CN114965815B - Method for classifying and identifying aroma-added cigarette paper based on chemometrics-sensory group - Google Patents
Method for classifying and identifying aroma-added cigarette paper based on chemometrics-sensory group Download PDFInfo
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
- G01N30/8686—Fingerprinting, e.g. without prior knowledge of the sample components
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G01N30/02—Column chromatography
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N31/00—Investigating or analysing non-biological materials by the use of the chemical methods specified in the subgroup; Apparatus specially adapted for such methods
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Abstract
The invention discloses a method for classifying and identifying a flavored cigarette paper based on chemometrics-sensory suites, which comprises the following steps: performing qualitative olfactory analysis on aroma components of the aroma-added essence sample; GC-IMS fingerprint analysis is carried out on the cigarette paper, the finished cigarette and the aroma components of the aroma cigarette paper and the clean water sample after burning; chemometric modeling analysis is performed on the aroma components of various types of aroma-imparting cigarette paper, and a first pattern recognition model for classifying a clear water sample, aroma-imparting cigarette paper, a finished product and cigarette paper after combustion, a second pattern recognition model for classifying aroma-imparting cigarette paper of different specifications, a third pattern recognition model for classifying aroma-imparting cigarette paper of finished cigarettes of different cigarette factories, and a fourth pattern recognition model for classifying aroma-imparting cigarette paper after combustion of different cigarette factories are established. The method for classifying and identifying the aroma-imparting cigarette paper based on chemometrics-sensory suites is beneficial to monitoring the attenuation change rule and traceability analysis of aroma-imparting components of the aroma-imparting cigarette paper.
Description
Technical Field
The invention relates to the technical field of tobacco product quality evaluation, in particular to a method for classifying and identifying flavored cigarette paper based on chemometrics-sensory suites.
Background
The flavored cigarette paper is special cigarette paper prepared by adding essence and spice with the purposes of flavoring, sweetening, coloring and the like, extract and the like in the cigarette paper manufacturing process. When the perfuming cigarette paper burns, the perfuming additive releases the fragrance components in a way of volatilization, cracking and the like to achieve the purpose of endowing a certain characteristic fragrance. In recent years, the cigarette paper flavoring technology is widely applied to high-end cigarette production to improve the smoking quality of cigarettes, and has the advantages of effectively covering up the miscellaneous gases of cigarettes, endowing the sweet feeling of the cigarettes with smoke, reducing the irritation of the cigarettes, increasing the softness and fineness of the smoke and the like. The raw materials of the flavored cigarette paper are difficult to trace, and an effective stability monitoring method is lacked because the flavored cigarette paper contains a plurality of volatile components, the components are complex and the content of flavoring components is low.
At present, the main method for quality control of the essence and the spice in China is still physical judgment indexes such as acidity, miscibility, refractive index, density and the like, the existing GC/MS method in national standard has the defects of insufficient specificity and sensitivity for detecting trace aroma compounds, and mainly uses the qualitative and quantitative analysis method of the targeting compound as a main method, and lacks a whole quality evaluation means for a complex system of the aroma-added cigarette paper.
Therefore, there is a need for a method of identifying classes of flavored cigarette paper based on chemometrics-sensory groups.
Disclosure of Invention
The invention aims to provide a chemometrics-sensory-group-based classification and identification method for the aroma-imparting cigarette paper, which is used for solving the problems in the prior art and monitoring the attenuation change rule and traceability analysis of aroma components of the aroma-imparting cigarette paper.
The invention provides a chemometrics-sensory group-based method for classifying and identifying flavored cigarette paper, which comprises the following steps:
performing qualitative olfactory analysis on aroma components of the aroma-added essence sample;
Performing GC-IMS fingerprint analysis on the aroma components of the aroma cigarette paper, the aroma cigarette paper of the finished cigarette, the aroma cigarette paper after combustion and the clean water sample by utilizing the qualitative olfactory analysis result of the aroma components of the aroma essence sample;
Based on GC-IMS fingerprint analysis results of various types of the flavored cigarette paper, chemometric modeling analysis is carried out on the flavored components of various types of the flavored cigarette paper, and a first mode identification qualitative identification model for classifying a clear water sample, the flavored cigarette paper of a finished cigarette, the flavored cigarette paper after burning, a second mode identification qualitative identification model for classifying the flavored cigarette paper with different specifications, a third mode identification qualitative identification model for classifying the flavored cigarette paper of the finished cigarette of different cigarette factories and a fourth mode identification qualitative identification model for classifying the flavored cigarette paper after burning of different cigarette factories are respectively established.
The method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory suites, as described above, wherein the qualitative olfactory analysis is preferably performed on the perfuming ingredients of the perfuming essence sample, specifically comprises the following steps:
qualitative olfactory analysis is carried out on the aroma components of the aroma-added essence sample by adopting a gas chromatograph-quadrupole-time-of-flight mass spectrometer and an olfactory analyzer;
And identifying the aroma substances from the aroma essence sample according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactometer of the aroma components of the aroma essence sample.
The method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory groups as described above, wherein the qualitative and olfactory analysis of the perfuming ingredients of the perfuming essence sample is preferably performed by adopting a gas chromatograph-quadrupole-time-of-flight mass spectrometer and an olfactory analyzer, and specifically comprises the following steps:
Sample pretreatment: taking 0.5mL of essential oil sample in a 15mL headspace sample bottle, and reserving by GC-O-MS; diluting the essential oil sample 100 times by using a methanol solvent, and taking 1mL of diluted solution in a 2mL sample bottle for later use;
Extraction and sample introduction: after the solid phase microextraction arrow is aged for 15min at 250 ℃, the adsorption extraction is started: extracting at 80deg.C for 30 min, and desorbing at 250deg.C for 5 min; after sample injection, the solid phase microextraction arrow was aged at 250℃for 10 min.
The method for classifying and identifying the flavored cigarette paper based on chemometrics-sensory groups as described above, wherein the gas chromatographic conditions of the GC-MS are preferably as follows when qualitative sniffing analysis is performed:
sample inlet temperature: 250 ℃;
programming temperature: maintaining at 40deg.C for 1min; raising the temperature to 150 ℃ at 5 ℃/min, keeping the temperature at 1min ℃, raising the temperature to 300 ℃ at 30 ℃/min, and keeping the temperature for 2min;
Carrier gas: he gas;
Sample injection mode: sample introduction without diversion;
The mass spectrum conditions are as follows: the ion source EI, electron energy 70eV, the transmission line temperature 250 ℃, the ion source temperature 230 ℃, the mass range 30-600 m/z, the agilent MassHunter Unknows software and the NIST14 spectrum library are utilized to carry out unknown identification analysis, and the unknown identification analysis is searched according to the similarity;
Conditions of GC-O:
Transmission line temperature: 300 ℃, sniffing temperature: 100 ℃; sensory evaluation is carried out by 3 testers in a room with the temperature of 25+/-2 ℃ and the relative humidity of 50% -60%, fresh air and no wind, the testers register the time points when all the aroma substances exist, meanwhile, the specific attributes are analyzed, the evaluation result is subjected to 4-point classification, 1-point represents the weakest and 4-point represents the strongest;
Parameters of MS:
Scanning range: 100-1350 m/z, ion source gas 1:50; ion source gas 2:50, curtain gas: 35; temperature: ion spray voltage floating at 500 ℃): 5500V-4500V, wherein 5500V corresponds to positive ion mode and 4500V corresponds to negative ion mode.
The method for classifying and identifying the flavoring cigarette paper based on chemometrics-sensory suites as described above, wherein the method preferably identifies and identifies flavoring substances from flavoring essence samples according to the qualitative analysis result of a gas chromatography-time-of-flight mass spectrometry tandem sniffing instrument of flavoring essence samples, specifically comprises:
Qualitative analysis software is adopted by GC-IMS Library Search V2.2.1, and a built-in IMS database is utilized to carry out qualitative analysis on the aroma substances in the sample, and identified aroma substances comprise alcohols, phenols, esters, ethers and ketones.
The method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory suites as described above, wherein the method preferably uses the qualitative olfactory analysis result of the perfuming ingredients of the perfuming essence sample to perform GC-IMS fingerprint analysis on the perfuming cigarette paper, the perfuming cigarette paper of the finished cigarette, the perfuming cigarette paper after burning and the perfuming ingredients of the clean water sample, and specifically comprises the following steps:
sample pretreatment: 0.5g of tobacco paper is taken and placed in a 20mL headspace bottle, and is incubated at 90 ℃ for 20 min and then injected;
And (3) carrying out a combustion test on the burnt aroma-added cigarette paper: carrying out a cigarette paper combustion test on different cigarette samples from which cut tobacco is removed on an automatic smoking machine, collecting smoke, collecting a bulk particulate matter by using 1 Cambridge filter sheet with the diameter of 44mm for each pore channel sample, collecting the smoke by using a silica gel gas collecting bag, and connecting the smoke to a GC-IMS sample injection device;
Detecting a cigarette paper sample, a finished cigarette, a burned fragrant cigarette paper sample and a clear water sample by adopting a GC-IMS flavor analyzer, repeatedly carrying out sample injection measurement on each sample for 3 times to obtain a gas chromatography tandem ion mobility spectrum of a fragrant volatile compound of each fragrant cigarette paper sample, wherein the cigarette paper sample comprises different batches of straight rib wood pulp cigarette paper samples, wood pulp cigarette paper samples and imported cross-grain cigarette paper samples, the fragrant cigarette paper sample of the finished cigarette comprises the fragrant cigarette papers of different production places and different batches of finished cigarettes, the burned fragrant cigarette paper sample comprises the burned finished fragrant cigarette paper, and the clear water sample comprises the non-textured cigarette paper base paper.
The method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory suites, as described above, preferably comprises the following steps of:
Sample injection volume: 200ul; incubation time: 20min; incubation temperature: 90 ℃; sample injection needle temperature: 95 ℃; incubation rotation speed: 500 rpm.
The chromatographic conditions are as follows:
Gas phase-ion mobility spectrometry: analysis time is 20min; type of column: WAX, column length 30m, inner diameter: 0.53mm, film thickness: column temperature 1 μm: 60 ℃; carrier gas/drift gas: n 2; IMS temperature: 45 ℃;
The GC chromatographic conditions were:
When the sample injection time is 0, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 2mL/min, and the acquisition state is rec; when the sample injection time is 2min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 10mL/min; when the sample injection time is 20min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 100mL/min; when the sample injection time is 30min, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 100mL/min, and the acquisition state is stop.
The method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory suites as described above, wherein the chemometrics modeling analysis is preferably performed on the perfuming components of various perfuming cigarette papers based on the GC-IMS fingerprint analysis results of various perfuming cigarette papers, and a first pattern recognition qualitative identification model, a second pattern recognition qualitative identification model, a third pattern recognition qualitative identification model and a fourth pattern recognition qualitative identification model are respectively established, and specifically comprise the following steps:
Performing chromatographic peak detection and integration treatment, background subtraction treatment and compound alignment treatment among samples on GC-IMS fingerprint data of various aroma-added cigarette paper sequentially by GC-IMS VOCal software;
And importing ModelLab Matman the GC-IMS fingerprint data of the aligned various flavored cigarette papers into software for chemometrics and machine learning modeling analysis, and respectively establishing a first mode identification qualitative identification model, a second mode identification qualitative identification model, a third mode identification qualitative identification model and a fourth mode identification qualitative identification model.
The method for classifying and identifying the flavored cigarette paper based on chemometrics-sensory suites, wherein modeling algorithms and parameters in the process of chemometrics and machine learning modeling analysis preferably comprise:
the non-supervision machine learning algorithm is a principal component analysis and self-organizing map neural network, wherein the principal component analysis corresponds to data preprocessing: UV scaling;
The supervised machine learning algorithm includes partial least squares discriminant analysis and random forests, wherein:
Partial least square discriminant analysis corresponding to the number of reserved latent variables: 5, a step of; cross-validation: leaving a first method; data preprocessing: UV scaling;
the number of decision trees corresponding to the random forest: 100; characteristic value algorithm: square root method; maximum tree depth: 30; minimal reduction in non-purity: 0.01; data preprocessing: UV scaling.
The method for classifying and identifying the flavoring cigarette paper based on chemometrics-sensory suites, as described above, preferably uses two unsupervised machine learning algorithms of principal component analysis and self-organizing map neural network for the first pattern recognition qualitative identification model, so that the differences of the flavoring cigarette paper, the flavoring cigarette paper of the finished cigarette and the flavoring cigarette paper after combustion are obvious; the recognition rate of the perfuming cigarette paper obtained by adopting the random forest and partial least square discriminant analysis of two supervised machine learning algorithms, the perfuming cigarette paper of the finished cigarette and the perfuming cigarette paper after combustion is higher;
for the second mode identification qualitative identification model, the differences of the different-specification flavored cigarette papers obtained by adopting a principal component analysis non-supervision machine learning algorithm are obvious; the recognition rate of the flavored cigarette paper with different specifications obtained by adopting the partial least square discriminant analysis supervised machine learning algorithm is higher;
For the third mode identification qualitative identification model, the differences of the flavored cigarette paper of the finished cigarettes of different cigarette factories obtained by adopting a principal component analysis non-supervision machine learning algorithm are common; the recognition rate of the flavored cigarette paper of the finished cigarettes in different cigarette factories obtained by adopting the partial least square discriminant analysis and the supervised machine learning algorithm is higher;
For the fourth mode identification qualitative identification model, the differences of the flavored cigarette paper after burning of different cigarette factories obtained by adopting two unsupervised machine learning algorithms of principal component analysis and self-organizing mapping neural network are not obvious; the recognition rate of the flavored cigarette paper obtained by adopting partial least square discriminant analysis and supervised machine learning algorithm, the flavored cigarette paper of the finished cigarette and the flavored cigarette paper after combustion is lower; the recognition rate of the flavored cigarette paper obtained by adopting the random forest supervised machine learning algorithm after burning in different cigarette factories is higher.
The invention provides a method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory group, which comprises the steps of carrying out GC-QTOF-O on-line sensory olfactory analysis and GC-IMS gas phase ion migration spectrum analysis around perfuming essence and different types of perfuming cigarette paper, carrying out chemometrics machine learning modeling research based on volatile component flavoring detection data, sequentially establishing 4 kinds of model recognition qualitative identification models of the perfuming cigarette paper-the classifying identification of the perfuming cigarette paper after combustion, the classifying identification of the perfuming cigarette paper with different specifications, the classifying identification of the perfuming cigarette paper of the finished cigarette in different cigarette factories, the classifying identification of the perfuming cigarette paper after combustion in different cigarette factories and the like through chemometrics modeling, and jointly forming a layering progressive analysis flow of the perfuming cigarette paper; the method reveals the composition change modes and trends of volatile aroma components from the aroma cigarette paper to the aroma cigarette paper of the finished cigarette to the aroma cigarette paper after burning, thereby providing theoretical basis and new research method for monitoring the aroma component attenuation change rule and tracing analysis of the aroma cigarette paper.
Drawings
For the purpose of making 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:
FIG. 1 is a flow chart of an embodiment of a chemometric-sensory group based method of identifying a class of flavored cigarette paper provided by the invention;
FIG. 2 is a total ion flow graph and sniffing signal intensity overlay of volatile components of a flavoring sample;
FIG. 3 is a GC-IMS fingerprint of the volatile components in 54 samples;
FIG. 4 is a GC-IMS fingerprint comparison analysis chart of the aroma components of various types of aroma-imparting cigarette paper samples;
FIG. 5 is a graph of a model V-plot of the identification of the GC-IMS data PLS-DA mode of the flavored cigarette paper of the finished cigarettes in different production plants;
fig. 6 is a hierarchical progressive analysis chart of the aroma components of GC-IMS fingerprint of various types of aroma-imparting cigarette paper.
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 should be construed as exemplary only and not limiting unless otherwise specifically stated.
"First", "second", as used in this disclosure: and similar words are not to be interpreted in any order, quantity, or importance, but rather are used to distinguish between different sections. The word "comprising" or "comprises" and the like means that elements preceding the word encompass the elements recited after the word, and not exclude the possibility of also encompassing other elements. "upper", "lower", etc. are used merely to denote relative positional relationships, which may also change accordingly when the absolute position of the object to be described changes.
In this disclosure, when a particular element is described as being located between a first element and a second element, there may or may not be intervening elements between the particular element and the first element or the second element. 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 intervening components, or may be directly connected to the other components without intervening components.
All terms (including technical or scientific terms) used in this disclosure 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 one of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and apparatus should be considered part of the specification.
As shown in fig. 1, the method for classifying and identifying the flavored cigarette paper based on chemometrics-sensory group provided in this embodiment specifically includes:
And S1, performing qualitative olfactory analysis on aroma components of the aroma-imparting essence sample.
In one embodiment of the chemometric-sensory group-based method for classifying and identifying flavored cigarette paper according to the invention, the step S1 may specifically include:
And S11, performing qualitative olfactory analysis on aroma components of the aroma-added essence sample by adopting a gas chromatography-quadrupole-time-of-flight mass spectrometer (GC/Q-TOF) and an olfactory analyzer.
In one embodiment, the invention can be applied to 7890B-7200 gas chromatograph-quadrupole-time-of-flight mass spectrometer (QF-TOF) of Agilent corporation in America and ODP4 sniffer of Gerstel corporation in Germany, and the invention is not limited to the manufacturer and model of GC/Q-TOF and sniffer.
The number of the flavoring essence samples is 1, the serial number is 01, and the specific sample information is shown in table 1. In one embodiment of the chemometric-sensory group-based method for classifying and identifying flavored cigarette paper of the present invention, the step S11 may specifically include:
Step S111, a sample pretreatment step: taking 0.5mL of essential oil sample in a 15mL headspace sample bottle, and reserving by GC-O-MS; the essential oil sample was diluted 100-fold with methanol solvent and 1mL of the diluted solution was taken in a 2mL sample bottle for use.
Step S112, extraction and sample injection: after the solid phase microextraction arrow is aged for 15min at 250 ℃, the adsorption extraction is started: extracting at 80deg.C for 30min, and desorbing at 250deg.C for 5min; after sample injection, the solid phase microextraction arrow was aged at 250℃for 10 min.
Wherein, in the qualitative olfactory analysis, the gas chromatographic conditions of the GC-MS are as follows:
sample inlet temperature: 250 ℃;
programming temperature: maintaining at 40deg.C for 1min; raising the temperature to 150 ℃ at 5 ℃/min, keeping the temperature at 1min ℃, raising the temperature to 300 ℃ at 30 ℃/min, and keeping the temperature for 2min;
Carrier gas: he gas;
Sample injection mode: sample introduction without diversion;
The mass spectrum conditions are as follows: the ion source EI, electron energy 70eV, the transmission line temperature 250 ℃, the ion source temperature 230 ℃, the mass range 30-600 m/z, the agilent MassHunter Unknows software and the NIST14 spectrum library are utilized to carry out unknown identification analysis, and the unknown identification analysis is searched according to the similarity;
Conditions of GC-O:
Transmission line temperature: 300 ℃, sniffing temperature: 100 ℃; sensory evaluation is carried out by 3 testers in a room with the temperature of 25+/-2 ℃ and the relative humidity of 50% -60%, fresh air and no wind, the testers register the time points when all the aroma substances exist, meanwhile, the specific attributes are analyzed, the evaluation result is subjected to 4-point classification, 1-point represents the weakest and 4-point represents the strongest;
Parameters of MS:
Scanning range: 100-1350 m/z, ion source gas 1:50; ion source gas 2:50, curtain gas: 35; temperature: ion spray voltage floating at 500 ℃): 5500V-4500V, wherein 5500V corresponds to positive ion mode and 4500V corresponds to negative ion mode.
And step S12, identifying and identifying the aroma substances from the aroma essence sample according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactometer of the aroma components of the aroma essence sample.
Specifically, GC-IMS Library Search V2.2.1 qualitative analysis software is adopted, and a built-in IMS database is utilized to carry out qualitative analysis on the aroma substances in the sample, so that the identified aroma substances comprise alcohols, phenols, esters, ethers and ketones.
And according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactometer of the aroma components of the aroma-imparting essence sample, and by combining NIST database retrieval (figure 2 and table 1), 29 aroma-imparting substances such as alcohol, phenol, ester, ether, ketone and the like are identified from the aroma-imparting essence sample. These substances are important aroma components in the aroma-imparting cigarette paper, giving the cigarette paper different style characteristics. Wherein, the alcohol not only has the function of moisture preservation in the cigarette smoking process, but also can improve the tobacco flavor, so that the main stream smoke of the cigarette is fine, smooth and rich in concentration. The ketone substances have strong influence on the taste, aroma and satisfaction of cigarettes, and can coordinate the aroma of cigarettes, mask miscellaneous gases and endow cigarettes with different characteristic aroma. The aldehydes and esters are also the main sources for forming the aroma characteristics of the aroma cigarette paper, and the difference in the content has important influence on the aroma style characteristics of the aroma cigarette paper, so that the aldehydes and esters can be used as important indexes for controlling the internal quality of the aroma cigarette paper. The large differences of different compounds in peak area response values, flavor types, flavor intensity and the like indicate that the flavor of the sample is the sensory integrated effect result of complex components, and the complex olfactory mechanism of organisms makes the complex flavor presented by the perfuming extract as a complex system not be characterized by the simple linear addition of the individual compounds.
According to the invention, the complex trace components of the flavored cigarette paper are finely characterized based on sensory flavor by a GC-MS column serial artificial olfactometer (GC-O) method, and the flavoring compounds are screened according to contribution degree according to the obtained flavor description and flavor intensity scoring results of the monomer compounds.
TABLE 1 GC-O-MS sensory evaluation and identification Table of volatile flavor substances
And S2, performing GC-IMS (gas chromatography tandem ion mobility) fingerprint analysis on the aroma components of the aroma essence sample by utilizing the qualitative olfactory analysis result of the aroma components of the aroma essence sample, the aroma cigarette paper of the aroma cigarette paper, the aroma cigarette paper after combustion and the aroma components of the clear water sample.
For example, detection may be performed using a germany g.a.s. FlavourSpec GC-IMS flavor analyzer, which has a CTC autoheadspace sampler. The invention is not particularly limited to 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, and can rapidly 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.
In one embodiment of the chemometric-sensory group-based method for classifying and identifying flavored cigarette paper according to the invention, the step S2 may specifically include:
step S21, sample pretreatment: 0.5g of cigarette paper is taken and placed in a 20mL headspace bottle, and is incubated at 90 ℃ for 20min g and then injected.
Wherein, headspace sampling conditions are: sample injection volume: 200ul; incubation time: 20min; incubation temperature: 90 ℃; sample injection needle temperature: 95 ℃; incubation rotation speed: 500 rpm.
Step S22, performing a combustion test on the burnt flavored cigarette paper: and (3) carrying out a cigarette paper combustion test on different cigarette samples from which cut tobacco is removed on an automatic smoking machine, collecting smoke, collecting a bulk particulate phase substance by using 1 Cambridge filter sheet with the diameter of 44mm for each pore channel sample, collecting the smoke by using a silica gel gas collecting bag, and connecting the smoke to a GC-IMS sample injection device.
And S23, detecting a cigarette paper sample, a finished cigarette, a burned cigarette paper sample and a clear water sample by adopting a GC-IMS flavor analyzer, repeatedly carrying out sample injection measurement on each sample for 3 times to obtain a gas chromatography tandem ion mobility spectrum of a volatile compound of each cigarette paper sample, wherein the cigarette paper sample comprises different batches of straight rib wood pulp cigarette paper samples, wood pulp cigarette paper samples and imported cross-grain cigarette paper samples, the cigarette paper sample of the finished cigarette comprises cigarette papers of different production places and different batches of finished cigarettes, the burned cigarette paper sample comprises burned cigarette papers, and the clear water sample comprises non-grain cigarette paper base paper.
The flavoring cigarette paper of the finished cigarette refers to the flavoring cigarette paper stripped from the finished cigarette, and the flavoring cigarette paper after burning refers to the flavoring cigarette paper on the finished cigarette after burning.
In the invention, when GC-IMS fingerprint analysis is carried out, the chromatographic conditions are as follows:
Gas phase-ion mobility spectrometry: analysis time is 20min; type of column: WAX, column length 30m, inner diameter: 0.53mm, film thickness: column temperature 1 μm: 60 ℃; carrier gas/drift gas: n 2; IMS temperature: 45 ℃;
The GC chromatographic conditions were:
When the sample injection time is 0, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 2mL/min, and the acquisition state is rec; when the sample injection time is 2min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 10mL/min; when the sample injection time is 20min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 100mL/min; when the sample injection time is 30min, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 100mL/min, and the acquisition state is stop.
In the invention, the number of the perfumed cigarette paper samples is 23, and the serial number is 02-24; the number of the perfumed cigarette paper samples of the finished cigarette is 20, and the serial number is 25-44; 10 samples of the burnt flavored cigarette paper are numbered 45-54; 1 sample of the clear water sample is provided with the serial number of 55, and specific sample information is shown in table 2, and it should be noted that the source, the place of production, the batch and the like of each sample are not particularly limited. Carrier gas N 2: the purity is more than 99.999 percent.
In GC-IMS fingerprint analysis of aroma components of various aroma-imparting cigarette paper, sample injection and measurement are repeated for each sample of 54 samples of 02-55 and the like for 3 times, and 162 groups of data are taken as a total. The parallel samples are prefixed with a/b/c to the sample number to show discrimination.
Table 2 sample information table
And S3, carrying out chemometric modeling analysis on aroma components of various aroma-imparting cigarette paper based on GC-IMS fingerprint analysis results of the various aroma-imparting cigarette paper, and respectively establishing a first pattern recognition qualitative identification model for classifying a clear water sample, the aroma-imparting cigarette paper of a finished cigarette, the aroma-imparting cigarette paper after burning, a second pattern recognition qualitative identification model for classifying the aroma-imparting cigarette paper of different specifications, a third pattern recognition qualitative identification model for classifying the aroma-imparting cigarette paper of the finished cigarette of different cigarette factories and a fourth pattern recognition qualitative identification model for classifying the aroma-imparting cigarette paper after burning of different cigarette factories.
In one embodiment of the chemometric-sensory group-based method for classifying and identifying flavored cigarette paper according to the invention, the step S3 may specifically include:
And S31, performing chromatographic peak detection and integration processing, background subtraction processing and compound alignment processing among samples on GC-IMS fingerprint data of various aroma-added cigarette papers through GC-IMS VOCal software.
In order to further comprehensively analyze and visually compare the composition characteristics of the aroma components of the aroma cigarette paper of different types and the aroma cigarette paper of different batches, GC-IMS fingerprint analysis is carried out on 54 samples, and chromatographic peak positioning and integration are carried out on the obtained GC-IMS two-dimensional spectrum, and the result is shown in figure 3. The superimposed graph of all sample compounds after alignment is shown in fig. 4, in which the abscissa indicates the peak area of the aroma substances in the sample and the ordinate indicates the sample number. As can be seen from fig. 4, the composition of the perfuming cigarette paper, the perfuming cigarette paper of the finished cigarette and the volatile compounds of the perfuming cigarette paper after combustion show a significant difference, and the perfuming substances of the sample have respective characteristic peak areas and also have a common similar area. The cigarette paper is mainly characterized in that the cigarette paper of the finished cigarette is increased in compound composition types compared with the cigarette paper with fragrance because volatile components in tobacco shreds are adsorbed, and the volatile matters of the cigarette paper after burning are obviously changed in both compound types and concentration.
And S32, importing ModelLab Matman software into GC-IMS fingerprint data of the aligned various aroma-imparting cigarette papers, performing chemometry and machine learning modeling analysis, and respectively establishing a first mode identification qualitative identification model, a second mode identification qualitative identification model, a third mode identification qualitative identification model and a fourth mode identification qualitative identification model.
In the present invention, the chemometric modeling analysis software may be, for example, modelLab Matman scientific big data system solution software 2021 version of Chemmind Technologies, china.
The modeling algorithm and parameters in the chemometric and machine learning modeling analysis include:
The unsupervised machine learning algorithm is a principal component analysis (PRINCIPAL COMPONENT ANALYSIS, PCA) and a Self-organizing map neural network (Self-organizing map network, SOM), wherein the principal component analysis corresponds to data preprocessing: UV scaling;
the supervised machine learning algorithms include partial least squares discriminant analysis (PARTIAL LEAST squares discrimination analysis, PLSDA) and random forest (random forest), where:
Partial least square discriminant analysis corresponding to the number of reserved latent variables: 5, a step of; cross-validation: leaving a first method; data preprocessing: UV scaling;
the number of decision trees corresponding to the random forest: 100; characteristic value algorithm: square root method; maximum tree depth: 30; minimal reduction in non-purity: 0.01; data preprocessing: UV scaling.
Specifically, for the first pattern recognition qualitative identification model, the differences of the aroma-added cigarette paper, the aroma-added cigarette paper of the finished cigarette and the aroma-added cigarette paper after combustion are obvious, which are obtained by adopting two non-supervision machine learning algorithms of principal component analysis and self-organizing mapping neural network; the recognition rate of the perfuming cigarette paper obtained by adopting the random forest and partial least square discriminant analysis of two supervised machine learning algorithms, the perfuming cigarette paper of the finished cigarette and the perfuming cigarette paper after combustion is higher;
for the second mode identification qualitative identification model, the differences of the different-specification flavored cigarette papers obtained by adopting a principal component analysis non-supervision machine learning algorithm are obvious; the recognition rate of the flavored cigarette paper with different specifications obtained by adopting the partial least square discriminant analysis supervised machine learning algorithm is higher;
For the third mode identification qualitative identification model, the differences of the flavored cigarette paper of the finished cigarettes of different cigarette factories obtained by adopting a principal component analysis non-supervision machine learning algorithm are common; the recognition rate of the flavored cigarette paper of the finished cigarettes in different cigarette factories obtained by adopting the partial least square discriminant analysis and the supervised machine learning algorithm is higher;
For the fourth mode identification qualitative identification model, the differences of the flavored cigarette paper after burning of different cigarette factories obtained by adopting two unsupervised machine learning algorithms of principal component analysis and self-organizing mapping neural network are not obvious; the recognition rate of the flavored cigarette paper obtained by adopting partial least square discriminant analysis and supervised machine learning algorithm, the flavored cigarette paper of the finished cigarette and the flavored cigarette paper after combustion is lower; the recognition rate of the flavored cigarette paper obtained by adopting the random forest supervised machine learning algorithm after burning in different cigarette factories is higher.
According to the invention, the differences of the aroma components of the aroma cigarette papers of different types and the aroma cigarette papers of different batches are comprehensively analyzed by a chemometric modeling method. And 4 kinds of mode identification qualitative identification models such as the classification identification of the flavoring cigarette paper-the flavoring cigarette paper of the finished cigarette-the flavoring cigarette paper after burning, the classification identification of the flavoring cigarette paper of different specifications, the classification identification of the flavoring cigarette paper of the finished cigarette of different cigarette factories, the classification identification of the flavoring cigarette paper after burning of different cigarette factories and the like are respectively built in sequence. The results are shown in Table 3, FIG. 5 and FIG. 6. For the established class 4 identification model, as the composition complexity of volatile compounds is continuously increased, higher requirements are put on the performance of the selected chemometric algorithm and the optimization of the training process, and the data modeling difficulty tends to increase. The classification effect of the non-supervision methods such as PCA and SOM on the 3rd and 4 th class models can not meet the requirements, but the model prediction accuracy of the supervised mode recognition algorithm is close to 100% through parameter optimization. Especially, the classification prediction effect of random forests is better than that of the partial least square method, which suggests that the difference between the sample compound compositions in the class 4 presents the characteristic of nonlinearity. Wherein the V-chart of fig. 5 shows the results of the significance ordering of the compounds of the differences in the flavoring components in the flavoring paper of the finished cigarettes of different cigarette factories in the class 3 model.
Analysis results of sensory stability evaluation model of table 34 class flavored cigarette paper are summarized
FIG. 5 is a graph showing the V-type independent variable (compound) scattergram drawn by PLS-DA pattern recognition based on the independent variable correlation (credibility) and VIP values as coordinate axes, illustrating the importance of the respective variables to regression classification prediction. Wherein the independent variables in the first quadrant of the coordinate axis (upper right plus sign) have a positive correlation effect on the classification, and the independent variables in the second quadrant of the coordinate axis (upper left plus sign) have a diametrically opposite negative correlation effect on the classification.
In specific implementation, for a to-be-detected perfuming cigarette paper sample, distinguishing which type of the to-be-detected perfuming cigarette paper sample is a clear water sample, the perfuming cigarette paper of a finished cigarette and the perfuming cigarette paper after combustion through a first mode identification qualitative identification model, if the to-be-detected perfuming cigarette paper is the perfuming cigarette paper, determining which type of the to-be-detected perfuming cigarette paper sample is the straight rib wood pulp cigarette paper, the wood pulp cigarette paper and the imported cross grain cigarette paper by utilizing a second mode identification qualitative identification model; if the cigarette paper is the flavoring cigarette paper of the finished cigarette, determining which cigarette factory produces the flavoring cigarette paper sample to be tested, in particular which one of the production factories A-F, by using a third pattern recognition qualitative identification model; if the cigarette paper is the burnt perfuming cigarette paper, a fourth mode identification qualitative identification model is utilized to determine which cigarette factory the sample of the perfuming cigarette paper to be detected is the burnt perfuming cigarette paper produced by, in particular which one of the production factories A-D.
The invention firstly carries out sensory olfactory analysis on the chemical composition and sensory evaluation of a complex system of the flavoring essence, and identifies 29 alcohol, phenol, ester, ether and ketone flavoring substances in a common identification way. And GC-IMS fingerprint analysis and GC-IMS differential marker screening are researched. The sensory stability of various types of flavored cigarette paper is evaluated based on a machine learning method, and a corresponding unsupervised and supervised algorithm is established, so that 4 major mode identification qualitative identification models of flavored cigarette paper-flavored cigarette paper of finished cigarettes, classification identification of flavored cigarette paper of different specifications, classification identification of flavored cigarette paper of finished cigarettes of different cigarette factories, classification identification of flavored cigarette paper of different cigarette factories and the like are sequentially established through chemometric modeling, and a layered progressive analysis flow of the flavored cigarette paper is formed together, as shown in fig. 6.
According to the method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory group, provided by the embodiment of the invention, 4 major mode recognition qualitative identification models such as the classification identification of the perfuming cigarette paper-the post-combustion perfuming cigarette paper of the perfuming cigarette paper-the finished cigarette, the classification identification of the perfuming cigarette paper of different specifications, the classification identification of the perfuming cigarette paper of the finished cigarette of different cigarette factories, the classification identification of the perfuming cigarette paper of the post-combustion perfuming cigarette paper of different cigarette factories and the like are sequentially established through chemometrics modeling by carrying out chemometrics machine learning modeling study on GC-IMS gas phase ion mobility spectrometry analysis and based on volatile component perfuming detection data, so that the layering progressive analysis flow of the perfuming cigarette paper is jointly formed; the method reveals the composition change modes and trends of volatile aroma components from the aroma cigarette paper to the aroma cigarette paper of the finished cigarette to the aroma cigarette paper after burning, thereby providing theoretical basis and new research method for monitoring the aroma component attenuation change rule and tracing analysis of the aroma cigarette paper.
Thus, various embodiments of the present disclosure have been described in detail. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.
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 above examples are for 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 the foregoing embodiments may be modified and equivalents substituted for elements thereof without departing from the scope and spirit of the disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (6)
1. A method for identifying a class of flavored cigarette paper based on chemometrics-sensory suites, comprising:
performing qualitative olfactory analysis on aroma components of the aroma-added essence sample;
Performing GC-IMS fingerprint analysis on the aroma components of the aroma cigarette paper, the aroma cigarette paper of the finished cigarette, the aroma cigarette paper after combustion and the clean water sample by utilizing the qualitative olfactory analysis result of the aroma components of the aroma essence sample;
Based on the GC-IMS fingerprint analysis result of various kinds of the perfuming cigarette paper, chemometric modeling analysis is carried out on the perfuming components of various kinds of the perfuming cigarette paper, a first pattern recognition qualitative identification model for classifying the water sample, the perfuming cigarette paper of the finished cigarette, the perfuming cigarette paper after burning, a second pattern recognition qualitative identification model for classifying the perfuming cigarette paper of different specifications, a third pattern recognition qualitative identification model for classifying the perfuming cigarette paper of the finished cigarette of different cigarette factories and a fourth pattern recognition qualitative identification model for classifying the perfuming cigarette paper after burning of different cigarette factories are respectively established,
The qualitative olfactory analysis is carried out on the aroma components of the aroma-imparting essence sample, and the method specifically comprises the following steps:
qualitative olfactory analysis is carried out on the aroma components of the aroma-added essence sample by adopting a gas chromatograph-quadrupole-time-of-flight mass spectrometer and an olfactory analyzer;
Identifying and identifying the aroma substances from the aroma sample according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactometer of the aroma components of the aroma sample,
When GC-IMS fingerprint analysis is carried out, headspace sample introduction conditions are as follows:
sample injection volume: 200ul; incubation time: 20min; incubation temperature: 90 ℃; sample injection needle temperature: 95 ℃; incubation rotation speed: 500 An rpm;
the chromatographic conditions are as follows:
Gas phase-ion mobility spectrometry: analysis time is 20min; type of column: WAX, column length 30m, inner diameter: 0.53mm, film thickness: column temperature 1 μm: 60 ℃; carrier gas/drift gas: n 2; IMS temperature: 45 ℃;
The GC chromatographic conditions were:
When the sample injection time is 0, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 2mL/min, and the acquisition state is rec; when the sample injection time is 2min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 10mL/min; when the sample injection time is 20min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 100mL/min; when the sample injection time is 30min, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 100mL/min, and the acquisition state is stop;
the method adopts a gas chromatograph-quadrupole-time-of-flight mass spectrometer and a sniffing instrument to perform qualitative sniffing analysis on aroma components of an aroma essence sample, and specifically comprises the following steps:
Sample pretreatment: taking 0.5mL of essential oil sample in a 15mL headspace sample bottle, and reserving by GC-O-MS; diluting the essential oil sample 100 times by using a methanol solvent, and taking 1mL of diluted solution in a 2mL sample bottle for later use;
Extraction and sample introduction: after the solid phase microextraction arrow is aged for 15min at 250 ℃, the adsorption extraction is started: extracting at 80deg.C for 30min, and desorbing at 250deg.C for 5 min; after sample injection is completed, aging the solid phase microextraction arrow for 10min at 250 ℃;
In the qualitative olfactory analysis, the conditions of the gas chromatograph-quadrupole-time-of-flight mass spectrometer are as follows:
sample inlet temperature: 250 ℃;
Programming temperature: maintaining at 40deg.C for 1min; raising the temperature to 150 ℃ at 5 ℃/min, keeping the temperature at 1min ℃, raising the temperature to 300 ℃ at 30 ℃/min, and keeping the temperature for 2min;
Carrier gas: he gas;
Sample injection mode: sample introduction without diversion;
The mass spectrum conditions are as follows: the ion source EI, electron energy 70eV, the transmission line temperature 250 ℃, the ion source temperature 230 ℃, the mass range 30-600 m/z, the agilent MassHunter Unknows software and the NIST14 spectrum library are utilized to carry out unknown identification analysis, and the unknown identification analysis is searched according to the similarity;
conditions of GC-O:
Transmission line temperature: 300 ℃, sniffing temperature: 100 ℃; sensory evaluation is carried out by 3 testers in a room with the temperature of 25+/-2 ℃ and the relative humidity of 50% -60%, fresh air and no wind, the testers register the time points when all the aroma substances exist, meanwhile, the specific attributes are analyzed, the evaluation result is subjected to 4-point classification, 1-point represents the weakest and 4-point represents the strongest;
parameters of MS:
scanning range: 100-1350 m/z, ion source gas 1:50; ion source gas 2:50, curtain gas: 35; temperature: ion spray voltage floating at 500 ℃): 5500V-4500V, wherein 5500V corresponds to positive ion mode and 4500V corresponds to negative ion mode.
2. The method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory groups according to claim 1, wherein the identifying and identifying the perfuming substances from the perfuming essence sample according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem sniffing instrument of the perfuming components of the perfuming essence sample specifically comprises:
Qualitative analysis software is adopted by GC-IMS Library Search V2.2.1, and a built-in IMS database is utilized to carry out qualitative analysis on the aroma substances in the sample, and identified aroma substances comprise alcohols, phenols, esters, ethers and ketones.
3. The method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory suites according to claim 1, wherein the GC-IMS fingerprint analysis is carried out on the perfuming cigarette paper, the perfuming cigarette paper of finished cigarettes, the perfuming cigarette paper after burning and the perfuming components like clear water by utilizing the qualitative olfactory analysis result of the perfuming components of the perfuming essence sample, and specifically comprises the following steps:
Sample pretreatment: 0.5g of tobacco paper is taken and placed in a 20mL headspace bottle, and is incubated at 90 ℃ for 20 min and then injected;
And (3) carrying out a combustion test on the burnt aroma-added cigarette paper: carrying out a cigarette paper combustion test on different cigarette samples from which cut tobacco is removed on an automatic smoking machine, collecting smoke, collecting a bulk particulate matter by using 1 Cambridge filter sheet with the diameter of 44mm for each pore channel sample, collecting the smoke by using a silica gel gas collecting bag, and connecting the smoke to a GC-IMS sample injection device;
Detecting a cigarette paper sample, a finished cigarette, a burned fragrant cigarette paper sample and a clear water sample by adopting a GC-IMS flavor analyzer, repeatedly carrying out sample injection measurement on each sample for 3 times to obtain a gas chromatography tandem ion mobility spectrum of a fragrant volatile compound of each fragrant cigarette paper sample, wherein the cigarette paper sample comprises different batches of straight rib wood pulp cigarette paper samples, wood pulp cigarette paper samples and imported cross-grain cigarette paper samples, the fragrant cigarette paper sample of the finished cigarette comprises the fragrant cigarette papers of different production places and different batches of finished cigarettes, the burned fragrant cigarette paper sample comprises the burned finished fragrant cigarette paper, and the clear water sample comprises the non-textured cigarette paper base paper.
4. The method for classifying and identifying the perfuming cigarette paper based on chemometrics-sensory suites according to claim 1, wherein the method for classifying and identifying the perfuming cigarette paper based on the GC-IMS fingerprint analysis result of each kind of the perfuming cigarette paper performs chemometrics modeling analysis on the perfuming components of each kind of the perfuming cigarette paper, respectively establishes a first pattern recognition qualitative identification model, a second pattern recognition qualitative identification model, a third pattern recognition qualitative identification model and a fourth pattern recognition qualitative identification model, and specifically comprises the following steps:
Performing chromatographic peak detection and integration treatment, background subtraction treatment and compound alignment treatment among samples on GC-IMS fingerprint data of various aroma-added cigarette paper sequentially by GC-IMS VOCal software;
And importing ModelLab Matman the GC-IMS fingerprint data of the aligned various flavored cigarette papers into software for chemometrics and machine learning modeling analysis, and respectively establishing a first mode identification qualitative identification model, a second mode identification qualitative identification model, a third mode identification qualitative identification model and a fourth mode identification qualitative identification model.
5. The method for chemometric-sensory group-based classification and identification of flavored cigarette paper of claim 4, wherein the modeling algorithms and parameters in performing chemometric and machine learning modeling analysis include:
the non-supervision machine learning algorithm is a principal component analysis and self-organizing map neural network, wherein the principal component analysis corresponds to data preprocessing: UV scaling;
The supervised machine learning algorithm includes partial least squares discriminant analysis and random forests, wherein:
Partial least square discriminant analysis corresponding to the number of reserved latent variables: 5, a step of; cross-validation: leaving a first method; data preprocessing: UV scaling;
the number of decision trees corresponding to the random forest: 100; characteristic value algorithm: square root method; maximum tree depth: 30; minimal reduction in non-purity: 0.01; data preprocessing: UV scaling.
6. The method for classifying and identifying the flavored cigarette paper based on chemometrics-sensory suites according to claim 5, wherein the first pattern recognition qualitative identification model is characterized in that the differences of the flavored cigarette paper, the flavored cigarette paper of the finished cigarette and the flavored cigarette paper after combustion are obvious by adopting two unsupervised machine learning algorithms of principal component analysis and self-organizing map neural network; the recognition rate of the perfuming cigarette paper obtained by adopting the random forest and partial least square discriminant analysis of two supervised machine learning algorithms, the perfuming cigarette paper of the finished cigarette and the perfuming cigarette paper after combustion is higher;
for the second mode identification qualitative identification model, the differences of the different-specification flavored cigarette papers obtained by adopting a principal component analysis non-supervision machine learning algorithm are obvious; the recognition rate of the flavored cigarette paper with different specifications obtained by adopting the partial least square discriminant analysis supervised machine learning algorithm is higher;
For the third mode identification qualitative identification model, the differences of the flavored cigarette paper of the finished cigarettes of different cigarette factories obtained by adopting a principal component analysis non-supervision machine learning algorithm are common; the recognition rate of the flavored cigarette paper of the finished cigarettes in different cigarette factories obtained by adopting the partial least square discriminant analysis and the supervised machine learning algorithm is higher;
For the fourth mode identification qualitative identification model, the differences of the flavored cigarette paper after burning of different cigarette factories obtained by adopting two unsupervised machine learning algorithms of principal component analysis and self-organizing mapping neural network are not obvious; the recognition rate of the flavored cigarette paper obtained by adopting partial least square discriminant analysis and supervised machine learning algorithm, the flavored cigarette paper of the finished cigarette and the flavored cigarette paper after combustion is lower; the recognition rate of the flavored cigarette paper obtained by adopting the random forest supervised machine learning algorithm after burning in different cigarette factories is higher.
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