Evaluation method and application of extraction process

Abstract

The invention discloses an evaluation method of an extraction process. The evaluation method comprises the following steps: (1) preparing a solution; (2) sample pretreatment; (3) obtaining a taste feature value; and (4) taste feature value evaluation: according to the taste feature value, comparing the taste differences of the extracts prepared by different processes through data analysis, and further evaluating different extraction processes. The invention further discloses application of the evaluation method.

Description

Technical Field

[0001] The invention belongs to the technical field of flavor processing, in particular to the technical field of tobacco flavor and fragrance production; and specifically relates to a method for evaluating and screening tobacco extract extraction processes by data analysis using taste characteristics as indicators. Background Art

[0002] In order to modify, supplement and highlight the original aroma and flavor of high-quality tobacco leaves, the tobacco industry generally uses natural aroma raw materials or synthetic fragrances to add flavor. Among them, tobacco extracts are derived from tobacco raw materials or waste raw materials generated during its processing. As an endogenous tobacco additive, the addition of tobacco extracts can restore the original aroma of tobacco to the greatest extent and enhance the characteristic aroma of tobacco. It is often used in tobacco flavor base modules and new tobacco flavoring. It is one of the main raw materials for tobacco flavors and fragrances.

[0003] As a natural raw material extract, the aroma, flavor and quality of tobacco extract are affected by the extraction process. The current common extraction method for tobacco extract is to use water or alcohol for reduced pressure concentration to extract; in addition, some researchers have tried to use microorganisms or enzyme preparations for fermentation or enzymatic hydrolysis in order to obtain more suitable tobacco extracts. However, the composition of tobacco extracts is very complex, and there can be hundreds of volatile compounds. For tobacco extracts obtained by different processes, their components are currently mainly analyzed by analytical testing methods such as GC-MS; however, these analytical testing methods cannot evaluate the style characteristics and quality of tobacco extracts, and it is difficult to provide effective guidance for the optimization of the extraction process.

[0004] The present invention has been proposed to solve the above-mentioned problems. Summary of the invention

[0005] The method of the present invention is based on the use requirements, uses bionic electronic tongue technology to identify the taste characteristics of tobacco extracts, and uses mathematical statistics to analyze sample differences, establishes a process processing taste difference recognition model, and reflects the influence of the extraction process on the taste characteristics of tobacco extracts. The tobacco extract processing technology evaluation method of the present invention is convenient and efficient, and can provide guidance for target-oriented tobacco extract processing.

[0006] The technical solution of the present invention is as follows:

[0007] The first aspect of the present invention discloses a method for evaluating an extraction process, comprising the following steps:

[0008] (1) Solution preparation: prepare reference solution, positive electrode cleaning solution and negative electrode cleaning solution;

[0009] (2) Sample pretreatment: using a reference solution as a solvent to dissolve a group of extracts to be tested that are prepared from the same raw materials but different processes, and diluting them by the same multiple to obtain multiple extract solutions;

[0010] (3) Obtaining taste characteristic values: respectively measure the reference solution, positive electrode cleaning solution, negative electrode cleaning solution and extract solution into the electronic tongue special sampling cup, and place them in the corresponding sample slot; perform detection according to the electronic tongue instrument operation requirements, measure the response of the sensor, and obtain the taste characteristic value of the extract; the reference solution is used as the detection solvent to eliminate the influence of the potential difference of different solutions;

[0011] (4) Evaluation of taste characteristic values: Based on the taste characteristic values ​​obtained in step (3), the taste differences of extracts prepared by different processes are compared through data analysis, and then different extraction processes are evaluated.

[0012] Preferably, the reference solution of step (1) is a mixed solution of potassium chloride and tartaric acid, and the molar ratio of potassium chloride to tartaric acid is (50-150):1; the positive electrode cleaning solution is a mixed solution of potassium hydroxide and potassium chloride in ethanol, and the molar ratio of potassium hydroxide to potassium chloride is 1:(5-15); the negative electrode cleaning solution is a mixed solution of hydrochloric acid and ethanol, wherein the concentration of hydrochloric acid in the mixed solution is 50-150mM.

[0013] Preferably, the extract to be tested in step (2) is one of a plant extract or a plant extract.

[0014] Preferably, the taste characteristics of step (3) include nine indicators: sour, sweet, bitter, salty, umami, astringent, bitter aftertaste, astringent aftertaste and umami richness.

[0015] Preferably, the data analysis in step (5) uses one-way analysis of variance combined with Duncan's multiple comparisons to specifically present the differences in the extracts to be tested prepared by different processes; first, one-way analysis of variance is performed to determine whether there are significant differences between different processes; then Duncan's multiple comparison analysis is performed to evaluate different extraction processes.

[0016] Preferably, one-way ANOVA is used to determine whether there are significant differences between the extracts prepared by different processes. The specific calculation steps are as follows:

[0017] (1) Calculate the sum of squared deviations (SST):

[0018] (11) Calculate the total sum of square deviations (SST): Where k is the number of groups, n i is the number of samples in the i-th group, x ij is the jth observation value of the i-th group, is the grand mean of all observations; its total degrees of freedom dfT =k×n i -1;

[0019] (12) Calculate the sum of squares of deviations between groups (SSA): in, is the mean of group i; its treatment degrees of freedom df t = k-1;

[0020] (13) Calculate the sum of squares of deviations within the group (SSE): SSE = SST-SSA; its error degrees of freedom df e =k×(n i -1);

[0021] (2) Calculate the mean square:

[0022] (21) Calculate the mean square between groups (MSA):

[0023] (22) Calculate the mean square error (MSE) within the group: Where N is the total number of samples;

[0024] (3) F test:

[0025] (31) Calculate the F value:

[0026] According to the F distribution table, the critical value (α = 0.05) was found and compared with the calculated F value to determine whether there was a significant difference between the extracts prepared by different processes; if F>F α =0.05, there was a significant difference, and Duncan multiple comparison analysis was performed.

[0027] Preferably, Duncan multiple comparison analysis is performed to evaluate different extraction processes. The specific calculation steps are as follows:

[0028] (A) Calculate the least significant range (LSR):

[0029] When the sample sizes of each group are equal: Among them, t α / 2，v is the critical value of the t distribution with v = Nk degrees of freedom (α = 0.05), a is the number of groups to be compared (usually from the largest mean to the smallest mean), and n is the sample size of each group;

[0030] (B) Comparison of means:

[0031] Calculate the difference between the means of the two process levels to be compared and compare the difference with the corresponding least significant range (LSR) 0.05 ) for comparison;

[0032] If the difference is greater than LSR 0.05 , then the means of the two groups (two processes) are considered to be significantly different at the (α=0.05) level.

[0033] The second aspect of the present invention discloses that the evaluation method is used for evaluating the extraction process of tobacco extract.

[0034] Beneficial effects of the present invention:

[0035] 1. The evaluation method of the present invention adopts one-way ANOVA combined with Duncan multiple comparisons for data analysis to improve the accuracy of the analysis. One-way ANOVA can effectively test whether a factor has a significant effect on the dependent variable. By comparing the mean differences of different groups, the effect of the factor can be preliminarily judged. Duncan's multiple comparison rule further refines this comparison, which can compare groups with significant differences after one-way ANOVA, thereby more accurately revealing the differences between different groups.

[0036] 2. The evaluation method of the present invention is suitable for small sample research. Duncan's multiple comparison method is particularly suitable for small sample research, which means that when the sample size is limited, the method can still provide reliable comparison results. This is undoubtedly an important advantage for some research fields where it is difficult to obtain a large number of samples (such as process level research under specific environments, etc.).

[0037] 3. The evaluation method of the present invention controls the alpha error rate. The Duncan multiple comparison method has strong hypothesis protection measures, which can ensure that the alpha error rate is controlled within a preset range. This means that when using this method for multiple comparisons, false positive results caused by multiple comparisons can be more effectively avoided, thereby improving the accuracy and reliability of the research.

[0038] 4. The evaluation method of the present invention is easy to understand and apply. Both one-way ANOVA and Duncan's multiple comparison method are relatively intuitive and easy to understand statistical methods. Their calculation process is relatively simple and can be easily implemented by statistical software, which makes them widely applicable in practical applications. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the technical solution of the present invention.

[0040] Example 1: According to product development and production requirements, nine tobacco extracts obtained by extracting three tobacco raw materials using water extraction, alcohol extraction, and water extraction and alcohol precipitation were evaluated. The specific steps include:

[0041] (1) Weigh 0.045 g of tartaric acid and dissolve it in 900 mL of distilled water, then add 2.24 g of potassium chloride and dilute it to 1000 mL with distilled water to prepare a reference solution; weigh 7.46 g of potassium chloride and dissolve it in 500 mL of distilled water, then add 300 mL of ethanol and 0.56 g of potassium hydroxide and dilute it to 1000 mL to prepare a positive electrode cleaning solution; measure 500 mL of distilled water and 300 mL of ethanol, mix them, add 8.3 mL of concentrated hydrochloric acid, and dilute it to 1000 mL with distilled water to prepare a negative electrode cleaning solution;

[0042] (2) Weigh 0.5 g of each of the nine tobacco extracts into a beaker, add the reference solution to 100 g, stir thoroughly to dissolve, and obtain nine test solutions;

[0043] (3) Measure 30.00 mL of reference solution, positive and negative electrode cleaning solution, and test solution respectively into the electronic tongue special sampling cup and place them in the corresponding sample slot; install each taste sensor and reference electrode according to the electronic tongue instrument operation requirements; set the detection program as follows: the sensor and reference electrode are first cleaned in the first group of positive and negative electrode cleaning solutions for 90 seconds, then cleaned in the second and third groups of reference solutions for 120 seconds respectively, and then returned to zero at the equilibrium position for 30 seconds. After reaching the equilibrium condition, start the test, measure the sample for 30 seconds, then perform aftertaste cleaning in the fourth and fifth groups of reference solutions for 3 seconds, and measure the aftertaste for 30 seconds;

[0044] When conducting electronic tongue testing, the temperatures of each sample solution and reagent solution were kept at room temperature, and the test was repeated 5 times according to the set program. The average value of the last 4 times was taken as the test result to reduce system errors. After the test of 9 test solutions was completed, the electronic tongue analysis system was used to convert the potential value of the test sample into a taste characteristic value, as shown in Table 1 below.

[0045] Table 1 Taste characteristic values ​​of the test liquid

[0046]

[0047] (4) Based on the 9 taste characteristic values ​​obtained, the results of one-way analysis of variance are shown in Table 2. It was found that the tobacco extracts obtained by different processes showed significant differences in the remaining 7 taste characteristic values ​​except bitterness and saltiness (P<0.05), indicating that the differences between the three processes can be specifically presented through subsequent Duncan multiple comparisons.

[0048] Table 2 Results of one-way ANOVA

[0049]

[0050] (5) Duncan multiple comparison of three processes

[0051] Calculating LSR 0.05The value is calculated by looking up the table when dfe = 6, α = 0.05 and a = 2, 3

[0052] a=2

[0053] a=3

[0054] The results are shown in Table 3.

[0055] Table 3 Duncan multiple comparison results of different processes

[0056]

[0057] As can be seen from Table 3, at the significant level (P<0.05), the sour taste value: alcohol extraction > water extraction and alcohol precipitation > water extraction; the astringent taste value: water extraction and alcohol precipitation = alcohol extraction > water extraction; the bitter aftertaste taste value: alcohol extraction = water extraction and alcohol precipitation > water extraction; the astringent aftertaste taste value: water extraction and alcohol precipitation = alcohol extraction > water extraction; the umami taste value: water extraction = water extraction and alcohol precipitation > alcohol extraction; the umami richness taste value: water extraction and alcohol precipitation > alcohol extraction = water extraction and alcohol precipitation; the sweetness taste value: water extraction > water extraction and alcohol precipitation > alcohol extraction.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0059] Note: The calculation process of the embodiment is as follows:

[0060]

[0061]

Claims

1. A method for evaluating an extraction process, characterized in that: The following steps are involved: (1) Solution preparation: prepare reference solution, positive electrode cleaning solution and negative electrode cleaning solution; (2) Sample pretreatment: using a reference solution as a solvent to dissolve a group of extracts to be tested that are prepared from the same raw materials but different processes, and diluting them by the same multiple to obtain multiple extract solutions; (3) Obtaining taste characteristic values: respectively measure the reference solution, positive electrode cleaning solution, negative electrode cleaning solution and extract solution into the electronic tongue special sampling cup, and place them in the corresponding sample slot; perform detection according to the electronic tongue instrument operation requirements, measure the response of the sensor, and obtain the taste characteristic value of the extract; the reference solution is used as the detection solvent to eliminate the influence of the potential difference of different solutions; (4) Evaluation of taste characteristic values: Based on the taste characteristic values ​​obtained in step (3), the taste differences of extracts prepared by different processes are compared through data analysis, and then different extraction processes are evaluated.

2. The evaluation method according to claim 1, characterized in that: The reference solution of step (1) is a mixed solution of potassium chloride and tartaric acid, and the molar ratio of potassium chloride to tartaric acid is (50-150):1; the positive electrode cleaning solution is a mixed solution of potassium hydroxide and potassium chloride in ethanol, and the molar ratio of potassium hydroxide to potassium chloride is 1:(5-15); the negative electrode cleaning solution is a mixed solution of hydrochloric acid and ethanol, wherein the concentration of hydrochloric acid in the mixed solution is 50-150mM.

3. The evaluation method according to claim 1, characterized in that: The extract to be tested in step (2) is one of plant extracts or plant extracts.

4. The evaluation method according to claim 1, characterized in that: The taste characteristics of step (3) include nine indicators: sour, sweet, bitter, salty, umami, astringent, bitter aftertaste, astringent aftertaste and umami richness.

5. The evaluation method according to claim 1, characterized in that: Step (5) Data analysis uses one-way ANOVA combined with Duncan's multiple comparisons to specifically present the differences in the test extracts prepared by different processes; first, one-way ANOVA is performed to determine whether there are significant differences between different processes; then Duncan's multiple comparison analysis is performed to evaluate different extraction processes.

6. The evaluation method according to claim 5, characterized in that: One-way ANOVA was used to determine whether there were significant differences in the extracts prepared by different processes. The specific calculation steps are as follows: (1) Calculate the sum of squared deviations (SST): (11) Calculate the total sum of square deviations (SST): Where k is the number of groups, n i is the number of samples in the i-th group, x ij is the jth observation value of the i-th group, is the grand mean of all observations; its total degrees of freedom df T =k×n i -1; (12) Calculate the sum of squares of deviations between groups (SSA): in, is the mean of group i; its treatment degrees of freedom df t = k-1; (13) Calculate the sum of squares of deviations within the group (SSE): SSE = SST-SSA; its error degrees of freedom df e = k × (ni-1); (2) Calculate the mean square: (21) Calculate the mean square between groups (MSA): (22) Calculate the mean square error (MSE) within the group: Where N is the total number of samples; (3) F test: (31) Calculate the F value: The critical value (α=0.05) was found according to the F distribution table and compared with the calculated F value to determine whether there were significant differences between the extracts prepared by different processes; if F>Fα=0.05, there were significant differences, and Duncan multiple comparison analysis was performed.

7. The evaluation method according to claim 5, characterized in that: Duncan multiple comparison analysis was performed to evaluate different extraction processes. The specific calculation steps are as follows: (A) Calculate the least significant range (LSR): When the sample sizes of each group are equal: Among them, t α / 2，v is the critical value of the t distribution with v = Nk degrees of freedom (α = 0.05), a is the number of groups to be compared, and n is the sample size of each group; (B) Comparison of means: Calculate the difference between the means of the two process levels to be compared and compare the difference with the corresponding LSR; If the difference is greater than the LSR, the two process means are considered to be significantly different at the (α=0.05) level.

8. The evaluation method according to any one of claims 1 to 7 is used for evaluating the extraction process of tobacco extract.

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