Method for identifying Chinese chestnuts of different producing areas and different varieties

Abstract

The invention provides a method for identifying Chinese chestnuts of different producing areas and different varieties, and belongs to the technical field of Chinese chestnut identification. The method for identifying the Chinese chestnuts of different producing areas and varieties comprises the following steps: respectively loading Chinese chestnut samples of different producing areas and varieties into headspace sample bottles, and carrying out GC-IMS detection on the headspace samples to obtain fingerprints of the different Chinese chestnut samples; carrying out data analysis processing on the fingerprints by adopting VOCa1 software to obtain differential volatile substance fingerprints of different Chinese chestnut samples; analyzing the differential volatile substance fingerprint spectrum by adopting SIMCA software to obtain key volatile components; and identifying Chinese chestnut samples of different producing areas and varieties according to the differential volatile substance fingerprints and the key volatile components. The method provided by the invention does not need complex pretreatment operation, is simple, convenient and rapid, has high sensitivity and accuracy, and has important value in comparison of volatile components in Chinese chestnuts of different producing areas and varieties.

Description

Technical Field

[0001] The invention belongs to the technical field of chestnut identification, and particularly relates to a method for identifying chestnuts of different varieties from different origins. Background Art

[0002] Chestnut (Castanea mollissima.cv.Banli) belongs to the Fagaceae family, Castanea genus. It is rich in protein, starch, fat, B vitamins, vitamin C, carotene, etc. Chestnut is delicious and has high nutritional value. Regular consumption has health benefits such as invigorating qi and strengthening spleen, tonifying kidney and strengthening tendons. At present, there are many varieties of chestnuts, with more than 300 varieties in various places. With the prosperity of the chestnut market, many problems such as unclear grades, confusion of origin and variety, and inferior goods have been exposed, which greatly restricted the healthy development of the chestnut industry. Therefore, it is urgent to study and compare chestnuts of different origins and varieties to provide a theoretical basis for the distinction and identification of chestnuts in the market.

[0003] At present, gas chromatography-mass spectrometry (GC-MS) and other equipment are commonly used to detect volatile components in food. This type of equipment has high sensitivity, but is large in size, has high requirements on the environment and operators, and takes a long time to analyze samples, which increases the difficulty of promoting it in various fields of food production and distribution. In addition, the varieties currently studied by GC-MS are relatively single and have limitations. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method that can simply and quickly realize the simultaneous identification of chestnuts from multiple origins and multiple varieties.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] The present invention provides a method for identifying chestnuts of different varieties from different origins, comprising the following steps:

[0007] The chestnut samples of different varieties from different origins were respectively placed in headspace sample bottles, headspace sampling was performed, and gas chromatography-ion mobility spectrometry was performed to obtain the fingerprints of different chestnut samples; the VOCa1 analysis software was used to perform data analysis and processing on the obtained fingerprints to obtain the differential volatile substance fingerprints of different chestnut samples; the SIMCA software was used to perform PCA and OPLS-DA analysis on the obtained differential volatile substance fingerprints to obtain the key volatile components; the chestnut samples of different varieties from different origins were identified according to the differential volatile substance fingerprints and key volatile components.

[0008] Preferably, the amount of chestnut sample used is 1 to 4 g.

[0009] Preferably, the conditions for headspace injection are: incubation at 40-60°C for 5-10 min; injection volume of 0.5-1.5 mL; incubation speed of 400-600 r / min; injection needle temperature of 80-90°C.

[0010] Preferably, the conditions of gas chromatography are: column temperature 50-70°C; running time 40-60 min; carrier gas is nitrogen; carrier gas flow rate is 2 mL / min for 0-2 min, 2-15 mL / min for 2-10 min, 15-100 mL / min for 10-25 min, and 100 mL / min for 25-30 min.

[0011] Preferably, the data analysis and processing of the obtained fingerprint using VOCa1 analysis software includes the following steps: using the Reporter plug-in to compare the spectral differences between different chestnut samples; using the NIST database and IMS database built into the VOCa1 analysis software to perform qualitative analysis on the differential volatile substances; using the GaleryPlot plug-in to draw the differential volatile substance fingerprints of different chestnut samples.

[0012] Preferably, during OPLS-DA analysis, VIP values ​​are used to screen the differential volatile components in different chestnut samples, and volatile components with VIP values ​​greater than 1 are selected as key volatile components.

[0013] Preferably, the different origins include Tangshan Zunhua, Tangshan Qianxi and Chengde Kuancheng; the different varieties include purple amber, aventurine pearl, Tafeng, Shuofeng, Dabanhong, Yanli, Zunyu, Yanshanhong, Short Ci, Yanshan Zaofeng, Zunda Li, Yanfeng, Shisheng Li, Yankui, Short Branch, Laoshu Li and Daqingchuang.

[0014] Preferably, the differential volatile substance fingerprint includes ketones, alcohols, aldehydes, acids, olefins, ethers, esters, furans and pyridines; the ketones include 5-methyl-3-heptanone, 3-heptanone, cyclohexanone, 2-pentanone, acetoin and 1-octen-3-one; the alcohols include 3-methyl-2-butanol, 3-hexanol, 2-methyl-1-propanol, isopentanol and 1-pentanol; the aldehydes include hexanal, 3-methylthiopropanal, butyraldehyde and heptaldehyde; the acids include hexanoic acid, isobutyric acid, valeric acid and 3-methylvaleric acid; the olefins include limonene, styrene and 3-butenenitrile; the ethers include ethylene glycol monobutyl ether; the esters include ethyl butyrate; the furans include 2-acetylfuran; the pyridines include 2-methylpyridine.

[0015] Preferably, the key volatile components include styrene, 1-pentanol, heptanal, 2-methylpyridine, isopentanol, valeric acid, acetoin, ethylene glycol monobutyl ether, butyraldehyde, 5-methyl-3-heptanone, 2-acetylfuran and 1-octen-3-one.

[0016] Preferably, the method further comprises the following step: analyzing the key volatile components using a cluster analysis method to obtain a key volatile component cluster heat map.

[0017] Beneficial effects of the present invention:

[0018] The method for identifying chestnuts of different origins and varieties based on gas chromatograph-ion mobility spectrometry (GC-IMS) provided by the present invention does not require complicated pre-treatment operations, is simple and fast, has high sensitivity and accuracy, and is of great value for comparing volatile components in chestnuts of different origins and varieties. The present invention provides a new method for rapid detection and flavor comparative analysis of chestnuts of different origins and varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The GC-IMS two-dimensional comparison spectra of 24 varieties of chestnuts, of which the upper figure is the gas phase-ion migration spectra of the 24 varieties, and the lower figure is the gas phase ion two-dimensional difference comparison spectra of the 24 varieties with Zunhua purple amber as the standard;

[0020] Figure 2 Stacked bar chart for 24 chestnut flavor substances;

[0021] Figure 3 It is the fingerprint of 24 chestnut flavors;

[0022] Figure 4 This is the PCA analysis result of chestnut volatile substances;

[0023] Figure 5 This is the OPLS-DA analysis result of chestnut volatile substances;

[0024] Figure 6 This is the cross-validation result of OPLS-DA model of chestnut volatile substances;

[0025] Figure 7 It is the variable importance projection (VIP) histogram of the key aroma substances of chestnut;

[0026] Figure 8 This is a heat map of the content of key volatile components. DETAILED DESCRIPTION

[0027] The present invention provides a method for identifying chestnuts of different varieties from different origins, comprising the following steps:

[0028] The chestnut samples of different varieties from different origins were respectively placed in headspace sample bottles, headspace sampling was performed, and gas chromatography-ion mobility spectrometry was performed to obtain the fingerprints of different chestnut samples; the VOCa1 analysis software was used to perform data analysis and processing on the obtained fingerprints to obtain the differential volatile substance fingerprints of different chestnut samples; the SIMCA software was used to perform PCA and OPLS-DA analysis on the obtained differential volatile substance fingerprints to obtain the key volatile components; the chestnut samples of different varieties from different origins were identified according to the differential volatile substance fingerprints and key volatile components.

[0029] The present invention adopts GC-IMS technology to simply and quickly realize the detection of volatile components in chestnuts of different origins and varieties, obtains the fingerprint spectrum of the substance through data processing, extracts characteristic substances to realize the comparison of volatile components in chestnuts of different origins and varieties, and further realizes the identification of chestnuts of different varieties and origins.

[0030] In the present invention, the different origins include Tangshan Zunhua, Tangshan Qianxi and Chengde Kuancheng; the different varieties include purple amber, aventurine pearl, Tafeng, Shuofeng, Dabanhong, Yanli, Zunyu, Yanshanhong, Short Ci, Yanshan Zaofeng, Zunda Li, Yanfeng, Shisheng Li, Yankui, Short Branch, Laoshu Li and Daqingchuang.

[0031] In the present invention, the amount of the chestnut sample is preferably 1 to 4 g, more preferably 2 to 3 g. In the present invention, the conditions for headspace injection are preferably: incubation at 40 to 60°C for 5 to 10 min; injection volume is 0.5 to 1.5 mL; incubation speed is 400 to 600 r / min; injection needle temperature is 80 to 90°C. The incubation temperature is preferably 45 to 50°C, the incubation time is preferably 6 to 8 min, the injection volume is preferably 1 to 1.2 mL, the incubation speed is preferably 450 to 550 r / min, and the injection needle temperature is preferably 85°C.

[0032] In the present invention, the conditions of gas chromatography are preferably: column temperature 50-70°C; running time 40-60min; carrier gas is nitrogen; carrier gas flow rate is 2mL / min from 0-2min, 2-15mL / min from 2-10min, 15-100mL / min from 10-25min, and 100mL / min from 25-30min. In the gas chromatography of the present invention, the chromatographic column is preferably MXT-WAX, the column temperature is preferably 55-65°C, the running time is preferably 45-55min, and the nitrogen content is preferably ≥99.999%. In the present invention, the carrier gas flow rate refers to reaching 2mL / min at 2min, 15mL / min at 10min, 100mL / min at 25min, and maintaining 100mL / min from 25min to 30min.

[0033] The present invention adopts VOCa1 analysis software for qualitative and quantitative analysis of spectra and data, and preferably uses built-in NIST database and IMS database of the application software to perform qualitative analysis on substances; the Reporter plug-in directly compares the spectra differences between samples, and the fingerprint spectrum comparison of the Reporter plug-in can intuitively and quantitatively compare the volatile organic matter differences between different samples; the TBtools-II software is used for cluster analysis and similarity analysis of samples to quickly determine the types of samples; the GalleryPlot plug-in draws the fingerprint spectrum of the differential volatile substances of the chestnut samples of different origins and varieties to be tested.

[0034] The present invention uses VOCal analysis software with built-in NIST database and IMS database of the GC-IMS instrument to perform qualitative and quantitative analysis of flavor substances, and uses two plug-ins, Reporter and Galleryplot, to perform differential analysis of volatile substances between samples, and then uses SIMCA software to perform variable importance in projection (VIP) analysis on volatile flavor substances, and selects differential volatile components with VIP values ​​greater than 1 as key volatile components. In the present invention, the key volatile components preferably include styrene, 1-pentanol, heptanal, 2-methylpyridine, isopentanol, valeric acid, acetoin, ethylene glycol monobutyl ether, butyraldehyde, 5-methyl-3-heptanone, 2-acetylfuran and 1-octen-3-one.

[0035] In the present invention, principal component analysis (PCA) is an unsupervised analysis tool, a dimensionality reduction method based on feature decomposition, and belongs to exploratory analysis. However, due to the complexity of volatile compound data, for samples with unclear inter-group differences, simple "unsupervised" analysis cannot well distinguish the inter-group differences of samples. Therefore, the present invention further uses orthogonal partial least squares discriminant analysis (OPLS-DA) to correct orthogonal transformation, which can filter out noise irrelevant to classification information, improve the analytical ability and effectiveness of the model, and make the discrimination effect and the visualization effect of the principal component score map more obvious.

[0036] In the present invention, the differential volatile substance fingerprint preferably includes ketones, alcohols, aldehydes, acids, olefins, ethers, esters, furans and pyridines; the ketones include 5-methyl-3-heptanone, 3-heptanone, cyclohexanone, 2-pentanone, acetoin and 1-octen-3-one; the alcohols include 3-methyl-2-butanol, 3-hexanol, 2-methyl-1-propanol, isopentanol and 1-pentanol; the aldehydes include hexanal, 3-methylthiopropanal, butyraldehyde and heptaldehyde; the acids include hexanoic acid, isobutyric acid, valeric acid and 3-methylvaleric acid; the olefins include limonene, styrene and 3-butenenitrile; the ethers include ethylene glycol monobutyl ether; the esters include ethyl butyrate; the furans include 2-acetylfuran; the pyridines include 2-methylpyridine.

[0037] In the method of the present invention, preferably, the following step is further included: using a cluster analysis method to analyze the key volatile components to obtain a key volatile component cluster heat map. The present invention has no special restrictions on the specific steps of cluster analysis.

[0038] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0039] In the following embodiments, unless otherwise specified, all of them are conventional methods.

[0040] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.

[0041] The gas chromatography-ion mobility spectrometer GC-IMS in the following embodiments adopts the GC-IMS gas chromatography-ion mobility spectrometer produced by GAS of Germany.

[0042] Example 1

[0043] A method for identifying chestnuts of different origins and varieties, comprising the following steps:

[0044] (1) 24 chestnut samples from three production areas (Zunhua City, Tangshan City, Hebei Province, Qianxi City, Tangshan City, and Kuancheng Manchu Autonomous County, Chengde City) were numbered, and the chestnut sample information is shown in Table 1. 2.5 g of each chestnut sample was weighed and placed in a 20 mL headspace bottle, and headspace injection was performed for GC-IMS detection to collect fingerprints of different chestnut samples. The conditions for the headspace injection were: incubation at 60°C for 10 min, injection of 1.0 mL, non-divided injection, incubation speed of 500 r / min, and injection needle temperature of 85°C. The gas chromatography column conditions for the GC-IMS detection are: chromatographic column MXT-WAX, ID: 0.53 mm, FT: 1 μm; chromatographic column temperature 60° C.; carrier gas is nitrogen (≥99.999%); carrier gas flow rate: 0-2 min: 2 mL / min; 2-10 min: 2-15 mL / min; 10-25 min: 15-100 mL / min; 25-30 min: 100 mL / min; chromatographic running time: 30 min; injection port temperature: 85° C. Each sample was measured three times in parallel.

[0045] Table 1 Information on chestnut varieties

[0046]

[0047] The fingerprint spectrum obtained in step (1) was analyzed using the GC-IMS instrument supporting software V0Ca1 analysis software:

[0048] First, the Reporter plug-in was used to directly compare the spectra differences between different chestnut samples. The results are as follows: Figure 1 shown. Figure 1 The red vertical line at the horizontal axis 1.0 in the upper middle figure represents the reactive ion peak (RIP peak). The migration time of volatile organic compounds in the sample is between 1.0 and 2.0 ms, and the retention time is concentrated between 100 and 1000 s. All volatile organic compounds are detected within 30 minutes. Each point on both sides of the RIP peak represents a volatile organic compound. The color of the point reflects the concentration of the substance. White indicates a lower concentration, red indicates a higher concentration, and the darker the color, the higher the concentration; otherwise, the lower the concentration. Figure 1 The lower middle figure uses the spectrum of Zunhua purple amber as a reference. The other spectra are subtracted from the reference to form a two-dimensional difference comparison spectrum. In the subtracted spectrum, substances with the same concentration are offset to white, red indicates that the concentration of the substance is higher than the reference, and blue indicates that the concentration of the substance is lower than the reference. The darker the color, the greater the difference. Most of the signals of different varieties of chestnuts on the migration spectrum appear between the retention times of 100 and 700 seconds. It can be seen that the gas phase-ion migration spectrum information presented by different chestnut samples is somewhat different.

[0049] Then, the NIST database and IMS database built into the V0Ca1 analysis software were used to conduct qualitative and quantitative analysis of the differential substances. The results are shown in Table 2. A total of 26 differential volatile components were identified in the 24 chestnut samples, including 6 ketones, 5 alcohols, 4 aldehydes, 4 acids, 3 olefins, 1 ether, 1 ester, 1 furan, and 1 pyridine. The results show that the volatile components are mainly ketones and alcohol compounds. Figure 2 As shown in the figure, different chestnut varieties have different volatile component contents. Among them, Chengde Kuancheng Daqinglie has the highest ketone content (46%), the highest acid content (12%), Tangshan Qianxi Shuofeng has the highest alcohol content (26%), the highest aldehyde content (11%), Tangshan Zunhua Short Ci has the highest olefin content (9%), and Tangshan Zunhua Dabanhong has the highest ether content (10%). The differences between these volatile components are the reason for the aroma differences between varieties.

[0050] Table 2 Qualitative information of chestnut volatile substances

[0051]

[0052]

[0053]

[0054] Then, the Galery Plot plug-in was used to draw the fingerprint spectra of the differential volatile substances of the chestnut samples for intuitive comparison. The results are as follows: Figure 3 As shown. Chestnut samples of different varieties and origins were observed and the corresponding characteristic fingerprints were established. Each row in the figure represents the signal peak of a sample, and each column represents the signal peak of the same volatile aroma component. 5-methyl-3-heptanone, 3-heptanone, cyclohexanone 、 The contents of 3-hexanol and 3-methyl-2-butanol were high in all samples, and the results were consistent with previous data ( Figure 2 ). The content of 1-pentanol and acetoin in Qianxi, Tangshan and Kuancheng, Chengde is much higher than that in Zunhua, Tangshan. The content of 3-butenenitrile and 2-acetylfuran in Kuancheng, Chengde is higher than that in the other two regions. Therefore, different varieties of chestnuts can be effectively distinguished based on the content of volatile components.

[0055] The PCA analysis of the differential volatile substances of the different chestnut samples obtained above was performed using SIMCA software. Specifically, the PCA analysis was performed with 26 aroma components as dependent variables and different varieties as independent variables. The results are as follows: Figure 4As shown in the figure, more than half of the sample characteristics can be explained by PC1 and PC2, with contribution rates of 63.3% and 17.2% respectively. The samples from Zunhua area are mainly located in the fourth quadrant, while the samples from Qianxi area are mainly located in the first quadrant, with some overlap. The samples from Chengde Kuancheng area are mainly located in the second quadrant, and there are significant differences in chestnut flavor from different origins.

[0056] The chestnut samples were subjected to OPLS-DA analysis using SIMCA software. Figure 5 As shown in the figure, the OPLS-DA model can effectively distinguish 24 chestnut samples from three origins. 2 X) is 0.944, and the dependent variable fitting index (R 2 Y) is 0.829, and the model prediction index (Q 2 ) is 0.703, R 2 and Q 2 If the value is greater than 0.5, the model fitting result is acceptable. After 200 permutation tests, Figure 6 As shown, the intersection point of the regression line and the vertical axis is less than 0, indicating that the model is not overfitting and the model verification is effective. It is believed that the result can be used for the identification and analysis of the origin of chestnut aroma.

[0057] The contribution of each volatile flavor compound to the classification is calculated based on the importance of the variable in the project (VIP). Therefore, the volatile compounds in the samples of different varieties were screened using the VIP value. Variables with a VIP value greater than 1 have the greatest impact on the model. The results are shown in Figure 7 As shown in the figure, there are 12 volatile compounds with VIP values ​​greater than 1. Among them, there are 2 alcohols, 2 aldehydes, 3 ketones, 1 ether, 1 acid, 1 olefin, 1 furan and 1 pyridine. They are styrene (VIP=1.28), 1-pentanol (VIP=1.25), heptanal (VIP=1.20), 2-methylpyridine (VIP=1.17), isopentanol (VIP=1.17), valeric acid (VIP=1.12), acetoin (VIP=1.11), ethylene glycol monobutyl ether (VIP=1.03), butyraldehyde (VIP=1.03), 5-methyl-3-heptanone (VIP=1.02), 2-acetylfuran (VIP=1.01) and 1-octen-3-one (VIP=1.01).

[0058] The 12 key volatile components were reanalyzed using cluster analysis using TBtools-II software. The horizontal axis represents the number of samples, and the vertical axis represents the key volatile substances detected. Blue represents a relatively low content, while red represents a relatively high content, such as Figure 8 As shown, the results of the cluster heat map indicated that the 12 characteristic volatile compounds effectively identified the differences between samples.

[0059] In summary, the present invention provides a method for accurately identifying chestnuts of different varieties and origins by combining flavor fingerprints with multivariate data analysis.

[0060] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying chestnuts of different varieties from different origins, characterized in that: The steps include: The chestnut samples of different varieties from different origins were respectively placed in headspace sample bottles, headspace sampling was performed, and gas chromatography-ion mobility spectrometry was performed to obtain the fingerprints of different chestnut samples; the VOCa1 analysis software was used to perform data analysis and processing on the obtained fingerprints to obtain the differential volatile substance fingerprints of different chestnut samples; the SIMCA software was used to perform PCA and OPLS-DA analysis on the obtained differential volatile substance fingerprints to obtain the key volatile components; the chestnut samples of different varieties from different origins were identified according to the differential volatile substance fingerprints and key volatile components.

2. The method according to claim 1, characterized in that The amount of chestnut sample used is 1 to 4 g.

3. The method according to claim 1, characterized in that The conditions for headspace injection are: incubation at 40-60°C for 5-10 minutes; injection volume of 0.5-1.5 mL; incubation speed of 400-600 r / min; injection needle temperature of 80-90°C.

4. The method according to claim 1, characterized in that: The conditions of gas chromatography are as follows: column temperature 50-70°C; running time 40-60 min; carrier gas is nitrogen; carrier gas flow rate is 0-2 min 2 mL / min, 2-10 min 2-15 mL / min, 10-25 min 15-100 mL / min, 25-30 min 100 mL / min.

5. The method according to claim 1, characterized in that: The data analysis and processing of the obtained fingerprint spectrum using VOCa1 analysis software includes the following steps: using the Reporter plug-in to compare the spectral differences between different chestnut samples; using the NIST database and IMS database built into the VOCa1 analysis software to perform qualitative analysis on the differential volatile substances; using the GaleryPlot plug-in to draw the differential volatile substance fingerprints of different chestnut samples.

6. The method according to claim 1, characterized in that During OPLS-DA analysis, VIP values ​​were used to screen the differential volatile components in different chestnut samples, and the volatile components with VIP values ​​greater than 1 were selected as key volatile components.

7. The method according to claim 1, characterized in that The different origins include Tangshan Zunhua, Tangshan Qianxi and Chengde Kuancheng; the different varieties include purple amber, aventurine pearl, Tafeng, Shuofeng, Dabanhong, Yanli, Zunyu, Yanshanhong, Short Ci, Yanshan Zaofeng, Zunda Li, Yanfeng, Shisheng Li, Yankui, Short Branch, Laoshu Li and Daqingchuang.

8. The method according to claim 1, characterized in that: The differential volatile substance fingerprint includes ketones, alcohols, aldehydes, acids, olefins, ethers, esters, furans and pyridines; the ketones include 5-methyl-3-heptanone, 3-heptanone, cyclohexanone, 2-pentanone, acetoin and 1-octen-3-one; the alcohols include 3-methyl-2-butanol, 3-hexanol, 2-methyl-1-propanol, isopentanol and 1-pentanol; the aldehydes include hexanal, 3-methylthiopropanal, butyraldehyde and heptaldehyde; the acids include hexanoic acid, isobutyric acid, valeric acid and 3-methylvaleric acid; the olefins include limonene, styrene and 3-butenenitrile; the ethers include ethylene glycol monobutyl ether; the esters include ethyl butyrate; the furans include 2-acetylfuran; the pyridines include 2-methylpyridine.

9. The method according to claim 1, characterized in that: The key volatile components include styrene, 1-pentanol, heptanal, 2-methylpyridine, isopentanol, valeric acid, acetoin, ethylene glycol monobutyl ether, butyraldehyde, 5-methyl-3-heptanone, 2-acetylfuran and 1-octen-3-one.

10. The method according to claim 1, characterized in that The method also includes the following steps: using a cluster analysis method to analyze the key volatile components to obtain a cluster heat map of the key volatile components.

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