Method for identifying varieties of smooth-peel kumquats and crisp and honey kumquats based on metabonomics and application

Abstract

The invention discloses a metabonomics-based method for identifying varieties of smooth-peel kumquats and crisp-honey kumquats and application of the metabonomics-based method, a metabonomics technical means is applied to identification of a new variety of crisp-honey kumquats and a traditional variety of smooth-peel kumquats for the first time, endogenous metabolites of the kumquats are detected to the maximum extent, the one-sidedness of a traditional analysis means is avoided, and the method is suitable for popularization and application. The result accuracy is improved; a large amount of original data detected based on LC-MS non-targeted scanning is combined with a multivariate statistical analysis method to obtain six characteristic metabolites for identifying the crisp and honey kumquats and the smooth-peel kumquats, and relative content thresholds of the characteristic metabolites of the two kumquat varieties are determined. The rapid identification of the two kumquat variety samples and the unknown variety kumquat sample is realized, and a scientific basis is provided for the quality control of the kumquat.

Description

Technical Field

[0001] The invention relates to the field of analysis and detection of secondary metabolites of specialty fruits, and in particular to a method for identifying smooth-skinned kumquat and crispy honey kumquat varieties based on metabolomics and an application thereof. Background Art

[0002] Kumquat, commonly known as kumquat, is a fruit tree of the genus Fortunella in the Rutaceae family. It has a long history of cultivation and consumption in Guangdong, Guangxi and other places, and is also widely planted in other provinces in South China and the middle and lower reaches of the Yangtze River. Rong'an County, Guangxi is one of the main production areas of kumquat. Rong'an kumquat (mandarin orange) was awarded the Guangxi Geographical Indication Protection Product in 2007. Slippery kumquat is the main traditional planting variety in Rong'an, Guangxi. Crisp honey kumquat is an excellent new kumquat variety bred from a single plant of bud mutation of Slippery kumquat. It was approved by the Guangxi Crop Variety Approval Committee in March 2014 (Guishen Guo 2014003). Crisp honey kumquat has the characteristics of good taste and excellent quality. The price of crisp honey kumquat is about three times that of slippery kumquat. As a high-end fruit with local characteristics, crisp honey kumquat has huge market potential.

[0003] At present, the comparative study of crisp honey kumquat and smooth skin kumquat mainly focuses on the differences in fruit quality, such as single fruit weight, longitudinal and transverse diameter, peel thickness and soluble solids, sucrose, total sugar, titratable acid content and juice rate, number of oil cells, and differences in the content of functional components such as flavonoids and phenolic acids. However, the biological characteristics and phenological periods of crisp honey kumquat and smooth skin kumquat are basically similar, and their appearance is highly similar, making it difficult to accurately distinguish and identify the two. At present, the distinction between the two is mainly based on their appearance and size (the average single fruit weight of crisp honey kumquat is about 35.7g, and the average single fruit weight of smooth skin kumquat is about 31.4g), which is highly subjective and experience-dependent, and when the two kumquats of different grades are mixed with each other, this judgment standard will not be applicable. Therefore, it is very difficult for ordinary consumers to distinguish between the two varieties of kumquats, and for relevant practitioners, empirical judgment will also 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 the field of crops or plants to reflect the changes in metabolites in different growth periods of plants, or to distinguish different varieties by the differences in metabolites. However, there are no reports on the identification and quality evaluation of kumquat varieties using metabolomics technology. In view of this, the present invention is proposed. Summary of the invention

[0005] In order to overcome the deficiencies in the above-mentioned prior art, the present invention provides a method and application for identifying the varieties of Slippery-skinned Kumquat and Crunchy-honeyed Kumquat based on metabolomics.

[0006] In a first aspect of the present invention, a method for identifying the varieties of Slippery Kumquat and Crunchy Honey Kumquat based on metabolomics is provided, wherein the method comprises the following steps:

[0007] S1) Extraction of kumquat samples;

[0008] S2) using a chromatography-mass spectrometer (LC-MS) to detect the extract obtained in step S1 to obtain detection data;

[0009] S3) processing and analyzing the detection data obtained in step S2, and then calculating the relative content of the metabolic marker;

[0010] When the relative content threshold of the metabolite markers in the kumquat sample meets the following conditions, the variety is determined to be crisp honey kumquat:

[0011] (-)-carvacrol = 8.63% to 11.78%,

[0012] Itaconic acid = 0.55% to 1.14%,

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

[0014] Retinol = 0.30% to 0.58%,

[0015] 4-terpene alcohol = 0.49% to 1.01%,

[0016] 1,2-cyclohexanediol = 1.01% to 1.61%;

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

[0018] (-)-carvacrol = 4.63% to 5.17%,

[0019] Itaconic acid = 0.10% to 0.33%,

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

[0021] Retinol = 0.08% to 0.20%,

[0022] 4-terpene alcohol = 1.31% to 2.96%,

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

[0024] In one embodiment of the present invention, when the relative content threshold (%) of the metabolite marker in the kumquat sample meets the following conditions, the variety is determined to be crisp honey kumquat:

[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 the metabolite markers in the kumquat sample meets the following conditions, the variety is determined to be 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, the step S1 also includes the step of collecting kumquat samples, specifically collecting crispy kumquat samples and smooth-skinned kumquat samples, chopping the crispy kumquat samples and the smooth-skinned kumquat samples, quick-freezing them, and storing them at -60°C to -100°C.

[0039] In one embodiment of the present invention, the step S1 also includes the step of collecting kumquat samples, specifically collecting crisp honey kumquat samples and smooth skin kumquat samples, chopping the crisp honey kumquat samples and the smooth skin kumquat samples, quick-freezing them with liquid nitrogen, and storing them at -80°C.

[0040] Furthermore, the step S1 includes the steps of mixing, oscillating, grinding, ultrasonicating and centrifuging the kumquat sample and the internal standard solution, and then filtering the supernatant obtained by centrifugation through a membrane to obtain a filtrate.

[0041] In one embodiment of the present invention, the step S1 comprises: 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), and vortexing for 30 seconds; adding steel beads, placing in a tissue grinder, and grinding at 55 Hz for 60 seconds; ultrasonicating at room temperature for 15 minutes; centrifuging at 12000 rpm and 4°C for 10 minutes, taking the supernatant and filtering it through a 0.22 μm membrane, and adding the filtrate to a detection bottle for LC-MS detection.

[0042] Furthermore, the chromatographic detection in step S2 is to perform chromatographic detection on the extract obtained in step S1 using both the positive ion mode (a) and the 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 in water, phase B: 0.1% formic acid in 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°C, 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 is to perform 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 of 3.50 kV, sheath gas 40 arb, auxiliary gas 10 arb, capillary temperature 325 °C, primary full scan with a resolution of 70000, primary ion scan range of m / z 100-1000, and HCD for secondary fragmentation, collision energy of 30 eV, secondary resolution of 17500, the first 3 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information.

[0047] (d) Negative ion mode, negative ion spray voltage was -2.50 kV, sheath gas was 40 arb, auxiliary gas was 10 arb, capillary temperature was 325 °C, primary full scan was performed at a resolution of 70000, primary ion scan range was m / z 100-1000, and HCD was used for secondary fragmentation, collision energy was 30 eV, secondary resolution was 17500, the first 3 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information.

[0048] Furthermore, the relative content of the metabolite marker described 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. The calculation method of the relative content of the metabolite marker is as follows:

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

[0050] The second aspect of the present invention provides a metabolic marker composition for identifying the smooth-skinned kumquat and the crisp honey kumquat varieties.

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

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

[0053] S1) Extraction of kumquat samples;

[0054] S2) using a chromatography-mass spectrometer (LC-MS) to detect the extract obtained in step S1 to obtain detection data;

[0055] S3) processing and analyzing the detection data obtained in step S2, and then identifying differential metabolites, and screening to obtain a metabolic marker composition for identifying the smooth skin kumquat and crisp honey kumquat varieties.

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

[0057] In one embodiment of the present invention, the data normalization process is to convert the original mass spectrum files of the LC-MS obtained in step S2 into the mzXML file format by the MSConvert tool in the Proteowizard software package; use the R XCMS software package to perform peak detection, peak filtering, and peak alignment, and set the parameters to bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdiff=0.01, method="centWave", obtain the retention time and peak area data of the substance, and use the total peak area normalization method to realize data correction. After normalization, the data is used to draw a cluster heat map by the R language Pheatmap package.

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

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

[0060] VIP≥1, FC≥2 or FC≤0.5 were used as the criteria for significant differences in metabolite expression, and the obtained differential metabolites were identified using the spectral database, thus obtaining a metabolic marker composition (i.e., characteristic metabolites) for identifying kumquats of the slippery skin variety and the crisp honey variety.

[0061] Furthermore, the spectrum database includes one or a combination of two or more 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 Ropls package, and PCA and OPLS-DA models are selected for principal component analysis and primary and secondary differential metabolite screening; VIP≥1, FC≥2 or FC≤0.5 are used as standards for significant differences in metabolite expression, and the obtained significant differential metabolites are identified using spectral databases such as HMDB, massbank, LipidMaps, mzcloud, and self-built metabolite standard databases, so as to obtain a metabolic marker composition (i.e., characteristic metabolites) for identifying kumquat and kumquat.

[0063] Furthermore, the step S1 also includes the step of collecting kumquat samples, specifically collecting crispy kumquat samples and smooth-skinned kumquat samples, chopping the crispy kumquat samples and the smooth-skinned kumquat samples, quick-freezing them, and storing them at -60°C to -100°C.

[0064] In one embodiment of the present invention, the step S1 also includes the step of collecting kumquat samples, specifically collecting crisp honey kumquat samples and smooth skin kumquat samples, chopping the crisp honey kumquat samples and the smooth skin kumquat samples, quick-freezing them with liquid nitrogen, and storing them at -80°C.

[0065] Furthermore, the step S1 includes the steps of mixing, oscillating, grinding, ultrasonicating and centrifuging the kumquat sample and the internal standard solution, and then filtering the supernatant obtained by centrifugation through a membrane to obtain a filtrate.

[0066] In one embodiment of the present invention, the step S1 comprises: 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), and vortexing for 30 seconds; adding steel beads, placing in a tissue grinder, and grinding at 55 Hz for 60 seconds; ultrasonicating at room temperature for 15 minutes; centrifuging at 12000 rpm and 4°C for 10 minutes, taking the supernatant and filtering it through a 0.22 μm membrane, and adding the filtrate to a detection bottle for LC-MS detection.

[0067] Furthermore, the chromatographic detection in step S2 is to perform chromatographic detection on the extract obtained in step S1 using both the positive ion mode (a) and the 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 in water, phase B: 0.1% formic acid in 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°C, 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 is to perform 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 of 3.50 kV, sheath gas 40 arb, auxiliary gas 10 arb, capillary temperature 325 °C, primary full scan with a resolution of 70000, primary ion scan range of m / z 100-1000, and HCD for secondary fragmentation, collision energy of 30 eV, secondary resolution of 17500, the first 3 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information.

[0072] (d) Negative ion mode, negative ion spray voltage was -2.50 kV, sheath gas was 40 arb, auxiliary gas was 10 arb, capillary temperature was 325 °C, primary full scan was performed at a resolution of 70000, primary ion scan range was m / z 100-1000, and HCD was used for secondary fragmentation, collision energy was 30 eV, secondary resolution was 17500, the first 3 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information.

[0073] The fourth aspect of the present invention provides an application of the metabolite marker composition as described in the second aspect in distinguishing the varieties of Slippery Kumquat and Crunchy Honey Kumquat.

[0074] The fifth aspect of the present invention provides a use of the metabolite marker composition as described in the second aspect in the preparation of a product for identifying the smooth-skinned kumquat and the crispy honey kumquat varieties.

[0075] Furthermore, the product for distinguishing the smooth-skinned kumquat and the crispy honey kumquat varieties is a reagent, a kit, a chip and / or an instrument for detecting the content of the metabolite marker composition in a kumquat sample.

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

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

[0078] Beneficial effects:

[0079] (1) The present invention is the first to apply metabolomics technology to the identification of a new kumquat variety, Crunchy Honey Kumquat, and a traditional variety, Smooth Skin Kumquat, to maximize the detection of endogenous metabolites in kumquat, avoid the one-sidedness of traditional analytical methods, improve the accuracy of the results, and provide new ideas and methods for the identification of kumquat varieties.

[0080] (2) The present invention combines a large amount of raw data detected by LC-MS non-targeted scanning with a multivariate statistical analysis method to obtain characteristic metabolites for identifying the crisp honey kumquat and the smooth skin kumquat, and determines the relative content threshold of the characteristic metabolites of these two kumquat varieties, thereby achieving rapid identification of samples of these two kumquat varieties and samples of kumquats of unknown varieties, and providing a scientific basis for the quality control of kumquats. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is a comparison chart between the Slippery Kumquat and the Crisp Honey Kumquat (Figure a is a bottom comparison chart between the Slippery Kumquat and the Crisp Honey Kumquat; Figure b is a side comparison chart between the Slippery Kumquat and the Crisp Honey Kumquat; A represents the Slippery Kumquat; B represents the Crisp Honey Kumquat).

[0082] Figure 2 These are the TIC spectra of Slippery Kumquat and Crunchy Honey Kumquat in positive ion mode and negative ion mode (Figure a is the TIC spectrum of Slippery Kumquat and Crunchy Honey Kumquat in positive ion mode; Figure b is the TIC spectrum of Slippery Kumquat and Crunchy Honey Kumquat in negative ion mode; red represents Slippery Kumquat; blue represents Crunchy Honey Kumquat).

[0083] Figure 3 These are the PCA score graph and loading graph of the principal component analysis of the Slippery Kumquat and the Crunchy Honey Kumquat (Figure a is the PCA score graph of the principal component analysis of the Slippery Kumquat and the Crunchy Honey Kumquat; Figure b is the loading graph of the PCA scores of the Slippery Kumquat and the Crunchy Honey Kumquat; A represents the Slippery Kumquat; B represents the Crunchy Honey Kumquat).

[0084] Figure 4 These are the OPLS-DA score graph and loading graph of the principal component analysis of Slippery Kumquat and Crunchy Honey Kumquat (Figure a is the OPLS-DA score graph of the principal component analysis of Slippery Kumquat and Crunchy Honey Kumquat; Figure b is the loading graph of the OPLS-DA scores of Slippery Kumquat and Crunchy Honey Kumquat; A represents Slippery Kumquat; B represents Crunchy Honey Kumquat).

[0085] Figure 5 This is the volcano plot of the differential metabolites between Smooth-skinned Kumquat and Crunchy-honeyed Kumquat (A represents Smooth-skinned Kumquat; B represents Crunchy-honeyed Kumquat).

[0086] Figure 6 This is the clustering heat map of differential metabolites between Smooth Skin Kumquat and Crunchy Honey Kumquat (A represents Smooth Skin Kumquat; B represents Crunchy Honey Kumquat). DETAILED DESCRIPTION

[0087] In order to more clearly understand the technical content of the present invention, the following embodiments are given in detail, the purpose of which is only to better understand the content of the present invention but not to limit the protection scope of the present invention.

[0088] Example 1 Screening of Metabolite Marker Compositions for Identifying Smooth-skinned Kumquat and Crunchy Honey Kumquat Varieties

[0089] 1. Instruments and test drugs

[0090] 1.1 Instrument

[0091] Thermo Vanquish ultra-high performance liquid chromatograph, Thermo Q Exactive Focus mass spectrometer.

[0092] 1.2 Drug testing

[0093] The ripe kumquat fruits were provided by Guangxi Rong'an Juxiangli Agriculture Co., Ltd. The samples were evenly collected from all around the fruit trees. 20 to 50 kumquat samples without pests and diseases and of the same size were randomly selected from each group for determination. The smooth-skinned kumquats were numbered A and the crispy honey kumquats were numbered B. The methanol, acetonitrile, and formic acid reagents were of chromatographic grade, the internal standard substance 2-chloro-L-phenylalanine was of analytical grade, and other reagents were of analytical grade.

[0094] 2. Collection of kumquat samples

[0095] The samples were minced and quickly 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 methanol containing 2-chloro-L-phenylalanine (4 ppm), vortex for 30 s; add steel beads, put into a tissue grinder, grind at 55 Hz for 60 s; ultrasonicate at room temperature for 15 min; centrifuge at 12000 rpm and 4 °C for 10 min, take the supernatant and filter it through a 0.22 μm membrane, add the filtrate to the detection bottle for LC-MS detection.

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

[0099] Chromatographic conditions: Thermo Vanquish (Thermo Fisher Scientific, USA) ultra-high performance liquid chromatography system, using ACQUITY HSS T3 (2.1×100 mm, 1.8 μm) (Waters, Milford, MA, USA) chromatographic column, flow rate of 0.3 mL / min, column temperature of 40°C, injection volume of 2 μL. Positive ion mode, mobile phase of 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 phases were acetonitrile (B3) and 5 mM ammonium formate water (A3), and the gradient elution program was: 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 ion source (ESI), data were collected in positive and negative ion modes. The positive ion spray voltage was 3.50 kV, the negative ion spray voltage was -2.50 kV, the sheath gas was 40 arb, and the auxiliary gas was 10 arb. The capillary temperature was 325 ° C, the first-level full scan was performed at a resolution of 70000, the first-level ion scan range was m / z 100-1000, and HCD was used for secondary fragmentation, the collision energy was 30 eV, the second-level resolution was 17500, and the first 3 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information.

[0101] 5. Metabolomics Data Processing and Analysis

[0102] (1) Data normalization

[0103] The original mass spectra of LC-MS of Kumquat and Kumquat were converted into mzXML file format by MSConvert tool in Proteowizard software package (v3.0.8789). Peak detection, peak filtering and peak alignment were performed by R XCMS software package, and the parameters were set as bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdiff=0.01, method=“centWave” to obtain the retention time and peak area data of the substance. The total peak area normalization method was used to realize data correction. After normalization, the data was used to draw the cluster heat map by Pheatmap package of R language.

[0104] (2) Multivariate statistical analysis

[0105] Multivariate statistical analysis was performed using the R language Ropls package, and PCA and OPLS-DA models were selected for principal component analysis and primary and secondary differential metabolite screening. VIP ≥ 1, FC ≥ 2 or FC ≤ 0.5 were used as the criteria for significant differences in metabolite expression, and the obtained differential metabolites were identified using spectral databases such as HMDB, massbank, LipidMaps, mzcloud, and self-built metabolite standard databases to obtain characteristic metabolites for identifying kumquat Slippery Skin and Crunchy Honey.

[0106] (3) Calculation of relative content

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

[0108] 6. Detection and analysis results

[0109] The TIC spectra of the smooth-skinned kumquat and the crisp honey kumquat in positive ion mode and negative ion mode are as follows Figure 2 a and Figure 2 As shown in b, the signal intensity and response time of the two kumquats in LC-MS detection are different. From the PCA score diagram and loading diagram ( Figure 3 a and Figure 3 b) OPLS-DA score and loading diagram ( Figure 4 a and Figure 4 b) It can be seen that the smooth skin kumquat and the crisp honey kumquat show a more obvious separation trend in the score diagram of the first principal component and the second principal component, indicating that there are significant differences in the metabolic components of these two kumquats, which can be distinguished by metabolomics technology. PCA and OPLS-DA models were further used to screen differential metabolites, and a volcano map of differential metabolites was drawn ( Figure 5 ) and differential metabolite clustering heat map ( Figure 6, the different colors in the drawing area are the colors filled with different values ​​obtained after the relative content of metabolites is standardized, red represents high content, and green represents low content). It can be seen that there are 172 non-differential metabolites and 43 differential metabolites in the samples of group B Crunchy Honey Kumquat and group A Slippery Skin Kumquat, of which 18 are up-regulated differential metabolites and 25 are down-regulated differential metabolites. There are significant differences in the expression levels of acids and alcohols between Slippery Skin Kumquat and Crunchy Honey Kumquat. With VIP≥1, FC≥2 or FC≤0.5 as the standard, the 6 most significant differential metabolites were screened out as shown in Table 1, namely: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpenol and 1,2-cyclohexanediol, which can be used as characteristic markers for distinguishing Slippery Skin Kumquat from Crunchy Honey Kumquat. By comparing the FC values, it can be seen that compared with the smooth kumquat (Group A), the (-)-carvacrol, itaconic acid, D-glucuronic acid and retinol in the crisp honey kumquat (Group B) showed an up-regulated expression trend, and 4-terpenol and 1,2-cyclohexanediol showed a down-regulated expression trend. That is, compared with the two kumquat varieties, the contents of (-)-carvacrol, itaconic acid, D-glucuronic acid and retinol were dominant in the crisp honey kumquat, and the contents of 4-terpenol and 1,2-cyclohexanediol were dominant in the smooth kumquat.

[0110] Table 1 Differential metabolites of kumquat and kumquat (B vs A, VIP≥1, FC≥2 or FC≤0.5)

[0111]

[0112] Table 2 Judgment thresholds of characteristic markers of Crunchy Honey Kumquat and Smooth Skin Kumquat

[0113]

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

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

[0116] 2. Detection and analysis results

[0117] Six significantly different metabolites were detected in the samples of Kumquat Slippery Skin and Kumquat Crunchy Honey: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpenol, and 1,2-cyclohexanediol. The relative contents are shown in Table 3, which are consistent with the judgment thresholds of characteristic markers of Kumquat Crunchy Honey and Kumquat Slippery Skin.

[0118] Table 3 Relative contents of different metabolites between Smooth-skinned Kumquat and Crunchy Honey Kumquat

[0119]

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

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

[0122] 2. Detection and analysis results

[0123] Six significantly different metabolites were detected in the samples of Kumquat Slippery Skin and Kumquat Crunchy Honey: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpenol, and 1,2-cyclohexanediol. The relative contents are shown in Table 4, which are consistent with the judgment thresholds of characteristic markers of Kumquat Crunchy Honey and Kumquat Slippery Skin.

[0124] Table 4 Relative contents of different metabolites between Smooth-skinned Kumquat and Crunchy Honey Kumquat

[0125]

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

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

[0128] 2. Detection and analysis results

[0129] The relative contents of the six characteristic markers in the method of the present invention: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol in the Rong'an kumquat samples are shown in Table 5, and their values ​​do not meet the judgment thresholds of the characteristic markers of Crisp Honey Kumquat and Slippery Kumquat in Table 1.

[0130] Table 5 Relative content of characteristic markers in comparative example 1 in Rong'an kumquat

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

[0132] From the results of the above embodiments and comparative examples, it can be seen that when the kumquat sample is the smooth-skinned kumquat or the crisp honey kumquat, the relative content values ​​of (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol and 1,2-cyclohexanediol meet the threshold range shown in Table 1, and the two kumquat varieties can be distinguished by the threshold range of Table 1; on the contrary, when the sample to be tested is other kumquat varieties, the relative content values ​​of (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol and 1,2-cyclohexanediol do not meet the threshold range shown in Table 1, which proves the feasibility of the method of the present invention for identifying kumquat varieties.

[0133] In summary, the method for identifying the varieties of Slippery Kumquat and Crunchy Kumquat based on metabolomics described in the present invention, through LC-MS metabolomics analysis, is used to quickly distinguish Slippery Kumquat and Crunchy Kumquat samples, providing a practical method for ensuring the quality of kumquats, preventing mixed sales of varieties on the market, and ensuring the healthy development of the industry.

Claims

1. A method for identifying the varieties of Slippery Kumquat and Crunchy Honey Kumquat based on metabolomics, the method comprising the following steps: S1) Extraction of kumquat samples; S2) using a chromatography-mass spectrometer (LC-MS) to detect the extract obtained in step S1 to obtain detection data; S3) processing and analyzing the detection data obtained in step S2, and then calculating the relative content of the metabolic marker; When the relative content threshold of the metabolite markers in the kumquat sample meets the following conditions, the variety is determined to be crisp honey kumquat: (-)-carvacrol = 8.63% to 11.78%, Itaconic acid = 0.55% to 1.14%, D-glucuronic acid = 0.90% to 2.65%, Retinol = 0.30% to 0.58%, 4-terpene alcohol = 0.49% to 1.01%, 1,2-cyclohexanediol = 1.01% to 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% to 5.17%, Itaconic acid = 0.10% to 0.33%, D-glucuronic acid = 0.18% to 0.30%, Retinol = 0.08% to 0.20%, 4-terpene alcohol = 1.31% to 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 is to perform chromatographic detection on the extract obtained in step S1 using both the positive ion mode (a) and the negative ion mode (b): (a) Positive ion mode, flow rate: 0.3 mL / min, column temperature: 40°C, phase A: 0.1% formic acid in water, phase B: 0.1% formic acid in 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; 10.1~12min, 8%B; (b) Negative ion mode, flow rate: 0.3 mL / min, column temperature: 40°C, 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; 10.1~12min, 8%B.

3. The method according to claim 1, characterized in that The mass spectrometry detection in step S2 is to perform 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 of 3.50 kV, sheath gas 40 arb, auxiliary gas 10 arb, capillary temperature 325 °C, full scan at a resolution of 70000, primary ion scan range of m / z 100-1000, and HCD for secondary fragmentation, collision energy of 30 eV, secondary resolution of 17500, the first 3 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information; (d) Negative ion mode, negative ion spray voltage was -2.50 kV, sheath gas was 40 arb, auxiliary gas was 10 arb, capillary temperature was 325 °C, primary full scan was performed at a resolution of 70000, primary ion scan range was m / z 100-1000, and HCD was used for secondary fragmentation, collision energy was 30 eV, secondary resolution was 17500, the first 3 ions of the acquisition signal were fragmented, and dynamic exclusion was used to remove unnecessary MS / MS information.

4. The method according to claim 1, characterized in that: The relative content of the metabolite marker described 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. The calculation method of the relative content of the metabolite marker is as follows: P s (%) = (A s / A t )×100, where P s is the relative content of metabolic markers, A s is the peak area of ​​the metabolic marker, A t is the total peak area of ​​all metabolites detected.

5. A metabolic marker composition for identifying the varieties of Slippery Kumquat and Crunchy Honey Kumquat, characterized in that: The metabolic marker composition comprises: (-)-carvacrol, itaconic acid, D-glucuronic acid, retinol, 4-terpene alcohol, and 1,2-cyclohexanediol.

6. A screening method for the metabolic marker composition for identifying the smooth skin kumquat and the crisp honey kumquat varieties as claimed in claim 5, the screening method comprising the following steps: S1) Extraction of kumquat samples; S2) using a chromatography-mass spectrometer (LC-MS) to detect the extract obtained in step S1 to obtain detection data; S3) processing and analyzing the detection data obtained in step S2, and then identifying differential metabolites, and screening to obtain a metabolic marker composition for identifying the smooth skin kumquat and crisp honey kumquat varieties.

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

8. The method according to claim 7, characterized in that The multivariate statistical analysis comprises the following steps: Construct the principal component analysis model (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) model; VIP≥1, FC≥2 or FC≤0.5 were used as the criteria for significant differences in metabolite expression, and the obtained differential metabolites were identified using the spectral database, thus obtaining a metabolic marker composition for identifying kumquat Slippery Skin and Kumquat Crunchy Honey.

9. Use of the metabolic marker composition as claimed in claim 5 in distinguishing the varieties of Slippery Kumquat and Crunchy Honey Kumquat.

10. Use of the metabolite marker composition according to claim 5 in preparing a product for identifying the varieties of Slippery Kumquat and Crunchy Honey Kumquat.

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