Method for identifying new and old soybeans based on volatile odor compounds

Abstract

The invention discloses a method for identifying new and old soybeans based on volatile odor compounds, and belongs to the field of soybean quality identification, and the method specifically comprises the following steps: crushing a soybean sample to obtain soybean particles; screening the obtained soybean particles to obtain refined soybean particles; treating the volatile odor compound of the refined soybean particles to obtain a substance to be detected; s3, processing the to-be-detected substance obtained in the step S3 by using a gas chromatograph-mass spectrometer to obtain original data, and performing qualitative and quantitative analysis on the original data to obtain volatile odor compound data; and processing the volatile odor compound data through software to obtain the odor difference of the new and old soybeans, thereby identifying the new and old soybeans. According to the method, a sample pretreatment method is simple to operate and widely applicable to the fields of soybean quality identification and the like, the new and old soybeans can be identified by analyzing odor compounds in different years, and the time for identifying and detecting the new and old soybeans is greatly shortened.

Description

Technical Field

[0001] The invention relates to the field of soybean quality identification, and in particular to a method for identifying the freshness of soybeans based on volatile odor compounds. Background Art

[0002] Soybean is an annual legume plant. It is divided into yellow beans, green beans, black beans, other soybeans and mixed soybeans according to the color of the seed coat. It has become the fourth largest reserve grain in my country after wheat, corn and rice, and is also an important economic crop for both grain and oil. Soybean is rich in protein, unsaturated fatty acids, minerals and multiple vitamins, which play an indispensable role in people's daily life and various production.

[0003] In recent years, the significant improvement in people's living standards and the rapid development of the food processing industry and animal husbandry have led to an increasing demand for soybeans. my country has become a major soybean importer from an initial soybean exporter. Compared with storage cereals, soybeans have higher and stricter storage and technical requirements. They must be prevented from heating, mold, oil immersion, rancidity, and deterioration, and their edible quality and commodity value must be maintained. Therefore, soybeans are more difficult to preserve during storage than other grains.

[0004] Flavor quality is the most important sensory attribute of soybeans and an important factor influencing consumer choice. The length of storage time (years) plays an important role in the quality of soybeans, especially the odor characteristics. At present, my country's self-produced and imported soybeans are widely distributed and have a wide variety of varieties. Storage in different years will affect the types and contents of volatile odor compounds rich in soybeans, resulting in soybeans from different harvest years having different sensory qualities and taste levels after processing, and will also cause the quality of subsequent processed products to be poor and the taste to be wrong. In severe cases, the beany smell of soybeans will be released, affecting the flavor level of the product.

[0005] Therefore, how to identify soybeans of different years and distinguish between fresh and old is an important issue. However, the existing soybean quality identification methods have problems such as complex pre-treatment process, long detection time, and significant subjective influence of human beings in the sensory evaluation process.

[0006] Therefore, how to design and develop a soybean identification method to quickly and accurately distinguish the fresh and old soybeans remains an urgent problem to be solved. Summary of the invention

[0007] In view of the above problems, the present invention provides a method for identifying the freshness of soybeans based on volatile odor compounds. The method has the advantages of simple sample pretreatment method and is widely applicable to the fields of soybean quality identification. The gas phase mass spectrometry-mass spectrometry technology is applied to soybean quality identification, which greatly shortens the time for soybean quality detection. The odor compound data of different years are analyzed and identified to avoid the advantages of human subjective influence. The specific technical scheme includes the following steps:

[0008] As a preferred technical solution,

[0009] The soybeans harvested that year had 3-pentanol- and octanal-substances as characteristic volatile odor compounds, and were free of nonanoic acid and (E,E)-3,5-octadien-2-one;

[0010] Tert-butyl alcohol, octanol and (E)-2-octenal were the characteristic volatile odor compounds of soybeans stored for one year;

[0011] The soybeans stored for two years have styraxol as the characteristic volatile odor compound;

[0012] Soybeans harvested after storage for three years or more have thioacetic acid, 3-octen-2-one, and heptanoic acid as characteristic volatile odor compounds;

[0013] Among them, nonanoic acid (sour taste) and (E,E)-3,5-octadien-2-one are used as auxiliary indicators for year identification, and their concentrations are positively correlated with storage time.

[0014] As a preferred technical solution, the concentration threshold of each characteristic volatile odor compound for identifying the freshness of soybeans meets the following requirements:

[0015] The threshold concentration of 3-pentanol and octanal substances is ≥4.125 mg / kg;

[0016] The threshold concentrations of soybean tert-butyl alcohol and (E)-2-octenal substances are ≥8.2 mg / kg and ≥0.003 mg / kg respectively;

[0017] The threshold concentration of soybean styrax alcohol substance is ≥0.479 mg / kg;

[0018] The threshold concentrations of soybean thioacetic acid and heptanoic acid substances are ≥0.001 mg / kg and ≥0.64 mg / kg, respectively.

[0019] As a preferred technical solution, the following steps are included:

[0020] S1. crushing the soybean sample to obtain soybean particles;

[0021] S2, screening the obtained soybean particles to obtain refined soybean particles;

[0022] S3, processing the volatile odor compounds of the refined soybean particles to obtain substances to be tested;

[0023] S4, using a gas chromatography-mass spectrometer to process the substance to be tested obtained in step S3 to obtain raw data, and performing qualitative and quantitative analysis on the raw data to obtain volatile odor compound data;

[0024] S5. Continue to process the volatile odor compound data to obtain the difference in odor between new and old soybeans, thereby identifying new and old soybeans.

[0025] As a preferred technical solution, the particle diameter of the refined soybean particles in step S2 is not greater than 100 mesh.

[0026] As a preferred technical solution, the treatment method used for volatile odor compounds in step S3 is solid phase microextraction, and the specific operation is: weigh 2.00g of the refined soybean particles and place them in a clean and odorless 20mL headspace bottle, then add 1μL of internal standard to the headspace bottle, tighten the stopper of the headspace bottle and place the headspace bottle in a water bath, and set the water bath temperature to 60°C and the water bath time to 20min. After the system is stabilized and balanced, insert the extraction fiber head with adsorption coating and push out the fiber coating, and adsorb for 40min to obtain the substance to be tested;

[0027] As a preferred technical solution, the internal standard is 2-methyl-3-heptanone with a concentration of 0.1632 mg / mL that has been diluted 5000 times with methanol;

[0028] The fiber coating had been aged at 250° C. for 0.5 h before use.

[0029] As a preferred technical solution, the qualitative analysis method in step S4 is to conduct qualitative analysis on the original data by combining mass spectrometry qualitative analysis MS with retention index RI comparison;

[0030] The positive and negative matching degree of the mass spectrometry qualitative MS is ≥750

[0031] The difference between the theoretical RI and the actual RI of the retention index RI does not exceed 100.

[0032] As a preferred technical solution, the quantitative analysis method in step S4 is to use an internal standard semi-quantitative method to calculate the concentration of the unknown substance by the peak area of ​​the unknown substance, the peak area of ​​the internal standard and the internal standard concentration. The calculation formula is as follows:

[0033]

[0034] Where, f represents the correction factor; A1, A xRepresent the peak area of ​​the internal standard and the peak area of ​​the unknown substance, C1, C x represent the mass concentration of internal standard and unknown substance, respectively.

[0035] As a preferred technical solution, the step S5 of continuing to process the volatile odor compound data is specifically as follows: after the volatile odor compound data is imported into the SIMCA 14.1 software, the model is verified through data preprocessing, OPLS-DA model construction and fitting, cross-validation and permutation test, and the score graph, loading graph and VIP value analysis are used to screen out the volatile compounds that contribute significantly to the classification, and finally obtain the differential characteristic compounds of samples of different categories.

[0036] The beneficial effects of the present invention are

[0037] (1) The sample pretreatment method of the present invention is relatively simple, low-cost, retains the most original flavor attributes of soybeans, and can be widely used in the field of soybean quality identification;

[0038] (2) The identification method of the present invention can select the volatile odor compounds of any soybean as a control, and can distinguish soybean samples of any year, and can also distinguish the same soybeans of a relatively recent year.

[0039] (3) The present invention applies gas chromatography-mass spectrometry-mass spectrometry technology to soybean quality identification, which significantly shortens the time for soybean quality detection and achieves rapid qualitative analysis of volatile odor compounds in soybeans, thereby improving the efficiency and accuracy of soybean quality identification, and providing the market with a fast and efficient method for detecting soybean quality changes.

[0040] (4) The OPLS-DA analysis used in the present invention is a regression modeling method for multiple dependent variables on independent variables. Its greatest feature is that it can remove data variations in the independent variable X that are irrelevant to the categorical variable Y, so that the categorical information is mainly concentrated in one principal component, thereby making the model simpler and easier to interpret. The discriminant effect and the identifiable effect of the principal component score graph are more obvious, and the influence of human subjectivity is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Comparison of the quantity of various volatile odor compounds in soybeans harvested in different years

[0042] Figure 2 Comparison of the concentrations of various volatile odor compounds in soybeans harvested in different years

[0043] Figure 3 The contribution rate of each principal component in the OPLS-DA analysis of various volatile odor compounds of soybeans harvested in different years

[0044] Figure 4VIP diagram of various volatile odor compounds of soybeans harvested in different years DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution of the present invention will be clearly and completely described below in conjunction with embodiments.

[0046] Implementation Case 1

[0047] This example is a method for identifying fresh and old soybeans based on volatile odor compounds. The soybean samples are soybeans picked in 2014. The specific steps are as follows:

[0048] S1. Use an ultrafine grinder to grind the 2014 soybean sample to obtain soybean particles.

[0049] S2. Screening the soybean particles through a 100-mesh sieve to obtain refined soybean particles with a particle diameter not greater than 100 mesh.

[0050] S3, accurately weigh 2.00g of the refined soybean particles and place them in a clean and odorless 20mL headspace bottle, then add 1μL of 0.1632mg / mL 2-methyl-3-heptanone diluted 5000 times with methanol to the headspace bottle, tighten the stopper of the headspace bottle and place the headspace bottle in a water bath, set the temperature to 60°C, and place it in a water bath for 20min until the system is stable and balanced, then insert the fiber extraction head with the adsorption coating, push out the fiber coating, and adsorb for 40min to obtain the substance to be tested;

[0051] The reason for using methanol to dilute the internal standard is that the relative molecular mass of methanol is 32, the mass spectrometry scanning range is 50-350, and methanol does not produce a peak and will not affect the chromatographic peak in the sample.

[0052] S4. Set up the program of the gas chromatography-mass spectrometer, specifically: The gas chromatography program is set as follows: the starting chromatographic column is selected as J&W DB-WAX quartz capillary column, with a specification of 60m×0.25mm×0.25μm; the starting chromatographic column temperature is set to 40℃, and after keeping warm for 5 minutes, it is set to increase the temperature to 190℃ at 3℃ / min, and then increase the temperature from 190℃ to 230℃ at 8℃ / min, and keep warm for 15 minutes; the injection port temperature is set to 230℃, and the detector temperature is set to 280℃; the carrier gas is set to nitrogen, and the flow rate is set to 1.5mL / min; the split ratio is set to non-split. The mass spectrometry program is set as follows: set electron bombardment, the electron energy is set to 70eV, the transmission line temperature is set to 280℃, the ion source temperature is set to 230℃, the quadrupole temperature is set to 150℃, the scanning mode is set to full scan, the mass scanning range is set to 50-350m / z, and the solvent delay is set to 5min. After the adsorption is completed, the fiber extraction head in the water bath is taken out and inserted into the 250°C gas phase inlet on the gas chromatograph-mass spectrometer with a set program. GC-MS analysis is performed 5 minutes after the analysis to obtain the original data of the chromatography and mass spectrum of the volatile odor compounds in the sample; the obtained raw data are qualitatively analyzed by combining mass spectrometry qualitative MS with retention index RI comparison, and the volatile odor compound data are obtained by internal standard semi-quantitative method.

[0053] S5. After importing the volatile odor compound data into SIMCA 14.1 software, the model was verified through data preprocessing, OPLS-DA model construction and fitting, cross-validation and permutation test. The volatile compounds that contributed significantly to the classification were screened out using score plots, loading plots and VIP value analysis. Finally, the differential characteristic compounds of samples of different categories were obtained, thereby identifying new and old soybeans.

[0054] Example 2

[0055] This embodiment 2 also identifies soybean samples, and the soybean samples are 2021 soybean samples. The specific identification method is the same as that of embodiment 1.

[0056] Example 3

[0057] This embodiment 3 also identifies soybean samples, and the soybean samples are 2022 soybean samples. The specific identification method is the same as that of embodiment 1.

[0058] Example 4

[0059] This embodiment 4 also identifies soybean samples, and the soybean samples are 2023 soybean samples. The specific identification method is the same as that of embodiment 1.

[0060] Comparative Example

[0061] This comparative example also identifies a soybean sample, which is a 2024 soybean sample. The specific identification method is the same as that of Example 1.

[0062] The qualitative and quantitative results of the volatile odor compounds of soybeans in the examples and comparative examples are shown in Table 1. The following results can be analyzed from Table 1:

[0063] By comparing the soybean samples harvested in the embodiment and the comparative example, it can be seen that 42 volatile odor compounds were identified in the soybeans harvested in 2024 of the comparative example, while 58, 51, 50 and 52 volatile odor compounds were identified in the soybeans harvested in Examples 2014, 2021, 2022 and 2023, respectively; there are 23 compounds in common in the soybean samples of the embodiment and the comparative example; substances such as 3-pentanol (herbal flavor), propylene glycol (alcohol flavor), 3-methylthiopropanol (sulfur flavor) and octanal (citrus flavor) are only present in the soybeans harvested in 2022 of the comparative example; 2-acetylpyrrole (musty flavor), 4-isopropylcyclohexanol (metallic flavor), 2-methyl -5-Propionylfuran (hazelnut flavor) and other 15 flavor substances only exist in the 2014 soybeans harvested in Example 1; thioacetic acid (barbecue flavor), heptanoic acid (sour sweat flavor), octyl formate (fruit flavor) and other substances only exist in the 2021 soybeans harvested in Example 2; styraxol (fresh flavor) substances only exist in the 2022 soybeans harvested in Example 3; tert-butyl alcohol (camphor flavor), octanol (mushroom flavor) and (E)-2-octenal (cucumber flavor) and other substances only exist in the 2023 soybeans harvested in Example 4; Overall, with the extension of storage time, the amount of flavor substances in soybeans has changed significantly, and the flavor profile composition has become more complicated.

[0064] Table 1 Volatile odor compounds of examples and comparative examples

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] Depend on Figure 1 , Figure 2Comparison of soybean samples harvested from the examples and comparative examples shows that the number and concentration of acids, ketones, esters and heterocyclic substances in the four examples increased, while alcohol and aldehyde compounds showed a downward trend. Acids mainly provide a sour taste; ketones mainly provide herbal, metallic and camphor tastes; esters have a strong musty taste in addition to a fruity taste; heterocyclic compounds mainly provide musty, nutty tastes, etc. Aldehydes mainly provide a fresh, green leafy taste. It can be seen that the flavor substances of soybeans have a significant change trend during storage, with the grassy taste decreasing and the irritating taste increasing.

[0074] By comparing the soybean samples harvested in the embodiment and the comparative example, it can be seen that the flavor substance composition of different soybean samples has obvious differences. Among them, in the fresh soybeans harvested in 2024 in the comparative example, octanal (citrus flavor) and 3-pentanol (herbal flavor) have higher concentrations; the concentrations of various volatile odor compounds in the soybean samples harvested in 2014 in Example 1 are significantly different from those in the comparative example and other embodiments; in the 2021 soybean samples harvested in Example 2, the accumulation of acidic substances is more significant, mostly straight-chain saturated acids, such as hexanoic acid (sweat smell), heptanoic acid (sweat smell), and octanoic acid (sweat smell), with obvious odor orientation; in the 2022 soybean samples harvested in Example 3, the concentration of alcohol substances further decreased, and the acidic substances showed an upward trend; in the 2023 soybean samples harvested in Example 4, the concentrations of alcohol, aldehyde, and ester substances increased, such as 4-methyl-1-pentanol (nutty flavor), 1-octen-3-ol (mushroom flavor), benzaldehyde (almond flavor), and 2-ethylbutyric acid allyl ester (nutty flavor).

[0075] Compared with the control example, the concentrations of nonanoic acid (sour taste), 2-acetylpyrrole (musty taste), 2-pentylfuran (earthy taste) and the like in the soybean samples harvested in Example 1 were significantly increased; while the concentrations of (Z)-2-hexen-1-ol (green leaf taste), 3-pentanol (herbal taste), and propylvalerolactone (herbal taste) and the like were significantly reduced.

[0076] In Example 2, compared with the control example, the concentrations of 2-butyl-2-octenal (metallic taste), acetic acid (vinegar taste), caprylic acid (sweaty taste) and the like in the soybean samples harvested from Example 2 were significantly increased; while the concentrations of 3-pentanol (grassy taste), caprylic aldehyde (citrus taste), methyl formate (fruity taste), 2-ethylfuran (beany taste) and the like were significantly low.

[0077] Example 3 Compared with the control example, the concentrations of 4-methyl-1-pentanol (nut flavor), benzaldehyde (almond flavor), heptyl formate (musty flavor), 2-methylpyrazine (cocoa flavor) and the like in the soybean samples harvested in Example 3 increased significantly; while the concentrations of 3-pentanol (grassy flavor), octanal (citrus flavor), methyl formate (fruity flavor), 2-ethylfuran (beany flavor) and the like decreased significantly.

[0078] Example 4 Compared with the control example, the concentrations of 2-methyl-2-butanol (greasy smell), benzaldehyde (almond smell), 2-methylpyrazine (cocoa smell) and the like in the soybean samples harvested from Example 4 increased significantly; while the contents of 3-pentanol (grassy smell), octanal (citrus smell), methyl formate (fruity smell) and the like decreased significantly.

[0079] The following characteristic volatile odor compounds can be obtained from the above examples and comparative examples:

[0080] Compounds such as 3-pentanol (herbal flavor) and octanal (citrus flavor) can serve as signature volatile odor compounds for identifying newly harvested soybeans in 2024;

[0081] Substances such as tert-butyl alcohol (camphor smell) and (E)-2-octenal (cucumber smell) can be used as signature volatile odor compounds to identify soybeans harvested in 2023;

[0082] Styraxol (fresh smell) substances can be used as the signature volatile odor compounds to identify soybeans harvested in 2022;

[0083] Substances such as thioacetic acid (barbecue smell) and heptanoic acid (sour sweat smell) can serve as signature volatile odor compounds for identifying soybeans harvested in 2021.

[0084] For soybeans with similar years, compounds such as nonanoic acid (sour taste) and (E,E)-3,5-octadien-2-one (grassy taste) can be used as auxiliary indicators for year identification, and their concentrations are positively correlated with storage time.

[0085] Figure 3 The principal component score diagram of flavor substances that have significant changes during soybean storage was screened out using OPLS-DA analysis. The figure shows that the first two principal components can explain 81.30% of the contribution, of which the first principal component and the second principal component contribute 61.70% and 19.60% of the variance, respectively. Soybean samples harvested at different times can be well separated in the principal component diagram.

[0086] The key variables are screened by the size of the VIP value. The results are as follows Figure 4 As shown in the figure, it is generally believed that substances with VIP values ​​greater than 1.0 have significant changes. Therefore, the concentrations of compounds that provide stimulating tastes such as sour sweat and cocoa, such as (E,E)-3,5-octadien-2-one, nonanoic acid, and propylvalerolactone, increase with the extension of storage period; while the concentrations of compounds that provide fruity, grassy, ​​and bean flavors, such as methyl formate, 3-pentanol, octanal, and 2-ethylfuran, decrease with the extension of storage time. This shows that the volatile odor compounds of any fresh soybeans can be used as a control to distinguish soybean samples of any year.

[0087] In general, the sample pretreatment method of the present invention is relatively simple, low-cost, retains the most original flavor attributes of soybeans, and can be widely used in the fields of soybean quality identification; the identification method of the present invention can select the volatile odor compounds of any soybean as a control, can distinguish any year of old soybean samples, and can also distinguish the same soybeans of relatively recent years. The present invention applies gas phase mass spectrometry-mass spectrometry technology to soybean quality identification, significantly shortens the time for identifying soybean quality detection, and quickly qualitatively identifies volatile odor compounds in soybeans, thereby improving the efficiency and accuracy of identifying soybean quality, and providing the market with a fast and efficient method for detecting soybean quality changes. The OPLS-DA analysis used in the present invention is a regression modeling method of multiple dependent variables to independent variables. Its biggest feature is that it can remove data variations in the independent variable X that are not related to the classification variable Y, so that the classification information is mainly concentrated in one principal component, thereby making the model simple and easy to interpret, and its discrimination effect and the identifiable effect of the principal component score graph are more obvious, and the influence of human subjectivity is also avoided.

[0088] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-restrictive from any point of view. In addition, it should be understood that although this specification is described in accordance with the implementation mode, it does not include only one technical solution. This narrative mode of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in the embodiments can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for identifying the freshness of soybeans based on volatile odor compounds, characterized in that: Year discrimination is achieved by detecting the different combinations of volatile odor compounds in soybeans from different storage years, specifically: The soybeans harvested that year had 3-pentanol- and octanal-substances as characteristic volatile odor compounds, and were free of nonanoic acid and (E,E)-3,5-octadien-2-one; Tert-butyl alcohol, octanol and (E)-2-octenal were the characteristic volatile odor compounds of soybeans stored for one year; The soybeans stored for two years have styraxol as the characteristic volatile odor compound; Soybeans harvested after storage for three years or more have thioacetic acid, 3-octen-2-one, and heptanoic acid as the characteristic volatile odor compounds; Among them, nonanoic acid (sour taste) and (E,E)-3,5-octadien-2-one are used as auxiliary indicators for year identification, and their concentrations are positively correlated with storage time.

2. The method for identifying the freshness of soybeans by using volatile odor compounds according to claim 1, characterized in that: The concentration thresholds of each characteristic volatile odor compound for identifying fresh soybeans meet the following requirements: The threshold concentration of 3-pentanol and octanal substances is ≥4.125 mg / kg; The threshold concentrations of soybean tert-butyl alcohol and (E)-2-octenal substances are ≥8.2 mg / kg and ≥0.003 mg / kg, respectively; The threshold concentration of soybean styrax alcohol substance is ≥0.479 mg / kg; The threshold concentrations of soybean thioacetic acid and heptanoic acid substances are ≥0.001 mg / kg and ≥0.64 mg / kg, respectively.

3. The method for identifying the freshness of soybeans by using volatile odor compounds according to claim 1, characterized in that: The detection of the volatile odor compounds comprises the following steps: S1. crushing the soybean sample to obtain soybean particles; S2, screening the obtained soybean particles to obtain refined soybean particles; S3, processing the volatile odor compounds of the refined soybean particles to obtain substances to be tested; S4, using a gas chromatography-mass spectrometer to process the substance to be tested obtained in step S3 to obtain raw data, and performing qualitative and quantitative analysis on the raw data to obtain volatile odor compound data; S5. Analyze and process the obtained volatile odor compound data to obtain the difference in odor between new and old soybeans, thereby identifying the new and old soybeans.

4. The method for identifying the freshness of soybeans by using volatile odor compounds according to claim 3, characterized in that: The particle diameter of the refined soybean particles in step S2 is not greater than 100 mesh.

5. The method for identifying the freshness of soybeans by using volatile odor compounds according to claim 3, characterized in that: In step S3, the treatment method used for volatile odor compounds is solid phase microextraction, and the specific operation is: weigh 2.00g of the refined soybean particles and place them in a clean and odorless 20mL headspace bottle, then add 1μL of internal standard to the headspace bottle, tighten the stopper of the headspace bottle and place the headspace bottle in a water bath, and set the water bath temperature to 60°C and the water bath time to 20min. After the system is stabilized and balanced, insert the extraction fiber head with adsorption coating and push out the fiber coating, and adsorb for 40min to obtain the substance to be tested.

6. The method for identifying the freshness of soybeans by using volatile odor compounds according to claim 5, characterized in that: The internal standard is 2-methyl-3-heptanone with a concentration of 0.1632 mg / mL diluted 5000 times with methanol; the fiber coating has been aged at 250° C. for 0.5 h before use.

7. The method for identifying the freshness of soybeans by using volatile odor compounds according to claim 3, characterized in that: The qualitative analysis method in step S4 is to conduct qualitative analysis on the raw data by combining mass spectrometry (MS) with retention index (RI) comparison; The positive and negative matching degree of the mass spectrometry qualitative MS is ≥750 The difference between the theoretical RI and the actual RI of the retention index RI does not exceed 100.

8. The method for identifying the freshness of soybeans by using volatile odor compounds according to claim 3, characterized in that: The quantitative analysis method in step S4 is to use an internal standard semi-quantitative method to calculate the concentration of the unknown substance by the peak area of ​​the unknown substance, the peak area of ​​the internal standard and the concentration of the internal standard. The calculation formula is as follows: Where, f represents the correction factor; A1, A x Represent the peak area of ​​the internal standard and the peak area of ​​the unknown substance, C1, C x represent the mass concentration of internal standard and unknown substance, respectively.

9. The method for identifying the freshness of soybeans by using volatile odor compounds according to claim 3, characterized in that: The specific steps of continuing to process the volatile odor compound data in step S5 are as follows: after importing the volatile odor compound data into the SIMCA14.1 software, the model is verified through data preprocessing, OPLS-DA model construction and fitting, cross-validation and permutation test, and the score graph, loading graph and VIP value analysis are used to screen out the volatile compounds that contribute significantly to the classification, and finally obtain the differential characteristic compounds of samples of different categories.

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