CN102901779A - Discrimination method for caramel sweet taste characteristics of tobacco leaf based on flavor components - Google Patents
Discrimination method for caramel sweet taste characteristics of tobacco leaf based on flavor components Download PDFInfo
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- CN102901779A CN102901779A CN2012103372331A CN201210337233A CN102901779A CN 102901779 A CN102901779 A CN 102901779A CN 2012103372331 A CN2012103372331 A CN 2012103372331A CN 201210337233 A CN201210337233 A CN 201210337233A CN 102901779 A CN102901779 A CN 102901779A
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Abstract
The invention provides a discrimination method for caramel sweet taste characteristics of a tobacco leaf based on flavor components. The discrimination method is characterized by comprising the following steps: (1) establishment of a sample database of tobacco leaves with caramel sweet taste characteristics; (2) artificial discrimination of the caramel sweet taste characteristics; (3) establishment of a flavor component database of the tobacco leaves with the caramel sweet taste characteristics; (4) screening of flavor component characterization indexes of the tobacco leaves with the caramel sweet taste characteristics; (5) establishment of a model for discrimination of the caramel sweet taste characteristics by using the flavor components of the tobacco leaves; (6) testing of the model; (7) determination of flavor components of an unknown sample; and (8) discrimination of caramel sweet taste characteristics of the unknown sample. The invention has the following advantages: the model for discrimination of caramel sweet taste characteristics based on flavor components is established, the problems of subjectivity and nondeterminacy of determination which is carried out mainly through subjective recognition of a smoke panel in the prior art are overcome, and scientificalness, objectivity and accuracy of determination of taste characteristics are improved.
Description
Technical field
The present invention relates to the composition measurement of tobacco leaf fragrance, specifically the method for discrimination of the burnt sweet sense mouthfeel feature of a kind of tobacco leaf based on flavor component.
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Background technology
The style characteristic of the burnt sweet sense mouthfeel feature centering type cigarette of tobacco leaf plays an important role.Mainly rely on sensory evaluating smoking expert that tobacco sample is smoked panel test manually to judge to the method for the burnt sweet sense mouthfeel feature of raw tobacco material at present, this method depends on the personnel's that smoke panel test Subjective, inevitably have certain subjectivity and uncertainty, the processing scheme of formulating differentiation according to the raw tobacco material characteristic for cigarette enterprise brings certain difficulty.
Because the burnt sweet sense mouthfeel feature of tobacco leaf has multiple fragrance composition synergy to form, this provides theoretical foundation for utilizing flavor component to differentiate tobacco leaf odor type type.The present invention just is being based on this mechanism and a kind of mathematical model of utilizing flavor component to make up of setting up is judged the method for the burnt sweet sense mouthfeel feature of tobacco leaf.
Summary of the invention
The method of discrimination of a kind of burnt sweet sense mouthfeel feature of tobacco leaf based on flavor component that purpose of the present invention provides in order to overcome the subjective impairment that the burnt sweet sense feature assessment method of above-mentioned existing tobacco leaf exists just.
The objective of the invention is to be achieved through the following technical solutions:
The method of discrimination of the burnt sweet sense mouthfeel feature of the tobacco leaf based on flavor component of the present invention may further comprise the steps:
1.The foundation in burnt sweet sense mouthfeel feature tobacco sample storehouse.Choose 60 of C3F grade Luzhou-flavor tobacco samples, roll, set up burnt sweet sense mouthfeel feature tobacco sample storehouse.
2.The artificial cognition of burnt sweet sense mouthfeel feature.To the sample in the tobacco sample storehouse of step 1 formation, organize the expert that smokes panel test, utilize scaling law that its burnt sweet sense mouthfeel feature is differentiated, finish the artificial cognition of the burnt sweet sense mouthfeel feature of these samples.
3.The foundation in burnt sweet sense mouthfeel feature tobacco leaf fragrance compositional data storehouse.To the tobacco sample storehouse that step 1 forms, adopt simultaneously distillation extraction method.Take by weighing offal 20.0g and place the 1000ml flat bottom flask, add 350ml distilled water, 90g sodium chloride, flask is placed in the simultaneously left side of distillation extraction instrument, then in the 100ml flat bottom flask, add the 45ml methylene chloride, place the simultaneously right side of distillation extraction instrument, carry out while distillation extraction 2h after the water boiling to be distilled, add the 9g anhydrous sodium sulfate to the dichloromethane extraction of experiment gained is middle mutually, and dried overnight.Dried extract is transferred in the concentrated bottle is concentrated into 1ml, add mark phenethyl acetate in the 10 μ l, be the GC/MS analytic liquid.Adopt gas chromatography-mass spectrum (GC/MS) combined instrument to measure.Gas chromatograph is the AT-7890A type, and mass spectrometer is the AT-5975C type.The GC condition: capillary column is HP-ULTRA2 (50m * 0.2mm * 0.33 μ m); 280 ℃ of injector temperatures; Split ratio 10:1; Sample size 1.0 μ l; Heating schedule: 60 ℃ of initial temperature, keep 1min, 2 ℃/min is warmed up to 280 ℃, and 280 ℃ keep 20min.MS condition: 230 ℃ of ion source temperatures; 150 ℃ of quadrupole rod temperature are selected ion detection.Flavor matter is qualitative: qualitative by GC/MS qualification result and Wiley library searching.Flavor matter is quantitative: adopt inner mark method ration.Utilize GC-MS to its Main Fragrance (furfural, furfuryl alcohol, 5 methyl furfural, benzaldehyde, 6-methyl-5-hepten-2-one, phenmethylol, phenylacetaldehyde, linalool, bata-phenethyl alcohol, isophorone, the oxidation isophorone, dorinone, β-damascone, farnesyl acetone, geranyl acetone, alpha, beta-lonone, the 2-acetyl furan, solanone, dihydroactinidiolide, Megastigmatrienone A, Megastigmatrienone B, Megastigmatrienone C, Megastigmatrienone D, 6,10,14-trimethyl-2-15 ketone) measure, set up burnt sweet sense mouthfeel feature tobacco leaf fragrance compositional data storehouse.
4.The screening of the flavor component characteristic index of burnt sweet sense mouthfeel feature tobacco leaf.Flavor component index and burnt sweet sense mouthfeel feature are carried out Successive Regression, introduce according to the principle of p＜0.05, and selected flavor component is respectively furfural, 5 methyl furfural, 2-acetyl furan, geranyl acetone.
5.Utilize the tobacco leaf flavor component to differentiate the foundation of burnt sweet sense mouthfeel feature.Choose at random 40 tobacco sample data as the modeling data collection, utilize the support vector machine regression model that the quantitative relationship of four kinds of characteristic indexs and the burnt sweet sense mouthfeel feature of tobacco leaf is simulated, set up the discrimination model based on the burnt sweet sense mouthfeel feature of tobacco leaf of flavor component, model parameter is as follows: the type that support vector machine returns is EPSILON-SVR, the kernel function type is RBF, Degree=3, Gamma=0.5, Coef0=0.001, Eps=0.001, C=1, nu=0.5, shrinking=1, p=0.01, probability=1.
6.The check of model.20 tobacco sample data definitions outside modeling data integrated are as the check data collection.Utilize the burnt sweet sense mouthfeel feature discrimination model of setting up in the step 2 that the burnt sweet sense mouthfeel feature of check data collection (n=20) is predicted, drawn the scatter diagram of measured value and predicted value, and calculated the related coefficient of predicted value and measured value, the coefficient of determination and average relative error the results are shown in Figure 1.Hence one can see that, and related coefficient is 0.9048 between the predicted value of model and the measured value, and the coefficient of determination is 0.8996, average relative error 9.01%.
7.The mensuration of unknown sample flavor component.Utilize in the step 3 describing method that the flavor component of unknown sample is measured.
8.The differentiation of the burnt sweet sense mouthfeel feature of unknown sample.To obtain unknown sample flavor component substitution model in the step 7, Output rusults is finished the differentiation of the burnt sweet sense mouthfeel feature of unknown sample.
The invention has the advantages that: set up the discrimination model based on the burnt sweet sense mouthfeel feature of tobacco leaf of flavor component, for the judgement of the burnt sweet sense mouthfeel feature of tobacco leaf provides objective, means accurately, overcome the Subjective that mainly depends on the personnel that smoke panel test in the prior art and judged existing subjectivity and uncertainty, improved science, objectivity and accuracy that the mouthfeel feature is judged.
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Embodiment
The present invention will be further described below in conjunction with instantiation:
Embodiment 1
1 part of C3F grade tobacco sample detects through GC-MS, and its furfural, 5 methyl furfural, 2-acetyl furan, geranyl acetone content are respectively 36.13 μ g/g, 5.06 μ g/g, 3.30 μ g/g, 30.65 μ g/g, bring model into, draw its burnt sweet sense mouthfeel feature and must be divided into 59.Smoke panel test through the expert, its actual burnt sweet sense mouthfeel feature must be divided into 52, and relative error rate is 13.46%.
Embodiment 2
1 part of C3F grade tobacco sample detects through GC-MS, and its furfural, 5 methyl furfural, 2-acetyl furan, geranyl acetone content are respectively 61.55 μ g/g, 3.05 μ g/g, 5.76 μ g/g, 14.38 μ g/g, bring model into, draw its burnt sweet sense mouthfeel feature and must be divided into 76.Smoke panel test through the expert, its actual burnt sweet sense mouthfeel feature must be divided into 70, and relative error rate is 8.57%.
Embodiment 3
1 part of C3F grade tobacco sample detects through GC-MS, and its furfural, 5 methyl furfural, 2-acetyl furan, geranyl acetone content are respectively 65.03 μ g/g, 4.92 μ g/g, 6.87 μ g/g, 12.07 μ g/g, bring model into, draw its burnt sweet sense mouthfeel feature and must be divided into 79.Smoke panel test through the expert, its actual burnt sweet sense mouthfeel feature highlights degree and must be divided into 85, and relative error rate is 7.06%.
Claims (3)
1. method of discrimination based on the burnt sweet sense mouthfeel feature of the tobacco leaf of flavor component, it is characterized in that: this method of discrimination may further comprise the steps:
(1) 60 of C3F grade Luzhou-flavor tobacco samples are chosen in the foundation in burnt sweet sense mouthfeel feature tobacco sample storehouse, roll, and set up burnt sweet sense mouthfeel feature tobacco sample storehouse;
(2) artificial cognition of burnt sweet sense mouthfeel feature, to the sample in the tobacco sample storehouse of step 1 formation, organize the expert that smokes panel test, with reference to tobacco and tobacco product sensory evaluation method YC/T138-1998, carry out the sensory evaluation of burnt sweet sense mouthfeel feature, standards of grading are: the most typical 100～76; Typical case 75～51; Inferior typical 50～26; Be not true to type 25～1, finish the artificial cognition of the burnt sweet sense mouthfeel feature of these samples;
(3) foundation in burnt sweet sense mouthfeel feature tobacco leaf fragrance compositional data storehouse, tobacco sample storehouse to step 1 formation, adopt while distillation extraction method that each sample is extracted, adopt gas chromatography-mass spectrum (GC/MS) combined instrument to analyze mensuration to each sample extract; Flavor matter is qualitative: qualitative by GC/MS qualification result and Wiley library searching; Flavor matter is quantitative: adopt inner mark method ration; Utilize GC-MS to its Main Fragrance (furfural, furfuryl alcohol, 5 methyl furfural, benzaldehyde, 6-methyl-5-hepten-2-one, phenmethylol, phenylacetaldehyde, linalool, bata-phenethyl alcohol, isophorone, the oxidation isophorone, dorinone, β-damascone, farnesyl acetone, geranyl acetone, alpha, beta-lonone, the 2-acetyl furan, solanone, dihydroactinidiolide, Megastigmatrienone A, Megastigmatrienone B, Megastigmatrienone C, Megastigmatrienone D, 6,10,14-trimethyl-2-15 ketone) measure, set up burnt sweet sense mouthfeel feature tobacco leaf fragrance compositional data storehouse;
(4) burnt sweet sense mouthfeel feature tobacco leaf fragrance component list is levied the screening of index, flavor component index and burnt sweet sense mouthfeel feature are carried out Successive Regression, principle according to p＜0.05 is introduced, and selected flavor component is respectively furfural, 5 methyl furfural, 2-acetyl furan, geranyl acetone;
(5) utilize the tobacco leaf flavor component to differentiate the foundation of burnt sweet sense mouthfeel characteristic model, choose at random 40 tobacco sample data as the modeling data collection, utilize the support vector machine regression model that the quantitative relationship of four kinds of characteristic indexs and the burnt sweet sense mouthfeel feature of tobacco leaf is simulated, set up the discrimination model based on the burnt sweet sense mouthfeel feature of tobacco leaf of flavor component, model parameter is as follows: the type that support vector machine returns is EPSILON-SVR, the kernel function type is RBF, Degree=3, Gamma=0.5, Coef0=0.001, Eps=0.001, C=1, nu=0.5, shrinking=1, p=0.01, probability=1;
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(6) check of model, 20 tobacco sample data definitions outside modeling data integrated are as the check data collection, utilize the burnt sweet sense mouthfeel feature discrimination model of setting up in the step 2 that the sweet sense mouthfeel of the dense Jiao feature of check data collection (n=20) is predicted, calculate the predicted value average relative error, average relative error 9.01%;
(7) mensuration of unknown sample flavor component utilizes in the step 3 describing method that the flavor component of unknown sample is measured;
(8) differentiation of the burnt sweet sense mouthfeel feature of unknown sample will obtain unknown sample flavor component substitution model in the step 7, and Output rusults is finished the differentiation of the burnt sweet sense mouthfeel feature of unknown sample.
2. the method for discrimination of the burnt sweet sense mouthfeel feature of the tobacco leaf based on flavor component according to claim 1, it is characterized in that: adopting simultaneously in the step (3), the distillation extraction method to the concrete grammar that each sample extracts is: take by weighing offal 20.0g and place the 1000ml flat bottom flask, add 350ml distilled water, 90g sodium chloride, flask is placed in the simultaneously left side of distillation extraction instrument, then in the 100ml flat bottom flask, add the 45ml methylene chloride, place the simultaneously right side of distillation extraction instrument, carry out while distillation extraction 2h after the water boiling to be distilled, add the 9g anhydrous sodium sulfate to the dichloromethane extraction of experiment gained in mutually, and dried overnight, dried extract is transferred in the concentrated bottle is concentrated into 1ml, add mark phenethyl acetate in the 10 μ l, be the GC/MS analytic liquid.
3. the method for discrimination of the burnt sweet sense mouthfeel feature of the tobacco leaf based on flavor component according to claim 1, it is characterized in that: used gas chromatograph is the AT-7890A type in the step (3), mass spectrometer is the AT-5975C type; The GC condition: capillary column is HP-ULTRA2 (50m * 0.2mm * 0.33 μ m); 280 ℃ of injector temperatures; Split ratio 10:1; Sample size 1.0 μ l; Heating schedule: 60 ℃ of initial temperature, keep 1min, 2 ℃/min is warmed up to 280 ℃, and 280 ℃ keep 20min; MS condition: 230 ℃ of ion source temperatures; 150 ℃ of quadrupole rod temperature are selected ion detection.
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| CN104991006A (en) * | 2015-07-10 | 2015-10-21 | 云南中烟工业有限责任公司 | Method for exactly characterizing and evaluating clean aroma characteristics in cigarette aroma |
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| CN106338555A (en) * | 2016-08-11 | 2017-01-18 | 云南中烟工业有限责任公司 | Method for quantification prediction of cigarette baking incense type |
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