Maotai-flavor liquor softness evaluation model construction method, softness determination method and equipment thereof

Abstract

The invention discloses a method for constructing a model for evaluating the softness of Maotai-flavor liquor, which comprises the following steps: carrying out sensory evaluation on a Maotai-flavor liquor sample to obtain a softness score, and measuring the content of volatile aroma compounds in the liquor sample; correlation analysis is carried out through the softness score and the content change rate of the volatile aroma compounds, and a first group of key flavor substances influencing the softness are determined from the multiple volatile aroma compounds; selecting a matrix sample, adding one or more of the first group of key flavor substances to the matrix sample, and comparing with the matrix sample for sensory analysis so as to determine a plurality of second key flavor substances from the first group of key flavor substances; and constructing a soft degree evaluation model of the Maotai-flavor liquor through the second group of key flavor substances. According to the method for evaluating the softness of the Maotai-flavor liquor based on the key flavor substance basic quality control model, the softness of the liquor body is scientifically regulated and controlled, and the quality control level of the Maotai-flavor liquor is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wine evaluation models, and in particular to a model for evaluating the softness of Maotai-flavor liquor, a construction method and applications thereof. Background Art

[0002] Wine is one of the oldest foods for human beings. For example, Chinese liquor has a long history. It uses a unique open solid-state brewing process. The organic substances such as starch and protein in raw materials such as sorghum and wheat are catalyzed and decomposed into different small molecular compounds under the action of microorganisms and enzymes. The different compounds react with each other, making the flavor substances in the wine complex. Take Maotai-flavor liquor as an example. As one of the twelve most distinctive flavor liquors in China, Maotai-flavor liquor has a complex and delicate brewing process, involving multiple strains of co-fermentation, high-temperature stacking, high-temperature distillation and other key links. The complex brewing process leads to different contents and proportions of trace components in the wine, creating different styles of Maotai-flavor liquor and giving it a unique taste. At the same time, when the contents and proportions of these complex trace substances cannot reach a proper balance, it will also cause irritation in the taste of the wine, affect the softness of the wine, and reduce the quality of the liquor. According to GB / T33405-2016 "Terms for Sensory Evaluation of Liquor", softness refers to the degree of softness felt when liquor enters the mouth. The evaluation of the softness of Maotai-flavor liquor currently relies mainly on the sensory evaluation of professional tasters, and tasters are easily affected by themselves and the environment, resulting in a large deviation in the evaluation results. Therefore, it is urgent to carry out scientific, objective, and digital research on the technical methods for evaluating the softness of liquor. Summary of the invention

[0003] The present invention aims to provide a model for evaluating the softness of Maotai-flavor liquor, a preparation method and its application, aiming to clarify the key flavor substances that affect the softness of Maotai-flavor liquor, establish a method for evaluating the softness of Maotai-flavor liquor based on a key flavor substance-based quality control model, scientifically regulate the softness of the wine body, and improve the quality control level of Maotai-flavor liquor

[0004] In order to achieve the above-mentioned purpose, in a first aspect, the present invention provides a method for constructing a model for evaluating the softness of Maotai-flavor liquor, comprising: blending a plurality of Maotai-flavor liquor samples; performing sensory evaluation on the softness of the plurality of Maotai-flavor liquor samples to obtain a softness score; measuring the content of a plurality of volatile aroma compounds in each of the plurality of Maotai-flavor liquor samples; determining a first group of key flavor substances that affect the softness of Maotai-flavor liquor from the plurality of volatile aroma compounds by performing a correlation analysis on the softness score and the content change rate of the plurality of volatile aroma compounds; selecting a matrix sample, adding one or more of the first group of key flavor substances to the matrix sample to prepare a backfill sample, performing sensory analysis by comparing the backfill sample with the matrix sample, and then determining a plurality of key flavor substances from the first group of key flavor substances, the plurality of key flavor substances determined being a second group of key flavor substances; constructing a softness evaluation model for Maotai-flavor liquor based on the second group of key flavor substances, the softness evaluation model using the content of the second group of key flavor substances as an independent variable and the softness score as a dependent variable.

[0005] In some embodiments, the multiple Maotai-flavor liquor samples have the same alcohol content, and the contents of multiple volatile aroma compound components contained in each liquor body are different.

[0006] In some embodiments, the multiple Maotai-flavor liquor samples are obtained by artificial blending by mixing base liquors from different brewing years or different rounds.

[0007] In some embodiments, the plurality of volatile aroma compounds are any plurality of the following components: acetaldehyde, isobutyraldehyde, ethyl formate, ethyl acetate, acetal, methanol, isovaleraldehyde, 2-pentanone, sec-butyl alcohol, ethyl butyrate, n-propyl alcohol, ethyl isovalerate, isobutyl alcohol, isoamyl acetate, ethyl valerate, n-butyl alcohol, isopentanol, ethyl hexanoate, n-pentanol, 2-methylpyrazine, acetoin, ethyl heptanoate, ethyl lactate, 2,3,5- Trimethylpyrazine, ethyl octanoate, acetic acid, furfural, tetramethylpyrazine, benzaldehyde, ethyl nonanoate, propionic acid, isobutyric acid, 2,3-butanediol, 1,2-propylene glycol, butyric acid, ethyl decanoate, furfuryl alcohol, isovaleric acid, diethyl succinate, valeric acid, ethyl phenylacetate, phenylethyl acetate, ethyl laurate, caproic acid, guaiacol, 2-phenylethanol, heptanoic acid, ethyl myristate, octanoic acid, ethyl palmitate, ethyl oleate, ethyl linoleate.

[0008] In some embodiments, the second group of key flavor substances are ethyl acetate, ethyl formate, acetaldehyde and acetal.

[0009] In some embodiments, the softness evaluation model for Maotai-flavor liquor based on the second group of key flavor substances is constructed, comprising: for multiple Maotai-flavor liquor samples used for model construction, determining the content of the multiple second group of key flavor substances in each liquor sample and performing sensory evaluation of the softness of each liquor sample to obtain a softness score; obtaining the softness evaluation model for Maotai-flavor liquor by performing regression analysis and partial least squares comparative analysis on the contents of ethyl acetate, ethyl formate, acetaldehyde and acetal in each liquor sample (unit: g / L) and the corresponding softness scores.

[0010] In some embodiments, the softness evaluation model of the Maotai-flavor liquor is: 8.928-0.121*acetaldehyde-4.955*ethyl formate-0.528*ethyl acetate-0.144*acetal.

[0011] In some embodiments, the regression analysis is ridge regression.

[0012] In some embodiments, the softness evaluation model for Maotai-flavor liquor based on the second group of key flavor substances is constructed, comprising: for multiple Maotai-flavor liquor samples used for model construction, determining the content of ethyl acetate, ethyl formate, acetaldehyde and acetal in each sample (unit: g / L); performing sensory evaluation of softness on the multiple Maotai-flavor liquor samples used for model construction to obtain a softness score; using the content of ethyl acetate, ethyl formate, acetaldehyde and acetal in each sample as input and the corresponding softness score as output, training a deep learning model to obtain the softness evaluation model for the Maotai-flavor liquor.

[0013] In a second aspect, the present application provides a method for determining the softness of an alcohol composition, comprising: obtaining a softness evaluation model corresponding to the alcohol composition, wherein the softness evaluation model is constructed based on a plurality of predetermined key flavor substances; obtaining the contents of the plurality of key flavor substances detected in the alcohol composition; inputting the contents of the plurality of key flavor substances into the softness evaluation model, obtaining an evaluation result output by the softness evaluation model, and using the evaluation result as a result of determining the softness of the alcohol composition.

[0014] In some embodiments, before obtaining the softness evaluation model corresponding to the alcohol composition, the method includes: determining a plurality of key flavor substances that affect the softness of the alcohol composition; for a plurality of alcohol composition wine samples used for model construction, determining the content of the plurality of key flavor substances in each wine sample and performing a sensory evaluation of the softness of each wine sample to obtain a softness score; and obtaining the softness evaluation model of the alcohol composition by performing a regression analysis on the content of the plurality of key flavor substances in each wine sample and the corresponding softness score.

[0015] In some embodiments, the alcohol composition is a Maotai-flavor liquor, and the multiple key flavor substances are ethyl acetate, ethyl formate, acetaldehyde and acetal.

[0016] In some embodiments, the softness evaluation model is 8.928-0.121*acetaldehyde-4.955*ethyl formate-0.528*ethyl acetate-0.144*acetal.

[0017] In some embodiments, the determining of multiple key flavor substances that affect the softness of the alcohol composition comprises: blending multiple alcohol composition wine samples; performing sensory evaluation of softness on the multiple alcohol composition wine samples to obtain a softness score; measuring the content of multiple volatile aroma compounds in each of the multiple alcohol composition wine samples; determining a first group of key flavor substances that affect the softness of the alcohol composition from the multiple volatile aroma compounds by performing a correlation analysis on the softness score and the content of the multiple volatile aroma compounds; selecting a matrix sample, adding one or more of the first group of key flavor substances to the matrix sample to prepare a backfill sample, performing a sensory analysis by comparing the backfill sample with the matrix sample, and then determining the multiple key flavor substances from the first group of key flavor substances.

[0018] In a third aspect, the present application also provides a device, including a processor and a display, wherein the processor is used to execute the above-mentioned method for determining the softness of the alcohol composition to obtain the softness determination result of the alcohol composition, and the display device is used to display the softness determination result of the alcohol composition.

[0019] In some embodiments, the device is connected to a concentration detection module of an alcohol composition flavor substance, for obtaining the contents of the multiple key flavor substances from the concentration detection module; or the device further includes an input device, and the processor obtains the contents of the multiple key flavor substances input through the input module.

[0020] Working principle and beneficial effects of the present invention:

[0021] The present application proposes a two-step method for screening key flavor substances, wherein the first step of the two-step method is: artificially blending a variety of Maotai-flavor liquor samples. Based on these liquor samples, the sensory evaluation team evaluates each liquor sample to obtain a softness score, and uses a detector to analyze the numerous volatile aroma compounds in the liquor, and performs a correlation analysis on the softness of the liquor sample and the measured volatile aroma change rate, and preliminarily determines the key flavor components that affect the softness of Maotai-flavor liquor; the second step of the two-step method is: sensory backfilling of each of the preliminarily confirmed key flavor substances, that is, selecting normal raw liquor as a matrix sample, adding a single or multiple target compounds of a certain concentration thereto, and the wine tasters perform sensory analysis on the samples with added target compounds and the matrix samples without added, respectively, and finally determine the key flavor substances. The above two-step method can accurately determine the key flavor substances that affect the softness of Maotai-flavor liquor, and construct a model for evaluating the softness of Maotai-flavor liquor based on the accurately determined key flavor substances, which can achieve accurate and stable softness evaluation through a small amount of strongly correlated data input, that is, the concentration of key flavor substances.

[0022] This method can be widely used to judge whether the softness style quality meets the product requirements in the process of product design of Maotai-flavor liquor. This technology has the advantages of being quantifiable, accurate, simple, and easy to operate. It is of great significance to the scientific regulation of the stimulation of Maotai-flavor liquor and helps to improve the quality control level of Maotai-flavor liquor. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of model construction for evaluating the softness of Maotai-flavor liquor is provided in one embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of the structure of a device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the terms "including" and "having" in the specification and claims of the present invention and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0027] In the description of the embodiments of the present invention, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present invention, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0028] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments. In the description of the embodiments of the present invention, the term "multiple" refers to more than two (including two).

[0029] The present application relates to an alcohol composition, especially liquor, and further, in particular, to the evaluation of the softness of sauce-flavor liquor. Softness refers to the degree of smoothness of liquor when it enters the mouth. The evaluation of the softness of liquor is generally carried out by a professional sensory evaluation team, and the evaluation of the patented sensory evaluation team is the gold standard. This method is recognized by people, but this evaluation method is poor in operability and not easy to implement for liquor lovers, consumers or small brewing companies. The present application provides a method for constructing a softness evaluation model and a method for measuring softness. The softness evaluation model constructed based on the method for constructing a softness evaluation model can conveniently and relatively accurately measure the softness of liquor, especially sauce-flavor liquor. Through the construction of a model once or a limited number of times, whether it is a liquor lover, a consumer or a small brewing company, a convenient evaluation of softness can be achieved.

[0030] The following is a further explanation of the construction method of the softness model of Maotai-flavor liquor. Figure 1 As shown in FIG. 1 , constructing a softness evaluation model for Maotai-flavor liquor includes the following steps:

[0031] Step 1: Blend multiple samples of Maotai-flavor liquor.

[0032] Specifically, multiple Maotai-flavor liquor samples are obtained by artificial blending and scientifically proportioning base liquors of different brewing years or non-rotation. In some embodiments, the alcohol content of the multiple Maotai-flavor liquors blended is the same, such as 53% Vol. The content of volatile aroma compound components contained in each sample of the multiple Maotai-flavor liquors blended is different, and then the softness of each sample is different. It should be noted that in order to construct a Maotai-flavor liquor softness evaluation model, a sufficient number of Maotai-flavor liquor samples need to be blended.

[0033] Step 2: Conducting sensory evaluation on the softness of the plurality of sauce-flavor liquor samples to obtain a softness score.

[0034] The sensory evaluation team conducts sensory evaluation of the softness of each wine sample blended in step 1 to obtain a softness score. In some embodiments, the sensory evaluation team is composed of provincial and first-level tasters engaged in liquor flavor research, and all members regularly participate in taste training and are experienced. In one model construction, 19 tasters were selected to participate in the evaluation, and 9 tasters were between 22 and 40 years old, all from the Guotai Liquor Tasting Team. All team members have national taster certificates and have at least one year of experience in base wine tasting. In some embodiments, the wine samples are sensory evaluated, and the intensity of the sensory dimension of the softness of the wine samples is scaled according to the 10-point scoring system. The higher the softness sensory score, the softer the wine body and the lower the irritation. Table 1 lists the sensory scores of some wine samples, and the sensory score of each wine sample is the average of the 19 groups of sensory scores.

[0035] Table 1 Evaluation results of the softness of Maotai-flavor liquor

[0036] Body Sensory score (N=19) Wine Sample 1 7.13 Wine Sample 2 7.25 Wine Sample 3 7.43 Wine Sample 4 7.43 Wine Sample 5 7.50

[0037] Step 3, measuring the content of multiple volatile aroma compounds in each of the multiple Maotai-flavor liquor samples.

[0038] Gas chromatography (GC) is used to determine the content of volatile aroma compounds in multiple Maotai-flavor liquor samples in step 1. In some embodiments, an Agilent 7890 gas chromatograph combined with an FID detector is used to analyze the volatile aroma compounds in the wine body, and all samples are analyzed on a DB-WAX UI gas chromatograph column (60m×0.25mm×0.25μm, Agilent). The carrier gas is commercially available high-purity nitrogen with a purity of 99.999%. Inlet conditions: heater: 240°C, split ratio: 30:1, split flow rate 15mL / min, septum purge volume: 3mL / min, injection volume: 1.0μL. Chromatographic column flow rate (gradient flow rate): initial flow rate 0.5mL / min, maintained for 18min, increased to 1mL / min at a rate of 1mL / min, and maintained for 58min. Column oven conditions: maximum column oven temperature of program heating: 250°C, maximum value of covering the chromatographic column: 250°C. Program temperature rise: initial temperature 35℃, hold for 2min, heat to 45℃ at a rate of 1℃ / min, hold for 1min, heat to 50℃ at a rate of 2℃ / min, hold for 2min, heat to 80℃ at a rate of 3℃ / min, heat to 110℃ at a rate of 6℃ / min, hold for 1min, heat to 180℃ at a rate of 3℃ / min, hold for 2min, heat to 230℃ at a rate of 20℃ / min, hold for 15min. Total running time 76.333min. Detector conditions: 240℃, FID detector, air flow rate: 300mL / min, hydrogen gas flow rate: 30mL / min, tail gas flow rate: 25mL / min. Tert-amyl alcohol (0.15g / L), n-amyl acetate (0.17g / L), and 2-ethylbutyric acid (0.17g / L) were used as internal standards. The wine sample was injected directly without pretreatment. 5 mL of wine sample was taken, 50 μL of internal standard was added, and the sample was shaken and placed in an injection bottle for analysis. The peak area ratio of the analyte to the corresponding internal standard was used as the horizontal axis, and the ratio of the concentration of the analyte to the internal standard substance was used as the vertical axis to establish a standard curve. The content of each component was calculated using the standard curve. All samples were measured three times.

[0039] In some embodiments, the plurality of volatile aroma compounds are any plurality of the following components: acetaldehyde, isobutyraldehyde, ethyl formate, ethyl acetate, acetal, methanol, isovaleraldehyde, 2-pentanone, sec-butyl alcohol, ethyl butyrate, n-propyl alcohol, ethyl isovalerate, isobutyl alcohol, isoamyl acetate, ethyl valerate, n-butyl alcohol, isopentanol, ethyl hexanoate, n-pentanol, 2-methylpyrazine, acetoin, ethyl heptanoate, ethyl lactate, 2,3,5-triazine, ethyl acetate, ethyl ester, ethyl lactate, ethyl ester ... Methylpyrazine, ethyl octanoate, acetic acid, furfural, tetramethylpyrazine, benzaldehyde, ethyl nonanoate, n-propionic acid, isobutyric acid, 2,3-butanediol, 1,2-propylene glycol, n-butyric acid, ethyl decanoate, furfuryl alcohol, isovaleric acid, diethyl succinate, valeric acid, ethyl phenylacetate, phenylethyl acetate, ethyl laurate, hexanoic acid, guaiacol, 2-phenylethanol, heptanoic acid, ethyl myristate, n-octanoic acid, ethyl palmitate, ethyl oleate, ethyl linoleate).

[0040] Further, the change rate of volatile aroma compounds is calculated according to the content of the detected volatile aroma compounds. Table 2 shows the measurement results of the change rate of some volatile aroma compounds in some wine bodies.

[0041] Table 2 Change rate of key volatile aroma compounds in wine (%)

[0042]

[0043]

[0044] Note: NA means not detected

[0045] Step 4, determining a first group of key flavor substances that affect the softness of Maotai-flavor liquor from the multiple volatile aroma compounds by performing correlation analysis on the softness score and the change rate of the multiple volatile aroma compounds.

[0046] The softness sensory evaluation score and the change rate of volatile aroma compounds were subjected to Pearson correlation analysis. The results are shown in Table 3. The softness of the wine body was extremely significantly negatively correlated with isobutyraldehyde, acetal, and isovaleraldehyde (P < 0.01), and was significantly negatively correlated with acetaldehyde, ethyl formate, ethyl acetate, 2-pentanone, ethyl butyrate, ethyl isovalerate, isobutyric acid, valeric acid, and ethyl palmitate (P < 0.05), and was significantly positively correlated with 2.3.5-trimethylpyrazine (P < 0.05). Thus, the key flavor substances that affect the softness of the wine body were preliminarily determined, that is, the first group of key flavor substances. In some embodiments, the first group of key flavor substances are acetaldehyde, isobutyraldehyde, ethyl formate, ethyl acetate, acetal, isovaleraldehyde, 2-pentanone, ethyl butyrate, ethyl isovalerate, 2.3.5-trimethylpyrazine, isobutyric acid, valeric acid, and ethyl palmitate.

[0047] It should be noted that the first group of key flavor substances determined above are the key flavor substances affecting the softness that are initially screened out for Maotai-flavor liquor. If the above steps are implemented for other types of liquor samples, the key flavor substances affecting the softness of other types of liquor will be determined.

[0048] Table 3 Pearson correlation analysis between sensory evaluation value of softness and change rate of main flavor substances content

[0049]

[0050] Step 5, select a matrix sample, add one or more of the first group of key flavor substances to the matrix sample, prepare a backfill sample, compare the backfill sample with the matrix sample for sensory analysis, and then determine multiple key flavor substances from the first group of key flavor substances, and the determined multiple key flavor substances are the second group of key flavor substances.

[0051] In some embodiments, according to the 2-AFC method, a normal raw wine is selected as a matrix sample, and a single or multiple target compounds of a certain concentration are added thereto. After the addition is completed, the sample is placed at room temperature. The wine glasses are randomly coded and presented to the tasters. 19 tasters conduct sensory analysis on the samples with added target compounds and the matrix samples without added. There should be no prompts or suggestive language during the process. The panel members use a 10-point scoring system to score the softness intensity, and provide a QR code to answer questions online. After all tasters complete the evaluation, the answer sheet is collected for statistical analysis. The final softness score is the arithmetic mean of the scoring results of the tasters. For Maotai-flavor liquor, according to the provisions of GB / T12310-2012 "Paired Comparison Test of Sensory Analysis Methods", based on the 2-AFC test, according to the statistical results as shown in Table 4, it can be seen from Table 4 that ethyl formate has a very significant effect on the softness of the wine body (a=0.01), ethyl acetate has a moderately significant effect on the softness of the wine body (a=0.10), acetaldehyde and acetal have a significant effect on the softness of the wine body (a=0.20), isobutyraldehyde, isovaleraldehyde, 2-pentanone, ethyl butyrate, ethyl isovalerate, 2.3.5-trimethylpyrazine, isobutyric acid, valeric acid, and ethyl palmitate have an effect on the softness of the wine body, but it does not reach a significant level. Acetaldehyde + acetal has a significant effect on the softness of the wine body (a=0.20), and ethyl acetate + ethyl formate and acetaldehyde + acetal + ethyl acetate + ethyl formate have a highly significant effect on the softness of the wine body (a=0.05). In summary, the key flavor substances that affect the softness are determined to be ethyl acetate, ethyl formate, acetaldehyde and acetal. That is, the second group of key flavor substances. It should be noted that the second group of key flavor substances determined above are the key flavor substances that affect the softness determined for sauce-flavor liquor. If the above steps are implemented for other types of liquor samples, the key flavor substances that affect the softness of other types of liquor are determined.

[0052] Table 4 Aroma addition test results

[0053] Serial number substance <![CDATA[n / N a ]]> <![CDATA[2-AFC / Significance b > 1 Ethyl acetate 13 / 19 0.10 2 Acetaldehyde 12 / 19 0.20 3 Acetal 12 / 19 0.20 4 Isobutyraldehyde 8 / 19 NA 5 Isovaleraldehyde 8 / 19 NA 6 Ethyl isovalerate 8 / 19 NA 7 Isobutyric acid 6 / 19 NA 8 Ethyl butyrate 10 / 19 NA 9 Ethyl palmitate 5 / 19 NA 10 Ethyl formate 15 / 19 0.01 11 Valeric acid 5 / 19 NA 12 2-Pentanone 3 / 19 NA 13 2.3.5-Trimethylpyrazine 3 / 19 NA 14 Acetaldehyde+acetal 12 / 19 0.20 15 Ethyl acetate + ethyl formate 14 / 19 0.05 16 Acetaldehyde + acetal + ethyl acetate + ethyl formate 14 / 19 0.05

[0054] Note: n / N a :n represents the number of correct evaluation answers; N represents the total number of participants in the evaluation; b Significance: “****”, α=0.01 (very significant); “***”, α=0.05 (highly significant); **, α=0.10 (moderately significant); “*”, α=0.20 (significant); “NA”, not significant.

[0055] Step 6: construct a softness evaluation model for Maotai-flavor liquor based on the second group of key flavor substances, wherein the softness evaluation model uses the content of the second group of key flavor substances as an independent variable and the softness score as a dependent variable.

[0056] In some embodiments, a softness evaluation model for Maotai-flavor liquor is constructed based on the second group of key flavor substances, including: for multiple Maotai-flavor liquor samples used for model construction, determining the content of the multiple second group of key flavor substances in each sample and performing sensory evaluation of the softness of each sample to obtain a softness score; obtaining the softness evaluation model for Maotai-flavor liquor by performing regression analysis and partial least squares comparative analysis on the contents of ethyl acetate, ethyl formate, acetaldehyde and acetal in each sample (unit: g / L) and the corresponding softness scores.

[0057] Specifically, multiple training samples and test samples of Maotai-flavor liquor are selected or prepared. The training samples are Maotai-flavor liquor samples used for model construction. The R language multivariate statistical analysis software is used to perform ridge regression analysis on the content of the second group of key flavor substances in the Maotai-flavor liquor samples used for model construction and the sensory evaluation score of the softness of each liquor sample to obtain a multivariate fitting equation, i.e., the softness evaluation model of Maotai-flavor liquor. In some embodiments, the softness evaluation model of Maotai-flavor liquor is: 8.928-0.121*acetaldehyde-4.955*ethyl formate-0.528*ethyl acetate-0.144*acetal.

[0058] It should be noted that if it is necessary to construct a softness evaluation model for other types of liquor, the training samples used will be the liquor samples of the corresponding types, and the softness evaluation model constructed will be the softness evaluation model for the liquor samples of the corresponding types.

[0059] Several groups of representative Maotai-flavor liquor samples were selected to verify the ridge regression analysis evaluation model of liquor softness. By measuring and analyzing the content of key flavor substances in the liquor, the liquor softness score was calculated by the model of softness sensory score = 8.928-0.121*acetaldehyde-4.955*ethyl formate-0.528*ethyl acetate-0.144*acetal, and compared with the sensory evaluation score. Based on the relative standard deviation of less than 5%, the relative standard deviation of the sensory score prediction value of the softness evaluation model and the taster score was verified to be <5%, as shown in Table 5. The full verification shows that the ridge regression model is accurate and reliable. On this basis, the ridge regression analysis model can effectively constrain the size of the regression coefficient and solve the problem of multicollinearity of data, reduce the complexity of the model, and thus improve the robustness of the model.

[0060] Table 5 Model validation results

[0061]

[0062]

[0063] In some embodiments, the softness evaluation model may also be constructed using a least squares method.

[0064] In some embodiments, a deep learning model can also be used to construct a softness evaluation model. Specifically, for multiple Maotai-flavor liquor samples used for model construction, the content of ethyl acetate, ethyl formate, acetaldehyde and acetal in each liquor sample is measured (unit: g / L); the multiple Maotai-flavor liquor samples used for model construction are subjected to sensory evaluation of softness to obtain a softness score; the content of ethyl acetate, ethyl formate, acetaldehyde and acetal in each liquor sample is used as input, and the corresponding softness score is used as output to train a deep learning model to obtain the softness evaluation model of the Maotai-flavor liquor.

[0065] This application proposes a two-step method to identify the key flavor substances that affect the softness of Maotai-flavor liquor, and to construct a Maotai-flavor liquor softness evaluation model based on the determined key flavor substances. By simply inputting a small amount of measured content of key flavor substances, the model can output an accurate softness score.

[0066] In summary, this application identified the important contributing substances that affect the softness of Maotai-flavor liquor, verified for the first time that ethyl formate is an important contributing substance that affects the softness of the liquor, and established a scientific digital quality control model for the sensory softness of the liquor. First, 52 key flavor substances in the skeleton were accurately and quantitatively detected based on GC. Then, the correlation analysis between the change rate of key flavor substances and the sensory score value was performed. Combined with the sensory backfill verification test, the key flavor substance basis that contributes to the softness of Maotai-flavor liquor was clarified, and a digital ridge regression model for the softness of Maotai-flavor liquor was established, that is, the sensory score of softness.

[0067] =8.928-0.121*acetaldehyde-4.955*ethyl formate-0.528*ethyl acetate-0.144*acetal. The model was used to calculate the wine body softness score, and compared with the sensory evaluation score, the relative standard deviation was less than 5%, indicating that the model was accurate and reliable, and the wine body softness score was obtained. The calculated value was ≥7.0, which was defined as good wine body softness.

[0068] In the second aspect, after the softness evaluation model is constructed, the present application provides a method for measuring the softness of an alcohol composition with respect to how to measure the softness using the model, comprising: obtaining the softness evaluation model corresponding to the alcohol composition, wherein the softness evaluation model is constructed based on the plurality of key flavor substances that are predetermined; obtaining the contents of the plurality of key flavor substances detected in the alcohol composition; inputting the contents of the plurality of key flavor substances into the softness evaluation model, obtaining an evaluation result output by the softness evaluation model, and using the evaluation result as a result of measuring the softness of the alcohol composition.

[0069] In some embodiments, before obtaining the softness evaluation model corresponding to the alcohol composition, the steps include: determining a plurality of key flavor substances that affect the softness of the alcohol composition; for a plurality of alcohol composition wine samples used for model construction, determining the content of the plurality of key flavor substances in each wine sample and performing a sensory evaluation of the softness of each wine sample to obtain a softness score; and obtaining the softness evaluation model of the alcohol composition by performing a ridge regression analysis on the content of the plurality of key flavor substances in each wine sample and the corresponding softness score. In some embodiments, the alcohol composition is a Maotai-flavor liquor, and the plurality of key flavor substances are ethyl acetate, ethyl formate, acetaldehyde, and acetal. In some embodiments, the softness evaluation model is 8.928-0.121*acetaldehyde-4.955*ethyl formate-0.528*ethyl acetate-0.144*acetal. In some embodiments, the determining of multiple key flavor substances that affect the softness of the alcohol composition comprises: blending multiple alcohol composition wine samples; performing sensory evaluation of softness on the multiple alcohol composition wine samples to obtain a softness score; measuring the content of multiple volatile aroma compounds in each of the multiple alcohol composition wine samples; determining a first group of key flavor substances that affect the softness of the alcohol composition from the multiple volatile aroma compounds by performing a correlation analysis on the softness score and the content of the multiple volatile aroma compounds; selecting a matrix sample, adding one or more of the first group of key flavor substances to the matrix sample to prepare a backfill sample, performing a sensory analysis by comparing the backfill sample with the matrix sample, and then determining the multiple key flavor substances from the first group of key flavor substances.

[0070] The determination method described above can be promoted and applied to judge whether the softness style quality meets the product requirements in the process of designing sauce-flavor liquor products. This technology has the advantages of being quantifiable, accurate, simple, and easy to operate. It is of great significance to the scientific regulation of the stimulation of sauce-flavor liquor and helps to improve the quality control level of sauce-flavor liquor. The present application also proposes a product that integrates the above-mentioned sauce-flavor liquor softness evaluation model. The specific form of the product is not limited. As long as it has an input device and a control chip that can integrate the sauce-flavor liquor softness evaluation model and an output device, it can be a wearable device, a desktop or cabinet instrument, or an online interconnected device. The product can be input or receive the flavor substance content data of the sauce-flavor liquor to be evaluated from the cloud device, and use the model to analyze the input or received data to obtain the softness score result of the sauce-flavor liquor, and the softness score result can be displayed, such as a display panel display or uploaded to a cloud display device for display.

[0071] Reference Figure 2Specifically, a device 10 is provided, comprising a processor 101 and a display 102, wherein the processor 101 is used to execute the above-mentioned method for determining the softness of the alcohol composition to obtain the softness determination result of the alcohol composition, and the display device 102 is used to display the softness determination result of the alcohol composition.

[0072] In some embodiments, the device 10 is further used to connect with a concentration detection module 104 of alcohol composition flavor substances, and is used to obtain the contents of the multiple key flavor substances from the concentration detection module 104, or the device is integrated with a concentration detection module; and / or the device 10 further includes an input device 103, and the processor 101 obtains the contents of the multiple key flavor substances input through the input module 103. In some embodiments, the device can also obtain the contents of the key flavor substances affecting the softness of the alcohol composition to be tested stored on a remote server.

[0073] The present application does not limit the specific form of the device 10. In some embodiments, the device 10 can be a mobile device that is easy to carry, such as a wearable device. In other embodiments, the device 10 can also be a desktop or cabinet instrument. Whether it is a mobile device or a desktop or cabinet instrument, it can have the function of supporting voice input or input from an external input device, or a built-in concentration detection module to quickly obtain the required flavor substance content data of the alcohol composition to be evaluated, and use its built-in softness model to analyze the data, and then obtain the softness score result of the sauce-flavored liquor. The display device of the device 10 displays the score result or the test result of whether the test is passed. Similarly, the display device can be a voice module, which is used to broadcast the score result or the test result of whether the test is passed, so as to achieve convenient detection of the softness of the alcohol composition to be tested.

[0074] In some embodiments, device 10 can also be an online interconnected device, that is, device 10 can receive flavor substance content data of the sauce-flavor liquor to be evaluated from a cloud device, and use a model to analyze the input or received data to obtain a softness score result of the sauce-flavor liquor, and can display the softness score result, such as displaying it on a display panel or uploading it to a cloud display device for display.

[0075] In some embodiments, the alcohol composition is a Maotai-flavor liquor. In other embodiments, the alcohol composition can also be other types of liquor.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention. In particular, as long as there is no structural conflict, the various technical features mentioned in each embodiment can be combined in any way. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A method for constructing a softness evaluation model for Maotai-flavor liquor, characterized in that: include: Blending multiple Maotai-flavor liquor samples; Conducting sensory evaluation of the softness of the multiple Maotai-flavor liquor samples to obtain a softness score; Determining the content of multiple volatile aroma compounds in each of the multiple Maotai-flavor liquor samples; Determining a first group of key flavor substances that affect the softness of Maotai-flavor liquor from the multiple volatile aroma compounds by performing a correlation analysis on the softness score and the content change rate of the multiple volatile aroma compounds, wherein the content change rate is calculated based on the content; Selecting a matrix sample, adding one or more of the first group of key flavor substances to the matrix sample to prepare a backfill sample, performing a sensory analysis on the backfill sample and the matrix sample, and then determining a plurality of key flavor substances from the first group of key flavor substances, wherein the determined plurality of key flavor substances are a second group of key flavor substances; A softness evaluation model for Maotai-flavor liquor is constructed based on the second group of key flavor substances. The softness evaluation model uses the content of the second group of key flavor substances as an independent variable and the softness score as a dependent variable.

2. The method for constructing a softness evaluation model for Maotai-flavor liquor according to claim 1, characterized in that: The alcohol content of the multiple Maotai-flavor liquor samples is the same, and the content of multiple volatile aroma compounds contained in each sample is different.

3. The method for constructing a softness evaluation model for Maotai-flavor liquor according to claim 2, characterized in that: The multiple Maotai-flavor liquor samples are obtained by artificial blending and proportioning base liquors from different brewing years or different rounds.

4. The method for constructing a softness evaluation model for Maotai-flavor liquor according to any one of claims 1 to 3, characterized in that: The second group of key flavor substances are ethyl acetate, ethyl formate, acetaldehyde and acetal.

5. The method for constructing a model for evaluating the softness of Maotai-flavor liquor according to claim 4, characterized in that: The method of constructing a softness evaluation model for Maotai-flavor liquor based on the second group of key flavor substances comprises: For multiple Maotai-flavor liquor samples used for model construction, the content of the multiple second group of key flavor substances in each liquor sample is measured, and each liquor sample is subjected to sensory evaluation of softness to obtain a softness score; The softness evaluation model of the Maotai-flavor liquor was obtained by performing ridge regression analysis on the contents of ethyl acetate, ethyl formate, acetaldehyde and acetal (unit: g / L) in each liquor sample and the corresponding softness scores.

6. The method for constructing a model for evaluating the softness of Maotai-flavor liquor according to claim 5, characterized in that: The softness evaluation model of the Maotai-flavor liquor is: 8.928-0.121*acetaldehyde-4.955*ethyl formate-0.528*ethyl acetate-0.144*acetal.

7. A method for determining the softness of an alcohol composition, characterized in that: include: Obtaining a softness evaluation model corresponding to the alcohol composition, wherein the softness evaluation model is constructed based on the plurality of key flavor substances determined in advance; Obtaining the contents of the plurality of key flavor substances detected in the alcohol composition; The contents of the plurality of key flavor substances are input into the softness evaluation model, an evaluation result output by the softness evaluation model is obtained, and the evaluation result is used as a softness measurement result of the alcohol composition.

8. The method for measuring the softness of an alcohol composition according to claim 7, characterized in that: Before obtaining the softness evaluation model corresponding to the alcohol composition, the method includes: Identifying a plurality of key flavor substances that affect the smoothness of the alcohol composition; For a plurality of alcohol composition wine samples used for model construction, the contents of the plurality of key flavor substances in each wine sample are measured, and a softness sensory evaluation is performed on each wine sample to obtain a softness score; The softness evaluation model of the alcohol composition is obtained by performing ridge regression analysis on the contents of the multiple key flavor substances in each wine sample and the corresponding softness scores.

9. The method for measuring the softness of an alcohol composition according to claim 8, characterized in that: The alcohol composition is a sauce-flavor liquor, the multiple key flavor substances are ethyl acetate, ethyl formate, acetaldehyde and acetal, and the softness evaluation model is 8.928-0.121*acetaldehyde-4.955*ethyl formate-0.528*ethyl acetate-0.144*acetal.

10. A device comprising a processor and a display, characterized in that: The processor is used to execute the alcohol composition softness determination method as described in claims 7-9 to obtain the alcohol composition softness determination result, and the display device is used to display the alcohol composition softness determination result.

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