CN114965815A - Aromatized cigarette paper classification and identification method based on chemometrics-sensory omics - Google Patents
Aromatized cigarette paper classification and identification method based on chemometrics-sensory omics Download PDFInfo
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- 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
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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
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Abstract
The invention discloses a method for classifying and identifying aromatized cigarette paper based on chemometrics-sensory omics, which comprises the following steps: performing qualitative and olfactory analysis on the aroma components of the aroma-endowing essence sample; carrying out GC-IMS fingerprint analysis on the aromatized cigarette paper, the finished cigarette, the combusted aromatized cigarette paper and the aroma components of the clear water sample; carrying out chemometric modeling analysis on the aroma components of various types of aroma-providing cigarette paper, and establishing a first mode identification model for classifying a clear water sample, the aroma-providing cigarette paper, a finished product and the burnt cigarette paper, a second mode identification model for classifying the aroma-providing cigarette paper with different specifications, a third mode identification model for classifying the aroma-providing cigarette paper of the finished product cigarettes of different cigarette factories and a fourth mode identification model for classifying the aroma-providing cigarette paper burnt in different cigarette factories. The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics is beneficial to monitoring the attenuation change rule and tracing analysis of the aroma components of the aromatized 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 aromatized cigarette paper based on chemometrics-sensory omics.
Background
The aromatized cigarette paper 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 paper is burnt, the flavoring additive releases the flavor components in modes of volatilization, cracking and the like to achieve the purpose of endowing the cigarette paper with certain characteristic flavor. In recent years, cigarette paper aroma endowing technology is widely applied to high-end cigarette production to improve the smoking quality of cigarettes, and has the advantages of effectively covering cigarette miscellaneous gas, endowing sweet and moist smoke, reducing cigarette irritation, increasing the softness and fineness of smoke and the like. The aromatic cigarette paper contains numerous volatile components, complex components and low content of aroma components, so that the source of the raw materials of the aromatic cigarette paper is difficult to trace, and an effective stability monitoring method is lacked.
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, 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 aroma-giving cigarette paper.
Therefore, a method for classifying and identifying the aromatized cigarette paper based on chemometrics-organoleptic group is needed.
Disclosure of Invention
The invention aims to provide a method for classifying and identifying aromatized cigarette paper based on chemometrics-sensoromics, which solves the problems in the prior art and can monitor the attenuation change rule and source tracing analysis of the aroma components of the aromatized cigarette paper.
The invention provides a method for classifying and identifying aromatized cigarette paper based on chemometrics-sensory omics, which comprises the following steps:
performing qualitative and olfactory analysis on the aroma components of the aroma-endowing essence sample;
performing GC-IMS fingerprint analysis on aroma components of the aroma-providing cigarette paper, the aroma-providing cigarette paper of the finished cigarette, the burnt aroma-providing cigarette paper and the clear water sample by utilizing the qualitative and olfactory analysis result of the aroma-providing component of the aroma-providing essence sample;
based on GC-IMS fingerprint spectrum analysis results of various types of aromatizing cigarette paper, carrying out chemometric modeling analysis on the aromatizing components of various types of aromatizing cigarette paper, and respectively establishing a first mode identification qualitative identification model for classifying clean water samples, aromatizing cigarette paper of finished cigarettes, a first mode identification qualitative identification model for classifying the combusted aromatizing cigarette paper, a second mode identification qualitative identification model for classifying the aromatizing cigarette paper with different specifications, a third mode identification qualitative identification model for classifying the aromatizing cigarette paper of finished cigarettes in different cigarette factories and a fourth mode identification qualitative identification model for classifying the aromatizing cigarette paper combusted in different cigarette factories.
The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics as described above, wherein preferably, the qualitative and olfactory analysis is performed on the aroma components of the aromatized flavor samples, and specifically comprises the following steps:
performing qualitative and olfactory analysis on aroma components of the aroma-endowing essence sample by adopting a gas chromatography-quadrupole-time-of-flight mass spectrometer and an olfactory analyzer;
according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactometer of the aroma components of the aroma-endowing essence sample, the aroma substances are identified and identified from the aroma-endowing essence sample.
The method for classifying and identifying the aromatized cigarette paper based on chemometrics-organoleptic omics preferably adopts a gas chromatography-quadrupole-time-of-flight mass spectrometer and a sniffer to perform qualitative and olfactory analysis on the aroma components of the aromatized essence sample, and specifically comprises the following steps:
a sample pretreatment step: taking 0.5mL of the essential oil sample in a 15mL headspace sample bottle, and keeping GC-O-MS for later use; diluting the essential oil sample by 100 times by using a methanol solvent, taking 1mL of diluent in a 2mL sample bottle, and performing LC-MS (liquid chromatography-mass spectrometry) for later use;
extraction and sample introduction: after aging at 250 ℃ for 15min by a solid phase microextraction arrow, adsorption extraction is started: extracting at 80 deg.C for 30min, and desorbing at 250 deg.C for 5 min; after the injection was completed, the solid phase microextraction arrow was aged at 250 ℃ for 10 min.
In the method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensormics, it is preferable that the gas chromatography conditions of LC-MS for the qualitative and olfactory analysis are as follows:
sample inlet temperature: 250 ℃;
temperature programming: maintaining the temperature at 40 deg.C for 1 min; heating to 150 deg.C at 5 deg.C/min, maintaining for 1min, heating to 300 deg.C at 30 deg.C/min, and maintaining for 2 min;
carrier gas: he gas;
and (3) sample introduction mode: no shunt sampling;
the mass spectrum conditions are as follows: the method comprises the following steps of carrying out unknown substance identification analysis on an ion source EI with electron energy of 70eV, transmission line temperature of 250 ℃, ion source temperature of 230 ℃ and mass range of 30-600 m/z by utilizing Agilent MassHunnows software and NIST14 spectrum library, and searching according to similarity;
conditions for GC-O:
transmission line temperature: 300 ℃, temperature of mouth of olfactory discrimination: 100 ℃; sensory evaluation is carried out by 3 testers in a room with the temperature of 25 +/-2 ℃, the relative humidity of 50-60%, fresh air and no wind, the testers register the time points of all aroma substances, and simultaneously analyze the specific attributes, the evaluation result is divided into 4 points, wherein 1 point represents the weakest point, and 4 points represent the strongest point;
parameters of the MS:
scanning range: 100-: 50; ion source gas 2: 50, curtain gas: 35; temperature: 500 ℃, ion spray voltage float: 5500V-4500V, wherein 5500V corresponds to positive ion mode and 4500V corresponds to negative ion mode.
The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics as described above, wherein preferably, the identifying and identifying of the aroma substances from the aromatized flavor samples is performed according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactory analyzer of the aroma components of the aromatized flavor samples, and specifically comprises the following steps:
and (3) carrying out qualitative analysis on the aroma substances in the sample by using GC-IMS Library Search V2.2.1 qualitative analysis software and using a built-in IMS database, and identifying the identified aroma substances comprising alcohols, phenols, esters, ethers and ketones.
The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics preferably performs GC-IMS fingerprint analysis on the aromatized cigarette paper, the aromatized cigarette paper of the finished cigarette, the combusted aromatized cigarette paper and the aromatized components of the clean water sample by using the qualitative and olfactory analysis result of the aromatized flavor sample, and specifically comprises the following steps:
sample pretreatment: taking 0.5g of cigarette paper, placing the cigarette paper in a 20mL headspace bottle, incubating for 20min at 90 ℃, and injecting a sample;
the burning test was performed on the cigarette paper after burning: performing a cigarette paper combustion test on different cigarette samples with cut tobaccos removed on an automatic smoking machine and collecting smoke, wherein 1 piece of 44mm Cambridge filter disc is used for collecting total particulate matters for each pore channel sample, and a silica gel gas collection bag is used for collecting the smoke and is connected to a GC-IMS sample introduction device;
detecting an aromatized cigarette paper sample, an aromatized cigarette paper sample of a finished product cigarette, a combusted aromatized cigarette paper sample and a clear water sample by adopting a GC-IMS flavor analyzer, repeatedly injecting and measuring each sample for 3 times to obtain a gas chromatography series ion mobility spectrometry of aromatized volatile compounds of each aromatized cigarette paper sample, wherein the aromatized cigarette paper samples comprise different batches of straight rib wood pulp cigarette paper samples, wood pulp cigarette paper samples and inlet cross-grain cigarette paper samples, the aromatized cigarette paper samples of the finished product cigarette comprise aromatized cigarette paper of finished products cigarettes of different production places and different batches, the combusted aromatized cigarette paper samples comprise combusted finished product aromatic cigarette paper, and the clear water sample comprises non-grain cigarette paper base paper.
In the method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensoromics, preferably, when the GC-IMS fingerprint spectrum analysis is performed, the headspace sampling condition is as follows:
sample introduction volume: 200 ul; incubation time: 20 min; incubation temperature: 90 ℃; temperature of the sample injection needle: 95 ℃; hatching rotation speed: 500 rpm.
The chromatographic conditions are as follows:
gas phase-ion mobility spectrometry: the analysis time is 20 min; type of column: WAX, column length 30m, inner diameter: 0.53mm, film thickness: 1 μm, column temperature: 60 ℃; carrier gas/drift gas: n is a radical of hydrogen 2 (ii) a IMS temperature: 45 ℃;
the GC chromatographic conditions were:
when the sample introduction time is 0, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 2mL/min, and the collection state is rec; when the sample introduction time is 2min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 10 mL/min; when the sample introduction time is 20min, the drift gas flow rate is 150mL/min, 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.
Preferably, the method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics comprises the steps of performing chemometrics modeling analysis on aroma components of various aromatized cigarette paper based on a GC-IMS fingerprint analysis result of the various aromatized cigarette paper, and respectively establishing a first pattern identification qualitative identification model, a second pattern identification qualitative identification model, a third pattern identification qualitative identification model and a fourth pattern identification qualitative identification model, and specifically comprises the following steps:
carrying out chromatographic peak detection and integral processing, background subtraction processing and compound alignment processing among samples on GC-IMS fingerprint data of various aromatized cigarette paper by GC-IMS VOCal software in sequence;
and importing the aligned GC-IMS fingerprint data of various types of aromatized cigarette paper into ModelLab Matman software, carrying out 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.
In the method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensormics, it is preferable that the modeling algorithm and parameters for performing chemometrics and machine learning modeling analysis include:
the unsupervised machine learning algorithm is a principal component analysis and self-organizing mapping neural network, wherein the data preprocessing corresponding to the principal component analysis comprises the following steps: UV scaling;
the supervised machine learning algorithm comprises partial least squares discriminant analysis and random forests, wherein:
and (3) performing partial least square discriminant analysis on the number of corresponding reserved latent variables: 5; and (3) cross validation: leaving one method; data preprocessing: UV scaling;
the number of decision trees corresponding to the random forest is as follows: 100, respectively; and (3) eigenvalue algorithm: square root method; maximum tree depth: 30; minimum impure degree drop: 0.01; data preprocessing: UV scaling.
The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics preferably includes that for the first pattern identification qualitative identification model, differences of the aromatized cigarette paper, the aromatized cigarette paper of a finished cigarette and the aromatized cigarette paper after combustion obtained by two unsupervised machine learning algorithms of principal component analysis and self-organized mapping neural network are obvious; the recognition rates of the flavored cigarette paper, the flavored cigarette paper of the finished cigarette and the flavored cigarette paper after combustion obtained by adopting two supervised machine learning algorithms of random forest and partial least square discriminant analysis are higher;
for the second pattern recognition qualitative identification model, the differences of the aromatized cigarette paper with different specifications obtained by adopting a principal component analysis unsupervised machine learning algorithm are obvious; the recognition rate of the fragrant cigarette paper with different specifications obtained by adopting partial least square discriminant analysis supervised machine learning algorithm is higher;
for the third pattern recognition qualitative identification model, the differences of the aromatized cigarette paper of finished cigarettes in different cigarette factories obtained by adopting a principal component analysis unsupervised machine learning algorithm are general; the recognition rate of the aromatized cigarette paper of the finished cigarettes in different cigarette factories obtained by adopting partial least square discriminant analysis and supervised machine learning algorithm is higher;
for the fourth pattern recognition qualitative identification model, the differences of the burnt aromatized cigarette paper 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 cigarette paper, the cigarette paper of the finished cigarette and the burnt cigarette paper obtained by adopting partial least square discriminant analysis supervised machine learning algorithm is lower; the recognition rate of the cigarette paper after burning in different cigarette factories is higher by adopting a random forest supervised machine learning algorithm.
The invention provides a method for classifying and identifying aromatized cigarette paper based on chemometrics-sensory omics, which develops GC-QTOF-O online sensory olfactory discrimination and GC-IMS gas phase ion mobility spectrometry analysis around aromatized essence and different types of aromatized cigarette paper, and carries out chemometrics machine learning modeling research based on volatile component aroma detection data, and sequentially establishes 4 large-mode identification qualitative identification models of the aromatized cigarette paper, the aromatized cigarette paper of finished cigarettes, classification identification of aromatized cigarette paper after combustion, classification identification of aromatized cigarette paper of different specifications, classification identification of aromatized cigarette paper of finished cigarettes of different cigarette factories, classification identification of aromatized cigarette paper after combustion of different cigarette factories and the like through chemometrics modeling, thereby jointly forming an aromatized cigarette paper layering progressive analysis flow; the change mode and trend of the compound composition of the volatile aroma components from the aromatized cigarette paper and the aromatized cigarette paper of the finished cigarette to the aromatized cigarette paper after combustion are disclosed, so that theoretical basis and a new research method are provided for monitoring the attenuation change rule of the aroma components of the aromatized cigarette paper and tracing analysis.
Drawings
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 chart of an embodiment of the method for classifying and identifying aromatized cigarette paper based on chemometrics-sensormics provided by the present invention;
FIG. 2 is a total ion flow diagram and a superimposed view of the sniffing signal intensity of the volatile components of the aromatized flavor sample;
FIG. 3 is a GC-IMS fingerprint of the volatile components in 54 samples;
FIG. 4 is a comparison analysis chart of GC-IMS fingerprint spectra of the aroma components of various aroma-providing cigarette paper samples;
FIG. 5 is a V-plot of a pattern recognition model V-plot of the GC-IMS data PLS-DA of the aromatized cigarette paper of finished cigarettes in different production plants;
FIG. 6 is a hierarchical progressive analysis diagram of aroma components of various aroma-providing cigarette paper GC-IMS fingerprint spectra.
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 covers the element listed after the word, and does not exclude the possibility that other elements are also covered. "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 located between a first component and a second component, there may or may not be intervening components 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, in the actual implementation process, the method for classifying and identifying aromatized cigarette paper based on chemometrics-sensory omics specifically includes:
and step S1, performing qualitative and olfactory analysis on the aroma components of the aroma-providing essence sample.
In an embodiment of the method for classifying and identifying aromatized cigarette paper based on chemometrics-sensormics of the present invention, the step S1 may specifically include:
and step S11, performing qualitative olfactory discrimination analysis on the aroma components of the aroma-providing essence sample by adopting a gas chromatography-quadrupole-time of flight mass spectrometer (GC/Q-TOF) and an olfactory discrimination instrument.
In one embodiment of the present invention, a 7890B-7200 GC-quadrupole-time-of-flight mass spectrometer (Agilent, usa) and an ODP4 sniffer (Gerstel, germany) can be used, but the manufacturer and model of the GC/Q-TOF and the sniffer are not particularly limited.
The number of the aromatized essence samples is 1, the serial number is 01, and the specific sample information is shown in table 1. In an embodiment of the method for classifying and identifying aromatized cigarette paper based on chemometrics-sensormics of the present invention, the step S11 may specifically include:
step S111, sample pretreatment: taking 0.5mL of the essential oil sample in a 15mL headspace sample bottle, and carrying out GC-O-MS for later use; the essential oil sample is diluted 100 times by using a methanol solvent, and 1mL of the diluted solution is put into a 2mL sample bottle and is kept for LC-MS.
Step S112, extraction and sample injection: after aging at 250 ℃ for 15min by a solid phase microextraction arrow, adsorption extraction is started: extracting at 80 deg.C for 30min, and desorbing at 250 deg.C for 5 min; after the injection was completed, the solid phase microextraction arrow was aged at 250 ℃ for 10 min.
Wherein, in the qualitative and olfactory analysis, the gas chromatography conditions of LC-MS are as follows:
sample inlet temperature: 250 ℃;
temperature programming: keeping the temperature at 40 ℃ for 1 min; heating to 150 deg.C at 5 deg.C/min, maintaining for 1min, heating to 300 deg.C at 30 deg.C/min, and maintaining for 2 min;
carrier gas: he gas;
and (3) sample introduction mode: no shunt sampling;
the mass spectrum conditions are as follows: the method comprises the following steps of carrying out unknown substance identification analysis on an ion source EI with electron energy of 70eV, transmission line temperature of 250 ℃, ion source temperature of 230 ℃ and mass range of 30-600 m/z by utilizing Agilent MassHunnows software and NIST14 spectrum library, and searching according to similarity;
conditions for GC-O:
transmission line temperature: 300 ℃, temperature of mouth of olfactory discrimination: 100 ℃; sensory evaluation is carried out by 3 testers in a room with the temperature of 25 +/-2 ℃, the relative humidity of 50-60%, fresh air and no wind, the testers register the time points of all aroma substances, and simultaneously analyze the specific attributes, the evaluation result is divided into 4 points, wherein 1 point represents the weakest point, and 4 points represent the strongest point;
parameters of the MS:
scanning range: 100-: 50; ion source gas 2: 50, curtain gas: 35; temperature: 500 ℃, ion spray voltage float: 5500V-4500V, wherein 5500V corresponds to positive ion mode and 4500V corresponds to negative ion mode.
And S12, identifying and identifying the aroma substances from the aroma-providing essence samples according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactometer of the aroma components of the aroma-providing essence samples.
Specifically, GC-IMS Library Search V2.2.1 qualitative analysis software is adopted, a built-in IMS database is utilized to carry out qualitative analysis on the fragrant substances in the sample, and the identified fragrant substances are identified to comprise alcohols, phenols, esters, ethers and ketones.
According to the qualitative analysis result of a gas chromatography-time-of-flight mass spectrometry tandem olfactometer of the aroma components of the aroma-providing essence sample, and by combining NIST database retrieval (figure 2 and table 1), 29 aroma substances such as alcohol, phenol, ester, ether, ketone and the like are identified and identified from the aroma-providing essence sample. These substances are important aroma components in the aromatized cigarette paper and impart different style characteristics to the cigarette paper. The alcohol can not only play a role in moisturizing in the smoking process of the cigarette, but also improve the fragrance of the tobacco, so that the mainstream smoke of the cigarette is fine, smooth and soft and has rich concentration. The ketone substances have strong influence on the smoking taste, aroma and satisfaction of the cigarettes, and can coordinate the cigarette aroma, cover up miscellaneous gas and endow the cigarettes with different characteristic aroma. The aldehydes and esters are also main sources for forming the aroma characteristics of the aromatized cigarette paper, and the content difference has important influence on the aroma style characteristics of the aromatized cigarette paper and can be used as an important index for controlling the internal quality of the aromatized cigarette paper. The different compounds have large differences in the aspects of peak area response values, aroma types, aroma intensities and the like, which indicates that the flavor of a sample is the result of comprehensive sensory effects of complex components, and the complex olfactory mechanism of organisms causes that the compound aroma presented by the aromatized extract as a complex system cannot be characterized by simple linear addition of the single compounds.
According to the invention, a GC-MS column is connected with an artificial olfactory identifier (GC-O) in series, the complex and trace components of the aromatized cigarette paper are subjected to fine characterization based on sensory flavor, and the results are graded according to the obtained flavor description and flavor intensity of the monomer compound, so that the aromatized compound is screened according to the contribution degree.
TABLE 1 GC-O-MS organoleptic evaluation and identification of volatile flavor substances
And step S2, performing GC-IMS (gas chromatography tandem ion mobility) fingerprint analysis on the aroma components of the aroma-providing cigarette paper, the aroma-providing cigarette paper of the finished cigarette, the burnt aroma-providing cigarette paper and the clear water sample by using the qualitative and olfactory analysis result of the aroma components of the aroma-providing essence sample.
Illustratively, detection can be performed using a german g.a.s.flavourpec GC-IMS flavor analyzer, which has a CTC auto-headspace injector. The manufacturer and the model of the GC-IMS flavor analyzer are not particularly limited.
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.
In an embodiment of the method for classifying and identifying aromatized cigarette paper based on chemometrics-sensormics of the present invention, the step S2 may specifically include:
step S21, sample pretreatment: 0.5g of the cigarette paper is put into a 20mL headspace bottle, incubated at 90 ℃ for 20min and then injected.
Wherein, the headspace sampling conditions are as follows: sample introduction volume: 200 ul; incubation time: 20 min; incubation temperature: 90 ℃; temperature of the sample injection needle: 95 ℃; hatching rotation speed: 500 rpm.
Step S22, carrying out a burning test on the burnt aromatized cigarette paper: 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, and a silica gel gas collection bag is used for collecting the smoke and is connected to a GC-IMS sample introduction device.
Step S23, detecting an aromatized cigarette paper sample, an aromatized cigarette paper sample of a finished cigarette, a combusted aromatized cigarette paper sample and a clear water sample respectively by adopting a GC-IMS flavor analyzer, repeatedly carrying out sample injection determination for 3 times on each sample to obtain a gas chromatography series ion mobility spectrometry of aroma volatile compounds of each aromatized cigarette paper sample, wherein the aromatized cigarette paper samples comprise different batches of straight rib wood pulp cigarette paper samples, wood pulp cigarette paper samples and inlet cross-grain cigarette paper samples, the aromatized cigarette paper samples of the finished cigarette comprise aromatized cigarette paper of finished cigarettes of different production places and different batches, the combusted aromatized cigarette paper samples comprise combusted finished aromatized cigarette paper, and the clear water sample comprises non-grain base paper.
Wherein, the aromatized cigarette paper of the finished cigarette refers to aromatized cigarette paper peeled off from the finished cigarette, and the combusted aromatized cigarette paper refers to aromatized cigarette paper on the combusted finished cigarette.
In the invention, when GC-IMS fingerprint analysis is carried out, the chromatographic conditions are as follows:
gas phase-ion mobility spectrometry: the analysis time is 20 min; type of column: WAX, column length 30m, inner diameter: 0.53mm, film thickness: 1 μm, column temperature: 60 ℃; carrier gas/drift gas: n is a radical of 2 (ii) a IMS temperature: 45 ℃;
the GC chromatographic conditions were:
when the sample introduction time is 0, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 2mL/min, and the collection state is rec; when the sample introduction time is 2min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 10 mL/min; when the sample introduction time is 20min, the drift gas flow rate is 150mL/min, 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.
In the invention, 23 cigarette paper samples with the serial numbers of 02-24 are endowed with fragrance; the number of the aromatized cigarette paper samples of the finished cigarette is 20, and the number is 25-44; the number of the burnt aromatized cigarette paper samples is 10, and the number is 45-54; the number of the clean water sample is 1, and is 55, the specific sample information is detailed 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 specifically limited in the present invention. Carrier gas N 2 : pureThe degree is more than 99.999 percent.
In the GC-IMS fingerprint spectrum analysis of the aroma components of various types of the aromatized cigarette paper, 54 samples such as 02-55 samples and the like are repeatedly injected and measured for 3 times, and 162 groups of data are calculated. Parallel samples are given the sample number suffix a/b/c for differentiation.
TABLE 2 sample information Table
And step S3, carrying out chemometric modeling analysis on aroma components of various aroma-providing cigarette paper based on GC-IMS fingerprint analysis results of the various aroma-providing cigarette paper, and respectively establishing a first mode identification qualitative identification model for classifying a clear water sample, the aroma-providing cigarette paper of a finished cigarette, the burnt aroma-providing cigarette paper, a second mode identification qualitative identification model for classifying the aroma-providing cigarette paper with different specifications, a third mode identification qualitative identification model for classifying the aroma-providing cigarette paper of the finished cigarettes of different cigarette factories and a fourth mode identification qualitative identification model for classifying the aroma-providing cigarette paper burnt in different cigarette factories.
In an embodiment of the method for classifying and identifying aromatized cigarette paper based on chemometrics-sensormics of the present invention, the step S3 may specifically include:
and step S31, carrying out chromatographic peak detection and integration processing, background subtraction processing and compound alignment processing among samples on the GC-IMS fingerprint data of various aromatized cigarette paper by GC-IMS VOCal software in sequence.
In order to further comprehensively analyze and visually compare the composition characteristics of aroma component compounds of different types of aroma-providing cigarette paper and different batches of aroma-providing cigarette paper, the GC-IMS fingerprint analysis is carried out on 54 samples, and the 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 overlay of all the sample compounds after alignment is shown in FIG. 4, in which the abscissa is the peak area of the aroma-forming substance in the sample and the ordinate is the sample number. As can be seen from fig. 4, the compositions of volatile compounds of the aromatized cigarette paper, the aromatized cigarette paper of the finished cigarette and the aromatized cigarette paper after combustion are obviously different, and the aroma substances of the samples have respective characteristic peak areas and also have common similar areas. The cigarette paper is mainly characterized in that the aroma-giving cigarette paper of the finished cigarette adsorbs volatile components in tobacco shreds, the composition types of compounds are increased compared with aroma-giving cigarette paper, and the volatile substances of the burnt cigarette paper are obviously changed in the aspects of compound types and concentrations.
And step S32, importing the aligned GC-IMS fingerprint data of each type of aromatized cigarette paper into ModelLab Matman software, carrying out 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.
In the present invention, the chemometric modeling analysis software may be, for example, ModelLab Matman scientific big data System solution software version 2021, of Chemind Technologies, Inc. of China.
The modeling algorithm and parameters during chemometrics and machine learning modeling analysis comprise:
unsupervised machine learning algorithms are Principal Component Analysis (PCA) and Self-organizing mapping neural network (SOM), in which the principal component analysis corresponds to data preprocessing: UV scaling;
supervised machine learning algorithms include Partial Least Squares Discriminant Analysis (PLSDA) and random forest (random forest), where:
and (3) performing partial least square discriminant analysis on the number of corresponding reserved latent variables: 5; and (3) cross validation: leaving one method; data preprocessing: UV scaling;
the number of decision trees corresponding to the random forest is as follows: 100, respectively; and (3) eigenvalue algorithm: square root method; maximum tree depth: 30, of a nitrogen-containing gas; minimum purity reduction: 0.01; data preprocessing: UV scaling.
Specifically, for the first pattern recognition qualitative identification model, differences of the aromatized cigarette paper, the aromatized cigarette paper of a finished cigarette and the aromatized cigarette paper after combustion obtained by adopting two unsupervised machine learning algorithms of principal component analysis and self-organizing mapping neural network are obvious; the recognition rates of the flavored cigarette paper, the flavored cigarette paper of the finished cigarette and the flavored cigarette paper after combustion obtained by adopting two supervised machine learning algorithms of random forest and partial least square discriminant analysis are higher;
for the second pattern recognition qualitative identification model, differences of the aromatized cigarette paper with different specifications obtained by adopting a principal component analysis unsupervised machine learning algorithm are obvious; the recognition rate of the fragrant cigarette paper with different specifications obtained by adopting partial least square discriminant analysis supervised machine learning algorithm is higher;
for the third pattern recognition qualitative identification model, the differences of the aromatized cigarette paper of finished cigarettes in different cigarette factories obtained by adopting a principal component analysis unsupervised machine learning algorithm are general; the recognition rate of the aromatized cigarette paper of the finished cigarettes in different cigarette factories obtained by adopting partial least square discriminant analysis and supervised machine learning algorithm is higher;
for the fourth pattern recognition qualitative identification model, the differences of the burnt aromatized cigarette paper 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 cigarette paper, the cigarette paper of the finished cigarette and the burnt cigarette paper obtained by adopting partial least square discriminant analysis supervised machine learning algorithm is lower; the recognition rate of the cigarette paper after burning in different cigarette factories is higher by adopting a random forest supervised machine learning algorithm.
The method comprehensively analyzes the differences of the aroma components of different types of aroma-providing cigarette paper and different batches of aroma-providing cigarette paper by a chemometrics modeling method. And 4 types of pattern recognition qualitative identification models such as the classification identification of the aromatizing cigarette paper, the aromatizing cigarette paper of finished cigarettes, the classification identification of the aromatizing cigarette paper after combustion, the classification identification of the aromatizing cigarette paper of different specifications, the classification identification of the aromatizing cigarette paper of finished cigarettes in different cigarette factories, the classification identification of the aromatizing cigarette paper after combustion in different cigarette factories and the like are respectively established in sequence. The results are shown in table 3, fig. 5 and fig. 6. For the established 4-class identification model, due to the fact that the composition complexity of the volatile compounds is increased continuously, higher requirements are put on the performance of the selected chemometric algorithm and the optimization of the training process, and the data modeling difficulty presents an increasing trend. Unsupervised methods such as PCA and SOM cannot meet the requirements on the classification effect of the 3 rd and 4 th models, but through parameter optimization, the model prediction precision of the supervised pattern recognition algorithm approaches 100%. Particularly, the classification prediction effect of the random forest is better than that of the partial least square method, and the fact that the difference between the sample compound compositions in the category 4 presents the characteristic of nonlinearity is suggested. Wherein the V-shaped plot of figure 5 shows the significant ranking results of the differential compounds of the aroma-forming components in the aromatized wrapper paper of the finished cigarettes of different cigarette factories in the type 3 model.
Table 34 type of flavored cigarette paper sensory stability evaluation model analysis result summary
FIG. 5 is a graph showing PLS-DA pattern recognition as a scatter plot of independent variables (compounds) of type V plotted on the basis of the correlation (confidence) of the independent variables and VIP values as coordinate axes, illustrating the importance of the respective variables for regression classification prediction. The independent variable positioned in the first quadrant (plus the upper right corner) of the coordinate axis plays a positive correlation role for classification, and the independent variable positioned in the second quadrant (plus the upper left corner) of the coordinate axis plays an opposite negative correlation role for classification.
In the specific implementation, for an aromatized cigarette paper sample to be detected, which type of the aromatized cigarette paper sample to be detected is a clear water sample, aromatized cigarette paper of a finished product cigarette and the combusted aromatized cigarette paper can be distinguished through a first mode identification qualitative identification model, and if the aromatized cigarette paper is the aromatized cigarette paper, different specifications of the aromatized cigarette paper sample to be detected are determined by a second mode identification qualitative identification model, and the types of the aromatized cigarette paper sample to be detected are the types of the straight rib wood pulp cigarette paper, the wood pulp cigarette paper and the imported cross-grain cigarette paper; if the cigarette paper is the aromatized cigarette paper of the finished product cigarette, determining which cigarette factory produces the aromatized cigarette paper sample to be tested by using a third mode identification qualitative identification model, specifically which cigarette factory A-F produces the aromatized cigarette paper; and if the cigarette paper is the burnt aromatized cigarette paper, determining which cigarette factory produces the aromatized cigarette paper sample to be tested by using a fourth mode identification qualitative identification model, specifically which production factory A-production factory D produces the aromatized cigarette paper.
The invention firstly develops sensory olfaction analysis on the chemical composition and sensory evaluation of a complex system of the aromatized essence, and identifies 29 kinds of aroma substances of alcohol, phenol, ester, ether and ketone. And the GC-IMS fingerprint analysis and the GC-IMS difference marker screening are researched. The method is characterized in that the sensory stability of various types of flavored cigarette paper is evaluated based on a machine learning method, and corresponding unsupervised and supervised algorithms are established, so that 4 types of pattern recognition qualitative identification models, such as flavored cigarette paper, flavored cigarette paper of finished cigarettes, classified recognition of flavored cigarette paper of different specifications, classified recognition of flavored cigarette paper of finished cigarettes of different cigarette factories, classified recognition of flavored cigarette paper of combusted 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 jointly formed, as shown in fig. 6.
According to the method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics, GC-QTOF-O online sensory olfactory discrimination and GC-IMS gas phase ion mobility spectrometry analysis are developed around aromatized essence and different types of aromatized cigarette paper, chemometrics machine learning modeling research is carried out based on volatile component aroma detection data, 4 large-class mode identification models such as the classifying identification of the aromatized cigarette paper after combustion, the classifying identification of aromatized cigarette paper of different specifications, the classifying identification of the aromatized cigarette paper of different cigarette factory finished cigarettes, the classifying identification of the aromatized cigarette paper after combustion of different cigarette factories and the like are sequentially established through chemometrics modeling, and an aromatized cigarette paper layering progressive analysis flow is jointly formed; the change mode and trend of the compound composition of the volatile aroma components from the aromatized cigarette paper and the aromatized cigarette paper of the finished cigarette to the aromatized cigarette paper after combustion are disclosed, so that theoretical basis and a new research method are provided for monitoring the attenuation change rule of the aroma components of the aromatized cigarette paper and tracing analysis.
Thus, various embodiments of the present disclosure have been described in detail. Some details 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 method for classifying and identifying aromatized cigarette paper based on chemometrics-sensory omics is characterized by comprising the following steps:
performing qualitative and olfactory analysis on the aroma components of the aroma-endowing essence sample;
performing GC-IMS fingerprint analysis on aroma components of the aroma-providing cigarette paper, the aroma-providing cigarette paper of the finished cigarette, the burnt aroma-providing cigarette paper and the clear water sample by utilizing the qualitative and olfactory analysis result of the aroma-providing component of the aroma-providing essence sample;
based on GC-IMS fingerprint spectrum analysis results of various types of aromatizing cigarette paper, carrying out chemometric modeling analysis on the aromatizing components of various types of aromatizing cigarette paper, and respectively establishing a first mode identification qualitative identification model for classifying clean water samples, aromatizing cigarette paper of finished cigarettes, a first mode identification qualitative identification model for classifying the combusted aromatizing cigarette paper, a second mode identification qualitative identification model for classifying the aromatizing cigarette paper with different specifications, a third mode identification qualitative identification model for classifying the aromatizing cigarette paper of finished cigarettes in different cigarette factories and a fourth mode identification qualitative identification model for classifying the aromatizing cigarette paper combusted in different cigarette factories.
2. The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics as claimed in claim 1, wherein the qualitative and olfactory analysis of the aroma components of the aromatized flavor sample specifically comprises:
performing qualitative and olfactory analysis on aroma components of the aroma-providing essence sample by adopting a gas chromatography-quadrupole-time-of-flight mass spectrometry combined instrument and an olfactory analyzer;
according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactometer of the aroma components of the aroma-endowing essence sample, the aroma substances are identified and identified from the aroma-endowing essence sample.
3. The method for classifying and identifying the aromatized cigarette paper based on chemometrics-organoleptic omics as claimed in claim 2, wherein the qualitative and olfactory analysis of the aroma components of the aromatized essence sample is carried out by using a gas chromatography-quadrupole-time-of-flight mass spectrometer and a sniffer, and specifically comprises the following steps:
a sample pretreatment step: taking 0.5mL of the essential oil sample in a 15mL headspace sample bottle, and keeping GC-O-MS for later use; diluting the essential oil sample by 100 times by using a methanol solvent, taking 1mL of diluent in a 2mL sample bottle, and performing LC-MS (liquid chromatography-mass spectrometry) for later use;
extraction and sample introduction: after aging at 250 ℃ for 15min by a solid phase microextraction arrow, adsorption extraction is started: extracting at 80 deg.C for 30min, and desorbing at 250 deg.C for 5 min; after the injection was completed, the solid phase microextraction arrow was aged at 250 ℃ for 10 min.
4. The method for classifying and identifying the aromatized cigarette paper based on chemometrics-organoleptic omics as claimed in claim 3, wherein the gas chromatography conditions of LC-MS are as follows when performing qualitative olfactory analysis:
sample inlet temperature: 250 ℃;
temperature programming: keeping the temperature at 40 ℃ for 1 min; heating to 150 deg.C at 5 deg.C/min, maintaining for 1min, heating to 300 deg.C at 30 deg.C/min, and maintaining for 2 min;
carrier gas: he gas;
and (3) sample introduction mode: no-shunt sample introduction;
the mass spectrum conditions are as follows: the method comprises the following steps of carrying out unknown substance identification analysis on an ion source EI with electron energy of 70eV, transmission line temperature of 250 ℃, ion source temperature of 230 ℃ and mass range of 30-600 m/z by utilizing Agilent MassHunnows software and NIST14 spectrum library, and searching according to similarity;
conditions for GC-O:
transmission line temperature: 300 ℃, temperature of mouth of olfactory discrimination: 100 ℃; sensory evaluation is carried out by 3 testers in a room with the temperature of 25 +/-2 ℃, the relative humidity of 50-60%, fresh air and no wind, the testers register the time points of all aroma substances, and simultaneously analyze the specific attributes, the evaluation result is divided into 4 points, wherein 1 point represents the weakest point, and 4 points represent the strongest point;
parameters of the MS:
scanning range: 100-: 50; ion source gas 2: 50, curtain gas: 35; temperature: 500 ℃, ion spray voltage float: 5500V-4500V, wherein 5500V corresponds to positive ion mode and 4500V corresponds to negative ion mode.
5. The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics as claimed in claim 3, wherein the identification and identification of the aroma substances from the aromatized flavor samples are carried out according to the qualitative analysis result of the gas chromatography-time-of-flight mass spectrometry tandem olfactory analyzer of the aroma components of the aromatized flavor samples, which specifically comprises the following steps:
and (3) carrying out qualitative analysis on the aroma substances in the sample by using GC-IMS Library Search V2.2.1 qualitative analysis software and using a built-in IMS database, and identifying the identified aroma substances comprising alcohols, phenols, esters, ethers and ketones.
6. The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics according to claim 1, wherein the GC-IMS fingerprint analysis is performed on the aromatized cigarette paper, the aromatized cigarette paper of the finished cigarette, the combusted aromatized cigarette paper and the aromatized components of the clean water sample by using the qualitative and olfactory analysis result of the aromatized essence sample, and specifically comprises the following steps:
sample pretreatment: taking 0.5g of cigarette paper, placing the cigarette paper in a 20mL headspace bottle, incubating for 20min at 90 ℃, and injecting a sample;
the burning test was performed on the cigarette paper with imparted fragrance after burning: performing a cigarette paper combustion test on different cigarette samples with cut tobaccos removed on an automatic smoking machine and collecting smoke, wherein 1 piece of 44mm Cambridge filter disc is used for collecting total particulate matters for each pore channel sample, and a silica gel gas collection bag is used for collecting the smoke and is connected to a GC-IMS sample introduction device;
detecting an aromatized cigarette paper sample, an aromatized cigarette paper sample of a finished product cigarette, a combusted aromatized cigarette paper sample and a clear water sample by adopting a GC-IMS flavor analyzer, repeatedly injecting and measuring each sample for 3 times to obtain a gas chromatography series ion mobility spectrometry of aromatized volatile compounds of each aromatized cigarette paper sample, wherein the aromatized cigarette paper samples comprise different batches of straight rib wood pulp cigarette paper samples, wood pulp cigarette paper samples and inlet cross-grain cigarette paper samples, the aromatized cigarette paper samples of the finished product cigarette comprise aromatized cigarette paper of finished products cigarettes of different production places and different batches, the combusted aromatized cigarette paper samples comprise combusted finished product aromatic cigarette paper, and the clear water sample comprises non-grain cigarette paper base paper.
7. The chemometrics-sensoromics-based aromatized cigarette paper classification and identification method according to claim 6, characterized in that in the GC-IMS fingerprint analysis, the headspace sample injection conditions are as follows:
sample introduction volume: 200 ul; incubation time: 20 min; incubation temperature: at 90 ℃; temperature of the sample injection needle: 95 ℃; hatching rotation speed: 500 rpm.
The chromatographic conditions are as follows:
gas phase-ion mobility spectrometry: the analysis time is 20 min; type of column: WAX, column length 30m, inner diameter: 0.53mm, film thickness: 1 μm, column temperature: 60 ℃; carrier gas/drift gas: n is a radical of 2 (ii) a IMS temperature: 45 ℃;
the GC chromatographic conditions were:
when the sample introduction time is 0, the drift gas flow rate is 150mL/min, the carrier gas flow rate is 2mL/min, and the collection state is rec; when the sample introduction time is 2min, the drift gas flow rate is 150mL/min, and the carrier gas flow rate is 10 mL/min; when the sample introduction time is 20min, the drift gas flow rate is 150mL/min, 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.
8. The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics according to claim 1, wherein chemometrics modeling analysis is performed on the aroma components of various types of aromatized cigarette paper based on the GC-IMS fingerprint analysis results of various types of aromatized cigarette paper, and a first pattern identification qualitative identification model, a second pattern identification qualitative identification model, a third pattern identification qualitative identification model and a fourth pattern identification qualitative identification model are respectively established, and specifically comprises the following steps:
carrying out chromatographic peak detection and integral processing, background subtraction processing and compound alignment processing among samples on GC-IMS fingerprint data of various aromatized cigarette paper by GC-IMS VOCal software in sequence;
and introducing the aligned GC-IMS fingerprint data of various types of aromatized cigarette paper into ModelLab Matman software, carrying out 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.
9. The chemometrics-sensoromics based aromatized cigarette paper classification and identification method according to claim 8, wherein the modeling algorithms and parameters in chemometrics and machine learning modeling analysis comprise:
the unsupervised machine learning algorithm is a principal component analysis and self-organizing mapping neural network, wherein the data preprocessing corresponding to the principal component analysis comprises the following steps: UV scaling;
the supervised machine learning algorithm comprises partial least squares discriminant analysis and random forests, wherein:
and (3) performing partial least square discriminant analysis on the number of corresponding reserved latent variables: 5; and (3) cross validation: leaving one method; data preprocessing: UV scaling;
the number of decision trees corresponding to the random forest is as follows: 100, respectively; and (3) eigenvalue algorithm: square root method; maximum tree depth: 30, of a nitrogen-containing gas; minimum purity reduction: 0.01; data preprocessing: UV scaling.
10. The method for classifying and identifying the aromatized cigarette paper based on chemometrics-sensory omics according to claim 9, wherein for the first pattern identification qualitative identification model, the differences of the aromatized cigarette paper, the aromatized cigarette paper of the finished cigarette and the aromatized cigarette paper after combustion obtained by adopting two unsupervised machine learning algorithms of principal component analysis and self-organized mapping neural network are significant; the recognition rates of the aromatized cigarette paper, the aromatized cigarette paper of the finished cigarette and the combusted aromatized cigarette paper obtained by adopting random forest and partial least square discriminant analysis two supervised machine learning algorithms are higher;
for the second pattern recognition qualitative identification model, differences of the aromatized cigarette paper with different specifications obtained by adopting a principal component analysis unsupervised machine learning algorithm are obvious; the recognition rate of the fragrant cigarette paper with different specifications obtained by adopting partial least square discriminant analysis supervised machine learning algorithm is higher;
for the third pattern recognition qualitative identification model, the differences of the aromatized cigarette paper of finished cigarettes in different cigarette factories are obtained by adopting a principal component analysis unsupervised machine learning algorithm; the recognition rate of the aromatized cigarette paper of the finished cigarettes in different cigarette factories obtained by adopting partial least square discriminant analysis and supervised machine learning algorithm is higher;
for the fourth pattern recognition qualitative identification model, the differences of the burnt aromatized cigarette paper 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 cigarette paper, the cigarette paper of the finished cigarette and the burnt cigarette paper obtained by adopting partial least square discriminant analysis supervised machine learning algorithm is lower; the recognition rate of the cigarette paper after burning in different cigarette factories is higher by adopting a random forest supervised machine learning algorithm.
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| CN102680627A (en) * | 2012-05-15 | 2012-09-19 | 上海烟草集团有限责任公司 | Method for analyzing and identifying key aromatic substances in tobacco leaf |
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