CN111474267A - Corn flavor quality evaluation method based on quantitative detection of flavor substances - Google Patents

Corn flavor quality evaluation method based on quantitative detection of flavor substances Download PDF

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CN111474267A
CN111474267A CN202010363306.9A CN202010363306A CN111474267A CN 111474267 A CN111474267 A CN 111474267A CN 202010363306 A CN202010363306 A CN 202010363306A CN 111474267 A CN111474267 A CN 111474267A
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flavor
corn
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代春华
袁惠琦
王金斌
李文
唐雪明
张倩
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Jiangsu University
Shanghai Academy of Agricultural Sciences
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Abstract

A corn flavor quality evaluation method based on quantitative detection of flavor substances is characterized by measuring volatile components in corn grains by adopting a headspace solid phase microextraction-gas chromatography-mass spectrometry combined method, carrying out qualitative and quantitative detection, determining a characteristic flavor compound of corn by utilizing a relative flavor activity value method, obtaining a multiple regression linear equation of the characteristic flavor compound of the corn and an original ecological flavor quality index, and establishing a corn original ecological flavor evaluation model; and substituting the quantitative result of the characteristic flavor compound of the corn kernel to be evaluated into the multiple regression linear equation, calculating to obtain a pre-estimated value of the original ecological flavor of the corn, and evaluating the flavor quality of the corn. The method has the characteristics of quantitative flavor evaluation data, objective results and high discrimination accuracy, is suitable for variety screening in the corn breeding process, and provides technical support for the conversion of the original ecological flavor evaluation result of the corn from subjective perception to data quantification.

Description

Corn flavor quality evaluation method based on quantitative detection of flavor substances
Technical Field
The invention belongs to the field of crop flavor quality judgment, and particularly relates to a corn flavor quality evaluation method based on quantitative detection of flavor substances.
Background
The original ecological flavor is one of the important qualities of the corn, the composition and the content of volatile flavor substances are the key for the flavor development of the fresh corn, and the flavor quality of the corn is more and more emphasized along with the development of the society and the improvement of the living standard of people.
Waxy corn (Zea mays L. certain Kulesh) is a subspecies of the genus Zea, and is generated by extremely reducing and even deleting amylose in endosperm due to invisible mutation of wx gene of chromosome nine of the corn.
At present, in the quantitative detection method of the flavor substance of fresh waxy corn, the quantitative detection of the flavor substance is heavier than that of waxy corn in an emulsion stage, and the original ecological flavor difference of the waxy corn in different growth stages is not considered; moreover, due to the reasons that the quality of the waxy corn flavor is difficult to quantify, objective discrimination standards are lacked and the like, the waxy corn gradually loses the original waxy corn flavor in the long-term waxy corn breeding process.
A method for quantitatively evaluating the original flavor quality according to the characteristic flavor substances of corn is not reported.
Disclosure of Invention
The invention aims to provide a corn flavor quality evaluation method based on quantitative detection of flavor substances, which is combined with a relative flavor activity value method, establishes a multiple regression linear equation of a characteristic flavor compound and an original ecological flavor quality index of corn, determines and establishes an analysis method of the original ecological flavor quality of the corn, has the advantages of data quantification, objective result and high discrimination accuracy, is suitable for variety screening in the corn breeding process, and provides technical support for the conversion of the original ecological flavor evaluation result of the corn from subjective susceptibility to data quantification.
In order to achieve the purpose, the invention provides the following technical scheme:
a corn flavor quality evaluation method based on quantitative detection of flavor substances comprises the following steps:
1) detection of
Taking a corn sample, and determining volatile components in corn grains by a headspace solid phase microextraction-gas chromatography-mass spectrometry combined method;
2) qualitative and quantitative analysis
Comparing the detected data of the volatile components with a NIST08 mass spectrum database, performing qualitative analysis, selecting a plurality of volatile components with the matching degree of more than 85%, performing quantitative analysis respectively, and calculating the relative percentage content of each selected volatile component respectively;
3) data processing
Selecting the components which have the greatest contribution to the total flavor aiming at the determined corn strains, and calculating the relative flavor activity value ROAV of each selected volatile component by using the quantitative analysis result, wherein the relative flavor activity value ROAV of a certain volatile component iiThe calculation formula of (2) is as follows:
Figure BDA0002475737410000021
in the formula, CiIs the relative percentage of a certain volatile component i, TiA detection threshold corresponding to the volatile component; cstanRelative percentage of the component that contributes most to the overall flavor, TstanA relative flavor activity value for the component that contributes most to the overall flavor of the sample of 100, which is the threshold of perception corresponding to the component that contributes most to the overall flavor;
selecting a plurality of characteristic flavor compounds of the corn according to ROAV of the volatile components, and performing principal component analysis by using data processing software to obtain a characteristic value, a characteristic vector and a principal component load matrix of a characteristic flavor compound correlation matrix, and further obtaining a multiple linear regression equation of the characteristic compounds of the corn and original ecological flavor quality indexes;
4) flavor assessment
Detecting the corn kernels to be evaluated by adopting a headspace solid phase microextraction-gas chromatography-mass spectrometry combined method to obtain a quantitative analysis result of each characteristic flavor compound;
substituting the obtained quantitative analysis result of the characteristic flavor compound into the multiple regression linear equation in the step 3), and calculating to obtain a predicted value; and judging the original ecological flavor quality of the corn to be detected according to the estimated value.
Preferably, in step 1), the corn to be tested is corn kernels in different growth stages.
Further, in step 1), the corn is selected from the corn in a kernel forming stage, a water soaking stage, a filling stage, a milk stage, a wax stage or/and a complete stage.
Preferably, in the step 1), in the headspace solid phase microextraction-gas chromatography-mass spectrometry combined method, the solid phase microextraction conditions are as follows: the solid phase extraction head has the advantages of 15min of balance time, 50 ℃ of extraction temperature, 30min of extraction time and 5min of desorption time.
In the step 1), in the combined method of headspace solid-phase microextraction-gas chromatography-mass spectrometry, the chromatographic conditions comprise a temperature rise program of 40 ℃ for holding for 5min, raising the temperature to 220 ℃ at 5 ℃/min without holding, then raising the temperature to 250 ℃ at 20 ℃/min for holding for 2.5min, a sample inlet temperature of 260 ℃ and a carrier gas (He) flow of 1.0m L/min.
Preferably, in the step 1), in the headspace solid phase microextraction-gas chromatography-mass spectrometry combined method, the mass spectrometry conditions are as follows: the ion source temperature is 230 ℃; an ionization mode EI; electron energy 70 eV; the mass range m/z is 20-400.
In step 2), the content of each selected volatile component is measured by an internal standard method.
Further, in step 3), volatile components with ROAV >1 are selected as the characteristic flavor compounds of the waxy corn.
The characteristic flavor compounds selected in the step 3) are: (E) -2-nonenal, 1-octen-3-one, hexanal, (E, Z) -2, 6-nonenal, (Z) -4-heptenal, n-octanal, dimethyl sulfide, (E, E) -2, 4-nonenal, (E, E) -2, 4-decadienal, 1-octen-3-ol, (E, E) -2, 6-nonenal, 2-n-pentylfuran, 2-heptenal, 1-nonanol, nonanal, n-hexanol, acetaldehyde, heptanal, (E) -2-octenal and 3-hydroxy-2-butanone.
Preferably, in step 1), the corn sample is waxy corn, and the component selected in step 3) that contributes most to the overall flavor is 1-octen-3-one.
The invention detects corn by a headspace solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) method, compares the detected volatile components with a NIST08 mass spectrum database for qualitative analysis, selects the volatile components with a matching degree of more than 85% in the NIST08 mass spectrum database, removes silicide impurities brought by an extraction head, makes qualitative and quantitative results more accurate, and calculates the relative percentage content of each volatile compound according to a peak area normalization method.
The research shows that the original ecological flavor of the corn is determined by the detection threshold of each volatile compound and the content of the volatile compound in a flavor system, and the relative percentage content of some alkane compounds is higher, but the influence on the original ecological flavor quality is smaller and almost negligible due to the larger detection threshold; the original ecological flavor of the corn is considered by combining the content of the volatile components and the detection threshold, so that the contribution degree of each volatile compound to the flavor of the waxy corn can be determined, and the evaluation result is more scientific and accurate.
The invention utilizes a relative flavor activity value method to obtain a relative flavor activity value ROAV of the component which has the greatest contribution to the overall flavorstanSetting as 100, calculating ROAV value of volatile component, selecting key characteristic flavor compound, wherein the larger ROAV value of the selected characteristic flavor compound is, the larger total flavor contribution of the group of samples is, and further determining characteristic flavor by principal component analysisMultiple regression linear equations of the compound and the original ecological flavor index can be evaluated by combining the estimation results of multiple regression linear equations, if the estimation value is a negative value, the flavor is thin, if the estimation value is between 0 and 10, the flavor is proper, and if the estimation value is between 0 and 10>10, the flavor is full and the flavor quality increases with increasing estimates.
According to the method, the corn kernels in different growth periods are selected for detection, so that key flavor substances of the corns in different growth periods can be determined, a multiple linear regression equation of the corn flavor quality in the whole growth period is established, and the original ecological flavor quality of the corns in different growth periods can be estimated.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the flavor perception threshold of the volatile compounds is combined, the relative flavor activity value of each volatile compound is calculated by using a relative flavor activity value method, the contribution degree of each volatile compound to the flavor of the waxy corn can be determined, key characteristic flavor compounds are selected, and the accuracy of evaluating the original ecological flavor of the corn is improved.
Establishing a multiple linear regression equation for the extracted main components, establishing a multiple linear regression equation for the characteristic flavor compounds of the corn and the original ecological flavor quality indexes, substituting the quantitative result of the characteristic flavor compounds of the corn to be evaluated into the multiple linear regression equation, calculating to obtain a predicted value of the original ecological flavor of the corn, and judging the flavor quality of the corn to be evaluated according to the height of the predicted value; according to the quantity of the main components, a plurality of estimated values can be obtained through calculation, and the plurality of estimated values are added to obtain a comprehensive estimated value, so that data quantization is realized, and the result is objective and reliable.
In the invention, 20 characteristic flavor substances are selected for waxy corns, ROAV values of the 20 characteristic flavor substances are all larger than 1, the 20 characteristic flavor substances play an important role in the overall flavor of the waxy corns and can cover the flavor characteristics of the waxy corns in different growth periods, wherein the cumulative contribution rate of 4 main components to the original ecological flavor quality index reaches 90.37%, and the 20 index comprehensive information can be fully represented.
The method provides technical support for evaluation of the original ecological flavor quality of the corn, has the advantages of simplicity and convenience in operation, data quantification, objective result and high discrimination accuracy, and provides effective technical support for discrimination of the flavor quality in the corn breeding process.
Drawings
FIG. 1 is an ion flow diagram of the volatile components of the Hu purple black glutinous corn No. 1 in the milk stage of the invention.
FIG. 2 is a principal component analysis lithotripsy chart in an embodiment of the present invention.
FIG. 3 is a diagram of principal component analysis components in an embodiment of the invention.
Detailed Description
The present invention is further illustrated by the following specific examples.
Test samples: shanghai purple black glutinous No. 1, Shenke glutinous No. 602 and glutinous corn seeds are all provided by Shanghai agricultural academy of sciences.
The instrument comprises the following steps: tri Plus AS full-automatic headspace solid phase microextraction autosampler, Thermo Fisher corporation, USA; 50/30 μm (DVB/CAR/PDMS) extraction fiber head, Supelco, USA; 6890N-5973N gas chromatography-Mass spectrometer, Agilent, USA; the data analysis software was IBM SPSS Statistics 23.0.
Examples
1. Detection of
Respectively bagging and marking the Shanghai purple and black waxy corn No. 1 after pollination, taking 4 parallel waxy corn samples every other 4 days from 8 days after pollination until the waxy corn is mature (36 days after pollination), namely respectively taking waxy corn seeds in a seed forming period, a water soaking period, a filling period, an early stage of milk maturity, an milk maturity period, a wax maturity period and a complete maturity period, preparing a sample to be detected of each corn sample, respectively placing the waxy corn samples in a tissue grinder for 60s under the condition of 60Hz/s, accurately weighing 5g (+ -0.01 g) of waxy corn, homogenizing the waxy corn in a 20ml sample inlet bottle, completing SPME-GC-MS detection in 24h after collection to obtain an ion flow diagram of volatile components, referring to the diagram 1, the horizontal coordinate represents the peak time, and the vertical coordinate represents the response intensity of each substance, wherein the detection conditions are as follows:
solid phase microextraction conditions: the solid phase extraction head is balanced for 15min, the extraction temperature is 50 ℃, the extraction time is 30min, and the desorption time is 5 min;
chromatographic conditions comprise a DB-WAX capillary column (30m × 0.25mm,0.25 μm), non-split-flow mode sample injection, a temperature raising program, wherein the initial temperature of the column is 40 ℃, the temperature is kept for 5min, the temperature is raised to 220 ℃ at 5 ℃/min without keeping, the temperature is raised to 250 ℃ at 20 ℃/min, the temperature is kept for 2.5min, the sample injection port temperature is 260 ℃, and the flow rate of carrier gas (He) is 1.0m L/min;
mass spectrum conditions: the ion source temperature is 230 ℃; an ionization mode EI; electron energy 70 eV; the mass range m/z is 20-400.
2. Qualitative and quantitative analysis
Comparing the detected ion flow diagrams of the volatile components by an NIST08 mass spectrum database for qualitative analysis, selecting a plurality of volatile components with mass spectrum data matching degree of more than 85%, and calculating the relative percentage content of the selected volatile components by an area normalization method.
121 volatile components are detected in Hu purple black waxy corn seeds No. 1 in 7 growth periods, aldehyde, ketone and alcohol compounds are the main flavor substances of the waxy corn seeds, and the types and the relative percentage contents of the aldehyde, ketone and alcohol compounds are obviously different in each growth period; the relative percentage content of ethers and ketones is highest in a grain forming period, the relative percentage content of aldehydes and alcohols is highest in a water soaking period, the relative percentage content of heterocyclic substances is highest in a grain filling period, the relative percentage content of hydrocarbons and other substances is highest in an early stage of milk maturity, the relative percentage content of esters is highest in a wax maturity period, and the relative percentage content of acids is highest in a maturity period; the waxy corn volatile components in the grain filling stage are complex, the types of hydrocarbon compounds, ester compounds, acid compounds and heterocyclic compounds tend to increase and decrease along with the growth and development of grains, and the types of alcohol compounds, aldehyde compounds, ketone compounds and ether compounds gradually decrease.
Determining characteristic flavor compounds of fresh waxy corn in different growth periods by using a relative flavor activity value method, and performing differential analysis, wherein 11, 7, 13, 8, 7, 8 and 7 volatile flavor components ROAV in waxy corn grains in 7 growth periods are more than 1 and are key substances influencing the flavor of No. 1 waxy corn in Shanghai purple black waxy corn, wherein (E) -2-nonenal, 1-octen-3-one, n-octanal, hexanal and 1-octen-3-ol are main flavor substances shared by the waxy corn grains in the whole growth period; 2-n-amyl furan, nonanal, heptanal, 2-heptenal, acetaldehyde, n-hexanol and other substances have important modification effect on the overall flavor of the Hu purple black glutinous corn No. 1.
3. Data analysis
Selecting 1-octene-3-ketone as a component which has the greatest contribution to the overall flavor from the determined waxy corns, setting ROAVstan of the component to be 100, and calculating the relative flavor activity value ROAV of each selected volatile component by using the quantitative analysis result, wherein the relative flavor activity value ROAV of a certain volatile component iiThe calculation formula of (2) is as follows:
Figure BDA0002475737410000071
in the formula, CiIs the relative percentage of a certain volatile component i, TiA detection threshold corresponding to the volatile component; cstanIs the relative percentage of 1-octen-3-one, TstanAs a threshold for detection of 1-octen-3-one, 100 is the relative flavor activity value for 1-octen-3-one.
The larger the ROAV value of a certain volatile component is, the larger the overall flavor contribution of the component is, and according to the condition that the ROAV value of the volatile component is more than 1, 20 characteristic flavor compounds of waxy corn are selected, including compounds shown as compound 1 to compound 20, wherein the compound 1 is (E) -2-nonenal, the compound 2 is 1-octen-3-one, the compound 3 is hexanal, the compound 4 is (E, Z) -2, 6-nonenal, the compound 5 is (Z) -4-heptenal, the compound 6 is n-octanal, the compound 7 is dimethyl sulfide, the compound 8 is (E, E) -2, 4-nonenal, the compound 9 is (E, E) -2, 4-decadienal, the compound 10 is 1-octen-3-ol, the compound 11 is (E, E) -2, 6-nonadienal, the compound 12 is 2-n-pentylfuran, the compound 13 is 2-heptenal, the compound 14 is 1-nonanol, the compound 15 is nonanal, the compound 16 is n-hexanol, the compound 17 is acetaldehyde, the compound 18 is heptanal, the compound 19 is (E) -2-octenal, and the compound 20 is 3-hydroxy-2-butanone.
Performing principal component analysis by using SPSS 23.0 software to obtain a principal component analysis lithotripsy graph and a component graph, wherein the lithotripsy graph takes a characteristic value as a vertical axis and a component as a horizontal axis, the characteristic value of a steep part in the front is large, the contained information is more, the characteristic value of a flat part in the back is small, and the contained information is also small, as shown in FIGS. 2-3; according to the analysis results, eigenvalues, eigenvectors and principal component load matrixes of correlation matrixes of the characteristic flavor compounds 1 to the compounds 20 are obtained, the cumulative contribution rate of 4 principal components to the original flavor quality reaches 90.37%, the comprehensive information of 20 indexes can be fully represented, 4 multiple linear regression equations of the characteristic compounds of corn and the original flavor quality indexes are further obtained, and a flavor evaluation model is established, wherein the 4 multiple linear regression equations are as follows:
F1＝-0.856X1+0.901X2+0.084X3-0.803X4-0.794X5+0.779X6+0.42X7+0.387X8+0.377X9+0.971X10+0.658X11-0.254X12-0.094X13+0.843X14+0.628X15+0.476X16-0.451X17-0.862X18+0.593X19-0.393X20；
F2＝0.402X1+0.123X2+0.742X3+0.114X4-0.22X5+0.389X6-0.88X7+0.553X8-0.92X9-0.153X10-0.056X11-0.024X12+0.81X13-0.043X14+0.557X15-0.102X16-0.436X17-0.439X18+0.601X19-0.293X20；
F3＝0.282X1-0.092X2-0.655X3+0.377X4+0.531X5+0.208X6+0.194X7+0.622X8+0.095X9-0.121X10+0.305X11+0.389X12+0.439X13+0.502X14-0.314X15+0.814X16+0.666X17+0.154X18+0.524X19-0.381X20；
F4＝-0.112X1+0.135X2+0061X3-0.002X4-0.336X6+0.104X7+0.393X8+0.036X9+0.092X10+0.059X11+0.754X12+0.22X13+0.042X14-0.193X15-0.284X16-0.26X17-0.2X18+0.092X19+0.539X20。
wherein F1, F2, F3 and F4 are four estimated values, and X is1To X20And adding the four predicted values of F1, F2, F3 and F4 to obtain an integrated predicted value, wherein the quantitative results are relative percentage contents.
4. Flavor assessment
The method comprises the steps of measuring quantitative results of two waxy corn varieties Hu purple black glutinous No. 1 and Shenke glutinous No. 602 to be evaluated by using SPME-GC-MS, respectively measuring the quantitative results of the waxy corn samples in seven growth periods, substituting the measured quantitative results into the multiple regression linear equation, and judging the original flavor quality of the corn to be measured according to the pre-estimated values, wherein the results are shown in Table 1, wherein FH1, FH2, FH3, FH4, FH5, FH6 and FH7 respectively represent a grain formation period, a blister period, a filling period, a milk maturity early stage, an milk maturity period, a wax maturity period and a complete maturity period of the Hu purple black glutinous No. 1, and FK1, FK2, FK3, FK4, FK5, FK6 and FK7 respectively represent grain formation period, a filling period, a water blister period, a milk maturity period, an milk maturity period, a wax maturity period and a complete maturity period of the Hu purple black glutinous corn No. 1, and each sample repeat each sample.
TABLE 1
Figure BDA0002475737410000081
Figure BDA0002475737410000091
According to the results, the differences of the original ecological flavors of the waxy corns in different growth periods are obvious, the estimated value of the original ecological flavor of the waxy corn in the soaking period of the No. 1 waxy purple and black waxy corns is the highest, and the original ecological flavor is the thinnest in the milk stage and in the full-maturation stage; the original ecological flavor prediction value of the Shenke waxy corn 602 in the milk stage is the highest, the original ecological flavor prediction value is the lowest in the milk stage, the edible quality is low in the full stage, and the quantitative evaluation of the original ecological flavor evaluation data of the waxy corn is realized.

Claims (10)

1. A corn flavor quality evaluation method based on quantitative detection of flavor substances comprises the following steps:
1) detection of
Taking a corn sample, and determining volatile components in corn grains by a headspace solid phase microextraction-gas chromatography-mass spectrometry combined method;
2) qualitative and quantitative analysis
Comparing the detected data of the volatile components with a NIST08 mass spectrum database, performing qualitative analysis, selecting a plurality of volatile components with the matching degree of more than 85%, performing quantitative analysis respectively, and calculating the relative percentage content of each selected volatile component respectively;
3) data processing
Selecting the components which have the greatest contribution to the total flavor aiming at the determined corn strains, and calculating the relative flavor activity value ROAV of each selected volatile component by using the quantitative analysis result, wherein the relative flavor activity value ROAV of a certain volatile component iiThe calculation formula of (2) is as follows:
Figure FDA0002475737400000011
in the formula, CiIs the relative percentage of a certain volatile component i, TiA detection threshold corresponding to the volatile component; cstanRelative percentage of the component that contributes most to the overall flavor, TstanA relative flavor activity value for the component that contributes most to the overall flavor of the sample of 100, which is the threshold of perception corresponding to the component that contributes most to the overall flavor;
selecting a plurality of characteristic flavor compounds of the corn according to ROAV of the volatile components, and performing principal component analysis by using data processing software to obtain a characteristic value, a characteristic vector and a principal component load matrix of a characteristic flavor compound correlation matrix, and further obtaining a multiple linear regression equation of the characteristic compounds of the corn and original ecological flavor quality indexes;
4) flavor assessment
Detecting the corn kernels to be evaluated by adopting a headspace solid phase microextraction-gas chromatography-mass spectrometry combined method to obtain a quantitative analysis result of each characteristic flavor compound;
substituting the obtained quantitative analysis result of the characteristic flavor compound into the multiple regression linear equation in the step 3), and calculating to obtain a predicted value; and judging the original ecological flavor quality of the corn to be detected according to the estimated value.
2. The corn flavor quality assessment method based on the quantitative detection of flavor substances according to claim 1, wherein in the step 1), the corn to be measured is corn kernels in different growth periods.
3. The corn flavor quality assessment method based on quantitative detection of flavor substances according to claim 1 or 2, wherein in the step 1), the corn is selected from the group consisting of kernel formation stage, blister stage, filling stage, pre-milk stage, waxy stage and/or complete stage.
4. The corn flavor quality assessment method based on the quantitative detection of flavor substances according to claim 1, wherein in the step 1), in the headspace solid phase microextraction-gas chromatography-mass spectrometry combined method, the solid phase microextraction conditions are as follows: the solid phase extraction head has the advantages of 15min of balance time, 50 ℃ of extraction temperature, 30min of extraction time and 5min of desorption time.
5. The corn flavor quality evaluation method based on the flavor substance quantitative detection is characterized in that in the step 1), in a headspace solid phase microextraction-gas chromatography-mass spectrometry combined method, the chromatographic conditions are that the temperature rise program is that the initial column temperature is 40 ℃, the temperature is kept for 5min, the temperature is increased to 220 ℃ at 5 ℃/min, the temperature is not kept, the temperature is increased to 250 ℃ at 20 ℃/min, the temperature is kept for 2.5min, the injection port temperature is 260 ℃, and the carrier gas flow rate is 1.0m L/min.
6. The corn flavor quality assessment method based on the quantitative detection of flavor substances according to claim 1, wherein in the step 1), in the headspace solid phase microextraction-gas chromatography-mass spectrometry combined method, the mass spectrometry conditions are as follows: the ion source temperature is 230 ℃; an ionization mode EI; electron energy 70 eV; the mass range m/z is 20-400.
7. The method for evaluating corn flavor quality based on quantitative detection of flavor substances according to claim 1, wherein the relative percentage of each selected volatile component is determined by an area normalization method when performing quantitative analysis in the step 2).
8. The corn flavor quality assessment method based on quantitative flavor detection according to claim 1, wherein in step 3), volatile components of ROAV >1 are selected as characteristic flavor compounds of corn.
9. The corn flavor quality assessment method based on quantitative detection of flavors according to claim 1 or 8, wherein the characteristic flavor compounds selected in the step 3) are: (E) -one or more of-2-nonenal, 1-octen-3-one, hexanal, (E, Z) -2, 6-nonenal, (Z) -4-heptenal, n-octanal, dimethyl sulfide, (E, E) -2, 4-nonenal, (E, E) -2, 4-decadienal, 1-octen-3-ol, (E, E) -2, 6-nonenal, 2-n-pentylfuran, 2-heptenal, 1-nonanol, nonanal, n-hexanol, acetaldehyde, heptanal, (E) -2-octenal, and 3-hydroxy-2-butanone.
10. The corn flavor quality assessment method based on quantitative detection of flavors according to any one of claims 1 to 9, wherein in step 1), the corn sample is waxy corn, and the component selected in step 3) having the greatest contribution to the overall flavor is 1-octen-3-one.
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