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

Abstract

A corn flavor quality evaluation method based on the quantitative detection of flavor substances, adopts headspace solid-phase microextraction-gas chromatography-mass spectrometry method to determine volatile components in corn kernels, and performs qualitative and quantitative detection, using relative flavor activity value The characteristic flavor compounds of corn were determined by the method, the multiple regression linear equations between the characteristic flavor compounds of corn and the original ecological flavor quality indexes were obtained, and the evaluation model of corn original ecological flavor was established; the quantitative results of the characteristic flavor compounds of the corn kernels to be evaluated were substituted into the multiple regression equations In the regression linear equation, the estimated value of the original ecological flavor of corn was calculated to evaluate the flavor quality of corn. The invention has the characteristics of flavor evaluation data quantification, has the advantages of objective results and high discrimination accuracy, is suitable for variety screening in the process of maize breeding, and provides for the transformation of the original ecological flavor evaluation results of maize from subjective perception type to data quantitative type. Technical Support.

Description

A method for evaluating corn flavor quality based on quantitative detection of flavor substances

technical field

The invention belongs to the field of crop flavor quality discrimination, in particular to a corn flavor quality evaluation method based on quantitative detection of flavor substances.

Background technique

The original ecological flavor is one of the important qualities of corn. The composition and content of volatile flavor substances are the key to the aroma of fresh corn. With the development of society and the improvement of people's living standards, the flavor quality of corn has been paid more and more attention.

Waxy maize (Zea mays L.certain Kulesh) is a subspecies of the genus Maize, which is produced by a recessive mutation of the wx gene of maize chromosome 9, resulting in extreme reduction or even deletion of amylose in the endosperm. Fresh waxy corn is rich in starch, fat, amino acids, trace elements and minerals, and has high nutritional value. After cooking, the grains are transparent, the color is crystal clear, the skin is thin and the residue is less, the soft and sticky is delicate, and the palatability is good.

At present, among the quantitative detection methods for flavor substances in fresh waxy corn, the quantitative detection of flavor substances focuses on the waxy corn in the milk-ripening stage, and does not consider the difference in the original ecological flavor of waxy corn in different growth stages; The quality is difficult to quantify, and there is a lack of objective criteria. During the long-term breeding process of waxy corn, the waxy corn gradually lost its original "waxy corn flavor".

There is no report on the quantitative assessment of the original flavor quality based on the characteristic flavor substances of corn.

SUMMARY OF THE INVENTION

The object of the present invention is to provide a corn flavor quality evaluation method based on quantitative detection of flavor substances, and to establish a multiple regression linear equation between the characteristic flavor compounds of corn and the original ecological flavor quality index in combination with the relative flavor activity value method, and to determine the establishment of the original corn flavor quality index. The analysis method of ecological flavor quality has the advantages of data quantification, objective results, and high discrimination accuracy. It is suitable for cultivar screening in the process of corn breeding, and provides for the transformation of corn original ecological flavor evaluation results from subjective perception type to data quantitative type. Technical Support.

In order to achieve the above object, the present invention provides the following technical solutions:

A corn flavor quality assessment method based on quantitative detection of flavor substances, comprising the following steps:

1) Detection

Taking corn samples, the volatile components in corn kernels were determined by headspace solid-phase microextraction-gas chromatography-mass spectrometry;

2) Qualitative and quantitative analysis

The detection data of the obtained volatile components were compared through the NIST08 mass spectrometry database, and qualitative analysis was carried out. Several volatile components with a matching degree of more than 85% were selected for quantitative analysis, and the relative ratio of each selected volatile component was calculated separately. percentage content;

3) Data processing

Select the components that contribute the most to the overall flavor for the measured corn strains, and use the results of quantitative analysis to calculate the relative flavor activity value ROAV of each volatile component selected. Among them, the relative flavor activity value of a volatile component i The formula for calculating ROAV _i is:

In the formula, C _i is the relative percentage content of a volatile component i, T _i is the corresponding detection threshold of the volatile component; C _stan is the relative percentage content of the component that contributes the most to the overall flavor, and T _stan is The detection threshold corresponding to the component that contributes the most to the overall flavor, 100 is the relative flavor activity value of the component that contributes the most to the overall flavor of the sample;

According to the ROAV of volatile components, several characteristic flavor compounds of corn were selected, and data processing software was used to perform principal component analysis to obtain the eigenvalues, eigenvectors and principal component loading matrix of the correlation matrix of characteristic flavor compounds, and further obtain the characteristic compounds of corn and The multiple linear regression equation of the original ecological flavor quality index;

4) Flavor Evaluation

The corn kernels to be evaluated were detected by headspace solid-phase microextraction-gas chromatography-mass spectrometry, and the quantitative analysis results of each characteristic flavor compound were obtained;

Substitute the obtained quantitative analysis results of characteristic flavor compounds into the multiple regression linear equation of step 3), and calculate the estimated value; judge the original ecological flavor quality of the corn to be tested according to the level of the estimated value.

Preferably, in step 1), the measured corn is corn kernels in different growth stages.

Further, in step 1), the corn is selected from the grain formation stage, the blister stage, the grain filling stage, the early stage of milk maturity, the milk maturity stage, the wax maturity stage or/and the full maturity stage.

Preferably, in step 1), in the headspace solid-phase micro-extraction-gas chromatography-mass spectrometry method, the solid-phase micro-extraction conditions are: solid-phase extraction head equilibration time 15min, extraction temperature 50 ℃, extraction time 30min, desorption time Time 5min.

In addition, in step 1), in the headspace solid-phase microextraction-gas chromatography-mass spectrometry method, the chromatographic conditions: heating program: the initial temperature of the column is 40 ° C, maintained for 5 min, and raised to 220 ° C at 5 ° C/min, without maintaining ; Then rise to 250°C at 20°C/min and hold for 2.5min; the inlet temperature is 260°C; the carrier gas (He) flow rate is 1.0 mL/min.

Preferably, in step 1), in the headspace solid phase microextraction-gas chromatography-mass spectrometry method, mass spectrometry conditions: ion source temperature 230°C; ionization mode EI; electron energy 70eV; mass range m/z 20-400.

Also, in step 2), the content of each selected volatile component is determined by the internal standard method.

Further, in step 3), volatile components with ROAV>1 are selected as characteristic flavor compounds of waxy corn.

In addition, the characteristic flavor compounds selected in the step 3) are: (E)-2-nonenal, 1-octen-3-one, hexanal, (E,Z)-2,6-nonenal Alkenal, (Z)-4-heptenal, n-octylaldehyde, dimethyl sulfide, (E,E)-2,4-nonadienal, (E,E)-2,4-decanedi Alkenal, 1-octen-3-ol, (E,E)-2,6-nonadienal, 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 the most to the overall flavor is 1-octen-3-one.

The headspace solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) method of the present invention is used to detect corn, and the detected volatile components are compared with the NIST08 mass spectrometry database for qualitative analysis, and the selected volatile components are compared with the NIST08 mass spectrometry database. If the matching degree is more than 85%, then remove the silicide impurity peak brought by the extraction head to make the qualitative and quantitative results more accurate, and then calculate the relative percentage content of each volatile compound according to the peak area normalization method.

After research, it was found that the original flavor of corn is determined by the detection threshold of each volatile compound and its content in the flavor system. Some alkane compounds have relatively high relative percentages, but because of their large detection threshold, they are not suitable for The influence of the original ecological flavor quality is small and can almost be ignored; the present invention considers the original ecological flavor of corn in combination with the content of volatile components and the detection threshold, and can clarify the contribution degree of each volatile compound to the flavor of waxy corn, and the evaluation result is better. Science is more accurate.

The invention uses the relative flavor activity value method, sets the relative flavor activity value ROAV _stan of the component that contributes the most to the overall flavor as 100, calculates the ROAV value of the volatile components, selects key characteristic flavor compounds, and the selected characteristic The larger the ROAV value of the flavor compounds, the greater the overall flavor contribution of the group of samples, and then through the principal component analysis, the multiple regression linear equations between the characteristic flavor compounds and the original ecological flavor indicators can be further determined, which can be combined with the predictions of multiple multiple regression linear equations. If the estimated value is negative, the flavor is weak; if the estimated value is between 0 and 10, the flavor is appropriate; if the estimated value is greater than 10, the flavor is full, and the flavor quality varies with the estimated value. increases as the value increases.

In the present invention, corn kernels in different growth periods are selected for detection, the key flavor substances of corn in different growth periods can be clarified, the multiple linear regression equation of the flavor quality of corn in the whole growth period can be established, and the original ecology of corn in different growth periods can be estimated. Flavor quality.

Compared with the prior art, the present invention has the following beneficial effects:

In the present invention, the relative flavor activity value of each volatile compound is calculated by using the relative flavor activity value method in combination with the flavor detection threshold of volatile compounds, so that the contribution degree of each volatile compound to the flavor of waxy corn can be determined, and the key characteristic flavor can be selected. Compounds that improve the accuracy of assessing the native flavor of maize.

The invention establishes a multiple linear regression equation for the extracted principal components, establishes a multiple regression linear equation between the characteristic flavor compounds of corn and the original ecological flavor quality index, and substitutes the quantitative results of the characteristic flavor compounds of the corn to be evaluated into the multiple regression linear equation, Calculate the estimated value of the original ecological flavor of corn, and determine the flavor quality of the corn to be tested according to the level of the estimated value. Valuation makes the data quantified and the results objective and reliable.

In the present invention, 20 kinds of characteristic flavor substances are selected for waxy corn, and their ROAV values are all greater than 1, which play an important role in the overall flavor of waxy corn, and can cover the flavor characteristics of waxy corn in different growth periods. The cumulative contribution rate of the original ecological flavor quality indicators reached 90.37%, which can fully represent the comprehensive information of 20 indicators.

The invention provides technical support for the evaluation of the flavor quality of the original ecology of corn. The method has the advantages of simple operation, data quantification, objective results and high discrimination accuracy, and provides effective technical support for the discrimination of the flavor quality in the process of corn breeding.

Description of drawings

Fig. 1 is the ion current diagram of the volatile components of the waxy corn in the milk-maturing stage of Huzi Hei Nuo No. 1 in the embodiment of the present invention.

FIG. 2 is a principal component analysis gravel diagram in an embodiment of the present invention.

FIG. 3 is a component diagram of principal component analysis in an embodiment of the present invention.

Detailed ways

The present invention will be further described below in conjunction with specific embodiments.

Test samples: Huzihei Nuo 1, Shenke Nuo 602, and waxy corn seeds were all provided by Shanghai Academy of Agricultural Sciences.

Instrument: Tri Plus AS automatic headspace solid-phase microextraction autosampler, Thermo Fisher, USA; 50/30 μm (DVB/CAR/PDMS) extraction fiber head, Supelco, USA; 6890N-5973N gas chromatography-mass spectrometer , Agilent, USA; data analysis software is IBM SPSS Statistics 23.0.

Example

1. Detection

After pollination, the waxy corn of Huzihei Nuo No. 1 was bagged and marked. From 8 days after pollination, 4 parallel samples were taken, and corn samples were taken every 4 days until the waxy corn was mature (36 days after pollination). Waxy corn kernels in the grain formation stage, blister stage, grain filling stage, early milk maturity stage, milk maturity stage, wax maturity stage and full maturity stage were prepared for each corn sample to be tested, and the waxy corn samples were placed in the tissue grinder respectively. Grind the sample for 60s under the condition of 60Hz/s, accurately weigh 5g (±0.01g) of waxy corn homogenate into a 20ml injection bottle, and complete the SPME-GC-MS detection within 24h after sampling to obtain the ion current of volatile components. Figure, see Figure 1, the abscissa represents the peak time, the ordinate represents the response intensity of each substance, the detection conditions are as follows:

Solid phase microextraction conditions: solid phase extraction head equilibration time 15min, extraction temperature 50℃, extraction time 30min, desorption time 5min;

Chromatographic conditions: DB-WAX capillary column (30m×0.25mm, 0.25μm); injection in splitless mode; temperature program: initial column temperature 40°C, hold for 5min, rise to 220°C at 5°C/min, no hold; then Rise to 250°C at 20°C/min, hold for 2.5min; inlet temperature 260°C; carrier gas (He) flow rate 1.0 mL/min;

Mass spectrometry conditions: ion source temperature 230°C; ionization mode EI; electron energy 70eV; mass range m/z 20-400.

2. Qualitative and quantitative analysis

The ion chromatograms of the detected volatile components were qualitatively analyzed by comparing the NIST08 mass spectrometry database, and a number of volatile components with a matching degree of more than 85% of the mass spectral data were selected, and then the area normalization method was used to calculate the selected volatile components. relative percentage.

A total of 121 volatile components were detected in the waxy corn kernels of Huzihei Nuo No. 1 in 7 growing periods. Aldehyde, ketone and alcohol compounds were the most important flavor substances in waxy corn kernels. There were obvious differences in the growth stages; the relative percentages of ethers and ketones were the highest in the grain formation stage, the relative percentages of aldehydes and alcohols were the highest in the blister stage, and the relative percentages of heterocyclic substances in the grain filling stage. The highest relative percentage of hydrocarbons and other substances in the early stage of milk maturity, the highest relative percentage of ester substances in the wax maturity stage, and the highest relative percentage of acid substances in the mature stage. The composition is more complex. With the growth and development of grains, the types of hydrocarbons, esters, acids, and heterocyclic compounds increased first and then decreased, and the types of alcohols, aldehydes, ketones, and ethers gradually decreased.

The relative flavor activity value method was used to determine the characteristic flavor compounds of fresh waxy maize in different growth periods and analyze the differences. There were 11, 7, 13, 8, 7, and 8 in the waxy maize grains of 7 growth periods. There are 7 kinds of volatile flavor components ROAV greater than 1, which are the key substances affecting the flavor of Huzihei Nuo No. 1 waxy corn. Among them, (E)-2-nonenal, 1-octen-3-one, n-octaldehyde , hexanal and 1-octen-3-ol are the main flavor substances shared by waxy corn grains in the whole growing period; 2-n-pentylfuran, nonanal, heptanal, 2-heptenal, acetaldehyde, n-hexanol, etc. The substances have an important modification effect on the overall flavor of Huzihei Nuo 1 waxy maize.

3. Data analysis

1-octen-3-one was selected as the component that contributed the most to the overall flavor of the measured waxy corn, and its ROAVstan was set to 100. Using the quantitative analysis results, the relative flavor activity of each selected volatile component was calculated. value ROAV, where the formula for calculating the relative flavor activity value ROAV _i of a volatile component i is:

In the formula, C _i is the relative percentage content of a volatile component i, T _i is the detection threshold corresponding to the volatile component; C _stan is the relative percentage content of 1-octen-3-one, and T _stan is The detection threshold for 1-octen-3-one, 100 is the relative flavor activity value of 1-octen-3-one.

The larger the ROAV value of a volatile component, the greater the overall flavor contribution of the component. According to the condition that the ROAV value of the volatile component is greater than 1, 20 characteristic flavor compounds of waxy corn were selected, including compounds 1 to 20. The compound shown, 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 -Nonadienal, compound 5 is (Z)-4-heptenal, compound 6 is n-octylaldehyde, compound 7 is dimethyl sulfide, compound 8 is (E,E)-2,4-nonanedi Alkenal, compound 9 is (E,E)-2,4-decadienal, compound 10 is 1-octen-3-ol, compound 11 is (E,E)-2,6-nonadienal , compound 12 is 2-n-pentylfuran, compound 13 is 2-heptenal, compound 14 is 1-nonanol, compound 15 is nonanal, compound 16 is n-hexanol, compound 17 is acetaldehyde, compound 18 is heptyl Aldehyde, compound 19 is (E)-2-octenal, and compound 20 is 3-hydroxy-2-butanone.

Use SPSS 23.0 software to carry out principal component analysis to obtain the principal component analysis gravel diagram and component diagram, see Figure 2-3, the gravel diagram takes the eigenvalue as the vertical axis and the component as the horizontal axis. It contains a lot of information, and the eigenvalues of the flat part in the back are small, and the information contained is also small; according to the analysis results, the eigenvalues, eigenvectors and principal component loading matrix of the correlation matrix of the characteristic flavor compounds compounds 1 to 20 are obtained, and four principal component pairs are obtained. The cumulative contribution rate of the original ecological flavor quality is 90.37%, which can fully represent the comprehensive information of 20 indicators, and further obtain four multiple linear regression equations between the characteristic compounds of corn and the original ecological flavor quality indicators, and establish a flavor evaluation model. The four multiple regression linear equations are as follows:

F1=-0.856X ₁ +0.901X ₂ +0.084X ₃ -0.803X ₄ -0.794X ₅ +0.779X ₆ +0.42X ₇ +0.387X ₈ +0.377X ₉ +0.971X ₁₀ +0.658X ₁₁ -0.254X ₁₂ -0.094X ₁₃ +0.843X ₁₄ +0.628X ₁₅ +0.476X ₁₆ -0.451X ₁₇ -0.862X ₁₈ +0.593X ₁₉ -0.393X ₂₀ ;

F2=0.402X ₁ +0.123X ₂ +0.742X ₃ +0.114X ₄ -0.22X ₅ +0.389X ₆ -0.88X ₇ +0.553X ₈ -0.92X ₉ -0.153X ₁₀ -0.056X ₁₁ -0.024X ₁₂ + 0.81X ₁₃ -0.043X ₁₄ +0.557X ₁₅ -0.102X ₁₆ -0.436X ₁₇ -0.439X ₁₈ +0.601X ₁₉ -0.293X ₂₀ ;

F3=0.282X ₁ -0.092X ₂ -0.655X ₃ +0.377X ₄ +0.531X ₅ +0.208X ₆ +0.194X ₇ +0.622X ₈ +0.095X ₉ -0.121X ₁₀ +0.305X ₁₁ +0.389X ₁₂ + 0.439X ₁₃ +0.502X ₁₄ -0.314X ₁₅ +0.814X ₁₆ +0.666X ₁₇ +0.154X ₁₈ +0.524X ₁₉ -0.381X ₂₀ ;

F4 = -0.112X ₁ +0.135X ₂ +0061X ₃ -0.002X ₄ -0.336X ₆ +0.104X ₇ +0.393X ₈ +0.036X ₉ +0.092X ₁₀ +0.059X ₁₁ +0.754X ₁₂ +0.22X ₁₃ + 0.042X ₁₄ -0.193X ₁₅ -0.284X ₁₆ -0.26X ₁₇ -0.2X ₁₈ +0.092X ₁₉ +0.539X ₂₀ .

Among them, F1, F2, F3, F4 are four estimated values, and the X ₁ to X ₂₀ are the quantitative results of compounds 1 to 20, and the quantitative results are relative percentages. F1, F2, F3, The four estimated values of F4 are added together to obtain the comprehensive estimated value.

4. Flavor assessment

The two waxy corn varieties to be evaluated, Huzihei Nuo 1 and Shenke Nuo 602, were determined by SPME-GC-MS, and the quantitative results of the waxy corn samples were determined in seven growth periods respectively, and the quantitative results were substituted into the above multiple regression line In the property equation, the original ecological flavor quality of the corn to be tested is determined according to the estimated value. The results are shown in Table 1. Among them, FH1, FH2, FH3, FH4, FH5, FH6 and FH7 represent the grains of Huzihei Nuo 1, respectively. Formation stage, blister stage, grain filling stage, early milk maturity stage, milk maturity stage, wax maturity stage and full maturity stage samples, FK1, FK2, FK3, FK4, FK5, FK6 and FK7 represent the grain formation stage of Huzihei Nuo 1, respectively , blister stage, filling stage, early milk maturity, milk maturity, wax maturity and full maturity samples, each sample was replicated 3 times.

Table 1

From the above results, it can be seen that the original ecological flavor of waxy corn has obvious differences in different growth periods. The estimated value of the original ecological flavor of the waxy corn No. 1 in the blister period is the highest, followed by the milk ripening period, and the original ecological flavor in the mature period is the weakest; Shenke The estimated value of the original ecological flavor of waxy 602 was the highest at the milk-maturing stage, followed by the filling stage, and the predicted value of the original ecological flavor at the full-ripening stage was the lowest, and the edible quality was low.

Claims (10)

1. a corn flavor quality assessment method based on quantitative detection of flavor substances, comprising the following steps: 1) Detection Taking corn samples, the volatile components in corn kernels were determined by headspace solid-phase microextraction-gas chromatography-mass spectrometry; 2) Qualitative and quantitative analysis The detection data of the obtained volatile components were compared through the NIST08 mass spectrometry database, and qualitative analysis was carried out. Several volatile components with a matching degree of more than 85% were selected for quantitative analysis, and the relative ratio of each selected volatile component was calculated separately. percentage content; 3) Data processing Select the components that contribute the most to the overall flavor for the measured corn strains, and use the results of quantitative analysis to calculate the relative flavor activity value ROAV of each volatile component selected. Among them, the relative flavor activity value of a volatile component i The formula for calculating ROAV _i is:

In the formula, C _i is the relative percentage content of a volatile component i, T _i is the corresponding detection threshold of the volatile component; C _stan is the relative percentage content of the component that contributes the most to the overall flavor, and T _stan is The detection threshold corresponding to the component that contributes the most to the overall flavor, 100 is the relative flavor activity value of the component that contributes the most to the overall flavor of the sample; According to the ROAV of volatile components, several characteristic flavor compounds of corn were selected, and data processing software was used to perform principal component analysis to obtain the eigenvalues, eigenvectors and principal component loading matrix of the correlation matrix of characteristic flavor compounds, and further obtain the characteristic compounds of corn and The multiple linear regression equation of the original ecological flavor quality index; 4) Flavor Evaluation The corn kernels to be evaluated were detected by headspace solid-phase microextraction-gas chromatography-mass spectrometry, and the quantitative analysis results of each characteristic flavor compound were obtained; Substitute the obtained quantitative analysis results of characteristic flavor compounds into the multiple regression linear equation of step 3), and calculate the estimated value; judge the original ecological flavor quality of the corn to be tested according to the level of the estimated value. 2. The corn flavor quality assessment method based on quantitative detection of flavor substances according to claim 1, wherein in step 1), the corn measured is corn kernels in different growth stages. 3. the corn flavor quality evaluation method based on the quantitative detection of flavor substance according to claim 1 and 2, is characterized in that, in step 1), described corn is selected from grain formation stage, blister stage, grain filling stage, milk ripening stage , milk-ripening, wax-ripening or/and full-ripening corn. 4. the corn flavor quality evaluation method based on the quantitative detection of flavor substances according to claim 1, is characterized in that, in step 1), in the headspace solid phase microextraction-gas chromatography-mass spectrometry method, the solid phase microextraction condition As follows: the equilibration time of the solid-phase extraction head is 15 minutes, the extraction temperature is 50 °C, the extraction time is 30 minutes, and the desorption time is 5 minutes. 5. the corn flavor quality evaluation method based on the quantitative detection of flavor substance according to claim 1, is characterized in that, in step 1), in headspace solid phase microextraction-gas chromatography-mass spectrometry method, chromatographic condition: temperature rise program : The initial column temperature is 40°C, held for 5 minutes, and then raised to 220°C at 5°C/min, without holding; then raised to 250°C at 20°C/min, held for 2.5 minutes; the inlet temperature is 260°C; the carrier gas flow rate is 1.0m L/min. 6. The corn flavor quality evaluation method based on quantitative detection of flavor substances according to claim 1, is characterized in that, in step 1), in the headspace solid phase microextraction-gas chromatography-mass spectrometry method, mass spectrometry condition: ion source Temperature 230℃; ionization mode EI; electron energy 70eV; mass range m/z 20～400. 7. the corn flavor quality assessment method based on the quantitative detection of flavor substance according to claim 1, is characterized in that, when carrying out quantitative analysis in step 2), measure the relative value of each volatile component selected by area normalization method. percent content. 8. The corn flavor quality assessment method based on quantitative detection of flavor substances according to claim 1, wherein in step 3), a volatile component with ROAV>1 is selected as the characteristic flavor compound of corn. 9. the corn flavor quality evaluation method based on the quantitative detection of flavor substance according to claim 1 or 8, is characterized in that, the characteristic flavor compound selected in described step 3) is: (E)-2-nonenal, 1-Octen-3-one, hexanal, (E,Z)-2,6-nonadienal, (Z)-4-heptenal, n-octal, dimethyl sulfide, (E, E)-2,4-nonadienal, (E,E)-2,4-decadienal, 1-octen-3-ol, (E,E)-2,6-nonadienal , 2-n-pentylfuran, 2-heptenal, 1-nonanol, nonanal, n-hexanol, acetaldehyde, heptanal, (E)-2-octenal and 3-hydroxy-2-butanone one or more of. 10. The corn flavor quality assessment method based on the quantitative detection of flavor substances according to any one of claims 1-9, characterized in that, in step 1), the corn sample is waxy corn, and the pair selected in step 3) is glutinous corn. The component with the largest overall flavor contribution was 1-octen-3-one.

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