CN113762775A - Evaluation method of sweet feeling of tobacco leaves based on total sugar content - Google Patents

Evaluation method of sweet feeling of tobacco leaves based on total sugar content Download PDF

Info

Publication number
CN113762775A
CN113762775A CN202111048721.6A CN202111048721A CN113762775A CN 113762775 A CN113762775 A CN 113762775A CN 202111048721 A CN202111048721 A CN 202111048721A CN 113762775 A CN113762775 A CN 113762775A
Authority
CN
China
Prior art keywords
tobacco leaves
tobacco
sugar content
total sugar
sweetness
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202111048721.6A
Other languages
Chinese (zh)
Other versions
CN113762775B (en
Inventor
董爱君
董平
宋旭艳
何昀潞
潘曦
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Tobacco Hubei Industrial LLC
Original Assignee
China Tobacco Hubei Industrial LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Tobacco Hubei Industrial LLC filed Critical China Tobacco Hubei Industrial LLC
Priority to CN202111048721.6A priority Critical patent/CN113762775B/en
Publication of CN113762775A publication Critical patent/CN113762775A/en
Application granted granted Critical
Publication of CN113762775B publication Critical patent/CN113762775B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Pure & Applied Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Mathematical Analysis (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Software Systems (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • Agronomy & Crop Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Algebra (AREA)
  • Quality & Reliability (AREA)
  • Databases & Information Systems (AREA)
  • Manufacture Of Tobacco Products (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Animal Husbandry (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Mining & Mineral Resources (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Evolutionary Biology (AREA)

Abstract

The invention discloses a method for evaluating the sweetness of tobacco leaves based on the total sugar content, and belongs to the technical field of evaluation of the mouthfeel of the tobacco leaves. According to the method, the sugar content of the tobacco leaf raw material is quantified, the sugar content has strong correlation with the sweetness score, a regression equation of the sweetness score and the sugar content of the tobacco leaf raw material is established by using a decision tree method, and then the sweetness score of the tobacco leaf raw material can be represented by using the sugar content according to the established equation.

Description

Evaluation method of sweet feeling of tobacco leaves based on total sugar content
Technical Field
The invention relates to the field of evaluation of tobacco taste, in particular to a method for evaluating the sweet taste of tobacco based on total sugar content.
Technical Field
Tobacco leaves are the basis of the cigarette industry, and the tobacco leaves used by cigarettes are important for stabilizing and improving the quality of cigarette products and expanding the market share of the products. Each high quality cigarette development is based on a steady supply of high quality tobacco leaf material. The quality of the tobacco leaves is a comprehensive fuzzy concept reflecting and reflecting the necessary character equilibrium condition of the tobacco leaves, and is influenced by the comprehensive action of a plurality of factors in the aspects of production areas, parts, appearance quality, physical characteristics, chemical components, smoke components, sensory evaluation quality and the like of the tobacco leaves. The sensory quality is an important component of the quality of cigarette products, is the basis and core of the product quality, and refers to the comprehensive feeling of the mainstream smoke generated in the combustion process of the cigarette on human body sense organs, such as the quality and quantity of aroma, the comfort level of taste and the like; and factors representing the style characteristics of the product, such as tobacco style types, aroma characteristics and the like.
The taste characteristics of the tobacco leaves mainly comprise aspects of irritation, bitter taste, spicy taste, sweetness, peculiar smell and the like. The 'sweet feeling' is an important index for measuring the quality of the tobacco leaves, and is obviously related to the appearance quality, physical characteristics, smoking quality, processing characteristics, inherent 'moisture retention' characteristics and the like of the tobacco leaves.
The sweet taste characteristics of the tobacco play an important role in the style and the characteristics of Chinese cigarettes. At present, the method for evaluating the sweet taste characteristics of the tobacco leaf raw materials mainly depends on sensory evaluation experts to evaluate tobacco leaf samples for manual judgment, for example, the sensory evaluation of the quality, style and characteristics of Xiangxi tobacco leaves published in Chinese tobacco science of journal discloses a method for evaluating the sweet taste, but the evaluation efficiency of the method is low.
Disclosure of Invention
In order to solve the problems, the invention provides a method for evaluating the sweet taste of tobacco leaves based on total sugar content, which overcomes the defect of low efficiency of the conventional method for evaluating the sweet taste of tobacco leaves.
The technical scheme of the invention is to provide a method for making tobacco sweet based on total sugar content, which comprises the following steps:
s1, rolling sample tobacco leaves to be single-material tobacco cigarettes, recording 10 single-material cigarettes as a group to be a pre-test sample, equally dividing a prediction sample by a 10-person sensory group according to people, carrying out sweetness evaluation on the prediction sample, and determining key attribute indexes of three grades, namely sweetness intensity, sweetness texture and sweetness perception time, wherein each index contains seven scales of extremely dislike, somewhat dislike, neither dislike nor unpleasant, somewhat like, like and very like;
s2, the key attribute indexes of the three ratings in the step S1 are subjected to weight judgment by the sensory evaluation personnel according to seven scales of unimportance, slightly important, somewhat important, more important, very important and extremely important respectively;
s3, counting the sensory evaluation results of the three graded key attribute indexes in the step S1, wherein seven scales of selection of the three graded key attribute indexes of very dislike, somewhat dislike, neither like nor dislike, somewhat like, like and very like are respectively marked as 1 point, 2 points, 3 points, 4 points, 5 points, 6 points and 7 points; further, 10 sensory evaluation results of the sweetness intensity index were each represented as A1 、A2 、A3 、…、A10And respectively recording 10 sensory evaluation results of the sweetness texture index as B1 、B2、B3、…、B10And respectively recording 10 sensory evaluation results of the sweetness feeling time index as C1、C2、C3、…、C10And the statistics can obtain:
the sensory evaluation average score of the sweetness intensity index is AAre all made of= QUOTE
Figure DEST_PATH_IMAGE002
Figure DEST_PATH_IMAGE002A
，
Sensory evaluation average score of sweetness texture index BAre all made of= QUOTE
Figure DEST_PATH_IMAGE004
Figure DEST_PATH_IMAGE004A
，
Sensory evaluation average score of sweetness perception time index is CAre all made of= QUOTE
Figure DEST_PATH_IMAGE006
Figure DEST_PATH_IMAGE006A
；
S4, counting the weight judgment results of the three rated key attribute indexes in the step S2, wherein the unimportant, slightly important, more important, very important and extremely important in the weight judgment are respectively marked as 1 point, 2 points, 3 points, 4 points, 5 points, 6 points and 7 points; and the 10 weight judgment results of the sweetness intensity index are respectively marked as D1, D2, D3, … and D10; the results of 10 weight judgments of the sweet texture are respectively recorded as E1, E2, E3, … and E10; the 10 weight evaluation results of the sweetness perception time index are respectively marked as F1, F2, F3, … and F10; the statistics can obtain:
the weight evaluation average score of the sweetness intensity index is ARights= QUOTE
Figure DEST_PATH_IMAGE008
Figure DEST_PATH_IMAGE008A
，
The weight judgment average of the sweetness texture index is BRights= QUOTE
Figure DEST_PATH_IMAGE010
Figure DEST_PATH_IMAGE010A
，
Weight judgment average score C of sweetness perception time indexRights= QUOTE
Figure DEST_PATH_IMAGE012
Figure DEST_PATH_IMAGE012A
，
Weight evaluation total average score S of attribute indexesRights=ARights+BRights+CRights，
The weight evaluation ratio of the sweetness intensity is ARatio of weights= QUOTE
Figure DEST_PATH_IMAGE014
Figure DEST_PATH_IMAGE014A
，
The weight evaluation ratio of the sweetness texture is BRatio of weights= QUOTE
Figure DEST_PATH_IMAGE016
Figure DEST_PATH_IMAGE016A
，
The weight evaluation ratio of the time of sweetness perception was CRatio of weights= QUOTE
Figure DEST_PATH_IMAGE018
Figure DEST_PATH_IMAGE018A
；
S5, according to the sensory evaluation average scores and the weight evaluation proportions of the three graded key attribute indexes obtained in the steps S3 and S4, obtaining:
the sweetness intensity scores were: s1= quat
Figure DEST_PATH_IMAGE020
Figure DEST_PATH_IMAGE020A
，
The sweetness texture score was: s2= quat
Figure DEST_PATH_IMAGE022
Figure DEST_PATH_IMAGE022A
，
The time-to-sweetness perception score was: s3= quat
Figure DEST_PATH_IMAGE024
Figure DEST_PATH_IMAGE024A
；
S6, calculating a sweet feeling value S = S1+ S2+ S3 of the sample tobacco leaves, and performing star evaluation on the sample tobacco leaves according to the sweet feeling value, wherein S is less than 50 and is 0 star tobacco leaves, S is more than or equal to 50 and is less than 60, 1 star tobacco leaves, S is more than or equal to 60 and is less than or equal to 70, 2 star tobacco leaves, S is more than or equal to 70 and is less than 80, 3 star tobacco leaves, S is more than or equal to 80 and is less than or equal to 90, 4 star tobacco leaves, S is more than or equal to 90, and 5 star tobacco leaves;
s7, pretreating sample tobacco leaves, and determining the content of total sugar in the pretreated sample tobacco leaves by using HPLC-ELSD;
s8, determining the sweetness values of different sample tobacco leaves according to the steps S1-S6, determining the total sugar content of the corresponding sample tobacco leaves according to the step S7, and establishing a tobacco leaf sweetness value score prediction model by a decision tree method;
s9, preprocessing the tobacco leaves to be detected, determining the content of total sugar in the preprocessed tobacco leaves to be detected by using HPLC-ELSD, taking the content as an input variable to be brought into the tobacco leaf sweet feeling value score prediction model to obtain a predicted value of the sweet feeling value in the tobacco leaf raw materials to be detected, and grading according to the star-grade evaluation standard of the step S6;
preferably, the decision tree method comprises the following steps:
K1. correspondingly listing the total sugar content and the sweet feeling value data in the sample tobacco leaves, and establishing a data sample set;
K2. establishing a tobacco leaf sweetness value score prediction model by using a decision tree algorithm;
preferably, the decision tree algorithm comprises the steps of:
l1, feature selection and data preprocessing;
l2, constructing a decision tree from top to bottom recursion by adopting a greedy algorithm;
l3, establishing a linear regression model for all leaf nodes for prediction;
l4, pruning the initial decision tree from bottom to top by adopting a post-pruning mode to avoid overfitting;
and L5, performing prediction verification on the test sample set according to the constructed decision tree.
Preferably, in the steps S7 and S9, the preprocessing includes the steps of:
p1, grinding tobacco leaves to obtain tobacco powder;
p2, adding an aqueous solution of sodium chloride into the tobacco powder, extracting with an extracting agent, and taking supernatant;
p3, adding a water removal agent into the supernatant, and filtering to obtain an extracting solution;
and P4, distilling and concentrating the extracting solution to obtain the pretreated tobacco leaves.
Preferably, the extractant is one or more of dichloromethane and trichloromethane.
Preferably, the temperature of the distillation concentration is 60-80 ℃.
Preferably, the water scavenger comprises anhydrous sodium sulfate.
Preferably, in the step S7 and the step S9, the conditions of the HPLC-ELSD are: the chromatographic column is a Prevail sugar column; the column temperature is 25 ℃; mobile phase: acetonitrile phase A and water phase B; gradient elution: 85% A +15% B (0min), 78% A +22% B (10 min), 75% A +25% B (18 min), 55% A + 45% B (25-30min), 85% A +15% B (35 min); flow rate: 1 ml/min; the flow rate is 20 ul; ELSD drift tube temperature: 90.7 ℃; nitrogen flow rate: 2.4L/min; gain: 1; a striker: and off.
Preferably, in the step S7, the determination of the total sugar content of the sample tobacco leaves is repeated three times for each sample, and an average value is obtained.
Preferably, the sweetness assessment comprises the steps of:
t1, pouring 50 ml of purified water with the temperature of 20-30 ℃ into the mouth by a sensory evaluation member, gargling for 15 seconds, spitting out, and repeating for 2 times;
t2. the sensory evaluation person takes one of the test samples, sucks 8-10 times, evaluates according to the key attribute indexes of the three grades of sweetness, and selects in seven scales of the evaluation indexes. The invention has the beneficial effects that:
1. the sweet taste characteristics of the tobacco leaves are mainly determined by the influence of sugar content, and the method for judging the sweet taste characteristics of the tobacco leaves by using a mathematical model established based on the mechanism is used for reducing the interference of subjective factors;
2. the method is simple and easy to implement, simple to operate and convenient for practical application;
3. according to the method, the sugar content is directly used to construct a prediction equation of the sensory score of the tobacco leaf raw material, so that the formulator has higher accuracy in the evaluation of the sweetness of the tobacco leaf raw material of the cigarette;
4. the prediction equation established by the decision tree method has strong compatibility, high reliability and accuracy, and better guidance and practicability for evaluating the sweetness of the tobacco leaves.
Drawings
FIG. 1 is a sensory evaluation result of three ranked key attribute indicators;
FIG. 2 shows the weight evaluation results of three key attribute indicators of the rating.
Detailed Description
The present invention will be described in further detail with reference to examples.
It will be appreciated by those skilled in the art that the following examples are illustrative of the invention only and should not be taken as limiting the scope of the invention. The examples do not specify particular techniques or conditions, and are performed according to the techniques or conditions described in the literature in the art or according to the product specifications. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available by purchase.
Example 1
A method for evaluating the sweet taste of tobacco leaves based on the total sugar content comprises the following steps:
s1, rolling the sample tobacco leaves into single-material tobacco cigarettes to obtain the sweet feeling value of the sample tobacco leaves;
s2, pretreating sample tobacco leaves, and determining the content of total sugar in the pretreated sample tobacco leaves by using HPLC-ELSD;
the pretreatment comprises the following steps:
p1, grinding tobacco leaves to obtain tobacco powder;
p2, adding an aqueous solution of sodium chloride into the tobacco powder, extracting with dichloromethane, and taking supernatant;
p3, adding anhydrous sodium sulfate into the supernatant, and filtering to obtain an extracting solution;
and P4, distilling and concentrating the extracting solution at the temperature of 60-80 ℃ to obtain the pretreated tobacco leaves.
The conditions of the HPLC-ELSD are as follows: the chromatographic column is a Prevail sugar column; the column temperature is 25 ℃; mobile phase: acetonitrile phase A and water phase B; gradient elution: 85% A +15% B (0min), 78% A +22% B (10 min), 75% A +25% B (18 min), 55% A + 45% B (25-30min), 85% A +15% B (35 min); flow rate: 1 ml/min; the flow rate is 20 ul; ELSD drift tube temperature: 90.7 ℃; nitrogen flow rate: 2.4L/min; gain: 1; a striker: and off.
Repeating the step S7 for three times, and taking an average value to obtain the tobacco leaf with the total sugar content of 3% in the sun-cured red tobacco sample.
S3, determining the sweet feeling values of different sample tobacco leaves, wherein the different sample tobacco leaves comprise one or more of different types of sun-cured red tobacco, yellow-yellow tobacco, Maryland tobacco, burley tobacco, sun-cured yellow tobacco, warble air-cured tobacco, cigar wrapped tobacco, Turkey tobacco, Zimbabwe flue-cured tobacco and Sichuan Liangshan flue-cured tobacco, measuring the total sugar content of the corresponding sample tobacco leaves according to the step S7, and establishing a tobacco leaf sweet feeling value score prediction model by a decision tree method;
the decision tree method comprises the following steps:
K1. correspondingly listing the total sugar content and the sweet feeling value data in different sample tobacco leaves, and establishing a data sample set;
K2. establishing a tobacco leaf sweetness value score prediction model by using a decision tree algorithm;
the finally obtained tobacco sweet feeling value score prediction model is as follows:
sugar content is less than or equal to 10%: the sweet value score is 23.5102 multiplied by the sugar content + 61.9532;
sugar content > 10%: the sweet value score is 29.1527 multiplied by the sugar content + 60.202;
wherein the decision tree algorithm comprises the steps of:
the input Data is a training sample matrix Data, each row of the matrix is a tobacco leaf sample, the first column is an input variable (sugar content), the second column is a class attribute (sweetness score) of the sample, namely an attribute of a target value, and the size of the matrix is the number of the samples multiplied by 2. The specific algorithm flow is as follows:
l1, feature selection and data preprocessing;
l1.1 deleting the sample of the lost input variable or class attribute value in the Data matrix;
l1.2 randomly selecting 70% of samples in the Data matrix as a training sample set TrainData and 30% of samples as a test sample set TestData;
l1.3 selects class attributes (sweetness score) as features for decision tree construction.
L2, constructing a decision tree from top to bottom recursion by adopting a greedy algorithm;
and L2.1 judges the class attribute value of the training sample set TrainData, if all samples in the TrainData belong to the same class Ck, setting T as a single node tree, and taking the Ck as the class of the node.
L2.2 if the samples in TrainData do not belong to the same class Ck, according to the formula
Calculating the information gain ratio of the class attribute value (sweetness value score) to TrainData, and selecting the characteristic Ag (here, the class attribute characteristic) with the largest information gain ratio, wherein the formula is as follows:
Figure DEST_PATH_IMAGE026
wherein, A represents a class attribute value, M represents the number of samples, D represents a training sample set TrainData, and Dj represents each classified non-empty subset.
And L2.3, if the information gain ratio of the Ag is smaller than the threshold epsilon, setting T as a single node tree, classifying the training sample set TrainData into one class as the class of the node, and establishing a regression prediction model for the whole training sample set by using the same formula.
L2.4 if the information gain ratio of Ag is larger than the threshold epsilon, for each possible value aj of Ag, dividing D into j non-empty subsets Dj according to the largest aj, constructing sub-nodes by using the continuously split attribute values as classification conditions (respectively establishing regression prediction models for the subsets Dj by using different formulas), and forming a tree T by using the nodes and the sub-nodes.
And (3) recursively calling L2.1-L2.4 to obtain a subtree Tj by using Dj as a training set for the node j by the L2.5, so as to construct and complete the whole decision tree.
L3, establishing a linear regression model for all leaf nodes for prediction;
and L3.1 dividing the training sample set TrainData into a left matrix and a right matrix according to the splitting attribute value, and respectively sending the left matrix and the right matrix into a left branch and a right branch.
L3.2 for all leaf nodes, the linear regression model is the average of class attributes of samples arriving at this node, QUOTE
Figure DEST_PATH_IMAGE028
Figure DEST_PATH_IMAGE028A
。
And L3.3, according to the maximum value aj of Ag, constructing a binary tree after obtaining the split points, and respectively establishing a regression prediction model for the two nodes. Wherein the left branch: the sugar content is less than or equal to 10 percent, and the sweet feeling value is 23.5102 multiplied by the sugar content + 61.9532; right branch: the sugar content is more than 10%, and the sweet value is 29.1527 multiplied by the sugar content + 60.202.
L4, pruning the initial decision tree from bottom to top by adopting a post-pruning mode to avoid overfitting;
l4.1 if the current node is a leaf node, pruning is not performed;
if the current node is not a leaf node, pruning the left branch and the right branch of the current node, and turning to L4.2;
and L4.2, establishing a linear regression model according to the samples reaching the current node and part (or all) of linear regression attributes of the samples, traversing all the linear models, and selecting the model which enables the error of the samples reaching the current node to be minimum as the linear regression model of the current node.
Comparing the error generated by the linear regression model of the current node with the error generated by the sub-tree of the current node, if the error of the linear regression model of the current node is smaller, cutting off the sub-tree of the current node, and only keeping the current node; otherwise, the subtree of the current node is retained.
L4.3, if the father node of the current node is not empty, setting the father node as the current node, pruning the father node, and converting to L4.2; and if the parent node of the current node is empty, pruning is finished.
L4.4 sets the leaf node number of the tree.
And L5, performing prediction verification on the test sample set according to the constructed decision tree.
S4, preprocessing a cured tobacco leaf of Sichuan Liangshan as a tobacco leaf to be detected, determining the content of total sugar in the preprocessed cured tobacco leaf to be detected by using HPLC-ELSD, and taking the content as an input variable to be brought into the tobacco leaf sweet feeling value score prediction model to obtain a predicted value of the sweet feeling value in the cured tobacco leaf raw material of Sichuan Liangshan as 65;
the pretreatment comprises the following steps:
p1, grinding tobacco leaves to obtain tobacco powder;
p2, adding aqueous solution of sodium chloride into tobacco powder, extracting with chloroform, and collecting supernatant;
p3, adding anhydrous sodium sulfate into the supernatant, and filtering to obtain an extracting solution;
and P4, distilling and concentrating the extracting solution at the temperature of 60-80 ℃ to obtain the pretreated tobacco leaves.
The conditions of the HPLC-ELSD are as follows: the chromatographic column is a Prevail sugar column; the column temperature is 25 ℃; mobile phase: acetonitrile phase A and water phase B; gradient elution: 85% A +15% B (0min), 78% A +22% B (10 min), 75% A +25% B (18 min), 55% A + 45% B (25-30min), 85% A +15% B (35 min); flow rate: 1 ml/min; the flow rate is 20 ul; ELSD drift tube temperature: 90.7 ℃; nitrogen flow rate: 2.4L/min; gain: 1; a striker: and off.
Example 2
The flue-cured tobacco leaves of the Sichuan Liangshan in example 1 are replaced by the flue-cured tobacco leaves of Zimbabwe, and other steps and parameters are the same, so that the predicted value of the sweet sensation value in the raw materials of the Zimbabwe flue-cured tobacco leaves is 73.
Example 3
The cured tobacco leaves in the Sichuan Liangshan in example 1 are replaced with burley tobacco leaves, and the other steps and parameters are the same, so that the predicted value of the sweetness value in the burley tobacco leaf raw material is 35.
Test method
The tobacco leaves described in the embodiments 1 to 3 are used as sample tobacco leaves to be subjected to sensory sweet evaluation to obtain sweet values, and the predicted values of the sweet values are calculated according to the prediction model of the scheme, so that the results are consistent.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (9)

1. The method for evaluating the sweet taste of the tobacco leaves based on the total sugar content is characterized by comprising the following steps of:
s1, rolling sample tobacco leaves into single-material tobacco cigarettes to obtain the sweet feeling value of the sample tobacco leaves;
s2, pretreating sample tobacco leaves, and determining the content of total sugar in the pretreated sample tobacco leaves by using HPLC-ELSD;
s3, determining the sweet feeling values of different sample tobacco leaves and the corresponding total sugar content of the sample tobacco leaves, and establishing a tobacco leaf sweet feeling value score prediction model by a decision tree method;
s4, preprocessing the tobacco leaves to be detected, measuring the content of total sugar in the preprocessed tobacco leaves to be detected by using HPLC-ELSD, and taking the content as an input variable to be brought into the tobacco leaf sweet feeling value score prediction model to obtain a predicted value of the sweet feeling value in the tobacco leaf raw materials to be detected.
2. The evaluation method of the sweetness of tobacco leaves based on the total sugar content of claim 1, wherein the decision tree method comprises the following steps:
K1. correspondingly listing the total sugar content and the sweet feeling value data in different sample tobacco leaves, and establishing a data sample set;
K2. and establishing a tobacco leaf sweetness value score prediction model by using a decision tree algorithm.
3. The method for evaluating the sweetness of tobacco leaves based on the total sugar content of claim 2, wherein the decision tree algorithm comprises the following steps:
l1, feature selection and data preprocessing;
l2, constructing a decision tree from top to bottom recursion by adopting a greedy algorithm;
l3, establishing a linear regression model for all leaf nodes for prediction;
l4, pruning the initial decision tree from bottom to top by adopting a post-pruning mode to avoid overfitting;
and L5, performing prediction verification on the test sample set according to the constructed decision tree.
4. The evaluation method of the sweet taste of the tobacco leaves based on the total sugar content according to claim 1, wherein the pretreatment comprises the following steps:
p1, grinding tobacco leaves to obtain tobacco powder;
p2, adding an aqueous solution of sodium chloride into the tobacco powder, extracting with an extracting agent, and taking supernatant;
p3, adding a water removal agent into the supernatant, and filtering to obtain an extracting solution;
and P4, distilling and concentrating the extracting solution to obtain the pretreated tobacco leaves.
5. The method for evaluating the sweetness of tobacco leaves based on the total sugar content of claim 4, wherein the extracting agent is one or more of dichloromethane and trichloromethane.
6. The method for evaluating the sweetness of tobacco leaves based on the total sugar content of claim 4, wherein the temperature of the distillation concentration is 60-80 ℃.
7. The method for evaluating the sweetness of tobacco leaves based on the total sugar content of claim 4, wherein the water removing agent comprises anhydrous sodium sulfate.
8. The method for evaluating the sweetness of tobacco leaves based on the total sugar content of claim 1, wherein the conditions of HPLC-ELSD are as follows: the chromatographic column is a Prevail sugar column; the column temperature is 25 ℃; mobile phase: acetonitrile phase A and water phase B; gradient elution: 85% A +15% B (0min), 78% A +22% B (10 min), 75% A +25% B (18 min), 55% A + 45% B (25-30min), 85% A +15% B (35 min); flow rate: 1 ml/min; the flow rate is 20 ul; ELSD drift tube temperature: 90.7 ℃; nitrogen flow rate: 2.4L/min; gain: 1; a striker: and off.
9. The method for evaluating the sweetness of tobacco leaves based on the total sugar content of claim 1, wherein the measurement of the total sugar content of the sample tobacco leaves is repeated three times per sample, and an average value is taken.
CN202111048721.6A 2021-09-08 2021-09-08 Tobacco leaf sweet feeling evaluation method based on total sugar content Active CN113762775B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111048721.6A CN113762775B (en) 2021-09-08 2021-09-08 Tobacco leaf sweet feeling evaluation method based on total sugar content

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111048721.6A CN113762775B (en) 2021-09-08 2021-09-08 Tobacco leaf sweet feeling evaluation method based on total sugar content

Publications (2)

Publication Number Publication Date
CN113762775A true CN113762775A (en) 2021-12-07
CN113762775B CN113762775B (en) 2024-01-12

Family

ID=78793750

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111048721.6A Active CN113762775B (en) 2021-09-08 2021-09-08 Tobacco leaf sweet feeling evaluation method based on total sugar content

Country Status (1)

Country Link
CN (1) CN113762775B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102901779A (en) * 2012-09-13 2013-01-30 中国烟草总公司郑州烟草研究院 Discrimination method for caramel sweet taste characteristics of tobacco leaf based on flavor components
CN103940696A (en) * 2014-03-12 2014-07-23 红塔烟草(集团)有限责任公司 Prejudging method for taste of smokeless tobacco product
CN108520276A (en) * 2018-04-09 2018-09-11 云南中烟工业有限责任公司 A kind of interior characterizing method in aesthetic quality of raw tobacco material
CN108845085A (en) * 2018-07-03 2018-11-20 云南中烟工业有限责任公司 A method of tobacco leaf odor type is predicted based on tobacco leaf chemical composition

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102901779A (en) * 2012-09-13 2013-01-30 中国烟草总公司郑州烟草研究院 Discrimination method for caramel sweet taste characteristics of tobacco leaf based on flavor components
CN103940696A (en) * 2014-03-12 2014-07-23 红塔烟草(集团)有限责任公司 Prejudging method for taste of smokeless tobacco product
CN108520276A (en) * 2018-04-09 2018-09-11 云南中烟工业有限责任公司 A kind of interior characterizing method in aesthetic quality of raw tobacco material
CN108845085A (en) * 2018-07-03 2018-11-20 云南中烟工业有限责任公司 A method of tobacco leaf odor type is predicted based on tobacco leaf chemical composition

Also Published As

Publication number Publication date
CN113762775B (en) 2024-01-12

Similar Documents

Publication Publication Date Title
CN107862456A (en) A kind of method of batch quality of tobacco overall merit
CN107796782B (en) Redrying quality stability evaluation method based on tobacco leaf characteristic spectrum consistency measurement
CN111160425A (en) Neural network-based flue-cured tobacco comfort classification evaluation method
Talhout et al. An inventory of methods suitable to assess additive-induced characterising flavours of tobacco products
CN106529584A (en) Flue-cured tobacco aroma type and quality judgment intelligent evaluation method
CN113190967B (en) Multi-objective screening-based fine cigarette material combination method
CN103070465B (en) A kind of tobacco composition compounding method based on compatibility
CN105954366B (en) A kind of essence spice for cigarette character surveillance method
CN108414471B (en) Method for distinguishing sensory characterization information based on near infrared spectrum and sensory evaluation mutual information
CN110132880B (en) Tobacco leaf overall sensory quality evaluation method based on near infrared spectrum
CN114587003B (en) Development method of medium and thin cigarette formula tobacco shreds with characteristic aroma
CN110132879A (en) A kind of flue-cured tobacco note discrimination method based near infrared spectrum
CN103344713A (en) Method for analyzing importance of flavoring in flue-cured tobacco fragrance type classification and forecasting fragrance type based on random forest method
CN101382557A (en) Cigarette quality analytical method
CN111896657B (en) Method for screening tobacco leaf source spice style characteristic components
CN113762775A (en) Evaluation method of sweet feeling of tobacco leaves based on total sugar content
CN108520276B (en) Characterization method for internal sensory quality of tobacco leaf raw material
CN102680427A (en) Method for identifying surface fragrance quality of cigarettes by near infrared spectral analysis technology
CN111652516A (en) Tobacco base applicability evaluation method based on formula efficacy
CN115983692A (en) Tobacco leaf moistening and sweet flavor evaluation method and device based on random forest algorithm
CN109781870B (en) Method for identifying flavor type of flue-cured tobacco
CN109744580B (en) Intelligent smoking control method and device
CN116519874A (en) Heated cigarette style sensory evaluation method
CN114689746B (en) Method, device, electronic equipment and medium for screening tobacco extract characteristics
CN111904020B (en) Method for screening sensory quality characteristic components of tobacco leaf source spice

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant