Quinoa flavor evaluation method based on multi-modal sensory detection

Abstract

The invention relates to a chenopodium quinoa willd flavor evaluation method based on multi-modal sensory detection, which is characterized in that a fatty acid analysis method combining headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), gas chromatography-ion mobility spectrometry (HS-GC-IMS), electronic nose (E-nose), electronic tongue (E-tongue) and gas chromatography (GC) is adopted to realize comprehensive evaluation of the flavor of chenopodium quinoa willd. And the correlation between the fatty acid and the flavor component is disclosed. The method is suitable for the research and development of quinoa quality grading, variety breeding and deep processing directions.

Description

Technical Field

[0001] The present invention belongs to the field of food detection and quality control, and particularly relates to a method for evaluating the flavor of quinoa based on multimodal sensory detection, especially a method for comprehensively analyzing the fatty acids and volatile flavor compounds of quinoa by using various detection technologies such as headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), gas chromatography-ion mobility spectrometry (HS-GC-IMS), electronic nose (E-nose) and electronic tongue (E-tongue). Background Art

[0002] As a food crop with extremely high nutritional value, quinoa has attracted wide attention due to its high protein content, excellent fatty acid composition, gluten-free and other characteristics. Flavor, as an important factor affecting the edible quality and consumer acceptance of quinoa, is affected by multiple factors such as the types and contents of fatty acids and the composition of volatile compounds. Existing flavor evaluation methods mostly rely on single technologies, which are difficult to comprehensively reflect the flavor characteristics of quinoa, and have defects such as limited detection dimensions, insufficient sensitivity, and complex analysis. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for evaluating the flavor of quinoa based on multimodal sensory detection, which can comprehensively, quickly and quantitatively obtain the flavor information of different quinoa varieties and reveal the relationship between fatty acids and flavor substances.

[0004] The present invention solves its technical problems by adopting the following technical solutions:

[0005] (1) Perform drying and pulverization treatment on quinoa samples: Thoroughly wash different-colored (white, red, black) quinoa samples to remove saponins, and then dry them at 40 °C for 4 hours, and pulverize all quinoa samples with a pulverizer.

[0006] (2) Use headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and gas chromatography-ion mobility spectrometry (HS-GC-IMS) technologies to obtain volatile compound data of the samples: a) Use headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) to detect volatile organic compounds in quinoa samples; b) Use gas chromatography-ion mobility spectrometry (HS-GC-IMS) for supplementary analysis to construct a flavor fingerprint map.

[0007] The headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) method includes:

[0008] a. Mix 5 g of the sample with 5 g of sodium chloride and water to make the total weight reach 50 g. After homogenization, transfer the mixture to a 20 mL headspace vial, inject 10 mL, add 50 μL of 0.02 mg / mL cyclohexanone standard solution to each vial, seal the vials, and place them in an autosampler for measurement in triplicate;

[0009] b. Extract using a DVB / CAR / PDMS fiber 50 / 30 μm under the following conditions: Incubate at 80 °C for 15 minutes, then extract at 80 °C for 15 minutes. Set the inlet temperature to 220 °C, the desorption time to 1 minute, use the split injection mode, and age the fiber at 250 °C for 10 minutes;

[0010] c. For chromatographic separation, use an HP-INNOWax capillary column 60 m × 0.25 mm × 0.25 μm. Use high-purity nitrogen ≥99.999% as the carrier gas with a flow rate of 1.0 mL / min. Set the inlet temperature to 220 °C. The temperature program is as follows: The initial temperature is 50 °C for 1 minute, then increase the temperature to 180 °C at a rate of 3 °C / min, and then increase the temperature to 230 °C at a rate of 10 °C / min and hold at this temperature for 10 minutes. The mass spectrometry parameters include an electron energy of 70 eV, an ion source temperature of 230 °C, and a scan mode of 50 to 500 m / z.

[0011] The described headspace gas chromatography-ion mobility spectrometry HS-GC-IMS is carried out by a gas chromatograph and an IMS instrument. The specific method includes: Weigh 2 g of quinoa flour and put it into a 20 mL headspace vial. Stir the sample and incubate at 60 °C for 20 minutes. Subsequently, 500 μL of the headspace gas is automatically injected into an injector set at 85 °C. At a temperature of 60 °C, use a chromatographic column FS-SE-54-CB-1, 15 m, inner diameter: 0.53 mm to separate aromatic compounds. Nitrogen is both the carrier gas and the drift gas. Set the carrier gas flow rate as follows: The initial flow rate is 2 mL / min, hold for 2 minutes, then linearly increase to 10 mL / min within 8 minutes, and further increase to 100 mL / min within the subsequent 10 minutes. Set the drift gas flow rate to 75 mL / min, the drift tube length to 5.3 cm, and the linear voltage inside the tube to 500 V / cm. Measure each sample three times.

[0012] (3) Use electronic nose and electronic tongue technologies to obtain the odor and taste data of the sample: Use electronic nose technology to identify and statistically analyze the odor response pattern of the quinoa sample; Use electronic tongue technology to detect the basic taste characteristics of the quinoa sample such as sour, sweet, bitter, salty, and umami;

[0013] The method for the electronic nose (E-nose) to obtain the odor data of the sample includes: 1 g of each sample was transferred to a headspace vial, with three replicates for each sample. The vials were equilibrated at room temperature for 40 minutes to release volatile substances before inserting the sampling probe. Statistical analysis was performed on the normalized sensor responses G / G0 of 10 metal oxide semiconductor (MOS) sensors. The principal component analysis (PCA) and radar chart were used to visually display the differences in the samples.

[0014] The headspace analysis in the headspace vial was carried out under the following parameters: sensor cleaning time: 120 s, baseline reset: 5 s, sample preparation: 5 s, injection flow rate detection time: 240 s. The sensor was purged after each measurement to remove residual compounds before the next analysis.

[0015] The method for the electronic tongue (E-tongue) to obtain the taste data of the sample includes: sample preparation and taste difference analysis. 3 g of each type of sample was weighed and placed into a beaker respectively. Each sample was mixed with 200 mL of pure water and cultured in a water bath at 40 °C for 2 hours to extract soluble compounds. After filtration, the supernatant was collected, and its pH value and total soluble solid content were measured. The pH value of the sample was adjusted to between 4 and 7, and it was ensured that the total soluble solid content was less than 5%. The treated sample was analyzed using the electronic tongue; further, the electronic tongue (E-tongue) was used to analyze the taste characteristics of the sample. 30 mL of the filtered supernatant was transferred to a sample cup, and the following taste attributes were recorded using the seven lipid membrane sensors of the electronic tongue (E-tongue): sourness, bitterness, astringency, aftertaste - B, aftertaste - A, umami, richness, saltiness, and sweetness. All samples were analyzed in triplicate, and the principal component analysis (PCA) was used to distinguish the taste characteristics.

[0016] (4) Detection of the fatty acid composition of the sample: Gas chromatography was used to quantitatively analyze the fatty acid composition in the quinoa sample;

[0017] The sample preparation method in the method for determining the fatty acids in the sample by gas chromatography (GC) includes: A homogeneous sample weighing 0.1 g to 10 g, accurate to 0.1 mg, containing about 100 mg to 200 mg of fat was transferred to a 250 mL round-bottom flask equipped with a reflux condenser. 2.0 mL of an 1 mg / mL tricaprylin internal standard solution in heptane, 100 mg of pyrogallic acid, and 3 - 4 boiling chips were added. The mixture was vortexed for 1 minute, and then 2.0 mL of 95% (v / v) ethanol and 4.0 mL of deionized water were added. The solution was magnetically stirred at 60 °C for 5 minutes.

[0018] In the method for determining fatty acids in the sample by gas chromatography (GC), the acid hydrolysis method is used for fat determination. The specific method includes: adding 10 ml of hydrochloric acid solution to the mixture and fully homogenizing it, immersing the flask in a water bath of 70° C. to 80° C. for 40 minutes, shaking the flask manually every 10 minutes during the hydrolysis process to resuspend the wall particles, cooling the mixture to room temperature after the hydrolysis is completed, then adding 10 mL of 95% ethanol, homogenizing the mixture, transferring the hydrolyzate to a separation funnel, rinsing the flask with v / v=50 mL of a 1:1 ether-petroleum ether mixture, mixing it with the hydrolyzate, covering the funnel cover, shaking vigorously for 5 minutes, standing for 10 minutes, collecting the ether layer in a 250 mL flask, repeating the extraction twice, rinsing the funnel with 10 mL of the ether mixture each time, concentrating the combined ether extract to dryness using a rotary evaporator to obtain a fat extract, and adding w / v=8 mL of 2% methanolic NaOH, reflux at 80±1℃ until the oil droplets are dissolved to saponify the extract. Then, add 7 ml of 15% boron trifluoride-methanol complex through a condenser and continue to reflux at 80±1℃ for 2 minutes. Immediately cool the flask to 25℃, then add 10-30mL of n-heptane. After shaking for 2 minutes, add saturated sodium chloride solution to induce phase separation. Transfer the upper n-heptane layer to a test tube containing 3-5g of anhydrous Na2SO4, shake for 1 minute, let stand for 5 minutes, and filter the dried supernatant into a gas chromatography bottle for analysis.

[0019] (5) Principal component analysis and flavor modeling were performed on the above data to evaluate the quinoa flavor: principal component analysis (PCA), heat map analysis and correlation analysis were performed on the above multimodal sensory detection data to establish a relationship model between fatty acids and flavor substances for quinoa flavor evaluation.

[0020] All experimental data were processed by Excel and Origin 2022, and SPSS27.0 software was used for statistical significance and correlation analysis. P<0.05 was considered significant.

[0021] The advantages and positive effects of the present invention are:

[0022] The present invention improves the comprehensiveness and accuracy of quinoa flavor detection through multimodal detection means, and through experiments, it is verified that HS-SPME-GC-MS has identified 32 volatile compounds in total. Among them, there are 29 (350.19 μg / kg) in white quinoa, 20 (298.75 μg / kg) in black quinoa, and 17 (254.85 μg / kg) in red quinoa. Aldehydes (such as hexanal and trans-2-nonenal) are the main components in the formation of quinoa flavor, and the content of aldehydes in white quinoa is the highest. HS-GC-IMS analysis further determines 54 volatile substances, and the contents of compounds such as benzaldehyde and heptanal in white quinoa are significantly higher than those of other varieties. Electronic nose and tongue analysis show that the aroma characteristics of white quinoa are significantly different from those of black quinoa and red quinoa, with a higher acidic reaction value and relatively balanced bitterness and sweetness. Fatty acid analysis shows that all three types of quinoa are mainly composed of oleic acid, linoleic acid, and palmitic acid, and the total fatty acid content of white quinoa is the highest (1.496 g / 100 g). Correlation analysis shows that there is a significant positive correlation between fatty acid content and volatile flavor substances (p<0.05), which can achieve rapid differentiation and classification of the flavors of different varieties of quinoa, revealing the internal relationship between fatty acid components and flavor substances, providing a multimodal evaluation method for characterizing the fatty acids and volatile flavor compounds of quinoa, and providing important application results for the variety breeding and product development of quinoa. Description of the Drawings

[0023] Figure 1 Types of volatile organic compounds in different varieties of quinoa;

[0024] Figure 2 Contents of volatile organic compounds in different varieties of quinoa;

[0025] Figure 3 Heat map of the contents of volatile organic compounds in different varieties of quinoa;

[0026] Figure 4 Principal component analysis of volatile organic compounds in tricolor quinoa;

[0027] Figure 5 PCA diagram of volatile components in tricolor quinoa;

[0028] Figure 6 Qualitative spectrum of volatile HS-GC-IMS in tricolor quinoa;

[0029] Figure 7 Two-dimensional spectrum of volatile components in tricolor quinoa samples by GC-IMS;

[0030] Figure 8 Fingerprint map of volatile components in tricolor quinoa;

[0031] Figure 9PCA diagram of volatile aroma of tricolor quinoa detected by electronic nose;

[0032] Figure 10 Loading diagram of volatile aroma of tricolor quinoa detected by electronic nose;

[0033] Figure 11 Radar diagram of tricolor electronic nose detected by electronic nose;

[0034] Figure 12 PCA diagram of volatile aroma of tricolor quinoa detected by electronic tongue;

[0035] Figure 13 Loading diagram of volatile aroma of tricolor quinoa detected by electronic tongue;

[0036] Figure 14 Radar diagram of tricolor quinoa aroma detected by electronic tongue. Detailed implementation manners

[0037] The present invention will be further described in detail below through specific embodiments. The following embodiments are only descriptive and not restrictive, and the protection scope of the present invention cannot be limited thereby.

[0038] In this embodiment, HS-SPME-GC-MS, HS-GC-IMS, electronic nose and electronic tongue were used to analyze the volatile flavor compounds of white, red and black quinoa in Qinghai. In addition, gas chromatography was used to analyze the fatty acid composition.

[0039] I. Materials and methods

[0040] 1.1 Materials and instruments

[0041] All three colors of quinoa (white, red and black) were from Mulan Agricultural Development Co., Ltd. (Golmud, Qinghai, China). Chemical reagents such as sodium chloride, cyclohexanone, pyrogallic acid, ethanol, hydrochloric acid, ether, petroleum ether, sodium hydroxide, methanol, boron trifluoride, heptane and sodium sulfate were purchased from Guangfu Technology Development Co., Ltd. (Tianjin, China).

[0042] Gas chromatography-mass spectrometer (Shimadzu 2010plus, Kyoto, Japan); Gas chromatograph (Agilent 490, California, USA); IMS instrument (G.A.S. Dortmund, Germany); Electronic nose and electronic tongue (AIRSENSE, Germany); Crusher (150T, Xichu, Jinhua, Zhejiang).

[0043] 1.2 Sample preparation

[0044] The three colors of quinoa were thoroughly washed to remove saponins and then dried at 40 °C for 4 hours. All quinoa samples were crushed with a crusher.

[0045] 1.3 HS-SPME-GC-MS Analysis

[0046] The volatile organic compounds in quinoa samples were analyzed by GC-MS. Specifically, 5 g of the sample was mixed with 5 g of sodium chloride and water to make the total weight reach 50 g. After homogenization, the mixture was transferred to a 20 mL headspace vial and 10 mL was injected. 50 μL of 0.02 mg / mL cyclohexanone standard solution was added to each vial. The vials were sealed and placed in an autosampler for measurement in triplicate.

[0047] Extraction was carried out using a DVB / CAR / PDMS fiber (50 / 30 μm) under the following conditions: incubation at 80 °C for 15 minutes, followed by extraction at 80 °C for 15 minutes. The inlet temperature was set at 220 °C, the desorption time was 1 minute, and the split injection mode was adopted. The fiber was aged at 250 °C for 10 minutes.

[0048] Chromatographic separation was performed using an HP-INNOWax capillary column (60 m × 0.25 mm × 0.25 μm, Agilent), with high-purity nitrogen (≥99.999%) as the carrier gas at a flow rate of 1.0 mL / min. The inlet temperature was 220 °C. The temperature program was as follows: the initial temperature was 50 °C for 1 minute, then it was increased to 180 °C at a rate of 3 °C / min, and then further increased to 230 °C at a rate of 10 °C / min and held at this temperature for 10 minutes.

[0049] The mass spectrometry parameters included an electron energy of 70 eV, an ion source temperature of 230 °C, and a scanning mode of 50 to 500 m / z.

[0050] 1.4 HS-GC-IMS Analysis

[0051] The analysis was carried out using a gas chromatograph and an IMS instrument. 2 g of quinoa flour was accurately weighed and placed in a 20 mL headspace vial. The sample was stirred and incubated at 60 °C for 20 minutes. Subsequently, 500 μL of the headspace gas was automatically injected into an injector set at 85 °C. Aromatic compounds were separated using a chromatographic column (FS-SE-54-CB-1, 15 m, inner diameter: 0.53 mm) at a temperature of 60 °C. Nitrogen was both the carrier gas and the drift gas. The carrier gas flow rate was set as follows: the initial flow rate was 2 mL / min, held for 2 minutes, then linearly increased to 10 mL / min in 8 minutes, and further increased to 100 mL / min in the subsequent 10 minutes. The drift gas flow rate was set at 75 mL / min, the drift tube length was 5.3 cm, and the linear voltage inside the tube was 500 V / cm. Each sample was measured three times.

[0052] 1.5 Electronic Nose Analysis

[0053] The flavor characteristics of the samples were compared using an electronic nose. 1 g of each sample was transferred to a headspace vial (three replicates for each sample). The vials were equilibrated at room temperature for 40 minutes to release volatile substances before inserting the sampling probe. Headspace analysis was carried out under the following parameters: sensor cleaning time: 120 s; baseline reset: 5 s; sample preparation: 5 s; sample injection flow rate detection time: 240 s. The sensor was purged after each measurement to remove residual compounds before the next analysis.

[0054] Statistical analysis was performed on the normalized sensor responses (G / G0) of 10 metal oxide semiconductor (MOS) sensors. Principal component analysis (PCA) and radar charts were used to visually display the differences in the samples.

[0055] 1.6 E-tongue analysis

[0056] 1.6.1 Sample preparation

[0057] Black quinoa, red quinoa, and white quinoa (3 g each) were weighed and placed separately in beakers. Each sample was mixed with 200 mL of pure water and incubated in a water bath at 40 °C for 2 hours to extract soluble compounds. After filtration, the supernatant was collected and its pH value and total soluble solid content were measured. The pH value of the sample was adjusted to between 4 and 7, and the total soluble solid content was ensured to be less than 5%. The treated samples were analyzed using an electronic tongue.

[0058] 1.6.2 Taste difference analysis method

[0059] The taste characteristics of the samples were analyzed using an electronic tongue. Approximately 30 mL of the filtered supernatant (Section 1.6.1) was transferred to a sample cup. The system was equipped with seven lipid membrane sensors that could record the following taste attributes: sourness, bitterness, astringency, aftertaste - B, aftertaste - A, umami, richness, saltiness, and sweetness. The operating parameters included: sensor cleaning: 5 minutes; sample testing: 30 s; aftertaste measurement: 5 minutes: 30 s; aftertaste measurement: 30 s. All samples were analyzed in triplicate. Principal component analysis (PCA) was used to distinguish the taste characteristics.

[0060] 1.7 Fatty acid determination

[0061] 1.7.1 Sample preparation

[0062] Transfer a homogeneous sample weighing from 0.1 g to 10 g (accurate to 0.1 mg, containing approximately 100 mg to 200 mg of fat) into a 250 mL round-bottom flask equipped with a reflux condenser. Exactly add 2.0 mL of a tricaprin internal standard solution (1 mg / mL in heptane), 100 mg of pyrogallic acid, and 3 - 4 boiling chips. Vortex the mixture for 1 minute, then add 2.0 mL of 95% (v / v) ethanol and 4.0 mL of deionized water. Stir the solution magnetically at 60 °C for 5 minutes.

[0063] 1.7.2 Acid Hydrolysis Method

[0064] Add 10 mL of hydrochloric acid solution to the mixture and homogenize thoroughly. Immerse the flask in a water bath at 70 °C to 80 °C for 40 minutes. During hydrolysis, manually shake the flask every 10 minutes to resuspend the particles adhering to the wall. After hydrolysis, cool the mixture to room temperature.

[0065] Subsequently, add 10 mL of 95% ethanol and homogenize the mixture. Transfer the hydrolyzate to a separating funnel, rinse the flask with 50 mL of a 1:1 (v / v) ether - petroleum ether mixture, and combine it with the hydrolyzate. Cover the funnel and shake vigorously for 5 minutes, then let it stand for 10 minutes. Collect the ether layer into a 250 mL flask. Repeat the extraction twice, rinsing the funnel with 10 mL of ether mixture each time.

[0066] Concentrate the combined ether extracts to dryness using a rotary evaporator (Heidolph, 200 mbar, 40 °C) to obtain a fat extract. Add 8 mL of 2% (w / v) methanol NaOH to the extract and reflux at 80 ± 1 °C until the oil droplets dissolve to saponify the extract.

[0067] Next, add 7 mL of 15% boron trifluoride - methanol complex through the condenser and continue refluxing at 80 ± 1 °C for 2 minutes. Immediately cool the flask to 25 °C, then add 10 - 30 mL of n - heptane. After shaking for 2 minutes, add saturated sodium chloride solution to induce phase separation. Transfer the upper n - heptane layer to a test tube containing 3 - 5 g of anhydrous Na2SO4, shake for 1 minute, and let it stand for 5 minutes. Filter the dried supernatant into a gas chromatography vial for analysis.

[0068] 1.8 Data Processing and Statistical Analysis

[0069] All experimental data were calculated by Excel, and the figures were plotted by Origin 2022. Statistical significance and correlation analysis were performed using SPSS 27.0 software, with P < 0.05 indicating significant differences

[0070] II. Results and Discussion

[0071] 2.1 HS-SPME-GC-MS Analysis

[0072] 2.1.1 Analysis of Volatile Components in Different Varieties of Quinoa Samples

[0073] Table 1 lists the results of detecting volatile flavor substances in three different varieties of quinoa samples by HS-SPME-GC-MS. A total of 32 volatile components were detected. 29 were detected in white quinoa, including 1 ester, 7 aldehydes, 12 alcohols, 5 ketones, 3 acids and 1 alkene; 20 were detected in black quinoa, including 1 ester, 6 aldehydes, 9 alcohols, 1 ketone, 2 acids and 1 alkene; 17 were detected in red quinoa, including 1 ester, 6 aldehydes, 6 alcohols, 1 ketone and 3 acids. These substances mainly come from lipid oxidation and the decomposition of proteins, carbohydrates and amino acids. White quinoa contains more aldehydes than black and red quinoa, and aldehydes are the main components forming the flavor of quinoa.

[0074] From Figure 1 、 Figure 2 it can be seen the types and contents of volatile organic compounds in different varieties of quinoa. The total volatile content of white quinoa is the highest, reaching 350.19 μg / kg; followed by black quinoa with a total content of 298.75 μg / kg; the total content of red quinoa is the lowest, at 254.85 μg / kg. Therefore, the flavor of white quinoa is better than that of the other two kinds of quinoa.

[0075]

[0076]

[0077] 2.1.2 Analyze the changes in the concentration of volatile organic compounds (VOCs) in different varieties of quinoa samples.

[0078] Figure 3 Shows the differences in the content of volatile compounds between different quinoa varieties. The darker the red, the higher the concentration of the compound, and the darker the blue, the lower the concentration. For compounds not detected, their values are set to zero and plotted in the corresponding graph.

[0079] Heat maps were used to visually show the changes in the concentration of volatile organic compounds (VOCs) in different samples. Each row represents a flavor compound and each column corresponds to a sample. In all three samples, the concentrations of compounds such as 2,2,4-trimethyl-1,3-pentanediol diisobutyrate, 1-nonanal, 1-hexanol and hexanoic acid are high, indicating that these substances may contribute to the common flavor characteristics of quinoa. Volatile organic compounds with higher ROAV values are generally considered to be the main components of the aromatic characteristics of grains. In Figure 2 , the voc values of alcohols are higher, so aldehydes have a greater impact on the flavor of quinoa

[0080] The quinoa samples also contain a large amount of 1-octanol, which has a strong greasy and citrus odor. In black and red quinoa, the contents of 2-hexenal and trans-2-heptenal are significantly reduced. As is well known, these two compounds produce an oily and certain vegetable-like flavor. In addition, in the quinoa samples, 2-methylbutyraldehyde has the lowest aroma intensity and has a coffee and cocoa flavor, indicating that this special odor is not obvious compared with other varieties.

[0081] Overall, the heatmap analysis shows that the concentration of volatile organic compounds in white quinoa is higher than that in black and red quinoa, indicating that white quinoa has a better flavor.

[0082] 2.1.3 Principal component analysis of different varieties of quinoa

[0083] Principal component analysis was performed on the 32 detected volatile organic compounds, and the results are shown in Figure 4 . The first principal component (PC1) and the second principal component (PC2) explained 74.1% and 25.9% of the total variance, respectively. Black and red quinoa are closely clustered in distribution, and the eigenvalue of PC1 is small. In contrast, white quinoa is located farther away from the other two quinoa, showing the largest eigenvalue of PC1. In the loading plot, the 32 volatile organic compounds are mainly distributed along the positive axes of PC1 and PC2. The main compounds causing the aroma differences among quinoa varieties include hexanoic acid, trans-2-nonenal, d-limonene, 1-nonanol, n-heptanol, (E)-linalool oxide (furan type), methyl heptenone, n-heptanal, n-hexanol, and 2-ethylhexanol. These compounds play a crucial role in distinguishing the aroma characteristics of the three quinoa varieties.

[0084] 2.2 HS-GC-IMS analysis

[0085] 2.2.1 Comparative analysis of the contents of volatile compounds in quinoa

[0086] Principal component analysis (PCA) is a powerful multivariate data processing technique, known for its ability to handle complex data and identify subtle variables. On the other hand, cluster analysis represents the similarity among multiple samples, and samples with closer clustering show smaller differences. As Figure 5 shown, blue represents white quinoa, red represents red quinoa, and black represents black quinoa. PCA shows significant differences among white, red, and black quinoa samples.

[0087] Figure 6 , Figure 7The two-dimensional spectrum of quinoa is shown. Sample 1 is white quinoa, sample 2 is red quinoa, and sample 3 is black quinoa. The vertical coordinate represents the retention time of gas chromatography separation, and the horizontal coordinate corresponds to the relative drift time. Each point to the right of the reaction ion peak represents a product, with blue as the background. The darker the color, the higher the content of the product.

[0088] The GC-IMS analysis of volatile compounds in the samples showed that there were 54 volatile compounds and 66 peaks in the three types of quinoa. The detailed list of these volatile compounds is shown in Table 2.

[0089] Table 2 Details of volatile components in the samples

[0090]

[0091]

[0092]

[0093]

[0094] 2.2.2 Comparative analysis of fingerprints of volatile components in the samples

[0095] As Figure 8 shown, white quinoa contains more various volatile compounds, including benzaldehyde, nonanal, octanal, heptanal, hexanal, pentanal, butanal, propanal, e-2-heptenal, e-2-pentenal, 3-methyl-2-butenal, 6-methyl-5-hepten-2-one, 1-octen-3-one, 1-hydroxy-2-propanone, acetylene, 2-heptenone, 2-hexanone, 2-pentanone, 2-butanone, acetone, 1-pentanol, 1-penten-3-ol, 1-butanol, 2-propanol, methyl butyrate, butyl acetate, o-xylene, p-xylene, ethylbenzene, styrene, dimethyl disulfide, dimethyl sulfide, and so on.

[0096] Red quinoa is characterized by containing more β-pinene, 2-butanol, 3-methyl-3-buten-1-ol, methyl acetate, γ-butyrolactone, 2-methyl-2-pentenal, acetal, and similar substances.

[0097] The contents of propionic acid, acetic acid, 1-methyl-2-pyrrole, 1-hexanol, 3-methyl-1-butanol, 2-methyl-1-propanol, 1-propanol, ethanol, (E)-2-hexenal, 3-methylbutanal, 2-methylpropanal, ethyl acetate, ethyl butyrate, propyl acetate, 2-aminofuran, and several other compounds in black quinoa are increased.

[0098] Aldehydes are the main components of quinoa flavor. Among the three varieties, the concentration of aldehydes in white quinoa is the highest. Notably, 2-pentylfuran, a furan compound associated with fruity and grassy aromas, was detected in all samples. The content of propionaldehyde in white quinoa is significantly higher, while that in red and black quinoa is significantly lower. On the other hand, ketone compounds such as 2-heptanone, which have nutty and buttery aromas, are significantly higher in white quinoa. This is consistent with the results of HS-SPME-GC-MS detection. This may be related to lipid oxidation and amino acid decomposition. Butyraldehyde shows a green aroma; γ-butyrolactone brings caramel, cheese, and fruit aromas, and its formation shows a significant baking intensity response.

[0099] 2.3 Analysis of electronic nose results

[0100] The electronic nose was used to further determine the effects of germination and baking on quinoa flavor, and the dimensions of the response values of the six sensors of the electronic nose were analyzed by the PCA method. Generally, it is considered that a cumulative variance contribution rate exceeding 85% is sufficient to represent the overall characteristics of the samples. As Figure 9 shown, the contribution rate of PC1 is 61.93%, PC2 is 24.23%, and the total variance contribution rate is 86.16% (greater than 85%). This indicates that the electronic nose can successfully distinguish the odor differences of quinoa in three colors. In addition, if the volatile components and contents of the samples are more similar, their positions in the PCA will be closer. As ([[]] Figure 9 shown, quinoa in three colors are clearly separated, the distance between white quinoa samples and red quinoa samples is far, and black quinoa samples are located between the two. This shows the differences in odor characteristics among quinoa in three colors, especially highlighting the significant differences between white quinoa and red quinoa, while black quinoa has some similarities with quinoa in these two colors.

[0101] As Figure 10 、 Figure 11 shown, further analysis of the loadings of the first and second principal components indicates that the three quinoa samples showed strong responses to the W1C (benzene and aromatic compounds), W3C (ammonia and aromatic compounds), and W5C (short-chain alkanes) sensors. This indicates that the presence of these compounds may have a greater impact on the volatile compounds in quinoa samples. In addition, the response values of W1S and W1W of red quinoa samples are the highest, indicating that red quinoa samples may contain more methyl analogs and sulfides than other samples.

[0102] Compared with black quinoa, the distance between white quinoa samples and red quinoa samples is farther, and its flavor is better than that of black quinoa samples and red quinoa samples, which is consistent with the results obtained by HS-SPME-GC-MS and HS-GC-IMS detection.

[0103] 2.4 Analysis of electronic tongue results

[0104] Figure 12 The results in it show that PC1 and PC2 explain 93.71% of the total variance. The distances between samples on the PCA plot can be used to measure the similarity or difference in their flavors. The smaller the distance, the higher the similarity, and the larger the distance, the greater the difference. The fact that the three quinoa samples are scattered in different regions indicates that there are significant differences in the overall flavors of the three quinoa varieties.

[0105] According to Figure 13 、 Figure 14 it can be seen that the taste sensors respond to the tastes of all three quinoa varieties. There are obvious differences in the sour tastes of different quinoa varieties. The bitter and sweet sensors produced positive response values for all three quinoa varieties, while the response values of the umami, strong taste, aftertaste bitterness, and aftertaste astringency sensors were close to zero. The response values of the sour and salty sensors were negative, and the sour taste response value of black quinoa was the lowest, at -28.81, indicating that the sour taste of black quinoa is weaker than that of other quinoa varieties. In addition, except for white quinoa, there were no obvious differences in the responses of the bitter and sweet sensors of the other two varieties.

[0106] Overall, the flavors of the three quinoa varieties show that the overall flavor of white quinoa is better, which is consistent with the results of the previously determined volatile flavor substances.

[0107] 2.5 Fatty Acid Analysis

[0108] The fatty acid profiles of black, red, and white quinoa varieties were evaluated. Approximately 17 different fatty acids were detected, and the specific components are shown in Table 3. The total fatty acid content of white quinoa was 1.496 g / 100 g, including 17 fatty acids. The total fatty acid content of black quinoa was 0.778 g / 100 g, and 13 fatty acids were detected. In red quinoa, the total fatty acid content was 2.792 g / 100 g, including 11 fatty acids. It is worth noting that the contents of palmitic acid, oleic acid, linoleic acid, and α-linolenic acid were relatively high. The palmitic acid content was the highest in red quinoa, while the oleic acid content was the highest in Qinghai white quinoa. Linoleic acid had the highest content in black quinoa, and the content differences of α-linolenic acid among different varieties were relatively small. Overall, oleic acid, linoleic acid, and palmitic acid are the most important fatty acids in quinoa, with slight differences among varieties of different colors.

[0109] Table 3 Detailed Fatty Acid Compositions of Samples

[0110]

[0111] Note: Different letters indicate significant differences between data in the same column (a = 0.05), the same below

[0112] The correlation coefficients between the fatty acid content and the volatile flavor compound content in white quinoa, red quinoa, and black quinoa were 0.34, 0.38, and 0.38, respectively, and all were greater than 0.05, indicating that there was a positive correlation between the fatty acid content and the volatile flavor compound content in quinoa of the three colors. This shows that as the fatty acid content increased, the content of volatile flavor compounds also showed an upward trend. That is to say, fatty acids may be the precursor substances for the formation of quinoa flavor. During the processing, storage, and cooking of quinoa, fatty acids may generate volatile flavor compounds through a series of chemical reactions such as oxidation and hydrolysis, thus affecting the flavor of quinoa.

[0113] 3 Conclusions

[0114] Based on the above experimental results, the conclusion was drawn that gas chromatography - mass spectrometry and gas chromatography - olfactometry showed the differences in volatile organic compounds in different samples. Among them, the total volatile compound content of white quinoa was the highest, followed by black quinoa, and red quinoa was the lowest. Electronic nose analysis showed that W1C (benzene and aromatic compounds), W3C (ammonia and aromatic compounds), and W5C (short - chain alkane aromatic components) had a greater impact on flavor differences. Electronic tongue analysis showed that the acidity of white quinoa and red quinoa was slightly higher than that of black quinoa, while the sweetness and bitterness of black quinoa and red quinoa were slightly higher than that of white quinoa. Approximately 17 different fatty acids were detected in quinoa, among which the contents of oleic acid, linoleic acid, and palmitic acid were relatively high.

[0115] The above experimental results proved that the present invention improved the comprehensiveness and accuracy of quinoa flavor detection through multi - modal detection means, provided a multi - modal evaluation method for characterizing the fatty acids and volatile flavor compounds of quinoa, and provided important application results for the variety breeding and product development of quinoa.

[0116] Although the embodiments of the present invention are disclosed for illustrative purposes, those skilled in the art can understand that: within the spirit and scope of the present invention and the appended claims, various substitutions, changes, and modifications are possible. Therefore, the scope of the present invention is not limited to the content disclosed in the embodiments.

Claims

1. A method for evaluating the flavor of quinoa based on multi-modal sensory detection, characterized in that: The method includes the following steps: (1) Dry and crush the quinoa sample; (2) Use headspace solid-phase microextraction-gas chromatography-mass spectrometry HS-SPME-GC-MS and gas chromatography-ion mobility spectrometry HS-GC-IMS method to obtain the volatile compound data of the sample; (3) Use an electronic nose E-nose and an electronic tongue E-tongue to obtain the odor and taste data of the sample; (4) Combine gas chromatography GC to detect the fatty acid composition of the sample; (5) Conduct principal component analysis PCA, heat map analysis and correlation analysis on the above data, establish a relationship model between fatty acids and flavor substances, and use it for quinoa flavor evaluation.

2. The quinoa flavor evaluation method based on multi-modal sensory detection according to claim 1, wherein: In the headspace solid-phase microextraction-gas chromatography-mass spectrometry HS-SPME-GC-MS detection method, extraction is carried out with a DVB / CAR / PDMS optical fiber 50 / 30μm. The conditions include: incubation at 80°C for 15 minutes, then extraction at 80°C for 15 minutes, the inlet temperature is set at 220°C, the desorption time is 1 minute, the split injection mode is adopted, and the fiber is aged at 250°C for 10 minutes.

3. A quinoa flavor evaluation method based on multimodal sensory detection according to claim 1, characterized in that: In the headspace solid-phase microextraction-gas chromatography-mass spectrometry HS-SPME-GC-MS detection method, chromatographic separation is carried out using an HP-INNOWax capillary column 60m×0.25mm×0.25μm, with high-purity nitrogen ≥99.999% as the carrier gas, the flow rate is 1.0 mL / min, the inlet temperature is 220°C, and the temperature program is as follows: the initial temperature is 50°C, held for 1 minute, then increased to 180°C at a rate of 3°C / minute, and then increased to 230°C at a rate of 10°C / minute and held at this temperature for 10 minutes.

4. A quinoa flavor evaluation method based on multimodal sensory detection according to claim 1, characterized in that: The mass spectrometry parameters in the headspace solid-phase microextraction-gas chromatography-mass spectrometry HS-SPME-GC-MS detection method include an electron energy of 70 eV, an ion source temperature of 230°C, and a scanning mode of 50 to 500 m / z.

5. The quinoa flavor evaluation method based on multi-modal sensory detection according to claim 1, wherein: In the gas chromatography-ion mobility spectrometry HS-GC-IMS method, the sample is at a temperature of 60°C, and the chromatographic column used is FS-SE-54-CB-1, 15m, inner diameter: 0.53 mm, for separating aromatic compounds. Nitrogen is both the carrier gas and the drift gas. The carrier gas flow rate is set as follows: the initial flow rate is 2 mL / min, held for 2 minutes, then linearly increased to 10 mL / min within 8 minutes, and further increased to 100 mL / min within the subsequent 10 minutes. The drift gas flow rate is set at 75 mL / min, the drift tube length is 5.3 cm, and the linear voltage inside the tube is 500 V / cm.

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