Method for identifying huangpi jincheng and buhumi jincheng varieties based on metabolomics and application thereof

Abstract

The application discloses a method for identifying varieties of smooth-skin kumquat and crisp honey kumquat based on metabolomics and application, for the first time, metabolomics technology is applied to the identification of the new variety of crisp honey kumquat and the traditional variety of smooth-skin kumquat, the endogenous metabolites of kumquat are detected to the maximum extent, the one-sidedness of the traditional analysis method is avoided, and the accuracy of the result is improved; a large amount of original data detected based on LC-MS non-target scanning is combined with a multivariate statistical analysis method, six characteristic metabolites for identifying the crisp honey kumquat and the smooth-skin kumquat are obtained, and the relative content threshold of the characteristic metabolites of the two kumquat varieties is determined, so that the two kumquat variety samples and unknown variety kumquat samples can be rapidly distinguished, and a scientific basis is provided for the quality control of the kumquat.

Description

A method and application for identifying smooth-skinned kumquat and crisp honey kumquat varieties based on metabolomics Technical Field

[0001] This invention relates to the field of secondary metabolite analysis and detection in specialty fruits, and in particular to a method and application for identifying smooth-skinned kumquat and crisp honey kumquat varieties based on metabolomics. Background Technology

[0002] Kumquat, also known as golden tangerine, is a fruit tree belonging to the genus *Fortunella* of the Rutaceae family. It has a long history of cultivation and consumption in Guangdong and Guangxi provinces, and is also widely grown in other provinces of South China and the middle and lower reaches of the Yangtze River. Rong'an County in Guangxi is one of the main production areas of kumquat, and Rong'an kumquats were recognized as a Guangxi geographical indication protected product in 2007. The smooth-skinned kumquat is the main traditional variety cultivated in Rong'an, Guangxi. The crispy honey kumquat is a superior new variety of kumquat bred from a single bud mutation of the smooth-skinned kumquat, and was approved by the Guangxi Crop Variety Approval Committee in March 2014 (Guangxi Approval No. 2014003). The crispy honey kumquat is characterized by its excellent taste and superior quality. Its price is approximately three times that of the smooth-skinned kumquat. As a high-end fruit with strong local characteristics, the crispy honey kumquat has huge market potential.

[0003] Currently, comparative studies on Crispy Honey Kumquat and Smooth Skin Kumquat mainly focus on differences in fruit quality, such as single fruit weight, longitudinal and transverse diameters, peel thickness, soluble solids, sucrose, total sugar, titratable acid content, juice yield, number of oil cells, and differences in the content of functional components such as flavonoids and phenolic acids. However, Crispy Honey Kumquat and Smooth Skin Kumquat have basically similar biological characteristics and phenological periods, and their appearance is highly similar, making it difficult to accurately distinguish between the two. Currently, the distinction between the two is mainly based on their size (the average single fruit weight of Crispy Honey Kumquat is about 35.7g, and that of Smooth Skin Kumquat is about 31.4g), which is highly subjective and relies heavily on experience. Furthermore, this criterion becomes inapplicable when different grades of the two types of kumquat are mixed together. Therefore, distinguishing between the two kumquat varieties is quite difficult for ordinary consumers, and even for professionals, experience-based judgment can affect the accuracy and reliability of the identification results.

[0004] Metabolomics can qualitatively and quantitatively analyze a large number of endogenous metabolites in organisms. In recent years, metabolomics methods based on liquid chromatography-mass spectrometry (LC-MS) have been widely used in crops and plants to reflect changes in metabolites at different growth stages or to distinguish different varieties based on differences in metabolites. However, there are currently no reports on the use of metabolomics technology for the identification and quality evaluation of kumquat varieties. Therefore, this invention is proposed. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, the present invention provides a method and application for identifying smooth-skinned kumquat and crisp honey kumquat varieties based on metabolomics.

[0006] In a first aspect, the present invention provides a method for identifying smooth-skinned kumquat and crisp honey kumquat varieties based on metabolomics, the method comprising the following steps:

[0007] S1) Extract the kumquat sample;

[0008] S2) The extract obtained in step S1 was detected using chromatography-mass spectrometry (LC-MS) to obtain detection data;

[0009] S3) Perform data processing and analysis on the detection data obtained in step S2, and then calculate the relative content of metabolic markers;

[0010] When the relative content threshold of metabolic markers in a kumquat sample meets the following conditions, the variety is determined to be Crispy Honey Kumquat:

[0011] (-)-Carvacrol = 8.63%–11.78%,

[0012] Itaconic acid = 0.55%–1.14%,

[0013] D-glucuronic acid = 0.90%–2.65%,

[0014] Retinol = 0.30%–0.58%,

[0015] 4-Terpenicillin alcohols = 0.49%–1.01%,

[0016] 1,2-Cyclohexanediol = 1.01%–1.61%;

[0017] When the relative content threshold of metabolites in a kumquat sample meets the following conditions, the variety is determined to be a smooth-skinned kumquat:

[0018] (-)-Carvacrol = 4.63%–5.17%,

[0019] Itaconic acid = 0.10%–0.33%,

[0020] D-glucuronic acid = 0.18%–0.30%,

[0021] Retinol = 0.08%–0.20%,

[0022] 4-Terpenicillin alcohols = 1.31%–2.96%,

[0023] 1,2-Cyclohexanediol = 2.32%–5.54%.

[0024] In one embodiment of the present invention, the variety is determined to be Crispy Honey Kumquat when the relative content threshold (%) of metabolic markers in a kumquat sample meets the following condition:

[0025] (-)-Carvacrol = 10.205 ± 1.575,

[0026] Itaconic acid = 0.845 ± 0.259

[0027] D-glucuronic acid = 1.775 ± 0.875

[0028] Retinol = 0.440 ± 0.140

[0029] 4-Terpene alcohol = 0.750 ± 0.260,

[0030] 1,2-Cyclohexanediol = 1.310 ± 0.300;

[0031] When the relative content threshold (%) of metabolic markers in a kumquat sample meets the following conditions, the variety is determined to be a smooth-skinned kumquat:

[0032] (-)-Carvacrol = 4.900 ± 0.270,

[0033] Itaconic acid = 0.215 ± 0.115

[0034] D-glucuronic acid = 0.240 ± 0.060

[0035] Retinol = 0.140 ± 0.060

[0036] 4-Terpene alcohol = 2.060 ± 0.750

[0037] 1,2-Cyclohexanediol = 3.930 ± 1.610.

[0038] Furthermore, before step S1, there is also a step of collecting kumquat samples, specifically collecting crisp honey kumquat samples and smooth skin kumquat samples, chopping the crisp honey kumquat samples and smooth skin kumquat samples, quick-freezing them, and storing them at -60℃ to -100℃.

[0039] In one embodiment of the present invention, the step of collecting kumquat samples is included before step S1. Specifically, crisp honey kumquat samples and smooth skin kumquat samples are collected, and the crisp honey kumquat samples and smooth skin kumquat samples are chopped and flash-frozen with liquid nitrogen and stored at -80°C.

[0040] Further, step S1 includes mixing, shaking, grinding, sonicating and centrifuging the kumquat sample with the internal standard solution, and then filtering the supernatant obtained by centrifugation through a membrane to obtain the filtrate.

[0041] In one embodiment of the present invention, step S1 includes: accurately weighing an appropriate amount of kumquat sample into a 2 mL centrifuge tube, adding 600 μL of methanol containing 2-chloro-L-phenylalanine (4 ppm), vortexing for 30 s; adding steel beads, placing the sample in a tissue homogenizer, and homogenizing at 55 Hz for 60 s; sonicating at room temperature for 15 min; centrifuging at 12000 rpm at 4℃ for 10 min, taking the supernatant and filtering it through a 0.22 μm membrane, adding the filtrate to a detection bottle for LC-MS detection.

[0042] Furthermore, the chromatographic detection in step S2 involves simultaneously performing chromatographic detection on the extract obtained in step S1 using both positive ion mode (a) and negative ion mode (b):

[0043] (a) Positive ion mode, flow rate: 0.3 mL / min, column temperature: 40 °C, phase A: 0.1% formic acid aqueous solution, phase B: 0.1% formic acid acetonitrile, gradient elution process: 0–1 min, 8% B; 1–8 min, 8%–98% B; 8–10 min, 98% B; 10–10.1 min, 98%–8% B; 10.1–12 min, 8% B;

[0044] (b) Negative ion mode, flow rate: 0.3 mL / min, column temperature: 40℃, phase A: 5 mM ammonium formate aqueous solution, phase B: acetonitrile, gradient elution process: 0–1 min, 8% B; 1–8 min, 8%–98% B; 8–10 min, 98% B; 10–10.1 min, 98%–8% B; 10.1–12 min, 8% B.

[0045] Furthermore, the mass spectrometry detection in step S2 involves simultaneously performing mass spectrometry detection on the extract obtained in step S1 using both positive ion mode (c) and negative ion mode (d):

[0046] (c) Positive ion mode, positive ion spray voltage is 3.50kV, sheath gas is 40arb, auxiliary gas is 10arb, capillary temperature is 325℃, first-stage full scan is performed with a resolution of 70000, the first-stage ion scan range is 100~1000m / z, and second-stage fragmentation is performed using HCD with a collision energy of 30eV and a second-stage resolution of 17500. The first 3 ions of the acquired signal are fragmented, and unnecessary MS / MS information is removed by dynamic exclusion.

[0047] (d) Negative ion mode: negative ion spray voltage is -2.50kV, sheath gas is 40arb, auxiliary gas is 10arb, capillary temperature is 325℃, first-stage full scan is performed with a resolution of 70000, the first-stage ion scan range is 100~1000m / z, and HCD is used for second-stage fragmentation with a collision energy of 30eV and a second-stage resolution of 17500. The first 3 ions of the acquired signal are fragmented, and unnecessary MS / MS information is removed by dynamic exclusion.

[0048] Furthermore, the relative content of the metabolic biomarker mentioned in step S3 is the ratio of the content of that substance to the total content of all substances detected in the sample, expressed as a ratio of peak areas. The method for calculating the relative content of the metabolic biomarker is as follows:

[0049] P s (%) = (A) s / A t )×100, where P s The relative content of metabolic markers, A s A represents the peak area of ​​a metabolic biomarker. t This represents the total peak area of ​​all detected metabolites.

[0050] In a second aspect, the present invention provides a metabolic marker composition for distinguishing between smooth-skinned kumquat and crisp honey kumquat varieties.

[0051] Furthermore, the metabolic marker composition comprises: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol.

[0052] A third aspect of the present invention provides a screening method for a metabolic marker composition for identifying smooth-skinned kumquat and crisp honey kumquat varieties as described in the second aspect, the screening method comprising the following steps:

[0053] S1) Extract the kumquat sample;

[0054] S2) The extract obtained in step S1 was detected using chromatography-mass spectrometry (LC-MS) to obtain detection data;

[0055] S3) The detection data obtained in step S2 is processed and analyzed, and then the differential metabolites are identified to screen out a metabolic marker composition for distinguishing between the smooth-skinned kumquat and the crisp honey kumquat varieties.

[0056] Furthermore, the data processing and analysis described in step S3 includes data normalization and multivariate statistical analysis of the detection data obtained in step S2.

[0057] In one embodiment of the present invention, the data normalization process involves converting the raw LC-MS mass spectrometry files of the smooth-skinned kumquat and crisp honey kumquat obtained in step S2 into mzXML file format using the MSConvert tool in the Proteowizard software package; peak detection, peak filtering, and peak alignment are performed using the R XCMS software package with parameters set to bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdiff=0.01, and method="centWave" to obtain the retention time and peak area data of the substances; data correction is achieved by normalizing the total peak area; and the normalized data is used to draw a cluster heatmap using the R language Pheatmap package.

[0058] Furthermore, the multivariate statistical analysis includes the following steps:

[0059] Construct principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models;

[0060] Using VIP≥1, FC≥2 or FC≤0.5 as the criteria for significant differences in metabolite expression, the obtained differentially expressed metabolites were identified using a spectral database, thus obtaining a metabolic marker composition (i.e., characteristic metabolites) for identifying smooth-skinned kumquats and crisp honey kumquats.

[0061] Furthermore, the spectral database includes one or more combinations of HMDB, massbank, LipidMaps, mzcloud, and a self-built metabolite standard database.

[0062] In one embodiment of the present invention, the multivariate statistical analysis is performed using the R language Rollls package, selecting PCA and OPLS-DA models for principal component analysis and screening of primary and secondary differential metabolites; using VIP≥1, FC≥2 or FC≤0.5 as the criteria for significant differences in metabolite expression, the obtained significantly differential metabolites are identified using spectral databases such as HMDB, massbank, LipidMaps, mzcloud and a self-built metabolite standard database, thus obtaining a metabolic marker composition (i.e., characteristic metabolites) for identifying smooth-skinned kumquat and crisp honey kumquat.

[0063] Furthermore, before step S1, there is also a step of collecting kumquat samples, specifically collecting crisp honey kumquat samples and smooth skin kumquat samples, chopping the crisp honey kumquat samples and smooth skin kumquat samples, quick-freezing them, and storing them at -60℃ to -100℃.

[0064] In one embodiment of the present invention, the step of collecting kumquat samples is included before step S1. Specifically, crisp honey kumquat samples and smooth skin kumquat samples are collected, and the crisp honey kumquat samples and smooth skin kumquat samples are chopped and flash-frozen with liquid nitrogen and stored at -80°C.

[0065] Further, step S1 includes mixing, shaking, grinding, sonicating and centrifuging the kumquat sample with the internal standard solution, and then filtering the supernatant obtained by centrifugation through a membrane to obtain the filtrate.

[0066] In one embodiment of the present invention, step S1 includes: accurately weighing an appropriate amount of kumquat sample into a 2 mL centrifuge tube, adding 600 μL of methanol containing 2-chloro-L-phenylalanine (4 ppm), vortexing for 30 s; adding steel beads, placing the sample in a tissue homogenizer, and homogenizing at 55 Hz for 60 s; sonicating at room temperature for 15 min; centrifuging at 12000 rpm at 4℃ for 10 min, taking the supernatant and filtering it through a 0.22 μm membrane, adding the filtrate to a detection bottle for LC-MS detection.

[0067] Furthermore, the chromatographic detection in step S2 involves simultaneously performing chromatographic detection on the extract obtained in step S1 using both positive ion mode (a) and negative ion mode (b):

[0068] (a) Positive ion mode, flow rate: 0.3 mL / min, column temperature: 40 °C, phase A: 0.1% formic acid aqueous solution, phase B: 0.1% formic acid acetonitrile, gradient elution process: 0–1 min, 8% B; 1–8 min, 8%–98% B; 8–10 min, 98% B; 10–10.1 min, 98%–8% B; 10.1–12 min, 8% B;

[0069] (b) Negative ion mode, flow rate: 0.3 mL / min, column temperature: 40℃, phase A: 5 mM ammonium formate aqueous solution, phase B: acetonitrile, gradient elution process: 0–1 min, 8% B; 1–8 min, 8%–98% B; 8–10 min, 98% B; 10–10.1 min, 98%–8% B; 10.1–12 min, 8% B.

[0070] Furthermore, the mass spectrometry detection in step S2 involves simultaneously performing mass spectrometry detection on the extract obtained in step S1 using both positive ion mode (c) and negative ion mode (d):

[0071] (c) Positive ion mode, positive ion spray voltage is 3.50kV, sheath gas is 40arb, auxiliary gas is 10arb, capillary temperature is 325℃, first-stage full scan is performed with a resolution of 70000, the first-stage ion scan range is 100~1000m / z, and second-stage fragmentation is performed using HCD with a collision energy of 30eV and a second-stage resolution of 17500. The first 3 ions of the acquired signal are fragmented, and unnecessary MS / MS information is removed by dynamic exclusion.

[0072] (d) Negative ion mode: negative ion spray voltage is -2.50kV, sheath gas is 40arb, auxiliary gas is 10arb, capillary temperature is 325℃, first-stage full scan is performed with a resolution of 70000, the first-stage ion scan range is 100~1000m / z, and HCD is used for second-stage fragmentation with a collision energy of 30eV and a second-stage resolution of 17500. The first 3 ions of the acquired signal are fragmented, and unnecessary MS / MS information is removed by dynamic exclusion.

[0073] A fourth aspect of the present invention provides the application of the metabolic marker composition as described in the second aspect in the identification of smooth-skinned kumquat and crisp honey kumquat varieties.

[0074] A fifth aspect of the invention provides the use of the metabolic marker composition as described in the second aspect in the preparation of a product for distinguishing between smooth-skinned kumquat and crisp honey kumquat varieties.

[0075] Furthermore, the product used to distinguish between smooth-skinned kumquat and crisp honey kumquat varieties is a reagent, kit, chip, and / or instrument for detecting the content of metabolic marker composition in kumquat samples.

[0076] In one embodiment of the present invention, the product for identifying the smooth-skinned kumquat and the crisp honey kumquat varieties is a reagent, kit, chip and / or instrument suitable for detecting the content of metabolic marker composition in kumquat samples by at least one of the following methods: liquid chromatography, ultra-high performance liquid chromatography, ultra-high performance liquid chromatography-tandem mass spectrometry, and liquid chromatography-tandem mass spectrometry.

[0077] In one embodiment of the present invention, the product for identifying the smooth-skinned kumquat and the crisp honey kumquat varieties is a reagent, kit, chip and / or instrument suitable for detecting the content of metabolic marker composition in kumquat samples by liquid chromatography-tandem mass spectrometry.

[0078] Beneficial effects:

[0079] (1) This invention is the first to apply metabolomics technology to the identification of the new variety of kumquat, Crispy Honey Kumquat, and the traditional variety, Smooth Skin Kumquat. It maximizes the detection of endogenous metabolites in kumquat, avoids the one-sidedness of traditional analytical methods, improves the accuracy of results, and provides new ideas and methods for the identification of kumquat varieties.

[0080] (2) This invention combines a large amount of raw data detected by LC-MS non-targeted scanning with multivariate statistical analysis methods to obtain characteristic metabolites for distinguishing between crisp honey kumquat and smooth skin kumquat, and determines the relative content thresholds of characteristic metabolites of these two kumquat varieties, so as to achieve rapid identification of samples of these two kumquat varieties as well as samples of unknown varieties, and provides a scientific basis for the quality control of kumquat. Attached Figure Description

[0081] Figure 1 shows a comparison between smooth-skinned kumquats and crispy honey kumquats (where, Figure a is a bottom comparison of smooth-skinned kumquats and crispy honey kumquats; Figure b is a side comparison of smooth-skinned kumquats and crispy honey kumquats; A represents smooth-skinned kumquats; B represents crispy honey kumquats).

[0082] Figure 2 shows the TIC spectra of smooth-skinned kumquat and crispy honey kumquat in positive ion mode and negative ion mode (where, Figure a shows the TIC spectra of smooth-skinned kumquat and crispy honey kumquat in positive ion mode; Figure b shows the TIC spectra of smooth-skinned kumquat and crispy honey kumquat in negative ion mode; red represents smooth-skinned kumquat; blue represents crispy honey kumquat).

[0083] Figure 3 shows the PCA score plots and loading plots of the principal component analysis for the smooth-skinned kumquat and the crispy honey kumquat (where, Figure a shows the PCA score plots of the principal component analysis for the smooth-skinned kumquat and the crispy honey kumquat; Figure b shows the loading plots of the PCA scores for the smooth-skinned kumquat and the crispy honey kumquat; A represents the smooth-skinned kumquat; B represents the crispy honey kumquat).

[0084] Figure 4 shows the OPLS-DA score plot and loading plot of the principal component analysis of the smooth-skinned kumquat and the crisp honey kumquat (where, Figure a is the OPLS-DA score plot of the principal component analysis of the smooth-skinned kumquat and the crisp honey kumquat; Figure b is the loading plot of the OPLS-DA score of the smooth-skinned kumquat and the crisp honey kumquat; A represents the smooth-skinned kumquat; B represents the crisp honey kumquat).

[0085] Figure 5 shows the differential metabolite volcano diagram of smooth-skinned kumquat and crispy honey kumquat (A represents smooth-skinned kumquat; B represents crispy honey kumquat).

[0086] Figure 6 shows a heatmap of differential metabolite clustering between smooth-skinned kumquat and crispy honey kumquat (A represents smooth-skinned kumquat; B represents crispy honey kumquat). Detailed Implementation

[0087] In order to better understand the technical content of the present invention, the following embodiments are provided in detail. The purpose of these embodiments is only to better understand the content of the present invention and not to limit the scope of protection of the present invention.

[0088] Example 1: Screening of metabolic marker compositions for differentiating between smooth-skinned kumquat and crisp honey kumquat varieties

[0089] 1. Instruments and reagents

[0090] 1.1 Instruments

[0091] Thermo Vanquish ultra-high performance liquid chromatograph and Thermo QExactive Focus mass spectrometer.

[0092] 1.2 Test Drugs

[0093] The mature kumquats were provided by Guangxi Rong'an Juxiangli Agricultural Co., Ltd. Samples were taken evenly from around the fruit trees, and 20-50 disease-free and uniformly sized kumquat samples were randomly selected from each group for testing. Smooth-skinned kumquats were numbered A, and crisp honey kumquats were numbered B. Methanol, acetonitrile, and formic acid were chromatographic grade reagents, and the internal standard 2-chloro-L-phenylalanine was analytical grade. All other reagents were analytical grade.

[0094] 2. Collection of Kumquat Samples

[0095] After the samples were chopped, they were flash-frozen in liquid nitrogen and stored at -80°C for later use.

[0096] 3. Pretreatment and preparation of kumquat samples

[0097] Accurately weigh an appropriate amount of kumquat sample into a 2 mL centrifuge tube, add 600 μL of methanol containing 2-chloro-L-phenylalanine (4 ppm), vortex for 30 s; add steel beads, place in a tissue homogenizer, and homogenize at 55 Hz for 60 s; sonicate at room temperature for 15 min; centrifuge at 12000 rpm at 4℃ for 10 min, take the supernatant and filter through a 0.22 μm membrane, add the filtrate to the detection bottle for LC-MS detection.

[0098] 4. Detection and identification of metabolites in kumquat samples

[0099] Chromatographic conditions: Thermo Vanquish (Thermo Fisher Scientific, USA) ultra-high performance liquid chromatography system, using ACQUITY. HSS T3 (2.1×100mm, 1.8μm) column (Waters, Milford, MA, USA), flow rate 0.3 mL / min, column temperature 40℃, injection volume 2 μL. Positive ion mode, mobile phase 0.1% formic acid acetonitrile (B2) and 0.1% formic acid water (A2), gradient elution program: 0–1 min, 8% B2; 1–8 min, 8%–98% B2; 8–10 min, 98% B2; 10–10.1 min, 98%–8% B2; 10.1–12 min, 8% B2. In negative ion mode, the mobile phase consisted of acetonitrile (B3) and 5 mM ammonium formate water (A3), with the gradient elution program as follows: 0–1 min, 8% B3; 1–8 min, 8%–98% B3; 8–10 min, 98% B3; 10–10.1 min, 98%–8% B3; 10.1–12 min, 8% B3.

[0100] Mass spectrometry conditions: Thermo Q Exactive Focus mass spectrometer (Thermo Fisher Scientific, USA), electrospray ionization (ESI) source, and positive and negative ion modes were used for data acquisition. The positive ion spray voltage was 3.50 kV, the negative ion spray voltage was -2.50 kV, the sheath gas was 40 alb, and the auxiliary gas was 10 alb. The capillary temperature was 325 °C. A first-stage full scan was performed at a resolution of 70,000 m / z, with a first-stage ion scan range of 100–1000 m / z. Second-stage fragmentation was performed using an HCD with a collision energy of 30 eV and a second-stage resolution of 17,500 m / z. The first three ions acquired were fragmented, and unnecessary MS / MS information was removed using dynamic exclusion.

[0101] 5. Processing and Analysis of Metabolomics Data

[0102] (1) Data normalization

[0103] The raw LC-MS mass spectrometry files of the obtained smooth-skinned kumquat and crisp honey kumquat were converted to mzXML file format using the MSConvert tool in the Proteowizard software package (v3.0.8789). Peak detection, peak filtering, and peak alignment were performed using the R XCMS software package with parameters set to bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdiff=0.01, and method="centWave". Retention time and peak area data of the substances were obtained. Data correction was performed using the total peak area normalization method. After normalization, the data were used to generate cluster heatmaps using the R language Pheatmap package.

[0104] (2) Multivariate statistical analysis

[0105] Multivariate statistical analysis was performed using the R language's Rolls package. Principal component analysis and primary and secondary differential metabolite screening were conducted using PCA and OPLS-DA models. VIP≥1, FC≥2, or FC≤0.5 were used as criteria for significant differences in metabolite expression. The obtained differential metabolites were identified using spectral databases such as HMDB, massbank, LipidMaps, mzcloud, and a self-built metabolite standard database to obtain characteristic metabolites for identifying smooth-skinned kumquats and crisp honey kumquats.

[0106] (3) Calculation of relative content

[0107] The relative content of characteristic metabolites is calculated using the following formula: P s (%) = (A) s / A t )×100, where P s For the relative content of the target differential metabolite, A s A represents the peak area of ​​the target metabolite. t The total peak area of ​​all detected metabolites

[0108] 6. Detection and Analysis Results

[0109] Figures 2a and 2b show the TIC spectra of the smooth-skinned kumquat and the crisp honey kumquat in positive and negative ion modes, respectively. The signal intensity and response time of the two types of kumquat differ in LC-MS detection. From the PCA score plots and loading plots (Figures 3a and 3b) and the OPLS-DA score plots and loading plots (Figures 4a and 4b), it can be seen that the smooth-skinned kumquat and the crisp honey kumquat show a clear separation trend in the first and second principal component score plots, indicating that there are significant differences in the metabolic components of these two types of kumquat, which can be distinguished using metabolomics techniques. Further screening of differential metabolites was conducted using PCA and OPLS-DA models, and a volcano plot (Figure 5) and a heatmap of differential metabolite clustering were plotted (Figure 6, where different colors in the plot area represent different values ​​obtained after standardization of the relative content of metabolites; red represents high content and green represents low content). It was found that there were 172 non-differential metabolites and 43 differential metabolites in the B group of crisp honey kumquat samples and the A group of smooth-skinned kumquat samples. Among these, 18 differential metabolites were upregulated and 25 were downregulated. Significant differences were found in the expression levels of acids and alcohols between smooth-skinned and crisp honey kumquats. Using VIP≥1, FC≥2, or FC≤0.5 as criteria, six of the most significant differential metabolites were selected, as shown in Table 1: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol. These can serve as characteristic markers for distinguishing between smooth-skinned and crisp honey kumquats. Comparing the FC values, it can be seen that compared with the smooth-skinned kumquat (Group A), the expression of (-)-carvacrol, itaconic acid, D-glucuronic acid and retinol in the crisp honey kumquat (Group B) showed an up-regulation trend, while the expression of 4-terpene alcohol and 1,2-cyclohexanediol showed a down-regulation trend. That is, compared with the two kumquat varieties, the content of (-)-carvacrol, itaconic acid, D-glucuronic acid and retinol is dominant in the crisp honey kumquat, while the content of 4-terpene alcohol and 1,2-cyclohexanediol is dominant in the smooth-skinned kumquat.

[0110] Table 1. Differential metabolites between smooth-skinned kumquats and crisp honey kumquats (B vsA, VIP≥1, FC≥2 or FC≤0.5)

[0111]

[0112] Table 2 Thresholds for Determining Characteristic Markers of Crisp Honey Kumquat and Smooth Skin Kumquat

[0113]

[0114] Example 2: Identification of Smooth-skinned Kumquat and Crispy Honey Kumquat Varieties

[0115] 1. All implementation steps in this embodiment, including the use of instruments and reagents, collection of kumquat samples, pretreatment and preparation of kumquat samples, detection and identification of kumquat sample metabolites, and processing and analysis of metabolomics data, are the same as in Example 1.

[0116] 2. Detection and Analysis Results

[0117] Six significantly different metabolites were detected in samples of smooth-skinned kumquat and crisp honey kumquat: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol. The relative contents are shown in Table 3, which meet the threshold for the identification of characteristic markers of crisp honey kumquat and smooth-skinned kumquat in Table 2.

[0118] Table 3. Relative contents of differential metabolites between smooth-skinned kumquats and crispy honey kumquats.

[0119]

[0120] Example 3: Identification of Smooth-skinned Kumquat and Crispy Honey Kumquat Varieties

[0121] 1. All implementation steps in this embodiment, including the use of instruments and reagents, collection of kumquat samples, pretreatment and preparation of kumquat samples, detection and identification of kumquat sample metabolites, and processing and analysis of metabolomics data, are the same as in Example 1.

[0122] 2. Detection and Analysis Results

[0123] Six significantly different metabolites were detected in samples of smooth-skinned kumquat and crisp honey kumquat: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol. The relative contents are shown in Table 4, which meet the threshold for the characteristic markers of crisp honey kumquat and smooth-skinned kumquat in Table 2.

[0124] Table 4. Relative contents of differential metabolites between smooth-skinned kumquats and crispy honey kumquats

[0125]

[0126] Comparative Example 1: Identification of Rong'an Kumquat Varieties

[0127] 1. The sample used in this comparative example was a mature Rong'an kumquat, which is currently another traditional main cultivated variety of kumquat besides the smooth-skinned kumquat. All implementation steps, including instruments and reagents, collection of kumquat samples, pretreatment and preparation of kumquat samples, detection and identification of kumquat sample metabolites, and processing and analysis of metabolomics data, were the same as in Example 1.

[0128] 2. Detection and Analysis Results

[0129] The relative contents of the six characteristic markers in the method of this invention—(-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol—in Rong'an kumquat samples are shown in Table 5. Their values ​​do not meet the threshold values ​​for the characteristic markers of crisp honey kumquat and smooth skin kumquat in Table 2.

[0130] Table 5. Relative contents of characteristic markers in Rong'an kumquats (Comparative Example 1).

[0131] Relative content of characteristic markers (%) (-) - Carvacrol 3.033±1.120 Itaconic acid 0.054±0.025 D- Glucuronic acid 3.910±0.781 Retinol 2.160±0.655 4- Terpenol 3.584±0.420 1,2-Cyclohexanediol-- surface

[0132] As can be seen from the above examples and comparative results, when the kumquat sample is a smooth-skinned kumquat or a crisp honey kumquat, the relative contents of (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol meet the threshold range shown in Table 2, and these two kumquat varieties can be distinguished by the threshold range in Table 2. Conversely, when the sample to be tested is another kumquat variety, the relative contents of (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol do not meet the threshold range shown in Table 2, which proves the feasibility of the method of the present invention for the identification of kumquat varieties.

[0133] In summary, the method for identifying the smooth-skinned kumquat and the crisp honey kumquat varieties based on metabolomics described in this invention provides a practical approach to ensure the quality of kumquats, prevent the sale of mixed varieties in the market, and safeguard the healthy development of the industry by using LC-MS metabolomics analysis to rapidly distinguish between smooth-skinned kumquat and crisp honey kumquat samples.

Claims

1. A method for identifying smooth-skinned kumquat and crisp honey kumquat varieties based on metabolomics, the method comprising the following steps: S1) extracting kumquat samples, including mixing the kumquat samples with methanol, shaking, grinding, sonicating, and centrifuging; S2) detecting the extract obtained in step S1 using chromatography-mass spectrometry (LC-MS) to obtain detection data; S3) processing and analyzing the detection data obtained in step S2, and then calculating the relative content of metabolic markers; the relative content of metabolic markers mentioned in step S3 is the ratio of the content of the substance to the total content of all substances detected in the sample, expressed as the ratio of peak areas, and the calculation method of the relative content of metabolic markers is as follows: P s (%) = (A) s / A t ) × 100, where, P s The relative content of metabolic markers, A s A represents the peak area of ​​a metabolic biomarker. t The total peak area of ​​all detected metabolites is used. The variety is identified as Crispy Honey Kumquat when the relative content thresholds of metabolic markers in the kumquat sample meet the following conditions: (-)-Carvacrol = 8.63%~11.78%, Itaconic acid = 0.55%~1.14%, D-glucuronic acid = 0.90%~2.65%, Retinol = 0.30%~0.58%, 4-terpene alcohol = 0.49%~1.01%, 1,2-cyclohexanediol = 1%. 0.01%~1.61%; When the relative content threshold of metabolites in the kumquat sample meets the following conditions, the variety is determined to be smooth-skinned kumquat: (-)-carvacrol = 4.63%~5.17%, itaconic acid = 0.10%~0.33%, D-glucuronic acid = 0.18%~0.30%, retinol = 0.08%~0.20%, 4-terpene alcohol = 1.31%~2.96%, 1,2-cyclohexanediol = 2.32%~5.54%.

2. The method according to claim 1, characterized in that, The chromatographic detection in step S2 involves simultaneously performing chromatographic detection on the extract obtained in step S1 using both positive ion mode (a) and negative ion mode (b): (a) Positive ion mode, flow rate: 0.3 mL / min, column temperature: 40℃, phase A: 0.1% formic acid aqueous solution, phase B: 0.1% formic acid acetonitrile, gradient elution process: 0~1 min, 8% B; 1~8 min, 8%~98% B; 8~10 min, 98% B; (a) Negative ion mode, flow rate: 0.3 mL / min, column temperature: 40℃, phase A: 5 mM ammonium formate aqueous solution, phase B: acetonitrile, gradient elution process: 0~1 min, 8% B; 1~8 min, 8%~98% B; 8~10 min, 98% B; 10~10.1 min, 98%~8% B; 10.1~12 min, 8% B.

3. The method according to claim 1, characterized in that, The mass spectrometry detection in step S2 involves simultaneously performing mass spectrometry detection on the extract obtained in step S1 using both positive ion mode (c) and negative ion mode (d): (c) Positive ion mode: positive ion spray voltage 3.50 kV, sheath gas 40 arb, auxiliary gas 10 arb, capillary temperature 325 ℃, first-stage full scan at a resolution of 70000, first-stage ion scan range 100~1000 m / z, and second-stage fragmentation using HCD at a collision energy of 30 eV, second-stage resolution 17500. The first 3 ions acquired are fragmented, and unnecessary MS / MS information is removed using dynamic exclusion; (d) Negative ion mode: negative ion spray voltage -2.50 kV, sheath gas 40 arb, auxiliary gas 10 arb, capillary temperature 325 ℃, first-stage full scan at a resolution of 70000, first-stage ion scan range 100~1000 m / z, and second-stage fragmentation using HCD at a collision energy of 30 eV. eV, secondary resolution of 17500, fragmentation of the first 3 ions in the acquired signal, and dynamic exclusion to remove unnecessary MS / MS information.

4. A metabolic marker composition for differentiating between smooth-skinned kumquat and crisp honey kumquat varieties, characterized in that, The metabolic marker composition comprises: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol.

5. A screening method for a metabolic marker composition for identifying smooth-skinned kumquat and crisp honey kumquat varieties as described in claim 4, the screening method comprising the following steps: S1) Extract kumquat samples; S2) Detect the extract obtained in step S1 using chromatography-mass spectrometry (LC-MS) to obtain detection data; S3) Process and analyze the detection data obtained in step S2, then identify the differential metabolites, and screen to obtain a metabolic marker composition for distinguishing between smooth-skinned kumquat and crisp honey kumquat varieties.

6. The method according to claim 5, characterized in that, The data processing and analysis described in step S3 includes data normalization and multivariate statistical analysis of the detection data obtained in step S2.

7. The method according to claim 6, characterized in that, The multivariate statistical analysis includes the following steps: constructing a principal component analysis (PCA) model and an orthogonal partial least squares discriminant analysis (OPLS-DA) model; using VIP≥1, FC≥2 or FC≤0.5 as the criteria for significant differences in metabolite expression, the obtained differentially expressed metabolites are identified using a spectral database, thus obtaining a metabolic biomarker composition for identifying smooth-skinned kumquats and crisp honey kumquats.

8. The use of the metabolic marker composition as described in claim 4 in the identification of smooth-skinned kumquat and crisp honey kumquat varieties.

9. The use of the metabolic marker composition as described in claim 4 in the preparation of a product for identifying the varieties of smooth-skinned kumquat and crisp honey kumquat.

Images