CN114527031A - Method for rapidly evaluating quality of tobacco essence perfume - Google Patents

Method for rapidly evaluating quality of tobacco essence perfume Download PDF

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CN114527031A
CN114527031A CN202210144080.2A CN202210144080A CN114527031A CN 114527031 A CN114527031 A CN 114527031A CN 202210144080 A CN202210144080 A CN 202210144080A CN 114527031 A CN114527031 A CN 114527031A
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杨继
李源栋
刘春波
唐石云
朱瑞芝
司晓喜
张凤梅
蒋薇
李振杰
刘志华
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China Tobacco Yunnan Industrial Co Ltd
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Abstract

The invention discloses a method for rapidly evaluating the quality of tobacco essence and flavor, which comprises the following steps: (1) preparing a plurality of essence and spice to-be-evaluated samples and essence and spice standard samples; (2) thermogravimetric experiments; (3) carrying out thermogravimetric curve treatment to obtain a differential weight loss curve; (4) calculating the similarity of the differential weightlessness curves; (5) and evaluating a plurality of samples to be evaluated of the flavors and fragrances. The method disclosed by the invention is simple and rapid to operate, overcomes the influence of subjective factors and the characterization of differences brought by artificial sensory evaluation in the traditional method, and is more objective and efficient, good in repeatability and high in sensitivity.

Description

Method for rapidly evaluating quality of tobacco essence perfume
Technical Field
The invention belongs to the technical field of tobacco, and particularly relates to a method for rapidly evaluating the quality of tobacco essence and flavor by converting thermogravimetric differential curve similarity into essence and flavor similarity through a similarity calculation method similar to a fingerprint.
Background
The essence and spice for cigarette is an indispensable raw material for cigarette production, the formula of the essence and spice is also one of core technologies of tobacco enterprises, and the application of the essence and spice and additives is closely related to the establishment and development of cigarette brands. Along with the improvement of living standard of people, the requirements on the quality grade, taste and style of tobacco products are higher and higher. The aroma of the cigarette as a special food is the core content of the tobacco quality, and the aroma characteristics of the cigarette directly influence the smoking habit of smokers. The flavoring and casing are added to strengthen the fragrance of the tobacco, improve the quality of the tobacco leaves and improve the utilization rate of the tobacco leaves, and are important measures for improving the quality of the tobacco. At present, mixed low tar cigarettes are a necessary trend for development, and the reduction of tar content is an important subject of cigarette production both internationally and domestically, while a lot of flavor substances in tobacco are present in tar, and along with the reduction of tar content, the smoke taste becomes light, the aroma is weakened, and cigarette products lose the original style and are difficult to be accepted by consumers. Therefore, the tar of the cigarette is reduced, the smoke concentration and the fragrance are improved, and only the flavoring and feeding technology is used. Therefore, the flavoring and casing have important functions in the aspects of improving the self quality of tobacco, stabilizing the quality of cigarettes, meeting individual requirements, improving safety and the like. With the establishment of brands and the deep development of tar and harm reduction work, tobacco charging and flavoring become essential process links in cigarette production.
The perfume can be divided into two categories, namely natural perfume and synthetic perfume according to the source of the perfume, wherein more than 3000 types of natural perfume are known at present, and more than 7000 types of synthetic perfume are known. The quality of the tobacco flavor and fragrance is uneven under the influence of various factors such as production places, processing and the like. The existing evaluation method of the quality of the tobacco flavor and fragrance is mainly based on artificial sensory evaluation and assisted by objective data such as physical and chemical index detection and the like. The artificial sensory evaluation mainly depends on experience accumulation and evaluation judgment of formula personnel, and has high working strength and large subjective difference; the evaluation of physical and chemical indexes is mainly realized by detection indexes such as refraction, density, acid value and the like, and has the defects of excessive detection contents, longer detection time, low efficiency and the like. Other methods for evaluating the tobacco essence and flavor also comprise comparison of infrared spectrograms and the like, and the method has the defects of complex operation, large sample demand, insufficient precision and the like.
The quality control measures of the flavors and fragrances detected according to the appearances and physical indexes of the flavors and fragrances for cigarettes cannot meet the development requirements, and a simpler, faster, standardized, programmed and standardized quality control management system for the flavors and fragrances for cigarettes needs to be established.
The thermogravimetric analysis (TG/DTA) can provide stable reaction conditions under the condition of temperature programming, and is the most ideal experimental tool in the research of tobacco pyrolysis. Derivative thermogravimetry, also known as derivative thermogravimetry, is derived from thermogravimetry, which is a technique to record the first derivative of a TG curve with respect to temperature or time. The results obtained from the experiments are the differential quotient thermogravimetric curves, i.e. DTG curves. The DTG curve is characterized by: the initial reaction temperature, the maximum reaction rate temperature and the reaction termination temperature of each weight loss stage can be accurately reflected; the area of each peak on the DTG curve is in direct proportion to the weight loss of the corresponding sample on the TG curve; when the TG curve is not obvious to the steps of some heating processes, the DTG curve can be obviously distinguished. It can be said that a substance has its unique DTG curve as long as it undergoes a mass change when heated. A tobacco essence and spice database and a mathematical model are established by thermogravimetry, and objective data are utilized to evaluate the quality of the tobacco essence and spice database, so that the method has important significance for reducing subjective factor influence and difference representation and improving the quality control management level of the tobacco essence and spice. The invention is therefore proposed.
Disclosure of Invention
The invention discloses a method for quickly evaluating tobacco essence and flavor. Particularly, the conversion from the similarity of thermogravimetric differential curves to the similarity of flavors and fragrances is realized by a similarity calculation method similar to a fingerprint, so that the quality of the flavors and fragrances for cigarettes is rapidly evaluated.
The technical scheme of the invention is as follows:
a method for rapidly evaluating the quality of essence and flavor for tobacco comprises the following steps:
(1) preparing a plurality of essence and spice to-be-evaluated samples and essence and spice standard samples;
(2) performing thermogravimetric experiment;
(3) carrying out thermogravimetric curve treatment to obtain a differential weight loss curve;
(4) calculating the similarity of the differential weightlessness curves;
(5) and evaluating a plurality of samples to be evaluated of the flavors and fragrances.
Preferably, the thermogravimetric experiment adopts a thermogravimetric analyzer, and the thermogravimetric experiment comprises the following steps: weighing a certain mass of sample, placing the sample in a thermogravimetric platinum crucible, wherein the temperature rise procedure is as follows: the initial temperature is 30 ℃, the heating rate is 10 ℃/min, the termination temperature is 900 ℃, and the test is carried out under the nitrogen flow of 50 mL/min; the sample mass is typically (5.00. + -. 0.05) mg. Before sample analysis, a thermogravimetric analyzer is arranged and kept for 10min at 900 ℃ so as to completely discharge impurities in the furnace body, and an empty crucible is taken as a reference.
Preferably, the balance sensitivity of the thermogravimetric analyzer is not less than 0.1 mug, and the curve resolution is not less than 5000 ten thousand resolution points.
Preferably, the thermogravimetric curve processing step (3) is as follows: and (3) monitoring the weight change of the sample in the pyrolysis process in real time by using a thermogravimetric analyzer, and deriving the weight data with respect to time according to the obtained time-weight relation graph to obtain a differential weight loss curve.
Preferably, the step (4) of calculating the similarity of the differential weight loss curve comprises the following steps:
1) performing LLE dimensionality reduction on the sample
LLE (localization Linear embedding) is an unsupervised dimension reduction method suitable for nonlinear data, and the main idea is that the relative position relationship between data points in a high-dimensional space can be represented in a low-dimensional space, LLE retains the relative relationship between local point clusters, and the LLE keeps the neighbor weight of each sample data relatively stable in the range of translation, rotation and expansion and contraction transformation. The LLE dimensionality reduction process is as follows:
step 1: forming a data set X (N multiplied by D) by D-dimensional differential weight loss curve data of a plurality of samples to be evaluated of the essence and spice and standard samples, wherein X is the number of samplesi(i ═ 1, 2.., N) select k neighbors;
step 2: calculating a local reconstruction weight matrix of the sample points, solving a reconstruction coefficient with the minimum approximation error, and setting the following cost function:
Figure BDA0003507982340000031
wherein x isj(j ═ 1, 2.. k.) is xiK neighbors of, wijIs expressed in reconstruction xiThe weight occupied by the jth neighbor thereof and satisfies
Figure BDA0003507982340000032
Constructing a local covariance matrix Zi＝(xi-xj)T(xi-xj) And solving a local optimization reconstruction weight matrix:
Figure BDA0003507982340000033
and step 3: mapping all sample points of the data set to a low-dimensional space, reconstructing data samples in the low-dimensional space under the condition of keeping a local linear structure unchanged, and setting the following cost function:
Figure BDA0003507982340000034
wherein, yiIs a sample xiIs performed in a low-dimensional space of the image,yj(i ═ 1, 2.., k) is yiK neighbor points of (a), satisfying the following constraint:
Figure BDA0003507982340000035
by WiDenotes the ith column, I, of the W matrixiRepresenting the ith column of the N-dimensional identity matrix, the cost function can be matrixed as:
Figure BDA0003507982340000041
wherein, M ═ W (I-W)T(I-W)；
And finally, solving the Y to obtain a solving matrix M eigenvector. To minimize the loss function value, the feature vector corresponding to the feature value from 2 nd to d +1 th is usually taken to constitute the low-dimensional embedding coordinate Y. (usually the first eigenvalue is nearly zero, thus being discarded), so Y (N × d) is taken as the eigenvalue after dimension reduction of LLE;
2) calculating Euclidean distance of the selected reduced-dimension sample data; the Euclidean distance is a simple and effective similarity calculation method. The Euclidean distance calculates and adds absolute data value differences among all dimensions of the data, and the numerical value differences among the data can be intuitively reflected;
according to the characteristic matrix Y (Nxd) after dimensionality reduction, the Euclidean distance between the essence and spice to-be-evaluated sample and the essence and spice standard sample is calculated according to the following formula:
Figure BDA0003507982340000042
wherein y isil、yjlRespectively representing the l-th dimension data of the sample i to be evaluated and the standard sample j after the LLE dimension reduction.
Preferably, d (y)i，yj) The smaller the difference, the higher the similarity between the selected essence and spice to be evaluated and the essence and spice standard sample, and the smaller the difference.
The invention has the following beneficial effects:
1. the method can well avoid the influence of subjective factors and the differential characterization brought by the traditional artificial sensory evaluation; the universality of the evaluation of the tobacco essence and flavor is improved, the workload of smoke panel evaluation personnel is reduced, and a simpler, quicker, standardized, programmed and normalized management system for the quality control of the tobacco essence and flavor is established.
2. The method is simple to operate, the sample usage amount is extremely small and is within 10 mg; the method does not use any wet chemical means such as solvent and the like, is nontoxic and harmless, has no harm to operators, and does not cause environmental pollution.
3. The method is more objective and efficient, has good repeatability and high sensitivity, and has unique advantages in tobacco flavor evaluation in the tobacco industry.
4. According to the method, the change information of the heated mass of the substances is provided, and simultaneously, through the dynamic research of the programmed temperature rise of thermogravimetry, a thermochemical reaction model of different substances can be established, more data such as activation energy, pre-index factors, reaction stages, heat and mass transfer information and the like can be obtained, so that a theoretical basis is provided for the use of the tobacco essence and flavor in the traditional cigarettes and the heated cigarettes.
Drawings
Figure 1 example 1 DTG curves for a perfume to be evaluated sample (sample) versus a perfume standard sample (standard).
FIG. 2 is the dimension-reducing LLE projection diagram of example 1.
Detailed Description
The present invention is further illustrated by the following examples, but is not limited to these examples. The experimental methods in which specific conditions are not specified in examples are generally commercially available under the conventional conditions and the conditions described in the manual, or under the general-purpose equipment, materials, reagents and the like used under the conditions recommended by the manufacturer, unless otherwise specified. The starting materials required in the following examples and comparative examples are all commercially available.
Example 1: eleven perfumery samples to be evaluated (samples a-K) and one perfumery standard sample (standard). The quality evaluation of twelve selected essence and spice samples comprises the following steps:
(1) twelve samples of essence and spice are taken, and each sample is 5 g;
(2) before sample analysis, a thermogravimetric analyzer is arranged to be kept for 10min at 900 ℃ so as to completely discharge impurities in the furnace body, and an empty crucible is taken as a reference; weigh (5.00 ± 0.05) mg of sample into a hot-weight platinum crucible, with the temperature program: the initial temperature is 30 ℃, the heating rate is 10 ℃/min, the termination temperature is 900 ℃, and the test is carried out under the nitrogen flow of 50 mL/min; the sensitivity of the instrument balance is 0.1 mu g, and the curve resolution is 5000 ten thousand resolution points;
(3) performing first-order derivation on the twelve thermogravimetric curves to obtain a thermogravimetric differential weight loss curve, as shown in fig. 1;
(4) calculating the similarity of the differential weight loss curve according to the following steps:
the differential weight loss curves of the twelve samples are formed into a data matrix X (N × D), where N is the number of samples and D is the data dimension of the differential weight loss curve, i.e., N is 12 and D is 5191. For each sample xi11 neighboring points are chosen, i.e., k 11. Setting a cost function
Figure BDA0003507982340000051
Wherein xj(j ═ 1, 2.. times, k) is sample xiK number of neighboring points, wijIs represented in reconstruction xiThe weight occupied by the jth neighbor thereof and satisfies
Figure BDA0003507982340000061
Constructing a local covariance matrix Zi＝(xi-xj)T(xi-xj) To find the local optimized reconstruction weight matrix
Figure BDA0003507982340000062
Wherein 1 iskIs a k-dimensional all-1 vector.
Under the condition of keeping the local linear structure unchanged, reconstructing the data sample in the low-dimensional space, and setting a cost function
Figure BDA0003507982340000063
Wherein y isiIs a sample xiOf a low-dimensional space of yj(j ═ 1, 2.. times, k) for sample yiK neighbor points of (2), and satisfy the constraint condition
Figure BDA0003507982340000064
And I is an identity matrix. To minimize the loss function value, the cost function may be matrixed
Figure BDA0003507982340000065
Where tr is the trace function, M ═ I-WT(I-W) is a symmetric, semi-positive definite matrix; after the eigenvectors corresponding to the small eigenvalues from 2 nd to D +1 th of the matrix M are used to form LLE dimensionality reduction, a matrix Y (N × D) is used instead of the original data matrix X (N × D), and in this example, D is taken to be 3.
The projection of the sample set for 3-dimensional dimensionality reduction is shown in fig. 2. Table 1 shows the 3-dimensional feature data of the twelve samples after the dimension reduction.
TABLE 1 3-D characteristic data of twelve samples after dimension reduction
Sample (I) Dimension 1 Dimension 2 Dimension 3
Standard sample 0.531181 0.002128 -0.10185
Sample A -0.0228 0.031346 -0.27849
Sample B 0.569987 0.200304 0.03139
Sample C -0.2232 0.22973 0.447963
Sample D 0.052806 0.348058 0.164714
… … … …
Sample J -0.18076 -0.4651 0.163829
Sample K -0.07449 -0.35019 -0.07766
The top 3 dimensional feature data was selected and the euclidean distance between eleven samples and the standard was calculated as shown in table 2.
TABLE 2 Euclidean distance between sample and standard
Sample (I) Standard sample Sample A Sample B Sample C Sample D Sample E Sample F Sample G Sample H Sample I Sample J Sample K
Euclidean distance 0 0.58 0.24 0.96 0.64 0.43 1.01 0.71 2.07 0.79 0.89 0.70
As can be seen from table 2, the euclidean distance between the sample B and the standard sample is the smallest, which indicates that the sample B has the highest similarity and the smallest difference with the standard sample.
In order to further verify the calculation results, the aforementioned flavor and fragrance standard samples and eleven flavor and fragrance sample organization experts were subjected to sensory evaluation, and the results are shown in table 3.
TABLE 3 sensory test results
Sample(s) Standard sample Sample A Sample B Sample C Sample D Sample E Sample F Sample G Sample H Sample I Sample J Sample K
Total score 89.5 87 90 83 85 91 82 85.5 70 86 85 84
As can be seen from the results in Table 3 above, the sensory differences between sample B and the standards were minimal, and the differences between samples H and F and the standards were greater, consistent with the above calculations. Based on the method, the sample B can be directly used as a substitute for the essence and the spice of the standard sample in the development of cigarette products.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (6)

1. A method for rapidly evaluating the quality of tobacco essence and flavor is characterized by comprising the following steps:
(1) preparing a plurality of essence and spice to-be-evaluated samples and essence and spice standard samples;
(2) thermogravimetric experiments;
(3) carrying out thermogravimetric curve treatment to obtain a differential weight loss curve;
(4) calculating the similarity of the differential weightlessness curves;
(5) and evaluating a plurality of samples to be evaluated of the flavors and fragrances.
2. The method of claim 1, wherein the thermogravimetric experiment uses a thermogravimetric analyzer, and the thermogravimetric experiment comprises the following steps: weighing a certain mass of sample, placing the sample in a hot gravimetric platinum crucible, and carrying out a temperature rise procedure: the initial temperature was 30 deg.C, the ramp rate was 10 deg.C/min, the end temperature was 900 deg.C, and the test was conducted under a nitrogen flow of 50 mL/min.
3. The method of claim 1, wherein the thermogravimetric analyzer has a balance sensitivity of no less than 0.1 μ g and a curve resolution of no less than 5000 ten thousand resolution points.
4. The method of claim 1, wherein the thermogravimetric curve processing step (3) is: and (3) monitoring the weight change of the sample in the pyrolysis process in real time by using a thermogravimetric analyzer, and deriving the weight data with respect to time according to the obtained time-weight relation graph to obtain a differential weight loss curve.
5. The method according to claim 1, wherein the step of calculating the degree of similarity of the differential weight loss curve in the step (4) is as follows:
1) performing LLE dimensionality reduction on the sample
Step 1: forming a data set X (N multiplied by D) by D-dimensional differential weight loss curve data of a plurality of samples to be evaluated of the essence and spice and standard samples, wherein X is the number of samplesi(i＝1,2, N) selecting k adjacent points;
step 2: calculating a local reconstruction weight matrix of the sample points, solving a reconstruction coefficient with the minimum approximation error, and setting the following cost function:
Figure FDA0003507982330000011
wherein x isj(j ═ 1, 2.. k.) is xiK number of neighboring points, wijIs expressed in reconstruction xiThe weight occupied by the jth neighbor thereof and satisfies
Figure FDA0003507982330000012
Constructing a local covariance matrix Zi＝(xi-xj)T(xi-xj) And solving a local optimization reconstruction weight matrix:
Figure FDA0003507982330000013
and step 3: mapping all sample points of the data set to a low-dimensional space, reconstructing data samples in the low-dimensional space under the condition of keeping a local linear structure unchanged, and setting the following cost functions:
Figure FDA0003507982330000021
wherein, yiIs a sample xiOf a low-dimensional space of yj(i ═ 1, 2.., k) is yiK neighbor points of (a), satisfying the following constraint:
Figure FDA0003507982330000022
by WiDenotes the ith column, I, of the W matrixiRepresenting the ith column of the N-dimensional identity matrix, the cost function can be matrixed as:
Figure FDA0003507982330000023
wherein, M ═ I (I-W)T(I-W)；
Solving the eigenvector of the matrix M, and taking the eigenvector corresponding to the eigenvalue of 2-d +1 to form a low-dimensional embedded coordinate Y; then Y (N x d) is taken as the feature point after dimension reduction of LLE;
2) calculating Euclidean distance of the selected reduced-dimension sample data
According to the characteristic matrix Y (N multiplied by d) after dimensionality reduction, the Euclidean distance between the essence and spice to be evaluated sample and the essence and spice standard sample is calculated according to the following formula:
Figure FDA0003507982330000024
wherein y isil、yjlRespectively representing the l-dimension data of the sample i to be evaluated and the standard sample j after the LLE dimension reduction.
6. The method of claim 5, wherein d (y)i，yj) The smaller the difference, the higher the similarity between the selected essence and spice to be evaluated and the essence and spice standard sample, and the smaller the difference.
CN202210144080.2A 2022-02-17 2022-02-17 Method for rapidly evaluating quality of tobacco essence perfume Pending CN114527031A (en)

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CN108120653A (en) * 2017-12-13 2018-06-05 安徽中烟工业有限责任公司 A kind of method of finished cigarettes quality stability between evaluation batch
CN113049436A (en) * 2021-02-08 2021-06-29 广东中烟工业有限责任公司 Method for rapidly analyzing consistency of tobacco flavor and fragrance and application thereof
CN113049438A (en) * 2021-04-01 2021-06-29 中国烟草总公司郑州烟草研究院 Method for rapidly identifying heat conversion characteristics of different tobaccos based on macroscopic quantity thermogravimetry
CN113405941A (en) * 2021-06-29 2021-09-17 湖北中烟工业有限责任公司 Method for identifying tobacco shred types by using thermogravimetric analyzer

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008039660A (en) * 2006-08-09 2008-02-21 Tokyo Electric Power Co Inc:The Simplified analysis method of asbestos
CN104266927A (en) * 2014-10-28 2015-01-07 福建中烟工业有限责任公司 Method and device for rapidly and objectively distinguishing real and counterfeit cigarettes
CN108061691A (en) * 2017-12-13 2018-05-22 安徽中烟工业有限责任公司 A kind of method of reconstituted tobacco quality stability between evaluation batch
CN108120653A (en) * 2017-12-13 2018-06-05 安徽中烟工业有限责任公司 A kind of method of finished cigarettes quality stability between evaluation batch
CN113049436A (en) * 2021-02-08 2021-06-29 广东中烟工业有限责任公司 Method for rapidly analyzing consistency of tobacco flavor and fragrance and application thereof
CN113049438A (en) * 2021-04-01 2021-06-29 中国烟草总公司郑州烟草研究院 Method for rapidly identifying heat conversion characteristics of different tobaccos based on macroscopic quantity thermogravimetry
CN113405941A (en) * 2021-06-29 2021-09-17 湖北中烟工业有限责任公司 Method for identifying tobacco shred types by using thermogravimetric analyzer

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