omics analysis method for finished cigarette paper after combustion. Summary of the invention

[0006] The purpose of the present invention is to provide a chemometrics and sensory omics analysis method for finished cigarette paper after combustion, so as to solve the problems in the above-mentioned prior art. It can use unsupervised combined with supervised pattern recognition methods to perform feature analysis and pattern recognition based on sample GC-IMS sensory omics data on finished cigarette paper samples after combustion, and evaluate the sensory consistency of cigarette products of different production points of multi-point processing varieties when ignited and smoked, which is helpful to achieve accurate evaluation of the overall quality of the product.

[0007] The present invention provides a chemometrics and sensory omics analysis method for finished cigarette paper after combustion, which includes:

[0008] The smoke of finished cigarette paper samples of the same brand processed in different places after burning is tested, and the gas chromatography tandem ion mobility spectrometry data of finished cigarette paper samples of the same brand produced by different cigarette factories after burning are obtained;

[0009] The sensory omics data were characterized and pattern recognized by combining unsupervised and supervised pattern recognition methods on the gas chromatography tandem ion mobility spectrometry data of the same brand of finished cigarette paper samples produced by different cigarette factories.

[0010] Based on the pattern recognition results, the sensory consistency of cigarette products from different production points of multi-point processed varieties when lit and smoked was evaluated.

[0011] The chemometrics and sensory omics analysis method of the finished cigarette paper after combustion as described above, wherein preferably, the smoke of the finished cigarette paper samples of the same brand processed in different places after combustion is detected to obtain the gas chromatography tandem ion mobility spectrometry data of the finished cigarette paper samples of the same brand after combustion produced by different cigarette factories, specifically includes:

[0012] Collect smoke from the combustion of finished cigarette paper samples of the same brand processed in a different location;

[0013] The collected smoke after combustion was tested by GC-IMS flavor analyzer, and the gas chromatography ion mobility spectrometry data of the finished cigarette paper samples of the same brand after combustion produced by different cigarette factories were obtained.

[0014] The chemometrics and sensory omics analysis method of the finished cigarette paper after combustion as described above, wherein preferably, the smoke of the finished cigarette paper sample of the same brand processed in a different place after combustion is collected, specifically comprises:

[0015] After removing the tobacco shreds, different cigarette samples were subjected to cigarette paper burning test on an automatic smoking machine and smoke was collected. A 44mm Cambridge filter was used to collect the total particulate matter for each pore sample, and the smoke was collected through a silica gel gas collection bag and connected to the GC-IMS sampling device.

[0016] The chemometrics and sensory omics analysis method of the finished product cigarette paper after combustion as described above, wherein preferably, the GC-IMS flavor analyzer is used to detect the collected smoke after combustion, and the gas chromatography tandem ion mobility spectrometry data of the finished product cigarette paper samples of the same brand after combustion produced by different cigarette factories are obtained, specifically including:

[0017] The GC-IMS flavor analyzer was used to test the finished cigarette paper samples of the same brand and different batches produced by different cigarette factories of the same tobacco group. Each sample was injected and measured three times to obtain the gas chromatography-tandem ion mobility spectra and typical compound identification results of the aroma volatile compounds of each finished cigarette paper sample.

[0018] The chemometrics and sensory omics analysis method of the finished cigarette paper after combustion as described above, wherein, preferably, before the detection by using a GC-IMS flavor analyzer, the method further comprises:

[0019] The samples were pre-treated, including:

[0020] 0.5 g of cigarette paper was placed in a 20 mL headspace vial and incubated at 90 °C for 20 min before sampling.

[0021] In the above-mentioned chemometric and sensory omics analysis method of the finished cigarette paper after combustion, preferably, the headspace sampling conditions when using a GC-IMS flavor analyzer for detection include:

[0022] The injection volume was 200ul; the incubation time was 20min; the incubation temperature was 90°C; the injection needle temperature was 95°C; the incubation speed was 500rpm;

[0023] The chromatographic conditions for the GC-IMS flavor analyzer include:

[0024] The chromatographic conditions of gas phase-ion mobility spectrometry were as follows: analysis time 20 min; column type WAX; column length 30 m; inner diameter ID-0.53 mm; film thickness FT 1 μm; column temperature 60 °C; carrier gas / drift gas N2; IMS temperature 45 °C;

[0025] GC chromatographic conditions: when the injection time is 0, the drift gas flow rate is 150 mL / min, the carrier gas flow rate is 2 ml / min, and the acquisition state is rec; when the injection time is 2 min, the drift gas flow rate is 150 mL / min, and the carrier gas flow rate is 10 ml / min; when the injection time is 20 min, the drift gas flow rate is 150 mL / min, and the carrier gas flow rate is 100 ml / min; when the injection time is 30 min, the drift gas flow rate is 150 mL / min, the carrier gas flow rate is 100 ml / min, and the acquisition state is stop.

[0026] The chemometrics and sensory omics analysis method for the finished product of burned cigarette paper as described above, wherein preferably, the unsupervised combined with supervised pattern recognition method is used to perform characteristic analysis and pattern recognition of sensory omics data on gas chromatography tandem ion mobility spectrometry data of the finished product of burned cigarette paper samples of the same brand produced by different cigarette factories, specifically comprising:

[0027] ModelLab Matman universal chemometrics solution software was used to perform sensory omics data feature analysis on the gas chromatography tandem ion mobility spectrometry data of the same brand of finished cigarette paper samples produced by different cigarette factories after combustion.

[0028] In the ModelLab Matman general chemometrics solution software, at least one of the principal component analysis modeling algorithm, the partial least squares discriminant modeling algorithm and the random forest pattern recognition modeling algorithm is used to perform feature analysis and pattern recognition on the gas chromatography tandem ion mobility spectrometry data of the same brand of finished cigarette paper samples after combustion produced by different cigarette factories, and a pattern recognition model is established.

[0029] The chemometric and sensory omics analysis method of the finished cigarette paper after combustion as described above, wherein, preferably, the preprocessing algorithm adopted by the principal component analysis modeling algorithm is UV scaling; the number of latent variables retained by the partial least squares discriminant modeling algorithm is 5, k=7 cross-validation is adopted in the cross-validation, the number of random simulations is 10 times, and the preprocessing algorithm adopted is UV scaling for the independent variable.

[0030] The chemometrics and sensory omics analysis method of the finished cigarette paper after combustion as described above, wherein, preferably, the number of decision trees used in the random forest pattern recognition modeling algorithm is 100; the eigenvalue algorithm used is Sqrt; the maximum tree depth is 30; the minimum impurity reduction is 0.01; and the preprocessing algorithm used is UV scaling.

[0031] The chemometrics and sensory omics analysis method of the finished cigarette paper after combustion as described above, wherein preferably, the sensory consistency of cigarette products of different production points of multi-point processing varieties when ignited and smoked is evaluated based on the pattern recognition results, specifically including:

[0032] Based on the pattern recognition results, the sensory consistency of cigarette products from different production points of multi-point processed varieties when ignited and smoked was evaluated, and the key iconic series of compounds that affect the composition of volatile compounds and aroma characteristics were screened out.

[0033] The present invention provides a chemometrics and sensory omics analysis method for finished cigarette paper after combustion, and adopts an unsupervised combined with supervised pattern recognition method to perform feature analysis and pattern recognition based on sample GC-IMS sensory omics data on finished cigarette paper samples after combustion in a complex system of flavors and fragrances. The sensory component analysis method can be used to identify the different components of smoke after ignition, and finished cigarette paper samples of the same brand after combustion that are processed in different places can be effectively distinguished, and key characteristic series compounds that affect the composition of volatile compounds and the aroma characteristics can be screened out, which is helpful to achieve accurate evaluation of the overall quality of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described below with reference to the accompanying drawings, in which:

[0035] Figure 1 A flow chart of an embodiment of the chemometrics and sensory omics analysis method of finished cigarette paper after combustion provided by the present invention;

[0036] Figure 2 The PCA analysis scores and Totelling's T2 interval diagrams of the GC-IMS data of the finished cigarette paper after combustion of the four manufacturers;

[0037] Figure 3 The cumulative prediction variance trend chart of PLS-DA pattern recognition of GC-IMS data of finished cigarette paper after combustion of four manufacturers;

[0038] Figure 4 This is a schematic diagram of the confusion matrix of random forest pattern recognition prediction of GC-IMS data of finished cigarette paper after combustion from four manufacturers;

[0039] Figure 5 A schematic flowchart of the random forest decision tree topology diagram for the GC-IMS data of the finished cigarette paper after combustion of four manufacturers;

[0040] Figure 6 This is a bar chart of the random forest independent variable contribution scores for the GC-IMS data of the finished cigarette paper after combustion from four manufacturers. DETAILED DESCRIPTION

[0041] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The description of the exemplary embodiments is merely illustrative and is in no way intended to limit the present disclosure and its application or use. The present disclosure can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to make the present disclosure thorough and complete and to fully express the scope of the present disclosure to those skilled in the art. It should be noted that unless otherwise specifically stated, the relative arrangement of the components and steps, the composition of the materials, the numerical expressions and the numerical values ​​set forth in these embodiments should be interpreted as being merely exemplary and not as limiting.

[0042] The words "first", "second" and similar words used in this disclosure do not indicate any order, quantity or importance, but are only used to distinguish different parts. The words "include" or "comprises" and similar words mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of including other elements. "Up", "down" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0043] In the present disclosure, when a specific component is described as being located between a first component and a second component, there may or may not be an intermediate component between the specific component and the first component or the second component. When a specific component is described as being connected to other components, the specific component may be directly connected to the other components without an intermediate component, or may not be directly connected to the other components but have an intermediate component.

[0044] All terms (including technical terms or scientific terms) used in the present disclosure have the same meanings as those understood by ordinary technicians in the field to which the present disclosure belongs, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries, such as general dictionaries, should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined herein.

[0045] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0046] like Figure 1 As shown, the chemometrics and sensory omics analysis method for the finished cigarette paper after combustion provided in this embodiment specifically includes:

[0047] Step S1, detecting smoke from the combustion of finished cigarette paper samples of the same brand processed in different places, and obtaining gas chromatography-tandem ion mobility spectrometry (GC-IMS) data of finished cigarette paper samples of the same brand produced by different cigarette factories after combustion.

[0048] In one embodiment of the chemometrics and sensory omics analysis method of the finished cigarette paper after combustion of the present invention, the step S1 may specifically include:

[0049] Step S11, collecting smoke from the combustion of finished cigarette paper samples of the same brand processed at a different location.

[0050] Specifically, different cigarette samples after removing tobacco shreds were subjected to a cigarette paper burning test on an automatic smoking machine and smoke was collected, wherein each channel sample used a 44 mm Cambridge filter to collect total particulate matter, and smoke was collected through a silica gel gas collection bag and connected to a GC-IMS sampling device. It should be noted that the present invention does not specifically limit the manufacturer and model of the automatic smoking machine.

[0051] Step S12: Using a GC-IMS flavor analyzer to perform detection, to obtain gas chromatography tandem ion mobility spectrometry data of finished cigarette paper samples of the same brand produced by different cigarette factories after combustion.

[0052] For example, the German GAS Flavor Spec GC-IMS flavor analyzer can be used for detection. The present invention does not specifically limit the manufacturer and model of the GC-IMS flavor analyzer.

[0053] Among them, GC-IMS combines the advantages of high separation of gas chromatography and high sensitivity of ion mobility spectrometry. It can quickly detect trace volatile organic compounds in samples without any special sample pretreatment, and is used to measure volatile headspace components in solid or liquid samples.

[0054] Specifically, the GC-IMS flavor analyzer was used to test the finished cigarette paper samples of the same brand and different batches from different cigarette factories of the same tobacco group (see Table 1). Each sample was injected and measured three times to obtain the gas chromatography tandem ion mobility spectra and typical compound identification results of the aroma volatile compounds of each finished cigarette paper sample after combustion.

[0055] In one embodiment of the present invention, cigarette paper samples of 20 batches of finished cigarettes of the same brand from four different manufacturers of Yunyan Group, namely, Dali (three batches), Shankun (two batches), Ulanhot (three batches), and Haihong (two batches), are tested. It should be noted that the present invention does not specifically limit the number of different manufacturers, the number of samples corresponding to each manufacturer, and the batches.

[0056] Table 1 Summary of the data of finished cigarette paper samples after combustion for modeling analysis

[0057]

[0058] Specifically, the headspace injection conditions when using the GC-IMS flavor analyzer for detection include:

[0059] The injection volume was 200ul; the incubation time was 20min; the incubation temperature was 90°C; the injection needle temperature was 95°C; the incubation speed was 500rpm;

[0060] The chromatographic conditions for the GC-IMS flavor analyzer include:

[0061] The chromatographic conditions of gas phase-ion mobility spectrometry were as follows: analysis time 20 min; column type WAX; column length 30 m; inner diameter ID-0.53 mm; film thickness FT 1 μm; column temperature 60 °C; carrier gas / drift gas N2; IMS temperature 45 °C;

[0062] GC chromatographic conditions: when the injection time is 0, the drift gas flow rate is 150 mL / min, the carrier gas flow rate is 2 ml / min, and the acquisition state is rec; when the injection time is 2 min, the drift gas flow rate is 150 mL / min, and the carrier gas flow rate is 10 ml / min; when the injection time is 20 min, the drift gas flow rate is 150 mL / min, and the carrier gas flow rate is 100 ml / min; when the injection time is 30 min, the drift gas flow rate is 150 mL / min, the carrier gas flow rate is 100 ml / min, and the acquisition state is stop.

[0063] Before using the GC-IMS flavor analyzer for detection, the method further comprises:

[0064] The samples were pre-treated, including:

[0065] 0.5 g of cigarette paper was placed in a 20 mL headspace vial and incubated at 90 °C for 20 min before sampling.

[0066] Step S2: using an unsupervised combined with supervised pattern recognition method, the sensory omics data are analyzed and pattern recognized on the gas chromatography tandem ion mobility spectrometry data of the same brand of finished cigarette paper samples produced by different cigarette factories after combustion.

[0067] Specifically, in the present invention, the gas chromatography tandem ion mobility spectrometry data of the same brand of finished cigarette paper samples after combustion produced by different cigarette factories are used for characteristic analysis of sensory omics data by using the ModelLab Matman general chemometrics solution software (Chemmind Technologies, Beijing, China), and in the ModelLab Matman general chemometrics solution software, the gas chromatography tandem ion mobility spectrometry data of the same brand of finished cigarette paper samples after combustion produced by different cigarette factories are used for pattern recognition of sensory omics data by using at least one of the principal component analysis (PCA) modeling algorithm, the partial least squares discriminant (PLS-DA) modeling algorithm and the random forest pattern recognition modeling algorithm.

[0068] The preprocessing algorithm used by the principal component analysis modeling algorithm is UV scaling.

[0069] Figure 2 The PCA analysis scores and the interval plot of Totelling's T2 distribution interval test (P=0.05) for dimensionality reduction analysis of GC-IMS data of finished cigarette samples of the same brand from four different cigarette factories processed in different locations after combustion; Figure 2 The results show that in terms of smoke composition after cigarettes are ignited, the differences in products of various manufacturers are further reduced, and the four types of samples cannot be ideally distinguished in the current model. At this time, the principal component analysis (PCA) method of the unsupervised algorithm cannot effectively distinguish the differences between the groups of smoke samples, indicating that the linearity of the compound differences between the sample groups is not high.

[0070] Figure 2 It shows that there are outlier samples in the manufacturer's samples that exceed Totelling's T2 95% confidence interval (indicated by the ellipse), and there are significant differences between 2 batches of cigarette paper samples and the other 18 batches of samples, indicating that there are objective differences in the volatile components of the relevant batches of samples, providing an analytical basis for the subsequent traceability of product sensory quality.

[0071] Furthermore, the number of latent variables retained by the partial least squares discriminant (PLS-DA) modeling algorithm is 5; k=7 cross-validation is used in cross-validation; the number of random simulations is 10 times; and the preprocessing algorithm used is UV scaling.

[0072] Figure 3 This is the cumulative prediction variance trend chart of PLS-DA pattern recognition of GC-IMS data of finished cigarette paper after combustion of four manufacturers. Figure 3The number of latent variables selected for model building shows the changes in the prediction accuracy (cumulative prediction variance) of the independent variable (compound peak area, curve 1) and the dependent variable (sample grouping, curve 2). The model trend line in the figure - curve 2 for the prediction fit of the post-combustion sample classification does not increase but decreases after reaching 0.7, indicating that the model is underfitting and the prediction accuracy is not optimal. Figure 3 The partial least squares discriminant analysis (PLS-DA) of the supervised algorithm also shows that the recognition rate is not high (<65%).

[0073] Furthermore, the number of decision trees used in the random forest pattern recognition modeling algorithm is 100; the eigenvalue algorithm used is Sqrt; the maximum tree depth is 30; the minimum impurity reduction is 0.01; and the preprocessing algorithm used is UV scaling.

[0074] Figure 4 Schematic diagram of the confusion matrix of random forest pattern recognition prediction for GC-IMS data of finished cigarette paper after combustion from four manufacturers. Figure 4 Displays the accuracy of the pattern recognition model's prediction results for each group of samples. When all predictions are correct, all samples will be located on the diagonal of the matrix in the figure. If the prediction does not match the actual grouping, or there is no attribution, the corresponding number of misjudged samples will deviate from the diagonal display.

[0075] Figure 5 This is a flowchart showing the random forest decision tree topology diagram of the GC-IMS data of the finished cigarette paper after combustion from four manufacturers. Figure 5 The decision topology of one of the trees in the random forest is shown as an example decision process. After the binary tree method is used to divide each key variable in turn (≤ comparison), the correct prediction for each type of sample is finally obtained (classification is marked with different symbols).

[0076] Figure 6 This is a bar chart of the random forest independent variable contribution scores for the GC-IMS data of the finished cigarette paper after combustion from four manufacturers. Figure 6 The statistical results show that all independent variables (compounds) contribute to the random forest prediction grouping (binary tree purity increases, entropy decreases), arranged from high to low.

[0077] Figure 4-Figure 6 The results show that the random forest algorithm can still correctly distinguish up to 100% of the samples, indicating that there are still nonlinear objective differences between different batches of products. It suggests that the smoke composition of the products of various manufacturers is further consistent during the puffing process after ignition, but there are still subtle differences that can be identified by sensory omics analysis methods.

[0078] Step S3: Based on the pattern recognition results, the sensory consistency of cigarette products of different production points of multi-point processed varieties when lit and smoked is evaluated.

[0079] Specifically, based on the pattern recognition results, the sensory consistency (difference) of cigarette products from multi-point processed varieties with different production points when ignited and smoked can be evaluated, and the key characteristic series of compounds that affect the composition of volatile compounds and aroma characteristics can be screened out.

[0080] Figure 2-Figure 6 The results show that the principal component analysis modeling algorithm used in the present invention has no significant effect on the fingerprint similarity evaluation of finished cigarette paper samples after burning from four different manufacturers, namely Dali, Shankun, Ulanhot and Haihong. The recognition rate of qualitative pattern recognition using the partial least squares discriminant modeling algorithm is less than 65%, and the recognition rate of qualitative pattern recognition using the random forest pattern recognition modeling algorithm can reach 100%. This suggests that the smoke composition of samples from different manufacturers tends to be more consistent during the puffing process after ignition, but there are still subtle differences that can be identified by sensory omics analysis methods.

[0081] The chemometrics and sensory omics analysis method for the finished cigarette paper after combustion provided in the embodiment of the present invention adopts an unsupervised combined with supervised pattern recognition method to perform feature analysis and pattern recognition based on the sample GC-IMS sensory omics data on the finished cigarette paper samples after combustion in the complex system of flavors and fragrances. The sensory component analysis method can be used to identify the different components of the smoke after ignition, and the finished cigarette paper samples of the same brand after combustion processed in different places can be effectively distinguished, and the key characteristic series of compounds that affect the composition of volatile compounds and the aroma characteristics can be screened out, which is helpful to achieve accurate evaluation of the overall quality of the product.

[0082] So far, various embodiments of the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0083] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified or some technical features may be replaced by equivalents without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for chemometrics and sensory omics analysis of finished cigarette paper after combustion, characterized in that: include: The smoke of finished cigarette paper samples of the same brand processed in different places after burning is tested, and the gas chromatography tandem ion mobility spectrometry data of finished cigarette paper samples of the same brand produced by different cigarette factories after burning are obtained; The sensory omics data were characterized and pattern recognized by combining unsupervised and supervised pattern recognition methods on the gas chromatography tandem ion mobility spectrometry data of the same brand of finished cigarette paper samples produced by different cigarette factories. Based on the pattern recognition results, the sensory consistency of cigarette products from different production points of multi-point processed varieties when lit and smoked is evaluated; ModelLab Matman universal chemometrics solution software was used to perform sensory omics data feature analysis on the gas chromatography tandem ion mobility spectrometry data of the same brand of finished cigarette paper samples produced by different cigarette factories after combustion. In the ModelLab Matman general chemometrics solution software, at least one of the principal component analysis modeling algorithm, the partial least squares discriminant modeling algorithm, and the random forest pattern recognition modeling algorithm is used to perform characteristic analysis and pattern recognition of sensory omics data on gas chromatography tandem ion mobility spectrometry data of finished cigarette paper samples of the same brand after combustion produced by different cigarette factories, and a pattern recognition model is established; Based on the pattern recognition results, the sensory consistency of cigarette products from different production points of multi-point processed varieties when ignited and smoked was evaluated, and the key iconic series of compounds that affect the composition of volatile compounds and aroma characteristics were screened out.

2. The chemometrics and sensory omics analysis method of finished cigarette paper after combustion according to claim 1, characterized in that: The smoke from the finished cigarette paper samples of the same brand processed in different places after combustion is detected to obtain the gas chromatography tandem ion mobility spectrometry data of the finished cigarette paper samples of the same brand produced by different cigarette factories after combustion, specifically including: Collect smoke from the combustion of finished cigarette paper samples of the same brand processed in a different location; The collected smoke after combustion was tested by GC-IMS flavor analyzer, and the gas chromatography ion mobility spectrometry data of the finished cigarette paper samples of the same brand after combustion produced by different cigarette factories were obtained.

3. The chemometrics and sensory omics analysis method of finished cigarette paper after combustion according to claim 2, characterized in that: The smoke collected after burning the finished cigarette paper samples of the same brand processed in a different place specifically includes: After removing the tobacco shreds, different cigarette samples were subjected to cigarette paper burning test on an automatic smoking machine and smoke was collected. A 44mm Cambridge filter was used to collect the total particulate matter for each pore sample, and the smoke was collected through a silica gel gas collection bag and connected to the GC-IMS sampling device.

4. The method for chemometrics and sensory omics analysis of finished cigarette paper after combustion according to claim 2, characterized in that: The GC-IMS flavor analyzer is used to detect the collected smoke after combustion, and the gas chromatography tandem ion mobility spectrometry data of the finished cigarette paper samples of the same brand after combustion produced by different cigarette factories are obtained, which specifically includes: The GC-IMS flavor analyzer was used to test the finished cigarette paper samples of the same brand and different batches produced by different cigarette factories of the same tobacco group. Each sample was injected and measured three times to obtain the gas chromatography-tandem ion mobility spectra and typical compound identification results of the aroma volatile compounds of each finished cigarette paper sample.

5. The method for chemometrics and sensory omics analysis of finished cigarette paper after combustion according to claim 2, characterized in that: Before using the GC-IMS flavor analyzer for detection, the method further comprises: The samples were pre-treated, including: 0.5 g of cigarette paper was placed in a 20 mL headspace vial and incubated at 90 °C for 20 min before sampling.

6. The method for chemometrics and sensory omics analysis of finished cigarette paper after combustion according to claim 2, characterized in that: The headspace injection conditions for the GC-IMS flavor analyzer include: The injection volume was 200ul; the incubation time was 20min; the incubation temperature was 90°C; the injection needle temperature was 95°C; the incubation speed was 500rpm; The chromatographic conditions for the GC-IMS flavor analyzer include: The chromatographic conditions of gas phase-ion mobility spectrometry were as follows: analysis time 20 min; column type WAX; column length 30 m; inner diameter ID-0.53 mm; film thickness FT 1 μm; column temperature 60 °C; carrier gas / drift gas N2; IMS temperature 45 °C; GC chromatographic conditions: when the injection time is 0, the drift gas flow rate is 150 mL / min, the carrier gas flow rate is 2 ml / min, and the acquisition state is rec; when the injection time is 2 min, the drift gas flow rate is 150 mL / min, and the carrier gas flow rate is 10 ml / min; when the injection time is 20 min, the drift gas flow rate is 150 mL / min, and the carrier gas flow rate is 100 ml / min; when the injection time is 30 min, the drift gas flow rate is 150 mL / min, the carrier gas flow rate is 100 ml / min, and the acquisition state is stop.

7. The method for chemometrics and sensory omics analysis of finished cigarette paper after combustion according to claim 1, characterized in that: The preprocessing algorithm used by the principal component analysis modeling algorithm is UV scaling; the number of latent variables retained by the partial least squares discriminant modeling algorithm is 5, k=7 cross-validation is used in cross-validation, the number of random simulations is 10 times, and the preprocessing algorithm used is UV scaling for the independent variable.

8. The method for chemometrics and sensory omics analysis of finished cigarette paper after combustion according to claim 1, characterized in that: The number of decision trees used in the random forest pattern recognition modeling algorithm is 100; the eigenvalue algorithm used is Sqrt; the maximum tree depth is 30; the minimum impurity reduction is 0.01; and the preprocessing algorithm used is UV scaling.

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