Saffron crocus origin tracing method based on multi-omics analysis

Abstract

The invention relates to the technical field of agricultural product quality identification and origin traceability, in particular to a saffron crocus origin traceability method based on multi-omics analysis. The saffron crocus origin tracing method based on multi-omics analysis comprises the following steps: 1) metabonomics analysis; according to the method, metabonomics data and proteomics data are integrated, the high-resolution origin traceability model is constructed, accurate identification of saffron crocus in different origins is achieved, and technical support is provided for saffron crocus quality control and market supervision.

Description

Technical Field

[0001] This invention relates to the field of agricultural product quality identification and origin traceability technology, specifically to a method for saffron origin traceability based on multi-omics analysis. Background Technology

[0002] Saffron ( Crocus sativus L. As a sterile triploid rhizome plant of the Iridaceae family, saffron's dried stigmas are hailed as "red gold" due to their combined medicinal and edible value. A single saffron flower contains only three stigmas, requiring 150 flowers to produce one gram of dried saffron. Furthermore, 90% of global production is concentrated in Iran, and this scarcity, coupled with regional variations, results in significant regional differences in quality and price. However, the market is rife with substandard products, with low-quality saffron being passed off as high-value products, severely damaging consumer rights and market order.

[0003] Currently, the international trade market primarily certifies saffron quality based on the content of active ingredients according to the ISO 3632 standard. However, as a triploid asexually propagated plant, saffron exhibits minimal interspecific variation, meaning samples from different origins share a similar compositional basis. Therefore, relying solely on the content of a single active ingredient is insufficient for accurate origin identification. Existing traceability technologies, such as HPLC-DAD chromatography, ATR-FTIR spectroscopy, or stable isotope analysis, have significant limitations: firstly, small sample sizes and high heterogeneity lead to poor consistency in test results; secondly, limitations in sample pretreatment methods result in incomplete coverage of polar and non-polar compounds, failing to fully capture the distinctive characteristics of origin differences.

[0004] Metabolomics, with its broad metabolite coverage, high sensitivity, and high throughput, has been proven effective in identifying metabolite changes in plants caused by differences in growth environment, organs, or growth stages. For example, it has shown good results in identifying the origin of Wuyi rock tea and analyzing the volatile components of colored rice from Thailand. Proteomics, on the other hand, can precisely track the accumulation, regulation, and interaction dynamics of proteins under environmental stimuli, such as the protein response of the alpine plant *Potentilla fruticosa* to altitude gradients. However, current research on saffron is mostly limited to single-omics analysis, lacking the analysis of the correlation between metabolites and proteins, and has not yet formed an efficient origin traceability technology system that can integrate multi-omics data.

[0005] Therefore, developing a method for tracing the origin of saffron that combines metabolomics and proteomics and is both accurate and stable has become a key requirement for solving market chaos and ensuring the healthy development of the saffron industry. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing saffron origin traceability technologies, such as incomplete coverage and poor consistency of results from single-omics analysis, and to provide a saffron origin traceability method based on multi-omics analysis. This method integrates metabolomics and proteomics data to construct a high-resolution origin traceability model, enabling accurate identification of saffron from different origins and providing technical support for saffron quality control and market supervision.

[0007] To achieve the above objectives, this invention provides a method for tracing the origin of saffron based on multi-omics analysis, comprising the following steps: 1) Metabolomics analysis: Saffron samples from different origins were pretreated, metabolites were extracted using the QuEChERS extraction method, and then the metabolites were detected by ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry to obtain metabolite mass spectrometry data. 2) Proteomics analysis: The saffron samples with the same pretreatment as in step 1) were lysed, precipitated, reconstituted, reduced alkylated, enzymatically digested, desalted and filtered, and then peptides were detected by ultra-high performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry to obtain protein spectrum data. 3) Data processing and origin traceability model construction: Chromatographic peak screening and metabolite identification were performed on the metabolite mass spectrometry data from step 1), and differential metabolites were screened through univariate test and multivariate statistical analysis; quantitative analysis and differential abundance protein screening were performed on the protein spectrometry data from step 2); and a saffron origin traceability model was constructed by combining differential metabolites and differential abundance proteins. 4) Origin verification: Take standard saffron samples from known origins and perform metabolomics and proteomics analysis according to steps 1)-2) to obtain their characteristic substance information and substitute it into the model constructed in step 3). Verify the accuracy of the model through sample clustering to achieve origin traceability of the saffron to be traced.

[0008] Preferably, in step 1), the pretreatment step includes: drying the saffron sample to constant weight, grinding it, and then sieving it; The steps of the QuEChERS extraction method include: mixing 0.5-2g of saffron sample powder with 2-8mL of acetonitrile, adding 0.5-2g of NaCl and 2-6g of MgSO4, stirring at a stirring rate of 1500-2500rpm for 30-90s, and then centrifuging at 15,000×g for 10-20min at 4℃; transferring 0.5mL of the supernatant to a 2mL centrifuge tube containing 150mg of N-propylethylenediamine, 150mg of octadecyl bonded silica gel and 900mg of MgSO4, vortexing for 2min, and then centrifuging at 15,000×g for 10min at 4℃, and filtering the supernatant through a 0.22μm microporous membrane.

[0009] Preferably, in step 1), the chromatographic conditions for the ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry include: The chromatographic column was a Thermo Fisher Scientific C18 column with a packing particle size of 5 μm, 100 mm × 2.1 mm, and the column temperature was set to 35℃. A gradient elution process is used; Mobile phase A is an aqueous solution containing 0.1% formic acid and 4 mM ammonium formate by volume, and mobile phase B is an acetonitrile solution containing 0.1% formic acid and 4 mM ammonium formate by volume. The flow rate of the mobile phases is set to 300 μL / min. -1 The injection volume was 10 μL; The mass spectrometry conditions for the ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry include: full scan and data-dependent two-stage scan modes; During the full-scan mass spectrometry acquisition process, the instrument is set to... m / z Data was collected in the range of 100-1500, with a resolution of 70,000 FWHM. The target ion number was controlled by automatic gain control at 2e6, and the maximum injection time was set to 250ms. The data-dependent secondary mass spectrometry scan parameters were set as follows: resolution of 35,000 FWHM, AGC target ion number of 2e5; step-normalized collision energies of 17.5 eV, 35 eV, and 52.5 eV. The following parameters were selected for electrospray ionization: sheath flow rate 48 Arb; auxiliary flow rate 10 Arb; spray voltage of 3.8 kV in positive ion mode; spray voltage of 3.8 kV in negative ion mode; atomizer temperature of 350℃; and heater temperature of 320℃.

[0010] Preferably, in step 1), before performing ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry, equal volumes of the supernatant from saffron samples from different origins are mixed to prepare a quality control sample, which is then tested together with the sample to be traced to correct the accuracy and reproducibility of the analysis.

[0011] Preferably, the specific conditions for proteomics analysis in step 2) are as follows: Take 1g of saffron powder and extract it with 4-6mL of lysis buffer containing 2.5% SDS and 0.1mol / L Tris-HCl at pH 8. Sonicate in an ice bath for 10-20min. Centrifuge at 15000×g for 10-30min at 4℃. Take 1mL of the supernatant and add it to 3-5mL of pre-cooled acetone solution. Precipitate at -20℃ for 8-16h and collect the precipitate. The precipitate was reconstituted with 1 mL of protein lysis buffer (pH 8) containing 0.1 mol / L Tris-HCl and 8 mol / L urea, and the pH was adjusted to 8. After sonication for 5–15 min, 1 mL of the supernatant was added to 5–15 mM dithiothreitol and stirred at 56 °C for 0.5–1.5 h. After cooling, 55 mM iodoacetamide was added and alkylated in the dark for 0.5 h. The alkylate was then transferred to a 10 kDa ultrafiltration filter and centrifuged at 12000 × g for 10–20 min at 4 °C. The precipitate was washed 2–4 times with 200–400 μL of UA buffer containing 8 M urea and 150 mM Tris and 1 mL of 50 mM NH4HCO3 solution. Trypsin was added and the reaction was carried out at 37 °C for 8–14 h. Digestion was terminated by adding 1% formic acid solution. The peptide was collected by centrifugation at 15000 × g for 10 min, desalted by C18 column and passed through a 0.22 μm ultrafiltration membrane. Mass spectrometry was performed after filtration through a PTFE membrane.

[0012] Preferably, in step 2), the chromatographic conditions for ultra-high performance liquid chromatography-quadrupole-orbit trap high-resolution mass spectrometry detection include: The chromatographic column used was a C18 reversed-phase column with a packing particle size of 5 μm and a packing density of 2 cm × 100 μm id. After fractionation, the particles were fed into a C18 analytical column online with a packing particle size of 3 μm and a packing density of 15 cm × 75 μm id. Mobile phase A is an aqueous solution containing 0.1% formic acid and 4mM ammonium formate by volume, and mobile phase B is an acetonitrile solution containing 0.1% formic acid and 4mM ammonium formate by volume. A gradient elution program was used at a flow rate of 0.3 mL / min. -1 ; The mass spectrometry conditions for ultra-high performance liquid chromatography-quadrupole-orbit trap high-resolution mass spectrometry detection include: full scan followed by 3 to 20 MS2 scans. The full scan resolution is 70000 FWHM, and the quality parameters are... m / z 100~150, AGC target ion number is 3×10 6 ; MS2 scan parameters are set as follows: Separate window is... m / z 1~3, AGC target value is 2×10 5 The normalized collision energy is 20-30%, the maximum injection time is 120ms, and the resolution is 35000FWHM.

[0013] Preferably, in step 3), the specific steps for screening chromatographic peaks and identifying metabolites from the metabolite mass spectrometry data obtained in step 1) include: acquiring mass spectrometry data using Xcalibur™ software version 4.1, retaining a signal-to-noise ratio of 3, peak width of 5~20s, and minimum peak intensity of 1×10⁻⁶. 7 The chromatographic peaks with Gaussian distribution were obtained, and the resulting data matrix was compared with the public standard spectral database. The secondary spectral data were compared with the HighChem Fragment Library™ and multi-stage mass spectrometry tree of MassFrontier version 8.0. The interference of isomers was eliminated by comparing with the secondary fragment spectral data of commercially available metabolite standards. The specific criteria for screening differentially expressed metabolites are as follows: Metabolite concentrations in samples from different origins are compared using a one-way t-test; principal component analysis and partial least squares discriminant analysis are performed using MetaboAnalyst 5.0 software to screen metabolites with a VIP score > 1 and a p-value < 0.05 as differentially expressed metabolites; external standard calibration is used to quantify the differentially expressed metabolites, selecting those with a VIP score > 1.0 and a response rate > 1 × 10⁻⁶. 6 Metabolites with p < 0.05 were considered representative compounds, with correlation coefficients ≥ 0.9991 and detection limits of 1.33–8.33 μg·kg⁻¹. -1 The limits of quantitation are 4.43–24.91 μg·kg⁻¹. -1 The recovery rate was 85.56%–105.18%, the intraday relative standard deviation was 1.05%–4.05%, and the interday relative standard deviation was 2.05%–4.59%.

[0014] Preferably, in step 3), the specific conditions for screening differentially abundant proteins in step 2) are as follows: quantitative analysis is performed using MaxQuant software version 1.6.17.0, with parameters set as follows: Trypsin / P cleavage enzyme, UniProt saffron database, initial search tolerance of 20 ppm, label-free quantification, allowing two missed cleavage sites, master search tolerance of 6 ppm, precursor peptide false discovery rate of 0.01, protein false discovery rate of 0.01, dynamic modification as methionine oxidation and protein N-terminal acetylation, static modification as cysteine ​​aminomethylation, and proteins with a fold change ≤0.5 or ≥2.0, VIP score >1.00, and p-value <0.05 as differentially abundant proteins; Differential abundance proteins were classified and annotated using a gene ontology database. Metabolic pathways were analyzed using the KEGG database, and significantly enriched pathways with p-values ​​≤0.05 were screened, including alanine, aspartic acid, and glutamate metabolic pathways; keratin, suberin, and wax biosynthesis pathways; phenylalanine, tyrosine, and tryptophan biosynthesis pathways; and cyanoamino acid metabolic pathways.

[0015] Preferably, in step 3), the specific conditions for constructing the origin traceability model are: the cumulative variance explained by principal component 1 and principal component 2 in the principal component analysis model is ≥70%, and the partial least squares discriminant analysis model is tested with p<0.001 after 1000 permutation tests.

[0016] Preferably, the specific conditions for origin verification in step 4) are: when the known origin standard sample is clustered with the corresponding origin sample in the principal component analysis and partial least squares discriminant analysis diagrams and the overlap is ≥85%, the model is deemed to have passed verification.

[0017] In the above technical solution, the saffron origin tracing method based on multi-omics analysis of the present invention is the first to combine metabolomics and proteomics, simultaneously capturing the differences in metabolites and proteins of saffron. 77 differentially expressed metabolites (such as crocin, crocinic acid, and flavonoids) and 14 differentially expressed proteins (such as GLT2 and CCD2) together constitute the origin characteristic markers, significantly improving the tracing resolution compared to single-omics analysis, with a 100% concordance rate for unknown samples. Using the QuEChERS extraction method combined with UHPLC-Q-Orbitrap-MS / MS, the metabolite detection limit is as low as 1.33 μg·kg⁻¹. -1 The recovery rate was 85.56%–105.18%, and the intra-day / inter-day RSD was <5%. In proteomics analysis, the FDR for differentially expressed proteins was <0.01, ensuring data reliability. The good overlap of QC samples further demonstrated the stability of the method.

[0018] The PCA and PLS-DA models constructed by the saffron origin traceability method based on multi-omics analysis of this invention have a cumulative variance explanation rate of ≥70%, a permutation test p<0.001, and no risk of overfitting; it can cover 7 major saffron producing areas including Spain, Iran, and China, meet the traceability needs of global saffron trade, and provide a powerful technical tool for market supervision.

[0019] The saffron origin tracing method based on multi-omics analysis of the present invention has clearly defined parameter ranges and operation steps (such as sample drying conditions, enzymatic hydrolysis time, mass spectrometry parameters, etc.) from sample pretreatment, extraction detection to data processing. It does not require complex customized equipment, can be reproduced in ordinary laboratories, and is convenient for industrial application.

[0020] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the following detailed description to explain the invention, but do not constitute a limitation thereof. In the drawings: Figure 1Principal component analysis (PCA) scores of saffron samples from different origins in this invention. Figure 2 This is a partial least squares discriminant analysis (PLS-DA) score graph of metabolomics of saffron samples from different origins in this invention; Figure 3 This is a permutation test plot of the saffron metabolomics PLS-DA model in this invention; Figure 4 This is a correlation diagram of differential abundance proteins (DAPs) in the proteomics of saffron samples from different origins in this invention. Figure 5 This is a principal component analysis (PCA) score graph of saffron samples from different origins in this invention. Detailed Implementation

[0022] The following provides a detailed description of specific embodiments of the present invention. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0023] The endpoints and any values ​​of the ranges disclosed herein are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of the various ranges, the endpoint values ​​of the various ranges and individual point values, and individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed herein.

[0024] This invention provides a method for tracing the origin of saffron based on multi-omics analysis, comprising the following steps: 1) Metabolomics analysis: Saffron samples from different origins were pretreated, and metabolites were extracted using the QuEChERS extraction method. The metabolites were then detected by ultra-high performance liquid chromatography-quadrupole-orbitrap-MS / MS to obtain metabolite mass spectrometry data. 2) Proteomics analysis: The saffron samples pretreated in the same way as in step 1) were lysed, precipitated, reconstituted, reduced alkylated, enzymatically digested, desalted and filtered, and then subjected to peptide detection by ultra-high performance liquid chromatography-quadrupole-electrostatic field orbital trap high resolution mass spectrometry (UHPLC-Q-OrbitrapHRMS) to obtain protein spectrum data. 3) Data processing and origin traceability model construction: Chromatographic peak screening and metabolite identification were performed on the metabolite mass spectrometry data from step 1), and differential metabolites were screened through univariate test and multivariate statistical analysis; quantitative analysis and differential abundance protein screening were performed on the protein spectrometry data from step 2); and a saffron origin traceability model was constructed by combining differential metabolites and differential abundance proteins. 4) Origin verification: Take standard saffron samples from known origins and perform metabolomics and proteomics analysis according to steps 1)-2) to obtain their characteristic substance information and substitute it into the model constructed in step 3). Verify the accuracy of the model through sample clustering to achieve origin traceability of the saffron to be traced.

[0025] Further, the pretreatment steps described in step 1) include: drying the saffron sample to constant weight using an automatic hot air dryer to avoid moisture interference with subsequent extraction; grinding the sample and passing it through a 0.5 mm sieve to ensure uniform sample particle size and improve extraction efficiency; storing the dried sample in a brown vial for low-temperature transportation; and storing it in a dark place in a desiccator during the experiment to prevent degradation of metabolites caused by light and high temperature.

[0026] Further, the QuEChERS extraction method described in step 1) includes: mixing 0.5-2g of saffron sample powder with 2-8mL of acetonitrile. Acetonitrile, as an extraction solvent, can efficiently extract polar and non-polar metabolites. Adding 0.5-2g of NaCl promotes aqueous phase separation and 2-6g of MgSO4 adsorbs water, improving extraction efficiency. Stirring at a stirring rate of 1500-2500rpm for 30-90s ensures thorough mixing of the solvent and sample. Then, centrifuging at 15,000×g for 10-20min at 4℃. Low-temperature centrifugation avoids metabolite denaturation, while high-speed centrifugation ensures thorough solid-liquid separation. Transferring 0.5mL of the supernatant to a mixture containing 150mg N-propylethylenediamine (PSA), 150mg octadecyl bonded silica gel (C18), and 900mg... In a 2 mL centrifuge tube containing MgSO4, PSA can remove impurities such as organic acids and pigments, while C18 can remove non-polar impurities. To enhance impurity adsorption, the mixture is vortexed for 2 min and then centrifuged at 15,000 × g for 10 min at 4 °C. The supernatant is then filtered through a 0.22 μm microporous membrane to remove small particles and protect the chromatographic column.

[0027] Further, the chromatographic conditions for the ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry described in step 1) include: a Thermo Fisher Scientific C18 column (5 μm particle size, 100 mm × 2.1 mm), a column temperature of 35 °C (to maintain column efficiency stability and reduce peak shape variation); a gradient elution program (specifically: 1 min, 5% mobile phase B; 5 min, 60% mobile phase B; 7 min, 85% mobile phase B; 9-14 min, 95% mobile phase B; 14.1-15 min, 5% mobile phase B) to achieve effective separation of metabolites of different polarities; mobile phase A is an aqueous solution containing 0.1% formic acid and 4 mM ammonium formate, and mobile phase B is an acetonitrile solution containing 0.1% formic acid and 4 mM ammonium formate, wherein formic acid and ammonium formate can improve the ionization efficiency of metabolites and increase detection sensitivity; and the flow rate is set to 300 μL·min. -1 The injection volume was 10 μL.

[0028] The mass spectrometry conditions for the ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry (UHPLC-MS / MS) include: full scan (Full MS) and data-dependent secondary scan (dd-MS2) modes; during full scan, data are acquired in the m / z range of 100-1500, covering the mass range of major metabolites in saffron, with a resolution of 70,000 FWHM to ensure mass accuracy and reduce false positive identification; automatic gain control (AGC) is used with a target ion number of 2e6 and a maximum injection time (MIT) of 250 ms; dd-MS... The scanning resolution was 35,000 FWHM, the AGC target ion number was 2e5, and the step-normalized collision energies were 17.5 eV, 35 eV, and 52.5 eV. By acquiring rich fragment information through multiple collision energies, the accuracy of metabolite identification was improved. The electrospray ionization (ESI) parameters were: sheath gas flow rate 48 Arb, auxiliary gas flow rate 10 Arb, spray voltage of 3.8 kV for both positive and negative ion modes, nebulizer temperature 350 °C, and heater temperature 320 °C. The ionization conditions were optimized to improve the detection signal intensity.

[0029] Furthermore, in step 1), before performing ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry (UHPLC-MS / MS), equal volumes of supernatant from saffron samples from different origins are mixed to prepare quality control (QC) samples, which are then analyzed together with the samples to be traced. The chromatographic peak overlap and signal stability of the QC samples are used to correct systematic errors in the analysis process, ensuring the accuracy and reproducibility of the detection results.

[0030] Further, the specific conditions for proteomics analysis in step 2) are as follows: Take 1g of saffron powder and extract with 4-6mL of lysis buffer containing 2.5% SDS and 0.1mol / L Tris-HCl at pH 8. SDS can disrupt the spatial structure of proteins, Tris-HCl maintains the buffer environment, and sonication in an ice bath for 10-20min enhances cell disruption and promotes protein release. Centrifuge at 15,000×g for 10-30min at 4℃ to remove cell debris and other impurities. Take 1mL of the supernatant and add 3-5mL of pre-cooled acetone solution (-20℃). Precipitate for 8-16h. Acetone denatures and precipitates proteins, and low temperature prevents protein degradation. After collecting the precipitate, use 1mL of... Reconstitute the protein with a pH 8 protein lysis buffer containing 0.1 mol / L Tris-HCl and 8 mol / L urea, and adjust the pH to 8. Urea can dissolve poorly soluble proteins. After sonication for 5-15 min, take 1 mL of the supernatant and add 5-15 mM dithiothreitol (DTT). Stir at 56 °C for 0.5-1.5 h. DTT reduces protein disulfide bonds and opens the spatial structure. After cooling to room temperature, add 55 mM iodoacetamide (IAM) and alkylate in the dark for 0.5 h. IAM blocks free thiol groups to prevent protein renaturation. Transfer the sample to a 10 kDa ultrafiltration filter and centrifuge at 12,000 × g for 10-20 min at 4 °C to retain large protein molecules and remove small molecule impurities. Sequentially lyse the sample with 200-400 μL of UA buffer containing 8 M urea and 150 mM Tris, and 1 mL of 50 mM urea... The peptides were washed 2-4 times by centrifugation with NH4HCO3 solution to remove urea and salts, thus avoiding interference with subsequent enzymatic digestion. Trypsin (1:50-1:100 mass ratio to protein) was added and the reaction was carried out at 37°C for 8-14 h. Trypsin specifically cleaves the carboxyl termini of lysine and arginine to generate short peptide fragments. After the reaction was completed, 1% formic acid solution was added to terminate the digestion. The peptides were collected by centrifugation at 15,000×g for 10 min, desalted by C18 column to remove residual impurities, filtered through a 0.22 μm PTFE membrane, and mass spectrometry was performed after protecting the mass spectrometer detector.

[0031] Further, the chromatographic conditions for ultra-high performance liquid chromatography-quadrupole-orbital trap high-resolution mass spectrometry detection described in step 2) include: the chromatographic column uses a C18 reversed-phase column (packing material particle size 5 μm, specification 2cm × 100 μm·d.) for fractionation, followed by online transfer to a C18 analytical column (packing material particle size 3 μm, specification 15cm × 75 μm·d.), and the two-stage column separation enables efficient separation of peptides; mobile phase A is an aqueous solution containing 0.1% formic acid and 4mM ammonium formate, and mobile phase B is a solution containing... An acetonitrile solution containing 0.1% formic acid and 4 mM ammonium formate was used; a gradient elution program was employed (0.0–50.0 min, 95.0–65.0% mobile phase A; 50.0–75.0 min, 65.0–0.0% mobile phase A; 75.0–90.0 min, 0.0% mobile phase A; 90.0–90.1 min, 0.0–95.0% mobile phase A; 90.1–100.0 min, 95.0% mobile phase A) at a flow rate of 0.3 mL / min. -1 .

[0032] The mass spectrometry conditions for ultra-high performance liquid chromatography-quadrupole-orbital trap high-resolution mass spectrometry detection include: 3–20 MS2 scans after a full scan; full scan resolution of 70,000 FWHM, mass range of m / z 100–1500, and AGC target ion number of 3 × 10⁶; MS2 scan parameters are: separation window m / z 1–3, and AGC target value of 2 × 10⁶. 5 With a normalized collision energy of 20-30%, a maximum injection time of 120ms, and a resolution of 35,000 FWHM, it obtains accurate peptide mass through high-resolution mass spectrometry, improving the reliability of protein identification.

[0033] Furthermore, step 3) involves the specific steps of chromatographic peak screening and metabolite identification of the metabolite mass spectrometry data from step 1), including: acquiring mass spectrometry data using Xcalibur™ software version 4.1, retaining a signal-to-noise ratio (S / N) of 3, peak width of 5–20 s, and minimum peak intensity of 1 × 10⁻⁶. 7Furthermore, the Gaussian-distributed chromatographic peaks eliminate interfering peaks, improving data reliability. The obtained data matrix is ​​compared with public standard spectral databases such as ChEBI, KEGG, and MassBank. The secondary spectral data is matched with the HighChemFragment Library™ and multi-level mass spectrometry (MSn) tree of MassFrontier version 8.0. By comparing with the secondary fragment spectra of commercially available metabolite standards (such as crocin, crocin, and crocin aldehyde), isomer interference is eliminated, achieving high-confidence identification of metabolites. The specific criteria for screening differential metabolites are as follows: Metabolite concentrations in samples from different origins were compared using a one-way t-test; principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed using MetaboAnalyst 5.0 software; metabolites with a VIP score >1 (indicating a high contribution of the metabolite to origin differences) and a p-value <0.05 (statistical significance) were selected as differential metabolites; external standard calibration was used to quantify the differential metabolites, selecting those with a VIP score >1.0 and a response rate >1×10⁻⁶. 6 Furthermore, metabolites with p < 0.05 were used as representative compounds, and their correlation coefficient (R²) ≥ 0.9991 indicated a good linear relationship. The limits of detection (LOD, S / N = 3) were 1.33–8.33 μg·kg⁻¹. -1 This indicates high detection sensitivity, with a limit of quantitation (LOQ, S / N=10) of 4.43–24.91 μg·kg⁻¹. -1 The recovery rate was 85.56%–105.18%, indicating that the accuracy met the requirements. The intraday relative standard deviation (RSD) was 1.05%–4.05%, and the interday RSD was 2.05%–4.59%, indicating good reproducibility.

[0034] Further, the specific conditions for screening differentially abundant proteins in step 2) in step 3) are as follows: protein quantification analysis is performed using MaxQuant software version 1.6.17.0, with the following parameters set: cleavage enzyme Trypsin / P (allowing two missed cleavage sites), database UniProt saffron database (82,528 entries, 315 proteins), initial search tolerance 20 ppm, main search tolerance 6 ppm, label-free quantification (LFQ) mode, precursor peptide false discovery rate (FDR) 0.01, protein FDR 0.01, and dynamic modifications including methionine oxidation and protein N-terminal acetyl groups. Static modification was performed to cysteine ​​aminomethylation (to reduce false positives and improve quantitative accuracy); proteins with fold change (FC) ≤0.5 or ≥2.0, VIP score >1.00, and p-value <0.05 were screened as differentially abundant proteins (DAPs). A total of 14 differentially abundant proteins were identified, including accD, adh, bch, CCD, CCD2, GLT2, ndhF, PSY1a, PAY1b, PSY2, rpoC2, ZCD, and ZDSC. These proteins are involved in key pathways such as saffron carotenoid synthesis and amino acid metabolism, and are closely related to adaptability to the production environment.

[0035] Differentially abundant proteins were classified and annotated using the Gene Ontology (GO) database, including biological processes, cellular components, and molecular functions. Metabolic pathways were analyzed using the KEGG database, and significantly enriched pathways with p-values ​​≤0.05 were screened. These pathways mainly included alanine, aspartic acid, and glutamate metabolic pathways; cutin, suberin, and wax biosynthesis pathways; phenylalanine, tyrosine, and tryptophan biosynthesis pathways; and cyanoamino acid metabolic pathways. The differential expression of these pathways is the molecular basis for the environmental adaptation of saffron from different origins and can serve as a key marker for tracing the origin of saffron.

[0036] Furthermore, the specific conditions for constructing the origin traceability model in step 3) are as follows: the cumulative variance explained by principal component analysis (PCA) model of principal component 1 (PC1) and principal component 2 (PC2) is ≥70%, ensuring that the model can effectively extract origin difference information and that samples from different origins can achieve clear clustering in the PCA score map; the partial least squares discriminant analysis (PLS-DA) model has passed 1000 permutation tests with p<0.001, proving that the model has no overfitting, high stability, and can further strengthen the differentiation between origins.

[0037] Further, the specific conditions for origin verification in step 4) are as follows: Take saffron standard samples from known origins, such as the standard products provided by the China National Institutes for Food and Drug Control, and perform metabolomics and proteomics analysis according to steps 1)-2) to obtain the characteristics of differential metabolites and differential abundance proteins; substitute the characteristic information into the origin traceability model constructed in step 3). When the standard samples from known origins cluster with the corresponding origin samples in the PCA and PLS-DA diagrams and the overlap is ≥85%, the model is deemed to have passed the verification. At this time, the model can be used to identify the origin of saffron to be traced, so as to achieve accurate traceability.

[0038] The present invention will be described in detail below through examples. In the following examples, the pharmaceuticals and agents are all conventional commercially available products.

[0039] Example 1

[0040] Origin traceability of saffron from different regions 1. Sample preparation: Saffron samples were collected from seven production areas: Spain, Greece, Iran, Shanghai (China), Tibet (China), Japan, and India. Three groups were collected from each production area, with 40 samples in each group (a total of 840 samples). The samples were hand-picked on the 8th day after flowering. Eight planting sites were randomly selected from farms in each production area. The stigmas of five saffron plants were collected from each planting site and mixed into one sample to reduce individual differences.

[0041] Each sample was dried to constant weight in an automated hot air dryer, then stored in brown vials and transported to the laboratory for analysis at low temperature. Fresh stigmas were ground in the same manner and sieved through a 0.5 mm sieve to obtain a uniform powder. Standard saffron samples were available from the National Institutes for Food and Drug Control (NIFDC). During the experiments, samples were kept in the dark within a desiccator.

[0042] 2. Metabolomics analysis Metabolites were extracted using the QuEChERS method. Saffron powder (1.0 g) was mixed with 5 mL of acetonitrile, followed by the addition of 1.0 g NaCl and 4.0 g MgSO4. The mixture was vigorously vortexed (2000 rpm) for 1 min, then centrifuged at 15,000 × g for 15 min at 4 °C. 0.5 mL of the supernatant was transferred to a 2 mL Eppendorf tube containing 150 mg PSA, 150 mg C18, and 900 mg MgSO4. The tube was vortexed for 2 min, then centrifuged at 15,000 × g for 10 min at 4 °C. The supernatant was filtered through a 0.22 μm microporous membrane and transferred to a separate brown vial. After sample preparation, aliquots of the supernatant from each sample were taken and mixed thoroughly to obtain the quality control (QC) sample. This sample, along with other samples, was analyzed by UPLC-MS / MS to correct for the accuracy and reproducibility of the analytical process.

[0043] Metabolites were identified using an UltiMate 3000 system connected to a Q-Orbitrap mass spectrometer. LC separations were performed on a ThermoFisher Scientific C18 column (5 μm; 2.1 mm id × 100 mm length) maintained at 35 °C. The mobile phase consisted of an aqueous solution containing 0.1% FA and 4 mM ammonium formate (solvent A) and an acetonitrile solution containing 0.1% FA and 4 mM ammonium formate (solvent B), suitable for both negative and positive ion modes. The elution gradients were set as follows: 1 min, 5% B; 5 min, 60% B; 7 min, 85% B; 9–14 min, 95% B; 14.1–15 min, 5% B. The injection volume was 10 μL, and the flow rate was 300 μL min⁻¹.

[0044] Data acquisition employed both full scan (Full MS) and data-dependent secondary scan (dd-MS2) acquisition modes. The full scan quality range was set to... m / z The range was 100-1500, resolution 70,000, maximum injection time (MIT) set to 250 ms, automatic gain control (AGC) target ion number 2e6. For dd-MS2 scans, MIT was set to 120 ms, resolution 35,000, AGC target ion number 2e5. Energy adjustments were made for stepped collision energy (CE) mode to 17.5, 35, and 52.5 eV. Electrospray ionization (ESI) parameters were selected as follows: auxiliary gas flow rate, 10 (units); sheath gas flow rate, 48 (units). Atomizer temperature was set to 350°C. Auxiliary gas heater temperature was changed to 320°C, and spray voltage was set to 3.80 kV.

[0045] 3. Proteomics analysis Saffron powder (1.0 g) was extracted with 5 volumes of lysis buffer (2.5% SDS, 0.1 mol / L Tris-HCl, pH 8.0). The mixture was sonicated in an ice bath for 15 min, then centrifuged at 15,000 × g for 20 min at 4 °C. 1 mL of the supernatant was collected, 4 mL of pre-chilled acetone solution was added, and precipitation was carried out at -20 °C for 12 h. 1 mL of protein lysis buffer (0.1 mol / L Tris-HCl, 8 mol / L urea) was added to the air-dried precipitate, and the pH was adjusted to 8.0. The lysis buffer was sonicated for 10 min, 1 mL of the supernatant was collected, 10 mM DTT was added, and the mixture was stirred at 56 °C for 1 h. After cooling to room temperature, 55 mM IAM was added, and alkylation was carried out in the dark for half an hour. The obtained sample was transferred to a 10 kDa ultrafiltration filter and centrifuged (12,000 × g, 15 min, 4 °C). It was washed three times sequentially with 300 μL UA buffer (8 M urea, 150 mM Tris, water, and HCl) and 1 mL NH4HCO3 solution (50 mM). Trypsin was added to the resulting protein suspension, and after reacting for 12 hours (37 °C), 1% FA (…) was added. v / v The digestion process was terminated. The eluted peptides were collected by centrifugation at 15,000 × g for 10 min. The peptides were desalted using a C18 column, filtered through a 0.22 μm PTFE membrane, and then analyzed on a UHPLC-Q-Orbitrap HRMS.

[0046] Peptides were fractionated on a C18 reversed-phase column (2 cm length × 100 μm id) with a typical particle size of 5 μm, and then separated online onto a C18 analytical column (15 cm length × 75 μm id) with a particle size of 3 μm. Chromatographic separation employed gradient elution using HPLC buffer A (water containing 4 mM ammonium formate and 0.1% FA) and HPLC buffer B (acetonitrile containing 4 mM ammonium formate and 0.1% FA). During separation, the elution gradient of buffer A was: 0.0–50.0 min 95.0–65.0%, 50.0–75.0 min 65.0–0.0%, 75.0–90.0 min 0.0%, 90.0–90.1 min 0.0–95.0%, 90.1–100.0 min 95.0%. Separation was performed at a flow rate of 0.3 mL min⁻¹. When analyzing the isolated peptides, a full scan (resolution = 70,000 FWHM) was performed. m / z After determining the target ion number (133.42000) and 3 x e6 AGC, perform up to 20 MS2 scans (separation window = m / z 2.0, AGC target value = 2e5, normalized collision energy (NCE) = 27%, maximum injection time = 120ms, resolution = 35,000 FWHM), using high-energy collision dissociation (HCD).

[0047] 4. Data Processing and Model Building (1) Metabolomics data processing: Mass spectrometry data were acquired using Xcalibur™ software version 4.1 to obtain data containing... m / z A data matrix containing retention time (RT) and peak intensity information. Prior to data analysis, chromatographic peak characteristics in the original dataset that did not meet the following criteria were removed: signal-to-noise ratio (S / N) = 3, peak width = 5–20 s, minimum peak intensity = 1 × 10⁻⁶. 7 The chromatographic peaks exhibited a Gaussian distribution. The resulting data matrix was then compared with publicly available standard spectral databases to identify the metabolites. Univariate statistical tests (t-tests) were used to compare the concentrations of potential metabolites in saffron samples from different origins. The data were imported into the statistical analysis (univariate) module of MetaboAnalyst 5.0, and multivariate data analysis was performed using PCA (principal component analysis) and PLS-DA (partial least squares discriminant analysis). The results are as follows: Figure 1-2 As shown. One-way ANOVA was used to calculate the p-values ​​of metabolites, and the characteristic metabolites with the highest discriminative power (VIP score > 1, p-value < 0.05) were selected.

[0048] PCA was used as a linear unsupervised feature extraction method to visualize metabolomics data. The results are as follows: Figure 1 As shown, the eight saffron samples clearly clustered according to different regions, with principal component 1 (48.3%) and principal component 2 (21.8%) accounting for 70.1% of the total variance. The tight clustering and good overlap of the QC samples indicate that the sample processing was reliable and the signal intensity drift over time was not significant.

[0049] To achieve a higher level of component separation, we used PLS-DA results as follows: Figure 2 As shown, principal component 1 and principal component 2 account for 50.5% and 26.1% of the total variance, respectively.

[0050] The PLS-DA model was validated using 1000 permutation tests (p<0.001), and the results are as follows: Figure 3 As shown, the model constructed from saffron sample mass spectrometry data is effective. In the PLS-DA model, a VIP score > 1 is used to identify metabolites that show significant changes among different saffron samples.

[0051] Data was acquired using Xcalibur™ 4.1 software, retaining values ​​of S / N=3, peak width 5–20 s, and intensity 1 × 10⁻⁶. 7 The chromatographic peaks were Gaussian distributed. Comparison with public databases and validation with standards identified 77 differential metabolites. PCA (PC1 48.3%, PC2 21.8%, cumulative 70.1%) and PLS-DA (PC1 50.5%, PC2 26.1%) were performed using MetaboAnalyst 5.0 to screen for differential metabolites with VIP>1 and p<0.05. External standard quantification showed that the Iranian sample had the highest content of crocin, crocin acid, and crocin; the Indian sample had the highest content of crocin aldehyde; and the Shanghai sample had the highest content of sea urchinone and canthaxanthin.

[0052] (2) Proteomics data processing: MaxQuant software version 1.6.17.0 was used for quantitative analysis of proteomics data. Specific parameters are as follows: (a) Cutting enzyme (Trypsin / P); (b) UniProt saffron (species) database (82,528 entries, 315 proteins); (c) Initial search tolerance (20 ppm); (d) Label-free quantification (LFQ); (e) A maximum of two omitted cut sites are allowed; (f) Master search tolerance (6 ppm); (g) Precursor peptide false discovery rate (FDR) cutoff value (0.01); (h) Protein FDR cutoff value (0.01); (i) Dynamic modifications (methionine oxidation and protein N-terminal acetylation); (j) Static modification (aminomethylation of cysteine); The remaining parameters are the default values.

[0053] Differentially abundant proteins (DAPs) were identified by fold change (FC) > 2-fold (FC ≤ 0.5 or FC ≥ 2.0), VIP score > 1.00, and p-value < 0.05; DAPs were classified and annotated using the Gene Ontology (GO) database; metabolite-protein pathway enrichment was analyzed using the KEGG database.

[0054] Due to the limited genomic resources specific to saffron, a search for saffron in the UniProt knowledge base returned only 420 results. Thirty-one saffron proteins were identified using the UniProt knowledge base, and then 14 differentially expressed proteins were obtained by manually removing contaminants (VIP score > 1, p-value < 0.05, FC > 2 or < 0.5). Figure 4 As shown. These 14 differentially expressed proteins include accD, adh, bch, bch, CCD, CCD2, GLT2, ndhF, PSY1a, PAY1b, PSY2, rpoC2, ZCD, and ZDSC.

[0055] PLS-DA plot for screening differentially accumulating proteins ( Figure 5 The results showed that the eight different saffron samples were significantly separated on PC1 and PC2, accounting for 49.8% and 28.7%, respectively.

[0056] Based on the above-mentioned non-targeted metabolomics results, 77 compounds were identified, which can be divided into 19 classes, including amino acids, peptides, fatty acids, aromatic ketones, flavonoids, monoterpenes, and alcohols. Forty-nine metabolites with KEGG IDs were used for metabolic pathway enrichment analysis. Using a p-value ≤ 0.05 as the criterion, four significantly enriched metabolic pathways were screened from 35 KEGG pathways, mainly including: alanine, aspartic acid, and glutamate metabolism; keratin, suberin, and wax biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; and cyanoamino acid metabolism.

[0057] By analyzing the untargeted metabolomics and proteomics of saffron from different origins, we revealed the changes in protein-to-metabolite conversion in saffron from different origins. Data from Spanish and Iranian saffron showed significant differences, consistent with previous studies. Saffron is typically characterized by the bioaccumulation and synthesis of its characteristic glucosylated derivative (crocetin) and its carotenoid derivative (crocetin acid). Zeaxanthin and echinenone originate from the same precursor, β-carotene. The 7,8 and 7',8' double bonds in zeaxanthin are cleaved by the plastidase CCD2, producing one molecule of crocetindialdehyde and two molecules of (3S)-3-hydroxycyclocitral. ZCD is an N-terminal truncated form of CCD4 and lacks the activity to convert zeaxanthin to cyanidin dialdehyde. (3S)-3-hydroxycyclocitral is converted to safranal during the drying process of the plant material through dehydration. The concentration of crocin increases during storage but decreases under sunlight and heat. Both India and Tibet, the two growing regions, are high-altitude areas. Tibet, with a higher altitude and similar climate, shows a significant increase in crocin and picocrocin content with increasing altitude. Picocrocin, a monoterpene glycoside precursor of crocin, is considered the main bitter component of saffron. Crocin dialdehyde is further oxidized to crocetin. Among the seven producing regions, India yielded the highest quantitative crocin content. Crocetin is a natural carotenoid with anti-glycation, antioxidant, and anti-inflammatory properties.

[0058] Subsequently, crocin becomes the substrate for GLT2 activity, catalyzing the production of crocin by transferring glucose to both ends of the molecule. Crocin is a dietary carotenoid nutrient with various medicinal properties. Among all the saffron samples we analyzed, those from Iran had the highest contents of crocin, crocin, and crocin. Furthermore, it exhibits high water solubility, which explains the characteristic bright yellow color of saffron. The saffron grown in Iran's Khorasan province is located in a semi-arid region. Semi-arid climates are among the ideal conditions for saffron growth.

[0059] In the carotenoid biosynthesis pathway, sea urchinone can be converted into canthaxanthin. Among eight saffron products, the highest content of sea urchinone was found in the Shanghai production area. Sea urchinone and canthaxanthin belong to the ketocarotenoid family. Canthaxanthin possesses potent antioxidant and immunomodulatory properties. It has been reported that canthaxanthin can enhance the digestive enzyme activity, antioxidant capacity, immune response activity, and hemolymphatic biochemical indicators in Penaeus monodon. Canthaxanthin exhibits strong photoprotective effects. Bright light conditions can stimulate the synthesis of zeaxanthin or canthaxanthin. Quantitative results indicate that the Shanghai region had the highest canthaxanthin content.

[0060] Polyphenols have previously been used to determine the authenticity and geographical origin of saffron. Kaempferol possesses various effects, including anti-inflammatory, anticancer, antiepileptic, antioxidant, antidepressant, and cerebral blood flow improvement. Similar to kaempferol, substantial evidence suggests that flavonoids offer numerous health benefits. Rutin, a citrus flavonoid glycoside composed of flavonols, quercetin, and the disaccharide rutin, exhibits diverse biological activities and pharmacological effects, including antibacterial, anti-inflammatory, antitumor, antioxidant, anti-apoptotic, and anti-allergic properties. The biosynthetic pathways of flavonoids and flavonols revealed that rutin and trifolin are downstream products of kaempferol, with a generally consistent accumulation trend across the eight different groups. The results indicated that the levels of flavonoids such as rutin, trifolin, and kaempferol in the INM (Indian metabolite) were significantly higher than in other groups.

[0061] Based on the aforementioned metabolomics data, the influence of origin on the amino acid profile was investigated. The results showed that all saffron samples contained eight free amino acids, with significant differences among different origins. Non-essential amino acids (alanine, glutamic acid, proline, L-asparagine, and L-leucine) were generally higher than essential amino acids (L-phenylalanine, L-tyrosine, and lysine). Among the essential amino acids, leucine was the most abundant, ranking fifth among all quantitatively determined amino acids in saffron. Leucine, as a substrate for various metabolic pathways (e.g., protein synthesis), was most abundant in Spanish saffron. L-phenylalanine was the most abundant in Spanish saffron. Lysine was the most abundant in Indian saffron. Among non-essential amino acids, alanine, proline, and L-asparagine were higher than in other regions. Glutamic acid was the most abundant in Indian saffron. L-tyrosine was the most abundant in Spanish saffron. Regardless of origin, alanine, glutamic acid, proline, and L-asparagine were the main amino acids in saffron flavoring. Alanine is found in human skeletal muscle and plays a role in the production of antibodies and other components of the immune system. Glutamic acid not only contributes to flavor but is also a major neurotransmitter in the central nervous system, playing a crucial role in physiological processes such as learning and memory. Proline is a component of collagen and promotes the formation of skin, joints, cartilage, and other tissues. L-Asparagine is a major precursor to essential amino acids such as lysine, threonine, isoleucine, and methionine.

[0062] 5. Origin Verification: Take Iranian and Tibetan saffron standards provided by the China National Institutes for Food and Drug Control and test them according to the above steps; the cluster overlap between the standards and the corresponding origin samples in the PCA model of characteristic metabolites, the PLS-DA model of characteristic metabolites, and the PLS-DA model of differentially accumulated proteins is 95%, 97%, and 92%, respectively, and the model verification is passed; The model was used to trace the origin of 30 saffron samples from unknown origins, and the results matched the actual origins by 100%.

[0063] In summary, the multi-omics analysis-based saffron origin tracing method of the present invention has clearly defined parameter ranges and operating procedures (such as sample drying conditions, enzymatic digestion time, mass spectrometry parameters, etc.) from sample pretreatment, extraction detection to data processing. It does not require complex customized equipment, can be reproduced in ordinary laboratories, and is convenient for industrial application.

[0064] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0065] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0066] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for tracing the origin of saffron based on multi-omics analysis, characterized in that, Includes the following steps: 1) Metabolomics analysis: Saffron samples from different origins were pretreated, metabolites were extracted using the QuEChERS extraction method, and then the metabolites were detected by ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry to obtain metabolite mass spectrometry data. 2) Proteomics analysis: The saffron samples with the same pretreatment as in step 1) were lysed, precipitated, reconstituted, reduced alkylated, enzymatically digested, desalted and filtered, and then peptides were detected by ultra-high performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry to obtain protein spectrum data. 3) Data processing and origin traceability model construction: Chromatographic peak screening and metabolite identification were performed on the metabolite mass spectrometry data from step 1), and differential metabolites were screened through univariate test and multivariate statistical analysis; quantitative analysis and differential abundance protein screening were performed on the protein spectrometry data from step 2); and a saffron origin traceability model was constructed by combining differential metabolites and differential abundance proteins. 4) Origin verification: Take standard saffron samples from known origins and perform metabolomics and proteomics analysis according to steps 1)-2) to obtain their characteristic substance information and substitute it into the model constructed in step 3). Verify the accuracy of the model through sample clustering to achieve origin traceability of the saffron to be traced.

2. The method according to claim 1, characterized in that, In step 1), the pretreatment step includes: drying the saffron sample to constant weight, grinding it, and then sieving it; The steps of the QuEChERS extraction method include: mixing 0.5-2g of saffron sample powder with 2-8mL of acetonitrile, adding 0.5-2g of NaCl and 2-6g of MgSO4, stirring at a stirring rate of 1500-2500rpm for 30-90s, and then centrifuging at 15,000×g for 10-20min at 4℃; transferring 0.5mL of the supernatant to a 2mL centrifuge tube containing 150mg of N-propylethylenediamine, 150mg of octadecyl bonded silica gel and 900mg of MgSO4, vortexing for 2min, and then centrifuging at 15,000×g for 10min at 4℃, and filtering the supernatant through a 0.22μm microporous membrane.

3. The method according to claim 1, characterized in that, In step 1), the chromatographic conditions for the ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry include: The chromatographic column was a Thermo Fisher Scientific C18 column with a packing particle size of 5 μm, 100 mm × 2.1 mm, and the column temperature was set to 35℃. A gradient elution process is used; Mobile phase A is an aqueous solution containing 0.1% formic acid and 4 mM ammonium formate by volume, and mobile phase B is an acetonitrile solution containing 0.1% formic acid and 4 mM ammonium formate by volume. The flow rate of the mobile phases is set to 300 μL / min. -1 The injection volume was 10 μL; The mass spectrometry conditions for the ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry include: full scan and data-dependent two-stage scan modes; During the full-scan mass spectrometry acquisition process, the instrument is set to... m / z Data was collected in the range of 100-1500, with a resolution of 70,000 FWHM. The target ion number was controlled by automatic gain control at 2e6, and the maximum injection time was set to 250ms. The data-dependent secondary mass spectrometry scan parameters were set as follows: resolution of 35,000 FWHM, AGC target ion number of 2e5; step-normalized collision energies of 17.5 eV, 35 eV, and 52.5 eV. The following parameters were selected for electrospray ionization: sheath flow rate 48 Arb; auxiliary flow rate 10 Arb; spray voltage of 3.8 kV in positive ion mode; spray voltage of 3.8 kV in negative ion mode; atomizer temperature of 350℃; and heater temperature of 320℃.

4. The method according to claim 1, characterized in that, In step 1), before performing ultra-high performance liquid chromatography-quadrupole-orbit trap tandem mass spectrometry, equal volumes of supernatant from saffron samples from different origins are mixed to prepare quality control samples, which are then tested together with the samples to be traced to correct the accuracy and reproducibility of the analysis.

5. The method according to claim 1, characterized in that, The specific conditions for proteomics analysis in step 2) are as follows: Take 1g of saffron powder and extract it with 4-6mL of lysis buffer containing 2.5% SDS and 0.1mol / L Tris-HCl at pH 8. Sonicate in an ice bath for 10-20min, centrifuge at 15000×g for 10-30min at 4℃, take 1mL of supernatant and add it to 3-5mL of pre-cooled acetone solution. Precipitate at -20℃ for 8-16h and collect the precipitate. The precipitate was reconstituted with 1 mL of protein lysis buffer (pH 8) containing 0.1 mol / L Tris-HCl and 8 mol / L urea, and the pH was adjusted to 8. After sonication for 5–15 min, 1 mL of the supernatant was added to 5–15 mM dithiothreitol and stirred at 56 °C for 0.5–1.5 h. After cooling, 55 mM iodoacetamide was added and alkylated in the dark for 0.5 h. The alkylate was then transferred to a 10 kDa ultrafiltration filter and centrifuged at 12000 × g for 10–20 min at 4 °C. The precipitate was washed 2–4 times with 200–400 μL of UA buffer containing 8 M urea and 150 mM Tris and 1 mL of 50 mM NH4HCO3 solution. Trypsin was added and the reaction was carried out at 37 °C for 8–14 h. Digestion was terminated by adding 1% formic acid solution. The peptide was collected by centrifugation at 15000 × g for 10 min, desalted by C18 column and passed through a 0.22 μm ultrafiltration membrane. Mass spectrometry was performed after filtration through a PTFE membrane.

6. The method according to claim 1, characterized in that, In step 2), the chromatographic conditions for ultra-high performance liquid chromatography-quadrupole-orbit trap high-resolution mass spectrometry detection include: The chromatographic column used was a C18 reversed-phase column with a packing particle size of 5 μm and a packing density of 2 cm × 100 μm id. After fractionation, the particles were fed into a C18 analytical column online with a packing particle size of 3 μm and a packing density of 15 cm × 75 μm id. Mobile phase A is an aqueous solution containing 0.1% formic acid and 4mM ammonium formate by volume, and mobile phase B is an acetonitrile solution containing 0.1% formic acid and 4mM ammonium formate by volume. A gradient elution program was used at a flow rate of 0.3 mL·min⁻¹; The mass spectrometry conditions for ultra-high performance liquid chromatography-quadrupole-orbit trap high-resolution mass spectrometry detection include: full scan followed by 3 to 20 MS2 scans. The full scan resolution is 70,000 FWHM, the mass parameter is m / z 100~150, and the AGC target ion number is 3×106. The MS2 scan parameters are set as follows: separation window is m / z 1~3, AGC target value is 2×105, normalized collision energy is 20~30%, maximum injection time is 120ms, and resolution is 35000FWHM.

7. The method according to claim 1, characterized in that, In step 3), the specific steps for screening chromatographic peaks and identifying metabolites from the metabolite mass spectrometry data in step 1) include: acquiring mass spectrometry data using Xcalibur™ software version 4.1, retaining chromatographic peaks with a signal-to-noise ratio of 3, peak width of 5~20s, minimum peak intensity of 1×107, and Gaussian distribution, comparing the obtained data matrix with a public standard spectrum database, comparing the secondary spectrum data with the HighChem Fragment Library™ and multi-level mass spectrometry tree of Mass Frontier version 8.0, and eliminating isomer interference by comparing with the secondary fragment spectra of commercially available metabolite standards; The specific criteria for screening differential metabolites are as follows: Metabolite concentrations in samples from different origins were compared using a one-way t-test; principal component analysis and partial least squares discriminant analysis were performed using MetaboAnalyst 5.0 software to screen metabolites with a VIP score > 1 and a p-value < 0.05 as differential metabolites; external standard calibration was used to quantify the differential metabolites, and metabolites with a VIP score > 1.0, a response rate > 1 × 10⁶, and a p-value < 0.05 were selected as representative compounds, with a correlation coefficient ≥ 0.9991, a detection limit of 1.33–8.33 μg·kg⁻¹, a quantitation limit of 4.43–24.91 μg·kg⁻¹, a recovery rate of 85.56–105.18%, an intra-day relative standard deviation of 1.05–4.05%, and an inter-day relative standard deviation of 2.05–4.59%.

8. The method according to claim 1, characterized in that, In step 3), the specific conditions for screening differentially abundant proteins in step 2) are as follows: quantitative analysis is performed using MaxQuant software version 1.6.17.0, with parameters set as follows: Trypsin / P cleavage enzyme, UniProt saffron database, initial search tolerance of 20 ppm, label-free quantification, allowing two missed cleavage sites, master search tolerance of 6 ppm, precursor peptide false discovery rate of 0.01, protein false discovery rate of 0.01, dynamic modification as methionine oxidation and protein N-terminal acetylation, static modification as cysteine ​​aminomethylation, and proteins with a fold change ≤0.5 or ≥2.0, VIP score >1.00, and p-value <0.05 as differentially abundant proteins; Differential abundance proteins were classified and annotated using a gene ontology database. Metabolic pathways were analyzed using the KEGG database, and significantly enriched pathways with p-values ​​≤0.05 were screened, including alanine, aspartic acid, and glutamate metabolic pathways; keratin, suberin, and wax biosynthesis pathways; phenylalanine, tyrosine, and tryptophan biosynthesis pathways; and cyanoamino acid metabolic pathways.

9. The method according to claim 1, characterized in that, In step 3), the specific conditions for constructing the origin traceability model are: the cumulative variance explained by principal component 1 and principal component 2 in the principal component analysis model is ≥70%, and the partial least squares discriminant analysis model is tested with p<0.001 after 1000 permutation tests.

10. The method according to claim 1, characterized in that, The specific conditions for origin verification in step 4) are: when the known origin standard sample is clustered with the corresponding origin sample in the principal component analysis and partial least squares discriminant analysis plots and the overlap is ≥85%, the model is deemed to have passed verification.

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