Method for judging mellowing degree of cinnabar tobacco leaves

Abstract

The invention discloses a cinnabar tobacco mellowing degree distinguishing method. The method comprises the following steps: 1) drying samples to be detected according to source classification to obtain appearance characteristic analysis data and characteristic component data; 2) judging whether the to-be-detected sample belongs to cinnabar tobacco leaves according to the characteristic component data, and outputting a judgment result; 3) according to the judgment result of the cinnabar tobacco leaves, if the cinnabar tobacco leaves do not belong to the cinnabar tobacco leaves, rejecting the group of data, and if the cinnabar tobacco leaves belong to the cinnabar tobacco leaves, constructing an alcoholization degree model according to the appearance characteristic data and the special component data of the group of tobacco leaves to obtain alcoholization degree data; and 4) optimizing the alcoholization degree data, screening noise data, outputting residual data, and enabling the noise data to enter the alcoholization degree model again. The method effectively overcomes the problem of inaccurate judgment of cinnabar tobacco leaf characteristic note in the prior art, the obtained conclusion can effectively and accurately reflect the alcoholization degree of cinnabar tobacco leaves, and the method has important guiding significance for development and design of novel tobacco leaves.

Description

Technical Field

[0001] The present invention relates to a method for judging the aging of tobacco leaves, specifically to a method for judging the aging degree of cinnabar tobacco leaves, and belongs to the technical field of tobacco leaf raw materials. Background Art

[0002] Common tobacco (Nicotiana tabacum) is an allopolyploid formed by the interspecific hybridization of Nicotiana tomentosiformis and Nicotiana sylvestris, but its alkaloid spectrum is very different from that of its two ancestors. In most common tobacco green and senescent leaves, nicotine accounts for about 95% of the total alkaloids. Nicotiana tomentosiformis accumulates nor-nicotine in both green and senescent leaves; while Nicotiana sylvestris mainly accumulates nicotine in green leaves, but most of the nicotine is converted into nor-nicotine during leaf senescence. Some mutant strains of flue-cured tobacco varieties show red spots on the cured leaves, which are called "cherry-red", that is, cherry red. Most of the nicotine in its tobacco leaves is demethylated to form nor-nicotine. Therefore, the phenotype of this flue-cured tobacco with nor-nicotine as the dominant alkaloid is similar to "atavism", which is a very meaningful evolutionary problem. Such research has been carried out in the United States and Japan, and it was first seen in the 1950s and 1960s. The natural occurrence probability of this mutation is very low. Some studies have shown that 3 mutant strains were found among 538 individual plants, and the probability is about 0.56%. In China's tobacco leaf production, this variant strain is called "cinnabar tobacco". Its aroma quality is unique and has strong development and application value. Since the "cinnabar tobacco" will produce quality changes during the aging process and the "cinnabar tobacco" is a new tobacco leaf variety, the cinnabar tobacco leaves have a unique "glutinous rice aroma" rhyme in terms of aroma style and have a unique vermilion color in appearance, which is different from traditional light aroma type, middle aroma type and strong aroma type tobacco leaves. The determination of its aging degree not only involves the quality of tobacco leaves, but also includes the "glutinous rice aroma" characteristics. Traditional methods such as near-infrared analysis method or ultraviolet analysis method often cannot accurately judge the aging degree of its characteristic aroma rhyme, resulting in inaccurate judgment. At present, the industry has not deeply studied the aging degree of cinnabar tobacco leaves, and there is no corresponding discrimination standard for the aging degree of cinnabar tobacco Summary of the Invention

[0003] Aiming at the problems existing in the prior art, the present invention proposes a method for judging the aging degree of cinnabar tobacco leaves. After the tobacco leaves are judged based on specific aromatic substances in the cinnabar tobacco leaves, a relevant aging degree model is established according to the appearance characteristic data and component characteristic data of the tobacco leaves, and the aging degree is determined. This method effectively overcomes the problem of inaccurate judgment of the characteristic aroma rhyme of cinnabar tobacco leaves in the prior art, and the obtained conclusion can effectively and accurately reflect the aging degree of cinnabar tobacco leaves, which has important guiding significance for the development and design of new tobacco leaves.

[0004] To achieve the above technical objectives, the present invention provides a method for determining the degree of aging of cinnabar tobacco leaves, comprising:

[0005] 1) After drying the samples according to their sources, obtain appearance characteristic analysis data and characteristic component data;

[0006] 2) judging whether the sample to be tested belongs to cinnabar tobacco leaves based on the characteristic component data, and outputting the judgment result;

[0007] 3) Based on the judgment result of the red sand tobacco leaves, if the tobacco leaves do not belong to the red sand tobacco leaves, the data set is eliminated; if the tobacco leaves belong to the red sand tobacco leaves, a mellowing degree model is constructed based on the appearance characteristic data and specific component data of the tobacco leaves to obtain mellowing degree data;

[0008] 4) The alcoholization degree data is optimized to screen out noise data, and the remaining data is output, and the noise data is re-entered into the alcoholization degree model.

[0009] As a preferred solution, the appearance characteristic analysis data includes: maturity, identity, structure, color, oil content and vermilion degree.

[0010] As a preferred solution, the characteristic component data are the NDFF (1-(1'-2'S nornicotine)-1-deoxy-β-D-fructofuranose) and NDFP (1-deoxy-1-[(S)-2-(3-pyridyl)-1-pyrrolidinyl]-β-D-fructopyranose) content values of the sample to be tested.

[0011] As a preferred solution, the process for determining whether a tobacco leaf is cinnabar tobacco is as follows: when the sum of the NDFF and NDFP content values of the sample to be tested is not 0, the sample is a cinnabar tobacco leaf sample.

[0012] As a preferred solution, the process of constructing the alcoholization degree model is as follows:

[0013] i) Classify and assign values to the appearance feature data of the sample to be tested, with the value range being an integer from 1 to 5;

[0014] ii) Calculate the B value and P value of the sample to be tested based on the appearance feature data assignment result and the NDFF and NDFP content values of the sample to be tested, and output the alcoholization degree result.

[0015] As a preferred solution, the alcoholization degree results include: excessive, moderate and slightly insufficient.

[0016] As a preferred solution, the maturity classification is assigned as follows: 1-falsely ripe, 2-underripe, 3-moderately ripe, 4-mature, 5-well ripe.

[0017] As a preferred solution, the classification assignment of the identity is: 1 - thin, 2 - slightly thin, 3 - medium, 4 - slightly thick, 5 - thick.

[0018] As a preferred solution, the classification assignment of the structure is: 1 - dense, 2 - tight, 3 - slightly dense, 4 - moderately loose, 5 - loose.

[0019] As a preferred solution, the classification assignment of the chromaticity is: 1 - light, 2 - weak, 3 - medium, 4 - strong, 5 - intense.

[0020] As a preferred solution, the classification assignment of the oil content is: 1 - weak, 2 - little, 3 - slightly, 4 - present, 5 - much.

[0021] As a preferred solution, the classification assignment of the vermilion chromaticity is: 1 - light, 2 - weak, 3 - medium, 4 - strong, 5 - intense.

[0022] As a preferred solution, the calculation process of the B value is as follows:

[0023] Equation 1: B = 0.0992X1 - 0.154X2 - 0.158X3 - 0.144X4 - 0.534X5 - 0.075X6 + 1.605;

[0024] The calculation process of the P value is as follows:

[0025] Equation 2: P = (Y1 + Y2 - 13500) / 13500;

[0026] In Equations 1 and 2: X1 is the maturity assignment, X2 is the identity assignment, X3 is the structure assignment, X4 is the chromaticity assignment, X5 is the oil content assignment, X6 is the vermilion chromaticity assignment, Y1 is the NDFF content, with the dimension of mg / kg, and Y2 is the NDFP content, with the dimension of mg / kg.

[0027] As a preferred solution, the judgment process of the aging degree is as follows: If the B value > -2 and the P value ≥ 0.70, the aging degree is slightly insufficient; if 0.70 > P value > -0.30, the aging degree is moderate; if -1 < P value ≤ -0.30, the aging degree is excessive; if the B value ≤ -2 and 0 < P value < 0.30, the aging degree is slightly insufficient; if the P value ≥ 0.30, the aging degree is moderate; if -1 < P value ≤ 0, the aging degree is excessive.

[0028] As a preferred solution, the process of optimizing the screening noise data is as follows: When the aging degree is excessive or slightly insufficient, the result is output; when the aging result is moderate, this set of data is returned to the aging degree model for cyclic calculation of the M value again, and the aging degree result is output according to the M value.

[0029] As a preferred solution, the calculation process of the M value is as follows:

[0030] Formula 3: M = P / 4 - (B + 2.0);

[0031] As a preferred solution, the process of outputting the aging degree result according to the M value is as follows: when the B value > -2, if the M value > -0.30, the aging degree is slightly insufficient; when the M value ≤ -0.3, the aging degree is moderate. When the B value ≤ -2, if the M value < 0.3, the aging degree is slightly insufficient; when the M value ≥ 0.3, the aging degree is moderate.

[0032] Compared with the prior art, the beneficial technical effects of the technical solution of the present invention are as follows:

[0033] 1) After the discrimination method provided by the present invention determines the tobacco leaves based on specific aromatic substances in cinnabar tobacco leaves, a relevant aging degree model is established according to the appearance characteristic data and component characteristic data of the tobacco leaves, and the aging degree is determined. This method effectively overcomes the problem of inaccurate determination of the characteristic aroma of cinnabar tobacco leaves in the prior art, and the obtained conclusion can effectively and accurately reflect the aging degree of cinnabar tobacco leaves, which has important guiding significance for the development and design of new tobacco leaves.

[0034] 2) In the solution provided by the present invention, by assigning values to the appearance characteristic data, the appearance information of the tobacco leaves is converted into data information, which is convenient for quantitative calculation. Further, combined with the data of the "glutinous rice fragrance" aroma characteristic substances in cinnabar tobacco leaves, the objectivity and accuracy of the aging degree of cinnabar tobacco leaves are improved, and the characteristic style of cinnabar tobacco leaves is more comprehensively represented. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a BP value diagram of cinnabar tobacco and different aging degrees of the present invention;

[0036] Figure 2 It is a chemical structure diagram of the characteristic substances contained in cinnabar tobacco leaves. DETAILED DESCRIPTION OF THE INVENTION

[0037] The following examples are used to illustrate the present invention, but are not used to limit the scope of the present invention. Without departing from the spirit and essence of the present invention, any modification or replacement of the method steps or conditions of the present invention belongs to the scope of the present invention.

[0038] Example 1

[0039] A method for discriminating the aging degree of cinnabar tobacco leaves includes the following steps:

[0040] 1) Collection and pretreatment of the test sample, analysis and detection of the appearance, analysis and detection of the characteristic components, aging degree discrimination equation, and aging degree discrimination method;

[0041] 2) Collection and pretreatment of the sample to be tested: Collect the standard samples of cinnabar tobacco leaves from Honghe, Qujing, and Yuxi. After drying the tobacco leaf samples, reserve them for use.

[0042] 3) Analysis and detection of appearance: Ten tobacco leaf appearance experts evaluate the appearance of the tobacco leaves, and analyze the maturity, identity, structure, chroma, oil content, and cinnabar color degree of the tobacco leaf samples. The assignment rules for appearance data are shown in Table 1.

[0043] 4) Extraction and detection of characteristic components: Ultrasonically extract the tobacco leaf samples in anhydrous methanol solution, and reserve the extract. The experimental conditions and methods for sample pretreatment are shown in Table 2.

[0044] Determine the high-performance liquid chromatography-mass spectrometry (HPLC-MS) spectrum of cinnabar tobacco leaves by high-performance liquid chromatography-mass spectrometry method. For the analysis and detection of characteristic components, analyze the extract by high-performance liquid chromatography, measure it in parallel three times, determine the contents of NDFF and NDFP, and detect the stability of the test results. Take the average values of the NDFF and NDFP contents of the three test results as the contents of characteristic substances. The appearance assignment of tobacco leaf samples and the detection results of characteristic components are shown in Table 3.

[0045] 5) The aging degree discrimination equation: Using X1, X2, X3, X4, X5, and X6 score values as variables, establish the aging degree discrimination equation:

[0046] B = 0.0992X1 - 0.154X2 - 0.158X3 - 0.144X4 - 0.534X5 - 0.075X6 + 1.605

[0047] P = (Y1 + Y2 - 13500) / 13500

[0048] X1 is the maturity score value, X2 is the identity score value, X3 is the structure score value, X4 is the chroma score value, X5 is the oil content score value, X6 is the cinnabar color degree score value, Y1 is the NDFF content (mg / kg), and Y2 is the NDFP content (mg / kg).

[0049] 6) Aging degree discrimination method: Calculate the B and P values based on the data obtained from the appearance analysis and detection and the analysis and detection of characteristic components. Judge the aging degree according to the results of the B and P values. If the B value > -2 and the P value ≥ 0.70, the aging degree is slightly less; if 0.70 > P value > -0.30, the aging degree is moderate; if -1 < P value ≤ -0.30, the aging degree is excessive. If the B value ≤ -2 and 0 < P value < 0.30, the aging degree is slightly less; if the P value ≥ 0.30, the aging degree is moderate; if -1 < P value ≤ 0, the aging degree is excessive.

[0050] 7) When the aging degree is excessive or slightly insufficient, the result is output. When the aging result is moderate, the group of data returns to the aging degree model for cyclic calculation of the M value. The judgment process of the aging degree is as follows: When the B value > -2, if the M value > -0.30, the aging degree is slightly insufficient; when the M value ≤ -0.30, the aging degree is moderate. When the B value ≤ -2, if the M value < 0.30, the aging degree is slightly insufficient; when the M value ≥ 0.30, the aging degree is moderate.

[0051] Table 1 Appearance Assignment Rules for Tobacco Leaf Samples

[0052]

[0053] Table 2 Experimental Conditions for Sample Pretreatment

[0054] Substance Extraction method Solvent Reaction time Cut tobacco Ultrasonic extraction Anhydrous methanol 30 - 120 min Cut tobacco Ultrasonic extraction Anhydrous ethanol 30 - 120 min

[0055] Table 3 Appearance Assignment Results and Characteristic Component Detection Results of Different Aged Tobacco Leaf Samples

[0056]

[0057] Table 4 B and P Value Results and Prediction Effects of Different Tobacco Leaf Samples

[0058]

[0059]

[0060] The experimental results prove that the initial discrimination accuracy of the method of the present invention for the aging degree of cinnabar tobacco leaves is above 95%. After cyclic verification and discrimination, the accuracy is increased to 100%. The method of the present invention can accurately discriminate the aging degree of cinnabar tobacco leaves, can provide effective support for the use and storage of tobacco leaves, and can be widely applied in the agricultural and industrial production of cinnabar tobacco leaves.

[0061] Although the present invention has been described herein with reference to a number of illustrative embodiments of the invention, it should be understood that those skilled in the art can design many other modifications and embodiments that will fall within the scope of the principles of this application and the spirit. More specifically, within the scope of this application, the drawings, and the claims, various variations and improvements can be made to the components and / or the layout of the subject combination layout. In addition to the variations and improvements made to the components and / or the layout, other uses will also be apparent to those skilled in the art.

Claims

1. A method for discriminating the aging degree of cinnabar tobacco leaves, characterized in that, include: 1) After drying the samples according to their sources, obtain appearance characteristic analysis data and characteristic component data; 2) judging whether the sample to be tested belongs to cinnabar tobacco leaves based on the characteristic component data, and outputting the judgment result; 3) Based on the judgment result of the red sand tobacco leaves, if the tobacco leaves do not belong to the red sand tobacco leaves, the data set is eliminated; if the tobacco leaves belong to the red sand tobacco leaves, a mellowing degree model is constructed based on the appearance characteristic data and specific component data of the tobacco leaves to obtain mellowing degree data; 4) The alcoholization degree data is optimized to screen out noise data, and the remaining data is output, and the noise data is re-entered into the alcoholization degree model.

2. The method for discriminating the aging degree of cinnabar tobacco leaves according to claim 1, characterized in that: The appearance characteristic analysis data includes: maturity, identity, structure, color, oil content and vermilion degree.

3. A method for discriminating the aging degree of cinnabar tobacco leaves according to claim 1, characterized in that: The characteristic component data are the NDFF (1-(1'-2'S nornicotine)-1-deoxy-β-D-fructofuranose) and NDFP (1-deoxy-1-[(S)-2-(3-pyridyl)-1-pyrrolidinyl]-β-D-fructopyranose) content values of the sample to be tested.

4. A method for discriminating the aging degree of cinnabar tobacco leaves according to claim 3, characterized in that: The process for determining whether a tobacco leaf is cinnabar-like is as follows: when the sum of the NDFF and NDFP content values of the sample to be tested is not 0, the sample is a cinnabar-like tobacco leaf sample.

5. A method for discriminating the aging degree of cinnabar tobacco leaves according to claim 1, characterized in that: The construction process of the alcoholization degree model is as follows: i) Classify and assign values to the appearance feature data of the sample to be tested, with the value range being an integer from 1 to 5; ii) calculating the B value and P value of the sample to be tested based on the appearance characteristic data assignment result and the NDFF and NDFP content values of the sample to be tested, and outputting the alcoholization degree result; The results for the degree of aging include: excessive, moderate, and slightly under-aging.

6. The method for discriminating the aging degree of cinnabar tobacco leaves according to claim 5, characterized in that: The classification assignment values of the maturity are: 1-falsely ripe, 2-underripe, 3-still ripe, 4-ripe, 5-fully ripe; the classification assignment values of the identity are: 1-thin, 2-slightly thin, 3-medium, 4-slightly thick, 5-thick; the classification assignment values of the structure are: 1-dense, 2-tight, 3-slightly dense, 4-still loose, 5-loose; the classification assignment values of the chromaticity are: 1-light, 2-weak, 3-medium, 4-strong, 5-dense; the classification assignment values of the oil content are: 1-weak, 2-little, 3-slightly, 4-present, 5-more; the classification assignment values of the vermilion degree are: 1-light, 2-weak, 3-medium, 4-strong, 5-dense.

7. A method for discriminating the aging degree of cinnabar tobacco leaves according to claim 5, characterized in that: The calculation process of the B value is: Formula 1: B = 0.0992X1 - 0.154X2 - 0.158X3 - 0.144X4 - 0.534X5 - 0.075X6 + 1.605; The calculation process of the P value is: Formula 2: P = (Y1 + Y2 - 13500) / 13500; In formulas 1 and 2: X1 is the maturity value, X2 is the identity value, X3 is the structure value, X4 is the chromaticity value, X5 is the oil value, X6 is the vermilion value, Y1 is the NDFF content, the unit is mg / kg, and Y2 is the NDFP content, the unit is mg / kg.

8. A method for discriminating the aging degree of cinnabar tobacco leaves according to claim 5, characterized in that: The process for judging the aging degree is as follows: If the B value > -2 and the P value ≥ 0.70, the aging degree is slightly insufficient; if 0.70 > P value > -0.30, the aging degree is moderate; if -1 < P value ≤ -0.30, the aging degree is excessive. If the B value ≤ -2 and 0 < P value < 0.30, the aging degree is slightly insufficient; if the P value ≥ 0.30, the aging degree is moderate; if -1 < P value ≤ 0, the aging degree is excessive.

9. The method for discriminating the aging degree of cinnabar tobacco leaves according to claim 1, characterized in that: The process for optimizing the screening noise data is as follows: When the aging degree is excessive or slightly insufficient, the result is output. When the aging result is moderate, the set of data is returned to the aging degree model for calculating the M value again in a loop, and the aging degree result is output according to the M value.

10. A method for discriminating the aging degree of cinnabar tobacco leaves according to claim 9, characterized in that: The calculation process of the M value is as follows: Equation 3: M = P / 4 - (B + 2.0); The process for outputting the aging degree result according to the M value is as follows: When the B value > -2, if the M value > -0.30, the aging degree is slightly insufficient; if the M value ≤ -0.3, the aging degree is moderate. When the B value ≤ -2, if the M value < 0.3, the aging degree is slightly insufficient; if the M value ≥ 0.3, the aging degree is moderate.

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