CN114755330A - Chemometrics and organoleptic analysis method of finished cigarette paper after combustion - Google Patents
Chemometrics and organoleptic analysis method of finished cigarette paper after combustion Download PDFInfo
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- 235000019504 cigarettes Nutrition 0.000 title claims abstract description 150
- 238000002485 combustion reaction Methods 0.000 title claims abstract description 68
- 238000004458 analytical method Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 claims abstract description 39
- 230000001953 sensory effect Effects 0.000 claims abstract description 33
- 238000003909 pattern recognition Methods 0.000 claims abstract description 29
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- 230000000391 smoking effect Effects 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 10
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 8
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- 238000004422 calculation algorithm Methods 0.000 claims description 35
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- 150000001875 compounds Chemical class 0.000 claims description 21
- 239000007789 gas Substances 0.000 claims description 18
- 238000007637 random forest analysis Methods 0.000 claims description 18
- 238000000513 principal component analysis Methods 0.000 claims description 13
- 239000012159 carrier gas Substances 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 9
- 239000000203 mixture Substances 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 8
- 241000208125 Nicotiana Species 0.000 claims description 7
- 235000002637 Nicotiana tabacum Nutrition 0.000 claims description 7
- 238000002347 injection Methods 0.000 claims description 7
- 239000007924 injection Substances 0.000 claims description 7
- 238000002790 cross-validation Methods 0.000 claims description 6
- 238000011534 incubation Methods 0.000 claims description 6
- 238000003066 decision tree Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 5
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 3
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- 239000011148 porous material Substances 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
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- 229910002027 silica gel Inorganic materials 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
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- 238000002203 pretreatment Methods 0.000 claims 1
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- DKNWSYNQZKUICI-UHFFFAOYSA-N amantadine Chemical compound C1C(C2)CC3CC2CC1(N)C3 DKNWSYNQZKUICI-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses a chemometrics and organoleptic analysis method of finished cigarette paper after combustion, which comprises the following steps: detecting the flue gas generated after the burning of the cigarette paper sample finished product of the same brand processed in different places to obtain GC-IMS data of the cigarette paper sample finished product of the burned brand; performing sensory omics data characteristic analysis and mode identification on GC-IMS data of the burnt finished cigarette paper samples produced by different cigarette factories by adopting an unsupervised and supervised mode identification method; and evaluating the sensory consistency of the cigarette products of different production points of the multi-point processing variety during lighting and smoking according to the pattern recognition result. According to the chemometrics and sensory omics analysis method for the finished cigarette paper after combustion, provided by the invention, the difference components of the ignited smoke can be identified by a sensory component analysis method, and the finished cigarette paper samples after combustion of the same brand processed in different places are effectively distinguished, so that the accurate evaluation of the overall quality of the product is facilitated.
Description
Technical Field
The invention relates to the technical field of tobacco product quality evaluation, in particular to a chemometrics and organoleptic analysis method of a finished cigarette paper after combustion.
Background
The essence and spice system is special cigarette paper prepared by adding essence, spice, extract and materials thereof with functions of increasing aroma, sweetening, coloring and the like in the cigarette paper manufacturing process. When the cigarette is burnt, the aroma-giving additive on the cigarette paper releases aroma components in modes of volatilization, cracking and the like so as to achieve the purpose of giving aroma with certain characteristics. In recent years, cigarette paper aroma endowing technology has been widely applied to high-end cigarette production to improve the smoking quality of cigarettes, and has the advantages of effectively covering cigarette offensive odor, endowing sweet feeling to smoke, reducing cigarette irritation, increasing the softness and fineness of the smoke and the like. Because the essence and spice system contains numerous volatile components, complex components and low aroma component content, the raw materials of the essence and spice system are difficult to trace, an effective stability monitoring method is lacked, and other problems, a mature and reliable quality control system aiming at the quality of the essence and spice system is not established at present, and the sensory quality stability of cigarette brands is influenced.
At present, the main methods for controlling the quality of the flavors and fragrances in China still are physical judgment indexes such as acidity, miscibility, refractive index, density and the like, in addition, the existing GC/MS method in China has the defects of specificity and insufficient sensitivity for detecting trace aroma compounds, mainly takes a targeted compound qualitative and quantitative analysis method as a main method, and lacks the integral quality evaluation means for a complex system of a flavor and fragrance system. The above problems constitute a short board for the quality control of current flavors and fragrances.
How to effectively carry out chemometrics and sensory omics analysis on the finished cigarette paper after combustion and further analyze an essence and spice complex system so as to realize accurate evaluation of the overall quality of the product, and the method becomes a key technical bottleneck to be solved urgently in the tobacco industry.
Therefore, a method for chemometric and organoleptic analysis of the finished cigarette paper after combustion is needed.
Disclosure of Invention
The invention aims to provide a chemometric and sensory omics analysis method of finished cigarette paper after combustion, which aims to solve the problems in the prior art, can perform characteristic analysis and mode identification based on sample GC-IMS sensory omics data on a finished cigarette paper sample after combustion by adopting an unsupervised and supervised mode identification method, evaluates the sensory consistency of cigarette products at different production points of a multi-point processing variety during ignition and smoking, and is favorable for realizing accurate evaluation of the overall quality of the product.
The invention provides a chemometrics and organoleptic analysis method of finished cigarette paper after combustion, which comprises the following steps:
detecting the smoke of the cigarette paper sample which is processed in different places and has the same brand after combustion to obtain the gas chromatography series ion mobility spectrometry data of the cigarette paper sample which is produced by different cigarette factories and has the same brand after combustion;
performing feature analysis and mode identification of sensory omics data on gas chromatography series ion mobility spectrometry data of cigarette paper samples of the same brand produced by different cigarette factories after combustion by adopting an unsupervised and supervised combined mode identification method;
and evaluating the sensory consistency of the cigarette products of different production points of the multi-point processing variety during lighting and smoking according to the pattern recognition result.
The chemometrics and organoleptic analysis method of the finished cigarette paper after combustion as described above, wherein preferably, the method for detecting the flue gas after combustion of the finished cigarette paper sample of the same brand processed in different places to obtain the gas chromatography tandem ion mobility spectrometry data of the finished cigarette paper sample of the same brand after combustion produced by different cigarette factories specifically comprises:
collecting flue gas generated after combustion of finished cigarette paper samples of the same brand processed in different places;
and detecting the collected burned smoke by adopting a GC-IMS flavor analyzer to obtain gas chromatography series ion mobility spectrometry data of the burned finished cigarette paper samples of the same brand produced by different cigarette factories.
The above method for analyzing the stoichiometry and organoleptic properties of the finished cigarette paper after burning preferably collects the burned smoke of the finished cigarette paper sample of the same brand processed in different places, and specifically comprises the following steps:
and performing a cigarette paper combustion test on different cigarette samples with cut tobaccos removed on an automatic smoking machine and collecting smoke, wherein 1 sheet of 44mm Cambridge filter disc is used for collecting total particulate matters for each pore channel sample, the smoke is collected through a silica gel gas collection bag, and the smoke is connected into a GC-IMS sample feeding device.
Preferably, the method for analyzing the stoichiometry and organoleptic properties of the finished cigarette paper after combustion uses a GC-IMS flavor analyzer to detect the collected flue gas after combustion, so as to obtain gas chromatography tandem ion mobility spectrometry data of cigarette paper samples of the same brand produced by different cigarette factories, and specifically comprises the following steps:
detecting the burnt finished cigarette paper samples of the same brand and different batches of different cigarette factories of the same tobacco group by adopting a GC-IMS flavor analyzer, and repeatedly injecting and measuring each sample for 3 times to obtain a gas chromatography series ion mobility spectrometry and a typical compound identification result of the aroma volatile compounds of each burnt finished cigarette paper sample.
The chemometric and organoleptic analysis method of the finished cigarette paper after burning as described above, wherein preferably, before the detection is performed by using the GC-IMS flavor analyzer, the method further comprises:
the pretreatment of the sample specifically comprises the following steps:
0.5g of the cigarette paper is put into a 20mL headspace bottle and is injected after being incubated for 20min at 90 ℃.
In the chemometric and organoleptic analysis method of the finished cigarette paper after combustion, preferably, the headspace sampling conditions in the detection by the GC-IMS flavor analyzer include:
the sample injection volume is 200 ul; the incubation time is 20 min; the incubation temperature is 90 ℃; the temperature of the sample injection needle is 95 ℃; the hatching rotating speed is 500 rpm;
the chromatographic conditions when the GC-IMS flavor analyzer is adopted for detection comprise that:
the chromatographic conditions of gas-ion mobility spectrometry are as follows: the analysis time is 20 min; the chromatographic column type is WAX; the column length is 30 m; the inner diameter is ID-0.53 mm; the film thickness is FT 1 μm; the column temperature is 60 ℃; carrier/drift gas of N2(ii) a The IMS temperature is 45 ℃;
GC chromatographic conditions were as follows: the drift gas flow rate is 150mL/min when the sample introduction time is 0, the carrier gas flow rate is 2mL/min, and the collection state is rec; the drift gas flow rate is 150mL/min and the carrier gas flow rate is 10mL/min when the sample introduction time is 2 min; the drift gas flow rate is 150mL/min when the sample introduction time is 20min, and the carrier gas flow rate is 100 mL/min; the drift gas flow rate is 150mL/min when the sample introduction time is 30min, the carrier gas flow rate is 100mL/min, and the collection state is stop.
The method for chemometric and organoleptic analysis of the finished cigarette paper after combustion as described above, wherein preferably, the method for performing characteristic analysis and pattern recognition of organoleptic data on gas chromatography tandem ion mobility spectrometry data of the finished cigarette paper samples of the same brand produced in different cigarette factories by adopting an unsupervised and supervised pattern recognition method specifically comprises:
performing feature analysis on sensory omics data on gas chromatography series ion mobility spectrometry data of cigarette paper samples of finished products produced by different cigarette factories after combustion with the same brand through ModelLab Matman general chemometrics solution software;
in ModelLab Matman general chemometrics solution software, by at least one of a principal component analysis modeling algorithm, a partial least squares discriminant modeling algorithm and a random forest pattern recognition modeling algorithm, sensory omics data characteristic analysis and pattern recognition are carried out on gas chromatography series ion mobility spectrometry data of cigarette paper samples of the same brand of finished products produced by different cigarette factories after combustion, and a pattern recognition model is established.
The chemometric and organoleptic analysis method of the finished cigarette paper after combustion is as described above, wherein preferably, the preprocessing algorithm adopted by the principal component analysis modeling algorithm is UV scaling; the number of latent variables reserved by the partial least square discriminant modeling algorithm is 5, the k-7-fold cross validation is adopted in the cross validation, the number of times of random simulation is 10, and the adopted preprocessing algorithm is UV scaling aiming at independent variables.
The chemometrics and organoleptic analysis method of the finished cigarette paper after combustion is characterized in that the number of decision trees adopted by the random forest pattern recognition modeling algorithm is preferably 100; the adopted characteristic value algorithm is Sqrt; the maximum tree depth is 30; the minimum purity is reduced to 0.01; the pre-processing algorithm used was UV scaling.
The chemometric and organoleptic analysis method of the finished cigarette paper after burning as described above, wherein preferably, the sensory consistency of the cigarette products of different production points of the multi-point processing variety when being smoked is evaluated according to the pattern recognition result, which specifically comprises:
and according to the pattern recognition result, evaluating the sensory consistency of the cigarette products of different production points of the multipoint processing varieties during lighting and smoking, and screening out key landmark series compounds influencing the composition and the aroma characteristics of the volatile compounds.
The invention provides a chemometric and sensory omics analysis method of finished cigarette paper after combustion, which is characterized in that a characteristic analysis and mode recognition based on sample GC-IMS sensory omics data are carried out on a finished cigarette paper sample after combustion in an essence and spice complex system by adopting an unsupervised and supervised mode recognition method, the different components of the smoke after ignition can be recognized by a sensory component analysis method, the finished cigarette paper sample after combustion adopting the same brand processed in different places can be effectively distinguished, and key marker series compounds influencing the composition and aroma characteristics of volatile compounds are screened, so that the accurate evaluation of the integral quality of products is facilitated.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the accompanying drawings, in which:
figure 1 is a flow diagram of an embodiment of a method of chemometric and organoleptic analysis of a finished cigarette paper after combustion provided by the present invention;
FIG. 2 is a graph of GC-IMS data PCA analysis score and Totelling's T2 interval of finished cigarette paper after burning for 4 manufacturing enterprises;
FIG. 3 is a graph of GC-IMS data PLS-DA pattern recognition cumulative predicted variance trend of finished cigarette paper after combustion for 4 manufacturing enterprises;
FIG. 4 is a schematic diagram of a GC-IMS data random forest pattern recognition prediction confusion matrix of a finished cigarette paper after combustion in 4 production enterprises;
FIG. 5 is a flow chart illustrating GC-IMS data random forest decision tree topology graphs of finished cigarette paper after combustion for 4 manufacturing enterprises;
figure 6 is a histogram of GC-IMS data random forest independent variable contribution scores for finished cigarette paper after combustion for 4 manufacturing enterprises.
Detailed Description
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 disclosure, its application, or uses. The present disclosure may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that: the relative arrangement of parts and steps, the composition of materials, numerical expressions and numerical values set forth in these embodiments are to be construed as merely illustrative, and not as limitative, unless specifically stated otherwise.
As used in this disclosure, "first", "second": and the like, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element preceding the word comprises the element listed after the word, and does not exclude the possibility that other elements may also be included. "upper", "lower", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
In the present disclosure, when a specific component is described as being positioned between a first component and a second component, there may or may not be an intervening component between the specific component and the first component or the second component. When it is described that a specific component is connected to other components, the specific component may be directly connected to the other components without having an intervening component, or may be directly connected to the other components without having an intervening component.
All terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
As shown in fig. 1, the chemometrics and organoleptic analysis method of the finished cigarette paper after combustion provided in this embodiment specifically includes, in an actual implementation process:
and S1, detecting the smoke of the cigarette paper sample which is processed in different places and has the same brand finished product after burning to obtain the gas chromatography series ion mobility spectrometry (GC-IMS) data of the cigarette paper sample which is produced by different cigarette factories and has the same brand finished product after burning.
In one embodiment of the method for chemometric and organoleptic analysis of the finished cigarette paper after burning of the present invention, the step S1 may specifically include:
and step S11, collecting the smoke generated after burning the finished cigarette paper sample of the same brand processed in different places.
Specifically, a cigarette paper combustion test is carried out on different cigarette samples with cut tobaccos removed on an automatic smoking machine, smoke is collected, wherein 1 sheet of 44mm Cambridge filter disc is used for collecting total particulate matters for each pore channel sample, the smoke is collected through a silica gel gas collection bag, and the smoke is connected into a GC-IMS sample injection device. It should be noted that the invention is not limited to the manufacturers and models of the automatic smoking machines.
And S12, detecting by using a GC-IMS flavor analyzer to obtain gas chromatography series ion mobility spectrometry data of the cigarette paper samples of the finished products produced by different cigarette factories after combustion with the same brand.
Illustratively, the detection can be performed using a german g.a.s.flavourpec GC-IMS flavour analyzer. The invention does not specifically limit the manufacturer and model of the GC-IMS flavor analyzer.
The GC-IMS combines the advantages of high separation degree of gas chromatography and high sensitivity of ion mobility spectrometry, can quickly detect trace volatile organic compounds in a sample without any special sample pretreatment, and is used for measuring volatile headspace components in a solid or liquid sample.
Specifically, a GC-IMS flavor analyzer is adopted to detect the burnt finished cigarette paper samples (shown in table 1) of the same brand and different batches of the same tobacco group and different cigarette factories, and the sample introduction and the measurement are repeated for 3 times for each sample, so that the gas chromatography series ion mobility spectrometry and the typical compound identification result of the aroma volatile compounds of each burnt finished cigarette paper sample are obtained.
Wherein the present invention, in one embodiment, tests cigarette paper samples from 20 batches of finished cigarettes of the same brand from 4 different manufacturers of the university (three batches), mountain queen (two batches), grand and amantadine (three batches), haihong (two batches) cloud. The present invention is not limited to the number of different manufacturing facilities, the number of samples corresponding to each manufacturing facility, and the number of batches.
TABLE 1 general data sheet of burnt finished cigarette paper samples for modeling analysis
Specifically, headspace sampling conditions when a GC-IMS flavor analyzer is used for detection include:
the sample injection volume is 200 ul; the incubation time is 20 min; the incubation temperature is 90 ℃; the temperature of the sample injection needle is 95 ℃; the hatching rotating speed is 500 rpm;
the chromatographic conditions when the GC-IMS flavor analyzer is adopted for detection comprise that:
the chromatographic conditions of gas-ion mobility spectrometry are as follows: the analysis time is 20 min; the chromatographic column type is WAX; the column length is 30 m; the inner diameter is ID-0.53 mm; the film thickness is FT 1 μm; the column temperature is 60 ℃; carrier/drift gas of N2(ii) a The IMS temperature is 45 ℃;
GC chromatographic conditions: the drift gas flow rate is 150mL/min when the sample introduction time is 0, the carrier gas flow rate is 2mL/min, and the collection state is rec; the drift gas flow rate is 150mL/min and the carrier gas flow rate is 10mL/min when the sample introduction time is 2 min; the drift gas flow rate is 150mL/min when the sample introduction time is 20min, and the carrier gas flow rate is 100 mL/min; the drift gas flow rate is 150mL/min when the sample introduction time is 30min, the carrier gas flow rate is 100mL/min, and the collection state is stop.
Before the detection is carried out by using the GC-IMS flavor analyzer, the method further comprises the following steps:
the pretreatment of the sample specifically comprises the following steps:
0.5g of the cigarette paper is put into a 20mL headspace bottle and is injected after being incubated for 20min at 90 ℃.
And S2, performing feature analysis and mode identification of sensory omics data on gas chromatography series ion mobility spectrometry data of the same brand of finished cigarette paper samples produced by different cigarette factories after combustion by adopting an unsupervised and supervised mode identification method.
Specifically, in the present invention, the sensory data characteristic analysis is performed on the gas chromatography tandem ion mobility spectrometry data of the same brand of post-combustion finished cigarette paper samples produced by different cigarette factories through the model lab mat man universal chemometrics solution software (Chemmind Technologies, Beijing, China), and the mode recognition of the sensory data is performed on the gas chromatography tandem ion mobility spectrometry data of the same brand of post-combustion finished cigarette paper samples produced by different cigarette factories through at least one of the Principal component analysis (Principal component analysis, PCA), the partial least squares discriminant (PLS-DA) modeling algorithm and the random forest (random forest) mode recognition modeling algorithm in the model lab mat man universal chemometrics solution software.
And the preprocessing algorithm adopted by the principal component analysis modeling algorithm is UV scaling.
FIG. 2 is a graph of PCA analysis score and Totelling's T2 distribution interval test (P is 0.05) of GC-IMS data after combustion of same brand finished cigarette samples from 4 different cigarette factories processed in different places; fig. 2 shows that in the aspect of smoke composition after the cigarette is ignited, product differences of each production enterprise are further reduced, 4 types of samples cannot be ideally distinguished in the current model, and at the moment, a Principal Component Analysis (PCA) method of an unsupervised algorithm cannot effectively distinguish differences of smoke samples of each group, so that the linearity degree of compound differences among sample groups is low.
Fig. 2 shows that in the factory samples, outliers (shown by ellipses) exceeding the tolling's T295% confidence interval exist, and there is a significant difference between the 2 batches of cigarette paper samples and the other 18 batches of samples, which indicates that there is an objective difference in the volatile components of the samples of the relevant batches, and provides an analysis basis for the subsequent product sensory quality tracing.
Further, the number of latent variables reserved by the partial least squares discriminant (PLS-DA) modeling algorithm is 5; adopting k-7-fold cross validation in the cross validation; the number of random simulation is 10; the pre-processing algorithm used was UV scaling.
Figure 3 is a graph of GC-IMS data PLS-DA pattern recognition cumulative predictive variance trend of finished cigarette paper after combustion for 4 production enterprises. Fig. 3 shows the variation of the number of latent variables selected for model modeling with respect to the accuracy of prediction (cumulative prediction variance) for independent variables (compound peak area, curve one) and dependent variables (sample grouping, curve two), respectively. The prediction fitting degree of the model trend line-curve pair in the graph for the classification of the burned samples does not rise or fall after reaching 0.7, and the model is prompted to have under-fitting and non-optimal prediction precision. Fig. 3 shows that the supervised algorithm partial least squares discrimination (PLS-DA) is also not high (< 65%) in recognition rate.
Furthermore, the number of decision trees adopted by the random forest pattern recognition modeling algorithm is 100; the adopted characteristic value algorithm is Sqrt; the maximum tree depth is 30; the minimum impure degree is reduced to 0.01; the pre-processing algorithm used was UV scaling.
FIG. 4 is a schematic diagram of a GC-IMS data random forest pattern recognition prediction confusion matrix of a finished cigarette paper after combustion in 4 production enterprises. FIG. 4 shows the accuracy of the pattern recognition model for the grouped predictions for each grouped sample. When all predictions are correct, all samples will be located on the diagonal of the matrix in the graph, and if the prediction does not conform to the actual grouping or is not attributed, the corresponding number of misjudged samples will deviate from the diagonal display.
Figure 5 is a flow chart illustrating a GC-IMS data random forest decision tree topology graph of finished cigarette paper after combustion for 4 manufacturing enterprises. FIG. 5 shows a decision topology for one of the trees in a random forest, used as an example decision flow. And sequentially dividing (comparing less than or equal to) each key variable through a binary tree method, and finally obtaining correct prediction (classifying by different symbol identifiers) of each type of sample.
Figure 6 is a bar graph of GC-IMS data random forest independent contribution scores for finished cigarette paper after combustion for 4 manufacturing enterprises. Fig. 6 shows the statistical results of ranking all independent variables (compounds) from high to low in the contribution degree (increase in binary tree purity, decrease in entropy) to random forest prediction groups.
Fig. 4-6 show that the random forest (random forest) algorithm can still obtain correct discrimination of up to 100% of samples, which indicates that there is still non-linear objective difference between different batches of products. The smoking process after ignition of the products of each production enterprise is further consistent in smoke composition, but slight differences which can be identified by a sensory omics analysis method still exist.
And step S3, evaluating the sensory consistency of the cigarette products of different production points of the multi-point processing variety during lighting and smoking according to the pattern recognition result.
Specifically, according to the pattern recognition result, the sensory consistency (difference) of cigarette products of different production points of the multipoint processing variety during lighting and smoking can be evaluated, and key symbolic series compounds which influence the composition and aroma characteristics of volatile compounds can be screened out.
The results of fig. 2-6 show that the distinguishing effect of the principal component analysis modeling algorithm adopted by the invention on the fingerprint similarity evaluation of finished cigarette paper samples after combustion in 4 different production enterprises such as big theory, mountain kun, grand orchid, and Haihong is not significant, the recognition rate of qualitative pattern recognition by adopting the partial least square discriminant modeling algorithm is lower than 65%, and the recognition rate of qualitative pattern recognition by adopting the random forest pattern recognition modeling algorithm can reach 100%. The smoking process after ignition of different production enterprise samples is further consistent in smoke composition, but slight differences which can be identified by a sensory omics analysis method still exist.
According to the chemometrics and organoleptic analysis method for the finished cigarette paper after combustion, provided by the embodiment of the invention, the characteristic analysis and the mode identification based on sample GC-IMS organoleptic data are carried out on the finished cigarette paper sample after combustion in an essence and spice complex system by adopting an unsupervised and supervised mode identification method, the difference component of the smoke after ignition can be identified by a sensory component analysis method, the finished cigarette paper sample after combustion of the same brand processed in different places can be effectively distinguished, and key landmark series compounds influencing the composition and the aroma characteristics of volatile compounds are screened, so that the accurate evaluation of the integral quality of the product is facilitated.
Thus far, various embodiments of the present disclosure have been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be understood by those skilled in the art that various changes may be made in the above embodiments or equivalents may be substituted for elements thereof without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (10)
1. A chemometrics and organoleptic analysis method of finished cigarette paper after combustion is characterized by comprising the following steps:
detecting the smoke of the cigarette paper sample which is processed in different places and has the same brand after combustion to obtain the gas chromatography series ion mobility spectrometry data of the cigarette paper sample which is produced by different cigarette factories and has the same brand after combustion;
performing feature analysis and mode identification of sensory omics data on gas chromatography series ion mobility spectrometry data of cigarette paper samples of the same brand produced by different cigarette factories after combustion by adopting an unsupervised and supervised combined mode identification method;
and evaluating the sensory consistency of the cigarette products of different production points of the multi-point processing variety during lighting and smoking according to the pattern recognition result.
2. The method for chemometric and sensoric analysis of finished cigarette paper after combustion according to claim 1, wherein the method for detecting flue gas after combustion of finished cigarette paper samples of the same brand processed in different places to obtain gas chromatography tandem ion mobility spectrometry data of the finished cigarette paper samples of the same brand produced in different cigarette factories specifically comprises:
collecting flue gas generated after combustion of finished cigarette paper samples of the same brand processed in different places;
and detecting the collected burned smoke by adopting a GC-IMS flavor analyzer to obtain gas chromatography series ion mobility spectrometry data of the burned finished cigarette paper samples of the same brand produced by different cigarette factories.
3. The chemometrics and sensorics analysis method of the finished cigarette paper after burning according to claim 2, wherein the collecting of the flue gas after burning of the finished cigarette paper sample of the same brand processed in a different place specifically comprises:
and performing a cigarette paper combustion test on different cigarette samples with cut tobaccos removed on an automatic smoking machine and collecting smoke, wherein 1 sheet of 44mm Cambridge filter disc is used for collecting total particulate matters for each pore channel sample, the smoke is collected through a silica gel gas collection bag, and the smoke is connected into a GC-IMS sample feeding device.
4. The chemometrics and sensorics analysis method of finished cigarette paper after combustion according to claim 2, characterized in that the collected flue gas after combustion is detected by a GC-IMS flavor analyzer to obtain gas chromatography tandem ion mobility spectrometry data of cigarette paper samples of finished cigarette paper after combustion of the same brand produced by different cigarette factories, specifically comprising:
detecting the burnt finished cigarette paper samples of the same brand and different batches of different cigarette factories of the same tobacco group by adopting a GC-IMS flavor analyzer, and repeatedly injecting and measuring each sample for 3 times to obtain a gas chromatography series ion mobility spectrometry and a typical compound identification result of the aroma volatile compounds of each burnt finished cigarette paper sample.
5. The method for chemometric and sensoromic analysis of finished cigarette paper after combustion according to claim 2, wherein prior to detection using a GC-IMS flavor analyzer, the method further comprises:
the pretreatment of the sample specifically comprises the following steps:
0.5g of cigarette paper is put into a 20mL headspace bottle, incubated at 90 ℃ for 20min and injected.
6. The method for chemometric and sensoromics analysis of finished cigarette paper after combustion according to claim 2, wherein the headspace sampling conditions when measured with a GC-IMS flavor analyzer comprise:
the sample injection volume is 200 ul; the incubation time is 20 min; the incubation temperature is 90 ℃; the temperature of the sample injection needle is 95 ℃; the hatching rotating speed is 500 rpm;
the chromatographic conditions when the GC-IMS flavor analyzer is adopted for detection comprise that:
the chromatographic conditions of gas-ion mobility spectrometry are as follows: the analysis time is 20 min; the chromatographic column type is WAX; the column length is 30 m; the inner diameter is ID-0.53 mm; the film thickness is FT 1 μm; the column temperature is 60 ℃; carrier/drift gas of N2(ii) a The IMS temperature is 45 ℃;
GC chromatographic conditions: the drift gas flow rate is 150mL/min when the sample introduction time is 0, the carrier gas flow rate is 2mL/min, and the collection state is rec; the drift gas flow rate is 150mL/min and the carrier gas flow rate is 10mL/min when the sample introduction time is 2 min; the drift gas flow rate is 150mL/min when the sample introduction time is 20min, and the carrier gas flow rate is 100 mL/min; the drift gas flow rate is 150mL/min when the sample introduction time is 30min, the carrier gas flow rate is 100mL/min, and the collection state is stop.
7. The chemometrics and sensorics analysis method of the finished cigarette paper after combustion according to claim 1, characterized in that the method of pattern recognition without supervision and with supervision is adopted to perform the characteristic analysis and pattern recognition of the sensorics data on the gas chromatography tandem ion mobility spectrometry data of the same brand finished cigarette paper after combustion samples produced by different cigarette factories, and specifically comprises the following steps:
performing feature analysis of sensory omics data on gas chromatography series ion mobility spectrometry data of cigarette paper samples of finished products produced by different cigarette factories after combustion with the same brand through ModelLab Matman general chemometrics solution software;
in ModelLab Matman general chemometrics solution software, by at least one of a principal component analysis modeling algorithm, a partial least squares discriminant modeling algorithm and a random forest pattern recognition modeling algorithm, sensory omics data characteristic analysis and pattern recognition are carried out on gas chromatography series ion mobility spectrometry data of cigarette paper samples of the same brand of finished products produced by different cigarette factories after combustion, and a pattern recognition model is established.
8. The method for chemometric and sensorics analysis of finished cigarette paper after burning of claim 7, wherein the pre-treatment algorithm employed by the principal component analysis modeling algorithm is UV scaling; the number of latent variables reserved by the partial least square discriminant modeling algorithm is 5, the k-7-fold cross validation is adopted in the cross validation, the number of times of random simulation is 10, and the adopted preprocessing algorithm is UV scaling aiming at independent variables.
9. The method for chemometric and sensoromics analysis of finished cigarette paper after combustion according to claim 7, wherein the number of decision trees employed by the random forest pattern recognition modeling algorithm is 100; the adopted characteristic value algorithm is Sqrt; the maximum tree depth is 30; the minimum impure degree is reduced to 0.01; the pre-processing algorithm used was UV scaling.
10. The method for chemometric and sensorinemic analysis of finished rolling paper after burning of claim 1, wherein the assessing sensory consistency of different production point cigarette products of a multi-point processing variety upon smoking based on pattern recognition results comprises:
and according to the pattern recognition result, evaluating the sensory consistency of the cigarette products of different production points of the multipoint processing varieties during lighting and smoking, and screening out key landmark series compounds influencing the composition and the aroma characteristics of the volatile compounds.
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