A quality evaluation method for a health-care wine for reducing liver damage and application thereof

Abstract

The present application belongs to the field of health wine quality evaluation, and particularly relates to a quality evaluation method and application of health wine with liver damage reduction function, which is based on GC-MS and LC-MS technology to establish a content detection method of various chemical components in health wine, combines chemometrics to distinguish health wines of different quality grades and screen differential markers of health wines of different quality grades, more comprehensively and objectively explores chemical components of health wines of different quality grades, and provides a reference for health wine quality control.

Description

Technical Field

[0001] This invention belongs to the field of health wine testing and analysis, specifically relating to a quality evaluation method and application for health wine that reduces liver damage. Background Technology

[0002] Health tonics, composed of three types of baijiu (Chinese liquor) and herbal extracts, possess complex chemical compositions. The quality and proportion of the three baijiu types, as well as the proportion of herbal extracts added, all significantly influence the quality of these tonics. For example, a health tonic prepared by combining light-aroma, strong-aroma, and sauce-aroma baijiu in a certain proportion with the addition of herbal extracts such as buckwheat, wolfberry, mulberry leaves, kudzu root, and acerola cherry fruit possesses the sensory characteristics of a rich baijiu aroma and a mellow, sweet taste, and also has the functions of reducing liver damage and assisting in lowering blood lipids. However, research on the flavor compounds, functional compounds, and quality evaluation of this health tonic is insufficient. The content and proportion of chemical components differ among different quality grades of health tonics, resulting in variations in flavor characteristics, functional effects, and product quality. Therefore, distinguishing between different quality grades of health tonics and identifying key indicators affecting quality levels are crucial factors in achieving quality evaluation and control. For beverages with complex chemical compositions, modern testing and analysis techniques should be employed to establish objective and effective methods for quality differentiation and evaluation, clarifying the material differences between products of different quality grades, and ensuring the quality control of health tonics. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a method for evaluating the quality of health wines that reduce liver damage. Based on GC-MS and LC-MS technologies, a method for detecting the content of multiple chemical components in health wines of different quality grades is established. Combined with chemometrics, different quality grades of health wines are distinguished and differential markers are screened. This objectively and effectively explores the changes in chemical components in health wines of different quality grades, providing a reference for the quality control and product preparation of health wines.

[0004] The technical solution of this invention is as follows:

[0005] A quality evaluation method for a health wine with liver-damage-reducing function includes the following steps:

[0006] (1) After pretreatment of the samples, the chemical composition content of health wines of different quality grades was determined by GC-MS and LC-MS techniques.

[0007] (2) Principal component analysis was used to classify and evaluate health wines of different quality grades; then partial least squares-discriminant analysis was used to screen out the differential markers among health wines of different quality grades.

[0008] (3) Cluster heatmap analysis was used to differentiate health wines of different quality grades;

[0009] The main differentiating compounds among the different quality grades of health wines are ethyl isovalerate, eugenol, 4-methylguaiacol, 4-ethylguaiacol, phenylacetaldehyde, ethyl hexanoate, rutin, furfural, quercetin-3-rutinoside-7-glucoside, vanillin, 2-ethyl-6-methylpyrazine, linalool, and 2,3,5-trimethylpyrazine.

[0010] Preferably, the sample pretreatment method in step (1) is as follows: the health wine sample to be tested is extracted and separated using a solvent-assisted evaporation device to obtain volatile and non-volatile chemical components, which are then extracted with organic solvents to obtain the test sample solution.

[0011] Preferably, step (1) specifically includes the following steps:

[0012] 1) Accurately transfer 10-20 mL of the health wine sample, add 20-40 μL of internal standard solution, mix thoroughly, and pour into the dropping funnel of the dual-channel vacuum distillation system. Set the system circulation temperature to 50-55℃, add liquid nitrogen to the cold trap, turn on the molecular turbo pump, and when the vacuum degree of the entire system is less than 1×10-4 Pa, slowly open the stopcock of the dropping funnel, control the flow rate at 0.8-1.2 mL / min, and collect the distillate in channel A bottle;

[0013] 2) Transfer all the distillate obtained in step 1) into a 250 mL separatory funnel, dilute with ultrapure water to an ethanol volume fraction of 5-15%, add sodium chloride until the solution is saturated, and then extract with dichloromethane three times. Combine the organic phases.

[0014] 3) Add anhydrous sodium sulfate to the organic phase obtained in step 2), dehydrate at 4°C for 12-24 h, and concentrate to 1-2 mL by nitrogen blowing at 40°C to obtain sample solution A, which is then analyzed by GC-MS.

[0015] 4) The standard solutions of volatile chemical components were analyzed under the same GC-MS conditions. A standard curve was plotted using the peak area internal standard method, and the content of volatile chemical components in health wines of different quality grades was calculated.

[0016] 5) Add 10-20 mL of 50% ethanol solution to bottle B in step 1), dissolve it completely, take 0.1 mL and put it into a sample bottle, dilute it to 10 mL with 50% methanol aqueous solution to obtain sample solution B, and analyze it by LC-MS.

[0017] 6) Analyze the standard solutions of non-volatile chemical components under the same LC-MS conditions, plot the standard curve using the external standard method of peak area, and calculate the content of non-volatile chemical components in health wines of different quality grades.

[0018] Preferably, in step 2), the extraction volume is 5 to 15 mL each time.

[0019] Preferably, the GC-MS conditions include: a stationary phase of polyethylene glycol nitrobenzene, an injection port temperature of 250°C, a split ratio of 10:1, constant flow mode, a flow rate of 1.0 mL / min, and an injection volume of 1 μL; a carrier gas of high-purity helium with a purity ≥99.999%; an initial column temperature of 50°C for 5 min, followed by a ramp-up to 230°C at 3.0°C / min and a hold for 5 min; a mass spectrometry ionization mode of electron impact source with an energy of 70 eV; a quadrupole temperature of 150°C; an ion source temperature of 230°C; a solvent delay of 5 min; and selected ion monitoring (SIM) acquisition.

[0020] Preferably, the LC-MS conditions include: ion source mode for LC-MS is ESI-, ESI+; nebulizer temperature: 450℃; ion source temperature: 150℃; nebulizer gas flow rate: 800 L / h; cone gas flow rate: 100 L / h; collision gas flow rate: 0.17 mL / min; Xselect HSS T3 column; mobile phase: water and acetonitrile; flow rate: 0.3 mL / min; column temperature: 40℃; injection volume: 5 μL; gradient elution conditions are as follows:

[0021] .

[0022] Preferably, the health wine is a health wine with liver-damaging function prepared by adding extracts of buckwheat, wolfberry, mulberry leaf, kudzu root and acerola cherry fruit.

[0023] The beneficial effects of this invention are:

[0024] This invention employs solvent-assisted evaporation extraction to separate volatile and non-volatile chemical components in health wine samples. The contents of these components are then determined using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), effectively improving detection efficiency and accuracy. Based on the content of volatile and non-volatile chemical components in health wines of different quality grades, principal component analysis is used to classify and evaluate these wines. Partial least squares discriminant analysis (PSA) is then used to screen for differential markers among the different quality grades. Finally, cluster heatmap analysis is employed to differentiate between the different quality grades. This method can detect multiple chemical components in health wines, differentiate and evaluate different quality grades, and boasts advantages such as high detection efficiency and high accuracy. Attached Figure Description

[0025] Figure 1PCA analysis of Grade A (Group A), Grade B (Group B), and Grade C (Group C) products in Example 1;

[0026] Figure 2 PLS-DA is the Grade A (Group A) and Premium (Group B) product in Example 1;

[0027] Figure 3 PLS-DA is the superior grade (Group B) and the special grade (Group C) in Example 1;

[0028] Figure 4 The VIP results are for Grade A (Group A) and Premium (Group B) products in Example 1;

[0029] Figure 5 The VIP results are for the superior grade (Group B) and the premium grade (Group C) in Example 1;

[0030] Figure 6 The results show the clustering of Grade A (Group A), Grade B (Group B), and Grade C (Group C) products in Example 1. Detailed Implementation

[0031] The following examples are for illustrative purposes only and are not intended to limit the scope of the invention. Where specific techniques or conditions are not specified in the examples, they should be performed according to the techniques or conditions described in the literature in this field, or according to the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased from legitimate channels.

[0032] In the following embodiments, the health wine used is a health wine prepared by combining light-aroma, strong-aroma, and sauce-aroma baijiu in a certain proportion and adding herbal extracts such as buckwheat, wolfberry, mulberry leaf, kudzu root, and acerola cherry. It has the sensory characteristics of baijiu with a rich aroma and mellow and sweet taste, as well as the functions of reducing liver damage and assisting in lowering blood lipids.

[0033] The quality evaluation method used includes the following steps:

[0034] 1) Accurately transfer 10–20 mL of the health wine sample, add 20–40 μL of internal standard solution, mix thoroughly, and pour into the dropping funnel of the dual-channel vacuum distillation system. Set the system circulation temperature to 50–55 °C, add liquid nitrogen to the cold trap, turn on the molecular turbopump, and wait until the vacuum degree of the entire system is less than 1 × 10⁻⁶. -4 When Pa, slowly open the stopcock of the dropping funnel, control the flow rate at 0.8-1.2 mL / min, and collect the distillate in channel A flask;

[0035] 2) Transfer all the distillate obtained in step 1) into a 250 mL separatory funnel, dilute with ultrapure water to an ethanol volume fraction of 5-15%, add sodium chloride until the solution is saturated, and then extract with dichloromethane 3 times (5-15 mL each time). Combine the organic phases (about 15-45 mL).

[0036] 3) Add anhydrous sodium sulfate to the organic phase obtained in step 2), dehydrate at 4°C for 12-24 h, and concentrate to 1-2 mL by nitrogen blowing at 40°C to obtain sample solution A, which is then analyzed by GC-MS.

[0037] 4) The standard solutions of volatile chemical components were analyzed under the same GC-MS conditions. A standard curve was plotted using the peak area internal standard method, and the content of volatile chemical components in health wines of different quality grades was calculated.

[0038] 5) Add 10-20 mL of 50% ethanol solution to bottle B in step 1), dissolve it completely, take 0.1 mL and put it into a sample bottle, dilute it to 10 mL with 50% methanol aqueous solution to obtain sample solution B, and analyze it by LC-MS.

[0039] 6) Analyze the standard solutions of non-volatile chemical components under the same LC-MS conditions, plot the standard curve using the external standard method of peak area, and calculate the content of non-volatile chemical components in health wines of different quality grades;

[0040] 7) Based on the determination results of volatile and non-volatile chemical components, principal component analysis was used to classify and evaluate health wines of different quality grades, and then partial least squares-discriminant analysis was used to screen out the differential markers between health wines of different quality grades.

[0041] 8) Cluster heatmap analysis was used to differentiate health wines of different quality grades.

[0042] Example 1

[0043] This embodiment provides a quality evaluation method and application method for a health-promoting wine that reduces liver damage, as detailed below:

[0044] 1. Reagents and test samples

[0045] Anhydrous ethanol, dichloromethane, methanol, and acetonitrile were all of chromatographic grade.

[0046] A total of 30 batches of health wine samples were collected, including 10 batches of samples from each of three different grades: Grade A (Group A), Superior Grade (Group B), and Special Grade (Group C). The samples were numbered A1~A10, B1-B10, and C1~C10, respectively. The alcohol content of all samples was 52% (V / V), and they were provided by Jingpai Co., Ltd.

[0047] 2. Standard products

[0048] Ethyl isovalerate, ethyl hexanoate, diethyl succinate, phenethyl acetate, 2,3-butanediol, 3-methylbutanal, phenylacetaldehyde, vanillin, furfural, 5-methylfurfural, 4-methylguaiacol, 4-ethylguaiacol, eugenol, 4-vinylguaiacol, α-cucurbitene, β-damascone, cucurbitone, linalool, 2,5-dimethylpyrazine, 2,6-dimethylpyrazine, 2,3-dimethylpyrazine, 2-ethyl-6-methylpyrazine, 2 3,5-Trimethylpyrazine, 2,6-Diethylpyrazine, Tetramethylpyrazine, and 2-Ethyl-3,5-Dimethylpyrazine, with a purity greater than 98%, were purchased from Shanghai Anpu Experimental Technology Co., Ltd.; Quercetin-3-rutin-7-glucoside, with a purity greater than 98%, were purchased from Weikeqi Biotechnology Co., Ltd.; Kaempferol-3-O-rutin, Puerarin, and Rutin standards, with a purity greater than 98%, were purchased from the China National Institutes for Food and Drug Control; Ethyl hexanoate-d 11 Standards: purity greater than 98.8%, purchased from TRC Canada; 2-octanol standard, n-alkanes (C7-C6) 30 ): Purity greater than 99.5%, purchased from Sigma America; 2-methoxy-d3-phenol standard: purity greater than 99.8%, purchased from CDNCanada; 2-methoxy-3-methylpyrazine standard: purity greater than 97.0%, purchased from CNW Germany.

[0049] 3. Preparation of standard solutions

[0050] Using anhydrous ethanol as a solvent, compound A, numbered 1 to 27 in Table 1, was prepared into a mixed standard solution and stored at -20°C. Using 50% methanol solution as a solvent, compound B, numbered 28 to 31 in Table 1, was prepared into a mixed standard solution with specific concentrations as shown in Table 1.

[0051] Using anhydrous ethanol as a solvent, ethyl hexanoate-d 11 2-Octanol, 2-methoxy-d3-phenol, and 2-methoxy-3-methylpyrazine were prepared as internal standard solutions with a concentration of 200 mg / L and stored at -20 °C.

[0052] Table 1. Concentrations (mg / L) of 31 chemical components

[0053]

[0054]

[0055] 4. Sample pretreatment process

[0056] Accurately transfer 10 mL (V1) of the health wine test sample B1, add 20 μL of internal standard solution, mix thoroughly, and pour into the dropping funnel of the dual-channel vacuum distillation system. Set the system circulation temperature to 50–55 °C, add liquid nitrogen to the cold trap, turn on the molecular turbopump, and wait until the vacuum degree of the entire system is less than 1 × 10⁻⁶. -4 At Pa, slowly open the stopcock of the dropping funnel, control the flow rate at 1.0 mL / min, collect the distillate from channel A and transfer it to a 250 mL separatory funnel. Dilute with ultrapure water to an ethanol volume fraction of 10%, add sodium chloride until the solution is saturated, then extract three times with dichloromethane (5 mL each time), combine the organic phases (about 15 mL), add excess anhydrous sodium sulfate, dehydrate at 4 °C for 24 h, and concentrate to 1 mL (V2) under nitrogen blowing at 40 °C to obtain sample solution A, which is analyzed by GC-MS.

[0057] Add 10 mL (V3) of 50% ethanol solution to channel B. After dissolving completely, take 0.1 mL (V4) and transfer it to a sample bottle. Dilute the sample to 10 mL (V5) with 50% methanol aqueous solution to obtain sample solution B, which is then analyzed by LC-MS.

[0058] 5. Instrument conditions

[0059] GC-MS conditions: Gas chromatography column stationary phase: polyethylene glycol nitrobenzene; injection port temperature: 250℃; split ratio: 50:1; constant flow mode; flow rate: 1.0 mL / min; injection volume: 1 μL; carrier gas: high-purity helium, purity ≥99.999%; column temperature: initial temperature 50℃, hold for 5 min, then ramp to 230℃ at 3.0℃ / min, hold for 5 min. Mass spectrometry ionization mode: electron impact source, energy 70 eV; quadrupole temperature: 150℃; ion source temperature: 230℃; solvent delay: 5 min.

[0060] LC-MS conditions: Xselect HSS T3 column (3.5 μm, 2.1*100 mm); mobile phase: gradient elution of water and acetonitrile; analysis time: 22 min; gradient elution conditions are shown in Table 2; flow rate: 0.3 mL / min; column temperature: 40 ℃; injection volume: 5 μL; LC-MS ion source mode: ESI-, ESI+; nebulizer temperature: 450 ℃; ion source temperature: 150 ℃; nebulizer gas flow rate: 800 L / h; cone gas flow rate: 100 L / h; collision gas flow rate: 0.17 mL / min.

[0061] Table 2 Gradient elution conditions for liquid chromatography

[0062]

[0063] 6. Qualitative Analysis

[0064] GC-MS qualitative analysis: Sample solution A and mixed standard solution A were injected into the GC-MS. Qualitative analysis was performed using NIST14 library search, retention index (RI), and standard comparison. The qualitative results are shown in Table 3.

[0065] Table 3 Qualitative results from GC-MS

[0066]

[0067]

[0068] Qualitative analysis by LC-MS: Diluent B and mixed standard solution B were simultaneously injected into the LC-MS, and qualitative analysis was performed by comparing retention times. The results are shown in Table 4.

[0069] Table 4 Qualitative results by LC-MS

[0070]

[0071] 7. Quantitative analysis

[0072] GC-MS: Selected ion scanning acquisition, monitored ions are shown in Table 5. First, qualitative analysis was performed using full scan mode (SCAN) to optimize instrument parameters, determine the retention times and characteristic ions of each volatile component and internal standard, and then the compounds were grouped and detected using selected ion mode (SIM), as shown in Table 5.

[0073] Table 5 GC-MS monitoring parameters

[0074]

[0075]

[0076] Note: * indicates the amount of ions in Table 5.

[0077] LC-MS: Instrument parameters are shown in Table 6.

[0078] Table 6 LC-MS Instrument Parameters

[0079]

[0080] Note: * indicates the amount of ions in Table 6.

[0081] 8. Draw the standard curve

[0082] GC-MS method: Pipette 0.05 mL, 0.10 mL, 0.2 mL, 0.5 mL, 1.0 mL, and 2.0 mL of mixed standard solution A (3.2) into 10 mL volumetric flasks, dilute to the mark with dichloromethane, and mix thoroughly to obtain standard solutions; accurately transfer 1 mL of the above standard solutions into sample bottles, add 20 μL of internal standard solution, mix thoroughly, and provide for GC-MS analysis; construct a standard curve with the peak area ratio of each chemical component to the internal standard as the ordinate and the corresponding concentration ratio as the abscissa. The results are shown in Table 7.

[0083] Table 7. Standard curve parameters for 27 volatile chemical components

[0084]

[0085] LC-MS method: Take a 10 mL volumetric flask, pipette 0.05 mL of mixed standard solution B (3.2), and dilute to the mark with 50% methanol aqueous solution to obtain intermediate solution B0; pipette 0.01 mL, 0.05 mL, 0.2 mL, 0.5 mL, 1.0 mL, and 2.0 mL of intermediate solution B0 into 10 mL volumetric flasks respectively, dissolve them with 50% methanol aqueous solution, and dilute to the mark. After mixing well, filter through a 0.22 μm filter membrane for LC-MS analysis; construct a standard curve with the peak area of ​​each chemical component as the ordinate and the concentration as the abscissa. The results are shown in Table 8.

[0086] Table 8. Standard curve parameters for four non-volatile chemical components

[0087]

[0088] 9. Measurement

[0089] GC-MS method: The sample solution and standard solution are measured under the same instrument conditions, and the peak areas A of each chemical component are calculated. i Peak area A corresponding to the internal standard f Substitute the values ​​into the standard curve to calculate the content X of the corresponding chemical component in the sample. i .

[0090] LC-MS method: The sample solution and standard solution are measured under the same instrument conditions, and the peak area A of the chemical components is obtained. i Substitute the values ​​into the standard curve to calculate the content Y of the corresponding chemical component in the sample. i .

[0091] 10. Calculate

[0092] The contents of 27 volatile chemical components in the sample were calculated according to formula (1).

[0093] In the formula:

[0094] Xi ---The content of the i-th volatile chemical component in the sample, in micrograms per liter (μg / L);

[0095] X f ---The content of the corresponding internal standard in the sample, in milligrams per liter (μg / L);

[0096] A i ---The chromatographic peak area of ​​the i-th volatile chemical component in the sample;

[0097] A f ---The chromatographic peak area of ​​the corresponding internal standard in the sample;

[0098] b --- the intercept value of the standard curve;

[0099] k --- the slope of the standard curve;

[0100] V1---Sample volume, in milliliters (mL);

[0101] V2 --- Concentrated volume of sample solution A, in milliliters (mL).

[0102] The calculation results are expressed as the arithmetic mean of two independent measurements obtained under repeatability conditions, and are retained to two decimal places.

[0103] The contents of the four non-volatile chemical components in the sample are calculated according to formula (2).

[0104] In the formula:

[0105] Y i ---The content of the i-th non-volatile chemical component in the sample, in micrograms per liter (μg / L);

[0106] A i ---The chromatographic peak area of ​​the i-th non-volatile chemical component in the sample;

[0107] b --- the intercept value of the standard curve;

[0108] k --- the slope of the standard curve.

[0109] V1---Sample volume, in milliliters (mL);

[0110] V3 --- The final volume of solution B in channel B, in milliliters (mL);

[0111] V4 --- The volume of solution sampled from channel B, in milliliters (mL);

[0112] V5 --- The final volume of sample solution B, in milliliters (mL).

[0113] The calculation results are expressed as the arithmetic mean of two independent measurements obtained under repeatability conditions, and are retained to two decimal places.

[0114] 11. Repeatability Experiment

[0115] Six samples (B1) were taken and labeled as C-1 to C-6. The sample solutions were processed according to the above method, the chemical components were determined, and the repeatability of the 31 chemical components was calculated. The RSD values ​​were all less than 10%, indicating that the method has good repeatability. The specific calculation results are shown in Table 9.

[0116] Table 9. Results of repeatability experiments for 31 chemical components

[0117]

[0118]

[0119] 12. Limit of Detection (LOD) and Limit of Quantification (LOQ)

[0120] The limit of detection (LOD) is the sample concentration at which the ratio of signal intensity (S) to baseline noise (N) is 3:1;

[0121] The limit of quantitation (LOQ) is the sample concentration at which the signal intensity (S) / baseline noise (N) ratio is 10:1.

[0122] In this embodiment, the limits of detection (LOD) for 31 chemical components ranged from 0.41 μg / L to 570.32 μg / L, and the limits of quantitation (LOQ) ranged from 1.37 μg / L to 1901.97 μg / L. The specific calculation results are shown in Table 10.

[0123] Table 10 Calculation results of the limits of detection (LOD) and limits of quantitation (LOQ) for 31 chemical components

[0124]

[0125] 13. Recovery rate experiment

[0126] Accurately pipette 50 mL of the sample to be tested (B1) into 100 mL centrifuge tubes, making a total of 6 portions, labeled as R1-1 and R1-2, R2-1 and R2-2, and R3-1 and R3-2 respectively;

[0127] Take mixed standard solution A from 3.2, add 0.01 mL to R1-1 and R1-2 respectively, add 0.025 mL to R2-1 and R2-2 respectively, and add 0.05 mL to R3-1 and R3-2 respectively;

[0128] Take mixed standard solution B from 3.2, add 0.1 mL to R1-1 and R1-2 respectively, add 0.25 mL to R2-1 and R2-2 respectively, and add 0.5 mL to R3-1 and R3-2 respectively;

[0129] Accurately transfer 10 mL of sample, add 20 μL of internal standard solution, mix thoroughly, and pour into the dropping funnel of the dual-channel vacuum distillation system. Other operations are the same as in step 4.

[0130] Substituting the peak areas of each chemical component and the internal standard into the standard curve, the content and recovery rate of each chemical component were calculated. The recovery rates of all 31 chemical components were greater than 85%, and the RSD values ​​were all less than 6%, indicating that the method has good accuracy. The specific calculation results are shown in Table 11.

[0131] Table 11 Detection results and recovery rates of 31 chemical components in health wine

[0132]

[0133]

[0134] 14. Sample Determination

[0135] GC-MS and LC-MS were used to quantitatively analyze 30 batches of samples to obtain the content of 31 chemical components in health wine samples of different quality grades. The results are shown in Table 12.

[0136] Table 12 Contents (μg / L) of 31 chemical components in health wine samples of different quality grades

[0137]

[0138]

[0139] Note: In Table 12, "ND" means Not Detected.

[0140] As shown in Table 12, the contents of ethyl isovalerate, ethyl hexanoate, and phenylacetaldehyde are higher in Grade 1 than in Grade 1 and Grade 2. The contents of chemical components such as vanillin, furfural, 4-methylguaiacol, 4-ethylguaiacol, eugenol, linalool, 2-ethyl-6-methylpyrazine, 2,3,5-trimethylpyrazine, rutin, and quercetin-3-rutinoside-7-glucoside are higher in Grade 2 than in Grade 1 and Grade 2.

[0141] 15. Principal Component Analysis (PCA)

[0142] Multivariate statistical analysis techniques can analyze the statistical regularities of a target sample when multiple objects and indicators are correlated. Principal component analysis (PCA) is a common statistical analysis method. PCA is an unsupervised statistical analysis method that uses dimensionality reduction techniques to transform multiple variables into a few principal components. It is an effective method for solving problems involving multiple variables and their correlations. Principal component analysis based on the quantitative results of chemical composition of 30 batches of samples of different grades is shown below. Figure 1 As shown. By Figure 1 It can be seen that the differences between the three groups of samples of different grades—Grade A (Superior Grade), Grade B (Superior Grade), and Grade C (Special Grade)—are quite obvious.

[0143] 16. Partial Least Squares Discriminant Analysis (PLS-DA)

[0144] PLS-DA is a novel multivariate statistical method, a regression modeling approach using multiple dependent variables on multiple independent variables. This method pre-groups the desired observed variables and then performs statistical analysis on the data based on their order properties, thereby accurately identifying the key variables influencing the grouping.

[0145] PLS-DA was used to perform discriminant analysis on the differences among three groups of health wine samples of different quality grades. The PLS-DA plots of the Grade 1 (Group A) and Superior (Group B) samples are shown in the figure below. Figure 2 As shown, Q 2 =0.956; PLS-DA graphs of the two groups of samples, namely, superior grade (Group B) and special grade (Group C), are shown below. Figure 3 As shown, Q 2 =0.966. To prevent overfitting, after cross-validation, all points were less than the original R-squared value. 2 This indicates that the model fits well and is feasible. The score plot shows that supervised analysis can effectively separate samples of different quality levels, thus deriving the projected variable weights (VIPs). VIP values ​​are typically used to reflect the importance of variables in the PLS-DA model. The higher the bar height in the plot, the greater its contribution to the model. Figure 4 , Figure 5 The more significant the difference, the more pronounced it becomes.

[0146] Based on the VIP diagram drawn from the PLS-DA model, chemical components with larger VIP values ​​are selected. The chemical components that contributed significantly to the differentiation between Grade A (Group A) and Grade B (Group B) samples (VIP>1) were eugenol (VIP=1.50977), 2,3,5-trimethylpyrazine (VIP=1.49590), ethyl hexanoate (VIP=1.48389), 4-ethylguaiacol (VIP=1.46305), phenylacetaldehyde (VIP=1.46305), rutin (VIP=1.45760), quercetin-3-rutinoside-7-glucoside (VIP=1.44261), ethyl isovalerate (VIP=1.41106), furfural (VIP=1.36029), 4-methylguaiacol (VIP=1.33736), 2-ethyl-6-methylpyrazine (VIP=1.29459), and vanillin (VIP=1.16313).

[0147] Based on the VIP diagram drawn from the PLS-DA model, chemical components with larger VIP values ​​are selected. The chemical components that contributed significantly to the differentiation between the superior grade (Group B) and the special grade (Group C) samples (VIP>1) were 4-ethylguaiacol (VIP=1.53602), 4-methylguaiacol (VIP=1.53133), vanillin (VIP=1.50816), 2-ethyl-6-methylpyrazine (VIP=1.50069), phenylacetaldehyde (VIP=1.48378), furfural (VIP=1.48087), ethyl hexanoate (VIP=1.44482), 2,3,5-trimethylpyrazine (VIP=1.42317), rutin (VIP=1.41030), ethyl isovalerate (VIP=1.36605), quercetin-3-rutinoside-7-glucoside (VIP=1.25861), and linalool (VIP=1.19231).

[0148] Comprehensive analysis suggests that 4-methylguaiacol, 4-ethylguaiacol, vanillin, 2-ethyl-6-methylpyrazine, quercetin-3-rutin-7-glucoside, eugenol, linalool, 2,3,5-trimethylpyrazine, rutin, ethyl isovalerate, ethyl hexanoate, phenylacetaldehyde, and furfural can serve as markers of differentiation between different quality grades of health wines.

[0149] 17. Cluster Heatmap Analysis (HCA)

[0150] Based on 13 significantly different chemical components, cluster heatmap analysis (HCA) was performed on Grade 1, Premium, and Special Grade health wine samples using the Heatmapper platform. The results are as follows: Figure 6 As shown. From Figure 6It can be seen that the content distribution of phenylacetaldehyde, ethyl isovalerate, and ethyl hexanoate is Grade 1 > Superior Grade > Premium Grade. The content distribution of 4-methylguaiacol, 4-ethylguaiacol, vanillin, 2-ethyl-6-methylpyrazine, quercetin-3-rutin-7-glucoside, eugenol, linalool, 2,3,5-trimethylpyrazine, rutin, and furfural is > Superior Grade > Grade 1. Based on these 13 different chemical components, the premium, superior, and grade 1 products can be well distinguished.

[0151] This application targets a health wine composed of herbal extracts and three types of baijiu (Chinese liquor). A solvent-assisted evaporation device was used to extract and separate the volatile and non-volatile chemical components of the health wine sample. GC-MS and LC-MS were used to identify and quantify 27 volatile components and 4 non-volatile components, respectively. Based on the PCA results of the quantitative results, the Grade 1, Superior, and Special Grade health wines were clustered into separate groups, indicating differences in chemical composition among different grades of health wines, which can be used to distinguish and evaluate different quality grades. Furthermore, PLS-DA was used to screen for differential markers between different quality grades of health wines, identifying 4-methylguaiacol, 4-ethylguaiacol, vanillin, 2-ethyl-6-methylpyrazine, quercetin-3-rutin-7-glucoside, eugenol, linalool, 2,3,5-trimethylpyrazine, rutin, ethyl isovalerate, ethyl hexanoate, phenylacetaldehyde, and furfural. Finally, cluster heatmap analysis was used to distinguish and verify the different quality grades of health wines.

[0152] Although the present invention has been described in detail above with general descriptions, specific embodiments, and experiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A quality evaluation method for a health care wine with reduced liver damage function, characterized by, Includes the following steps: (1) After pretreatment of the samples, the chemical composition content of health wines of different quality grades was determined by GC-MS and LC-MS techniques. The pretreatment method is as follows: The health wine sample to be tested is extracted and separated using a solvent-assisted evaporation device to obtain volatile and non-volatile chemical components. These components are then extracted separately with organic solvents to obtain the test sample solution. The specific steps include the following: 1) Accurately pipette 10~20 mL of the healthcare wine sample, add 20~40 μL of the internal standard solution, mix thoroughly, and pour into the double-channel vacuum distillation system dropping funnel. Set the system cycle temperature to 50~55 ℃, add liquid nitrogen in the cold trap, turn on the molecular turbine pump, and wait until the vacuum degree of the entire system is less than 1×10 -4 Pa. Slowly open the tap of the dropping funnel, control the flow rate at 0.8~1.2 mL / min, and collect the distillate in the channel A bottle. 2) Transfer all the distillate obtained in step 1) into a 250 mL separatory funnel, dilute with ultrapure water to an ethanol volume fraction of 5-15%, add sodium chloride until the solution is saturated, and then extract with dichloromethane 3 times, each time with an extraction volume of 5-15 mL, and combine the organic phases; 3) Add anhydrous sodium sulfate to the organic phase obtained in step 2), dehydrate at 4 °C for 12-24 h, and concentrate to 1-2 mL by nitrogen blowing at 40 °C to obtain sample solution A, which is then analyzed by GC-MS. 4) The standard solutions of volatile chemical components were analyzed under the same GC-MS conditions. A standard curve was plotted using the peak area internal standard method, and the content of volatile chemical components in health wines of different quality grades was calculated. 5) Add 10-20 mL of 50% ethanol solution to bottle B in step 1), dissolve it completely, take 0.1 mL and put it into a sample bottle, dilute it with 50% methanol aqueous solution to 10 mL to obtain sample solution B, and analyze it by LC-MS. 6) Analyze the standard solutions of non-volatile chemical components under the same LC-MS conditions, plot the standard curve using the external standard method of peak area, and calculate the content of non-volatile chemical components in health wines of different quality grades; (2) Principal component analysis was used to classify and evaluate health wines of different quality grades; then partial least squares-discriminant analysis was used to screen out the differential markers among health wines of different quality grades. (3) Cluster heatmap analysis was used to differentiate health wines of different quality grades; The main differentiating compounds among the different quality grades of health wines are ethyl isovalerate, eugenol, 4-methylguaiacol, 4-ethylguaiacol, phenylacetaldehyde, ethyl hexanoate, rutin, furfural, quercetin-3-rutinoside-7-glucoside, vanillin, 2-ethyl-6-methylpyrazine, linalool, and 2,3,5-trimethylpyrazine. The GC-MS conditions included: a stationary phase of polyethylene glycol nitrobenzene, an injection port temperature of 250 °C, a split ratio of 10:1, constant flow mode, a flow rate of 1.0 mL / min, and an injection volume of 1 μL; high-purity helium as the carrier gas, with a purity ≥ 99.999%; an initial column temperature of 50 °C for 5 min, followed by a ramp-up to 230 °C at 3.0 °C / min and a hold for 5 min; mass spectrometry ionization mode: electron impact source with an energy of 70 eV; quadrupole temperature: 150 °C; ion source temperature: 230 °C; solvent delay: 5 min; and selected ion monitoring (SIM) acquisition. The LC-MS conditions included: ion source mode for LC-MS was ESI-, ESI+; nebulizer temperature: 450 ℃; ion source temperature: 150 ℃; nebulizer gas flow rate: 800 L / h; cone gas flow rate: 100 L / h; collision gas flow rate: 0.17 mL / min; Xselect HSS T3 column; mobile phase: water and acetonitrile; flow rate: 0.3 mL / min; column temperature: 40 ℃; injection volume: 5 μL; gradient elution conditions were as follows: ； The health wine is a health wine with liver-damaging function, prepared by adding extracts of buckwheat, wolfberry, mulberry leaf, kudzu root and acerola cherry fruit.

2. The use of the method of claim 1 in the identification and quality evaluation of health-care wine of different quality grades, characterized in that, The health wine is a health wine with liver-damaging function, prepared by adding extracts of buckwheat, wolfberry, mulberry leaf, kudzu root and acerola cherry fruit.

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