Non-targeted analysis method for volatile components in rose essential oil and hydrolat

Abstract

The invention discloses a non-targeted analysis method for volatile components in rose essential oil and hydrolat, which comprises the following steps: (1) picking, treating and preparing a sample: picking full rose flowers, and extracting rose essential oil and hydrolat by using a steam distillation method; detecting the treated sample by using a gas chromatography-electrostatic field orbitrap high-resolution mass spectrometry system; and (2) statistical analysis: carrying out analysis and detection on a detection result, including identification of unknown trace compounds in rose essential oil and hydrolat, analysis of changes of volatile trace compounds and determination of differences of metabolite types in roses based on different metabonomics methods. According to the method, database retrieval and spectrogram analysis technologies are combined, so that the unknown components can be subjected to preliminary structural identification, trace components in the rose aroma can be accurately and quantitatively analyzed, the composition and content change of the rose aroma components can be deeply researched, and powerful support is provided for finding new aroma components and researching the formation mechanism of the rose aroma.

Description

Technical Field

[0001] This invention relates to an analytical method, and more particularly to a non-targeted analytical method for the analysis of volatile components in rose essential oil and hydrosol using GC-Orbitrap MS. Background Technology

[0002] Roses, originating in China, are a flower used for both medicinal and culinary purposes. They are also an important raw material for fragrances and flavorings, possessing both ornamental and economic value, and are widely used in landscaping, food, pharmaceuticals, and fine chemicals. Roses contain various active ingredients, such as amino acids, phenolic acids, terpenes, and alcohols. These bioactive components have been proven to act as antioxidants and antibacterial agents in cosmetics and pharmaceuticals. Roses can be eaten fresh or processed into rose jam, rose petal cakes, rose wine, and rose milk beverages. However, fresh roses are prone to spoilage during storage. Processing roses into rose essential oil, hydrosol, and other rose byproducts can extend their shelf life and facilitate further utilization, thereby increasing the economic value of roses. The pursuit of health and a high-quality life, along with the emphasis on mental health and spiritual well-being, has led to a continuous increase in consumer demand for natural plant extracts such as rose essential oil and hydrosol. Rose essential oil and hydrosol enjoy a high reputation among consumers worldwide for their rich nutritional components and unique aroma. Aroma, as an important component of sensory evaluation, directly influences consumers' purchasing preferences for plant essential oils and hydrosols.

[0003] Roses are rich in various chemical components, including flavonoids, amino acids, proteins, fats, and vitamins. Rose extract has antibacterial, antioxidant, analgesic, antidepressant, immunomodulatory, anticholinesterase activity, and moisturizing effects. Phenotypically, roses do not have an advantage over wild roses and hybrid tea roses. However, in important aroma components such as citronellol, phenylethyl alcohol, farnesol, nerol, and rose ether, roses are significantly superior to fragrant hybrid tea roses and other aromatic species of the Rosa genus. Aromatic components are an important indicator for evaluating aromatic materials, and the aromatic components contained in roses are the important material basis for their use as ornamental flowers, edible plants, and medicinal aromatic plants. The fragrance components are not only affected by environmental changes such as temperature, day length, and humidity, but also by differences between different varieties of the same species. Some aromatic compounds also exhibit different volatilization patterns depending on the plant's diurnal rhythm, and aroma release changes with the plant's developmental stage. The types of compounds contained in the aroma components of roses include terpenes, alcohols and esters, aldehydes, ketones, ethers, and aliphatic hydrocarbons. While different rose varieties share similar aroma components, their concentrations vary significantly. Aroma compounds form the basis of the fragrance in rose essential oils and hydrosols. With the advancement of analytical techniques and the rapid development of instruments, an increasing number of aroma compounds and their contributions are being discovered. These aroma compounds exhibit a wide range of polarities, solubilities, volatility, and thermal stability, posing a significant challenge to the non-targeted analysis of aroma compounds.

[0004] Due to technological limitations, the resolution and precision of GC-MS, currently used for volatile component analysis, cannot accurately detect trace components at extremely low concentrations (ppb level), making it insufficient for comprehensive detection of aroma components in roses. Although trace components in roses are present in small amounts, they can significantly impact the overall aroma characteristics and quality of roses. They are key factors contributing to the unique aroma of rose varieties, causing subtle differences in aroma between different varieties and serving as important markers for distinguishing their fragrances. Trace components in roses synergistically enhance or regulate the intensity, complexity, and balance of the overall rose aroma, resulting in a fuller and more harmonious fragrance. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a non-targeted analysis method for volatile components in rose essential oil and hydrosol.

[0006] This invention discloses a non-targeted analysis method for volatile components in rose essential oil and hydrosol. The method includes the following steps:

[0007] (1) Sample collection, processing and preparation: whole rose flowers were collected and rose essential oil and hydrosol were extracted by steam distillation; the processed samples were detected by gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry system.

[0008] (2) Statistical analysis: The test results were analyzed, including the identification of unknown trace compounds in rose essential oil and hydrosol, analysis of changes in volatile trace compounds, and determination of differences in the types of metabolites in roses based on different metabolomics methods.

[0009] The non-targeted analysis method for volatile components in rose essential oil and hydrosol described in this invention, wherein:

[0010] Statistical analysis was performed using Xcalibur Qual Browser version 4.2 and Compound Discoverer software for data acquisition and analysis of rose essential oil and hydrosol samples. Quan Browser and Qual Browser were used for processing. Peak detection was performed using spectral deconvolution. The built-in deconvolution plugin in the software was used to extract individual peaks from the total ion chromatograms of each EI-MS data file, and candidate metabolites were identified according to the GC-orbitrap flavor compound high-resolution library NIST 2020. Each analysis was performed in duplicate. The deconvolutioned spectra were matched with the standard spectra of metabolites in the NIST 2020 database to determine the molecular formula. Retention indices were obtained by injecting a mixture of C6–C24 n-alkanes under the same chromatographic conditions. Qualitative analysis of metabolites was performed by combining the calculated RI values, mass spectrometry information, and retention indices. The relative abundance of metabolites in the samples was expressed as peak area ratio, and each sample was injected three times. Multivariate analysis was performed on the data, including principal component analysis and hierarchical cluster analysis. The data were visualized using cluster heatmaps and volcano plots.

[0011] The non-targeted analysis method for volatile components in rose essential oil and hydrosol described in this invention, wherein:

[0012] The identification of unknown trace compounds in rose essential oil and hydrosol described in step (2) includes the following steps:

[0013] (A) Overall score and retention index;

[0014] (B) Chemical ionization is used to identify molecular ion peaks and obtain mass spectra. Information about sample molecules is inferred based on the mass-to-charge ratio of the molecular ion peaks.

[0015] (C) Detailed comparison of fragment ions to distinguish isomers: By comparing the mass-to-charge ratio of fragment ions in the mass spectrum, the differences between isomers can be found. By comparing the relative abundance of fragment ions and finding and identifying characteristic fragment ions, isomers can be distinguished more precisely.

[0016] (D) Chemical Standard Validation: The standard substance and the sample to be identified are analyzed under the same chromatographic conditions, and their chromatographic parameters such as retention time and retention index are compared. If the chromatographic parameters of the sample to be identified are consistent with those of the standard substance, it indicates that they behave similarly in the chromatographic separation process and may be the same metabolite. The search results are comprehensively ranked based on the similarity index SI, high-resolution matching factor HRF value and retention index RI in the spectral library. When the comprehensive score is 90 or above, the SI index is not less than 700, the HRF value is not less than 90 and the ΔRI is 50 or below, the identification result is reliable.

[0017] Trace components in rose essential oil and hydrosol were analyzed using principal component analysis, volcanic analysis, and partial least squares discriminant analysis.

[0018] The non-targeted analysis method for volatile components in rose essential oil and hydrosol described in this invention, wherein:

[0019] In step (2), the analysis of changes in volatile trace compounds uses strict statistical criteria to screen differential metabolites. With a peak area ratio of log2 N>1, for each compound, the peak area ratio between different sample groups is calculated, and then the log2N value is obtained by taking the base-2 logarithm. P<0.05 is used as the significance threshold to identify differential metabolites between different sample groups from the dataset. Differential analysis is performed on the screened metabolites, and the differences between the samples in each comparison group are displayed in the form of a differential metabolite volcano plot.

[0020] The peak areas of each metabolite in the dataset are logarithmically transformed to base 2, and metabolites with log2N>4 are selected to form an initial subset. Then, the peak area ratio of metabolites between different sample groups is calculated, and metabolites with a ratio>16 or a ratio<1 / 16 are retained. Finally, the high-confidence differential metabolites that simultaneously satisfy log2N>4 and peak area ratio>16 are obtained by taking the intersection.

[0021] The non-targeted analysis method for volatile components in rose essential oil and hydrosol described in this invention, wherein:

[0022] In step (2), the differences in the types of metabolites in roses determined by different metabolomics methods were analyzed using a multi-platform strategy to systematically resolve the metabolomics characteristics of rose products. Based on the results obtained from previous full-spectrum metabolomics GC-MS and UPLC-MS, high-resolution orbital trap mass spectrometry coupled with gas chromatography-GC-MS was further used to detect metabolites in rose essential oil and hydrosol. Through multi-platform data integration analysis, the consistency of detection of characteristic metabolites of roses was systematically verified. In the multi-platform data integration analysis method, a number of high-confidence metabolites screened based on the ΔRI<20 standard of the GC-Orbitrap / MS method were compared with a number of metabolites identified by the GC-MS platform and the UPLC-MS platform, and the key metabolite types were obtained through cross-validation.

[0023] The non-targeted analysis method for volatile components in rose essential oil and hydrosol described in this invention, wherein: the distillation method in step (1) includes the following steps:

[0024] Rose essential oil and hydrosol are extracted using steam distillation. Based on Dalton's Law, the distillation equipment is pre-treated, with separate compartments for flowers and water. First, purified water is added at a 1:1 ratio. This purified water is heated and converted into steam through heating pipes in the jacket. The steam is then piped to the jacket containing the treated roses, where the roses are heated and pressurized for distillation. The distillation pressure is controlled at 0.18-0.2 MPa. The steam passes through a condenser, and the water circulates back into the distillation vessel for further heating and distillation. During distillation, water is continuously added to the jacket containing the purified water to maintain a 4:1 water-to-flower ratio. When collecting rose essential oil, the temperature is controlled at 35℃, and when collecting rose hydrosol, the dew temperature is controlled at 25℃. The ratio of rose hydrosol extracted to fresh roses is 1:1.

[0025] The non-targeted analysis method for volatile components in rose essential oil and hydrosol described in this invention, wherein the detection method in step (1) includes the following steps:

[0026] Accurately transfer 5 mL of rose hydrosol aqueous solution sample to a 20 mL headspace vial, add 3 g of NaCl, and repeat in triplicate, labeling them as Pure-1, Pure-2, and Pure-3. Weigh 5 mL of ultrapure water and add 3 g of NaCl as a blank control. Accurately weigh 0.5 g of rose essential oil sample to a 20 mL headspace vial, tighten the cap, and repeat in triplicate, labeling them as Oil-1, Oil-2, and Oil-3. Use air as a blank control. Accurately weigh 4 mg of rose essential oil sample, add 1 mL of n-hexane to completely dissolve it, and then dilute it by half with n-hexane, repeating in triplicate.

[0027] Rose essential oil and hydrosol samples were injected and analyzed. Sample vials were sealed with metal caps equipped with PTFE / silicone septa and then placed in a headspace autosampler. Blank analysis was performed strictly following the same procedures as sample pretreatment to identify systematic or background contamination originating from experimental containers, septa, or column wear. Each sample and blank sample was analyzed at least three times in random order using a gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry system equipped with TriPlus. An RSH autosampler, including headspace sampling, was used to analyze volatile metabolites in rose essential oil and hydrosol. The injection port temperature was 240°C. The chromatographic column was a TG-WaxMS capillary column, 60m × 0.25mm × 0.25μm. The chromatographic temperature program was as follows: 40°C for 3 min, then increased to 240°C at a rate of 5°C / min, held for 12 min, for a total of 55 min. The carrier gas was helium, purity: 99.999%, flow rate: 1.2mL / min. Full scan MS acquisition was performed in profile mode using a mass-to-charge ratio range of 41-500m / z. Each sample was analyzed in EI mode, with an electron energy of 70eV, and the mass spectrometry resolution power was set to its maximum at @200m / z, with a full width at half maximum (FWHM) of 60000.

[0028] The non-targeted analysis method for volatile components in rose essential oil and hydrosol described in this invention is applied in any of the following:

[0029] (I) Establish a database of functional components of roses;

[0030] (II) To gain a comprehensive understanding of the composition of rose aroma components and to distinguish the aroma differences between different rose varieties;

[0031] (III) Selection and improvement of rose varieties: By analyzing and tracking trace components, it is possible to cultivate new rose varieties with more distinctive features and better fragrance.

[0032] (IV) To more accurately replicate and simulate the natural aroma of roses, improve the quality of rose by-products and the realism of rose aroma, and develop more distinctive rose products;

[0033] (V) Identification and analysis of rose varieties.

[0034] The non-targeted analysis method for volatile components in rose essential oil and hydrosol of this invention differs from existing technologies in that:

[0035] This invention presents a non-targeted analysis method for volatile components in rose essential oil and hydrosol. Based on gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry (GC-Orbitrap MS), a high-resolution database of volatile compounds in rose essential oil and hydrosol is established to improve identification accuracy. The method analyzes the volatiles released from rose essential oil and hydrosol under specific headspace conditions. Trace components in rose essential oil and hydrosol are qualitatively analyzed using four identification steps: Step 1: comprehensive scoring and retention index; Step 2: chemical ionization identification of molecular ion peaks; Step 3: detailed comparison of fragment ions to distinguish isomers; Step 4: chemical standard verification. Principal component analysis, volcanic eruption analysis, and partial least squares discriminant analysis are employed for trace components in rose essential oil and hydrosol. A total of 1709 volatile compounds were identified in rose essential oil and hydrosol. By establishing a high-resolution database (ΔRI < 20), 236 trace compounds were identified from these 1709 compounds, and the identified substances were divided into 13 subclasses. Terpenes, alcohols, esters, and ketones are the first four groups of substances identified in rose essential oil and hydrosol. Volatile analysis can not only explain the aroma types of rose essential oil and hydrosol, but may also be another promising method for rose variety identification and analysis.

[0036] To more accurately and comprehensively determine the composition of volatile components in roses, this invention employs gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry (GC-MS) to determine trace components in rose essential oil and hydrosol. Orbital trap mass spectrometry (Orbitrap-MS) possesses ultra-high resolution and mass accuracy, providing highly accurate mass-to-charge ratio data, with mass measurement errors typically less than 1 ppm (parts per million). Combined with database retrieval and spectral analysis techniques, preliminary structural identification of unknown components can be performed, and trace components in rose aroma can be precisely quantitatively analyzed. This allows for in-depth research into the composition and content variations of rose aroma components, providing strong support for the discovery of new aroma components and the study of the formation mechanism of rose aroma.

[0037] The non-targeted analysis method for volatile components in rose essential oil and hydrosol of the present invention will be further described below with reference to the accompanying drawings. Attached Figure Description

[0038] Figure 1 The whole 'Hanxiang' rose petals that are cold-stored after being picked, as described in the method of this invention;

[0039] Figure 2 These are the total ion chromatography (TIC) chromatograms of different samples in the method of this invention; from top to bottom, they are the TIC chromatograms of three replicate samples of hydrosol-SPME; the TIC chromatograms of three replicate samples of essential oil-SPME; and the TIC chromatograms of three replicate samples of essential oil-liquid injection.

[0040] Figure 3This is a principal component analysis score graph of all test samples in the method of this invention;

[0041] Figure 4 This is a heatmap showing the clustering of 'Hanxiang' rose essential oil and hydrosol samples in the method of this invention. The sample clustering selection criteria are: total qualitative score > 90 and retention index difference (ΔRI) < 50. Each colored cell corresponds to the value of a different category of volatile metabolites; red indicates low content, while blue indicates high content.

[0042] Figure 5 This is a ring diagram of trace components in 'Hanxiang' rose essential oil and hydrosol samples in the method of this invention; wherein, the sample screening conditions are: total qualitative score > 90, retention index difference (△RI) < 20;

[0043] Figure 6 This is a metabolite volcano diagram comparing the peak areas of essential oil-SPME and hydrosol-SPME in the method of this invention.

[0044] Figure 7 This is a metabolite volcano diagram comparing the peak areas of essential oil-liquid and hydrosol-SPME in the method of this invention.

[0045] Figure 8 This is a volcano diagram showing the difference in peak area between essential oil-SPME and essential oil-liquid in the method of this invention;

[0046] Figure 9 This is a graph showing the results of significantly upregulating the amount of differentially expressed compounds in 'Hanxiang' rose essential oil and hydrosol using the method of the present invention.

[0047] Figure 10 This is a Venn diagram showing the differences between the groups in the method of this invention. Detailed Implementation

[0048] 1. Materials and Methods

[0049] 1.1 Materials and Reagents

[0050] Fresh, disease-free, and fully blooming whole flowers of the 'Hanxiang' rose variety were collected from the Rose Germplasm Resource Nursery of the Beijing Academy of Agricultural and Forestry Sciences (Beilangzhong Village, Zhaoquanying Town, Shunyi District, Beijing). Chemical standards, including methanol (HPLC grade), sodium chloride (analytical grade), and n-hexane (HPLC grade), were purchased from Sinopharm Chemical Reagent Co., Ltd. (Beijing, China).

[0051] 1.2 Sample processing and preparation

[0052] Harvest disease- and pest-free 'Hanxiang' roses (whole flowers) Figure 1This method uses steam distillation to extract rose essential oil and hydrosol. Based on Dalton's Law, the distillation equipment is pre-treated to ensure internal cleanliness. The distillation equipment (MG-200 model, Shandong Lize Machinery Co., Ltd.) has separate flower and water compartments. First, purified water is added at a 1:1 ratio of water to flowers. The purified water is heated and converted into steam through heating pipes in the jacket. The steam is then piped to the jacket containing the treated 'Hanxiang' roses, where the roses are heated and pressurized for distillation. The distillation pressure is controlled at 0.18-0.2 MPa. The steam passes through a condenser, and the water circulates back into the distillation vessel for further heating and distillation. During distillation, water is continuously added to the jacket containing the purified water to maintain a final water-to-flower ratio of 4:1. When collecting rose essential oil, the temperature is controlled at approximately 35℃; when collecting rose hydrosol, the temperature is controlled at approximately 25℃. The ratio of rose hydrosol extraction to fresh flower extraction is 1:1.

[0053] This invention utilizes a gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry system (GC-Orbitrap / MS) to perform qualitative analysis and differential comparison of metabolites in rose hydrosol and rose essential oil. Accurately transfer 5 mL of rose hydrosol aqueous solution sample to a 20 mL headspace vial, add 3 g of NaCl, and repeat in triplicate, labeled as pure-1, pure-2, and pure-3. Weigh 5 mL of ultrapure water and add 3 g of NaCl as a blank control. Accurately weigh 0.5 g of rose essential oil sample to a 20 mL headspace vial, tighten the cap, and repeat in triplicate, labeled as oil-1, oil-2, and oil-3. Air serves as a blank control. Accurately weigh 4 mg of rose essential oil sample, add 1 mL of n-hexane to completely dissolve it, and then dilute with n-hexane by half, repeating in triplicate.

[0054] Rose essential oil and hydrosol samples were injected and analyzed. Sample vials were sealed with metal caps equipped with PTFE / silicone septa and then placed in a headspace autosampler. Blank analysis was performed strictly following the same procedures as sample pretreatment to identify systematic or background contamination that may originate from experimental containers, gaskets, column wear, etc. Each sample and blank sample was analyzed in at least three parallel injections in random order. Volatile metabolites in rose essential oil and hydrosol were analyzed using a gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry system (GC-Orbitrap-MS, Thermo Scientific, Bremen, Germany) equipped with a TriPlus RSH autosampler (including headspace sampling function). The injection port temperature was 240°C. The chromatographic column was a TG-WaxMS capillary column (60m × 0.25mm × 0.25μm). The chromatographic temperature program was as follows: 40℃ for 3 min, then increased to 240℃ at a rate of 5℃ / min and held for 12 min, for a total of 55 min. Helium (purity: 99.999%) was used as the carrier gas at a flow rate of 1.2 mL / min. Full scan MS acquisition was performed in profile mode using a mass-to-charge ratio range of 41–500 m / z. Each sample was analyzed in EI mode with an electron energy of 70 eV. The mass spectrometry resolution power was set to its maximum at 200 m / z, with a full width at half maximum (FWHM) of 60,000.

[0055] 1.3 Statistical Analysis Methods

[0056] This study used Xcalibur Qual Browser version 4.2 and Compound Discoverer software for data acquisition and analysis of rose essential oil and hydrosol samples. The software used was Quan Browser and Qual Browser (ThermoFisher Scientific, Les Ulis, France). This tool allows peak detection using spectral deconvolution, which effectively separates overlapping peaks and minimizes noise, improving peak resolution. Individual peaks were extracted from the total ion chromatograms of each EI-MS data file using the software's built-in deconvolution plugin. Candidate metabolites were identified according to the GC-orbitrap flavor compound high-resolution library NIST 2020, with each analysis performed in duplicate. The deconvolutioned spectra were matched with standard spectra of metabolites in the NIST 2020 database to determine the molecular formula. Under the same chromatographic conditions, retention indices were obtained by injecting a mixture of C6–C24 n-alkanes (Supelco, Bellefonte, PA, USA). Qualitative analysis of metabolites was performed by combining calculated RI values, mass spectrometry information, and retention indices. The relative abundance of metabolites in the samples was expressed as peak area ratio. Each sample was injected three times. Multivariate analysis was performed on the data, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), and the data were visualized using cluster heatmaps and volcano plots.

[0057] The method of this invention includes the identification of unknown trace compounds in rose essential oil and hydrosol, analysis of changes in volatile trace compounds, and determination of differences in the types of metabolites in roses based on different metabolomics methods.

[0058] Identification of unknown trace compounds in rose essential oil and hydrosol:

[0059] As previously mentioned, the presumptive identification of migrating chemicals was performed using the Compound Discoverer software workflow, which allows for automated data processing based on selected pre-built or custom workflows. After data processing using this workflow, a results table is generated containing information on compound identification scores, including total score, HRF and RHRF scores (aiding in accurate metabolite identification), SI and RSI (aiding in qualitative analysis), reference m / z (reference mass-to-charge ratio, used to identify characteristic peaks of metabolites in mass spectra), comparisons with experimental values, and annotation fragments. Post-processing functions for descriptive statistics and differential analysis allow for statistical comparisons of identified metabolites. High-precision molecular weight measurements are possible using gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry (GC-Orbitrap MS). This study performed qualitative analysis of trace components in rose essential oil and hydrosol according to four identification steps. First, the overall score and retention index are examined. Then, the molecular ion peak is identified based on chemical ionization to obtain a mass spectrum. The mass-to-charge ratio (m / z) of the molecular ion peak is used to infer information such as the molecular formula of the sample molecule, providing crucial information for compound identification. Next, the fragment ion details of isomers are compared to distinguish them. Isomers are metabolites with the same molecular formula but different structures. They may produce different fragment ions in mass spectrometry. By comparing the mass-to-charge ratio of fragment ions in the mass spectrum, differences between isomers can be identified. By comparing the relative abundance of fragment ions and identifying characteristic fragment ions, isomers can be distinguished more precisely. Finally, chemical standard verification is performed by analyzing the standard substance and the sample to be identified under the same chromatographic conditions and comparing their retention times, retention indices, and other chromatographic parameters. If the chromatographic parameters of the sample to be identified are consistent with those of the standard substance, it indicates that they behave similarly during chromatographic separation and may be the same metabolite. Based on the similarity index (SI), high-resolution matching factor (HRF) value, and retention index (RI) in the spectral library, the search results are comprehensively ranked. Generally speaking, a retention index difference (RIDelta) of less than 50 can greatly enhance the reliability of qualitative identification. However, some metabolites lack retention index data in the NIST spectral library, making it impossible to obtain their RI. Theoretically, when the comprehensive score is 90 or above, the SI index is not lower than 700, the HRF value is not lower than 90, and the RI is 50 or below, the identification results have high reliability (Li et al., 2022).

[0060] This invention employs a rigorous data quality control process: firstly, background interference is removed using deconvolution techniques, and chromatographic peaks with a relative standard deviation (RSD) > 20% and peaks with poor peak shape (possibly baseline noise) are eliminated from three biological replicates; subsequently, rose essential oil and hydrosol samples are searched and matched based on the NIST 2020 mass spectrometry database. Total ion chromatogram (TIC) overlap analysis shows that the experimental data have good repeatability. Figure 2 Principal component analysis (PCA) was further performed on 5206 chromatographic peaks identified by matching the NIST database. Figure 3 The results showed that, regardless of whether solid-phase microextraction (SPME) or direct liquid injection was used, essential oil and hydrosol samples exhibited significant separation at the PC1 (51.7% contribution rate) level, confirming an essential metabolic difference between the two products. For essential oil samples, different injection methods (SPME vs. liquid injection) also showed significant differentiation at the PC2 (37.6% contribution rate) level, reflecting the significant impact of analytical methods on the detection results. This analysis not only verified the reliability of the experimental data but also revealed the differences in chemical composition between essential oils and hydrosols at the metabolomics level.

[0061] This invention established stringent compound screening criteria, using a total qualitative score >90 points and a retention index difference (ΔRI) <50 or no ΔRI data as screening conditions. 2128 stable chromatographic peaks were identified from the raw data. After deduplication, 1709 structurally well-defined compounds were obtained. These metabolites were stable and reliably identified in three biological replicates. To better visualize and understand the changes in metabolites in rose essential oil and hydrosol, hierarchical cluster analysis (HCA) was performed on these metabolites, and the results were visualized in a heatmap (see [link to heatmap]). Figure 4 The results showed significant differences in the types and amounts of metabolites between rose essential oil and hydrosol samples. Using a more stringent screening criterion of ΔRI < 20, we precisely identified 236 trace compounds from 1709 compounds. These substances can be classified into 13 different chemical categories (e.g., Figure 5The 236 trace compounds included 50 terpenes (such as linalool, α-farnesene, geraniol, carotene, and 2-phenylethanol), 41 alcohols (such as citronellol, nerol, and benzyl alcohol), 34 esters (such as ethyl acetate, methyl cinnamate, and methyl geraniate), 28 ketones (such as carvone and 2-nonanone), 22 aromatics (such as elemol, flavin, and p-xylene), 17 aldehydes (such as phenylacetaldehyde, citronellol, and nerol), 11 acids (such as octanoic acid and nonanoic acid), 11 hydrocarbons (such as nonadecane and styrene), 9 ethers (such as trans-rosinone and 1-4-cineole), 6 heterocyclic compounds (such as isoquinoline), 3 phenols (such as methyl eugenol and eugenol), 3 other compounds, and 1 halogenated hydrocarbon. This analysis not only revealed the differences in the characteristic metabolic profiles of essential oils and hydrosols but also provided important evidence for the identification of key active ingredients.

[0062] Changes in volatile trace compounds in rose essential oil and hydrosol

[0063] This invention employs rigorous statistical criteria to screen differentially expressed metabolites. A peak area ratio (log₂N) > 1 (for each compound, the peak area ratio between different sample groups is calculated, and then the log₂N value is obtained by taking the base-2 logarithm) and P < 0.05 are used as the significance threshold to identify differentially expressed metabolites between different sample groups from the dataset. Differential analysis is performed on the screened metabolites, and the differences between the comparison groups are displayed in the form of a differential metabolite volcano plot. Figure 6 As shown, there are 1539 different metabolites between essential oil-SPME and hydrosol-SPME samples (829 metabolites were upregulated and 710 metabolites were downregulated in essential oil-SPME samples compared to hydrosol-SPME samples). Figure 7 As shown, there were 1543 different metabolites between the essential oil-liquid and hydrosol-SPME samples (compared to the hydrosol-SPME samples, the essential oil-liquid samples had 772 upregulated metabolites and 771 downregulated metabolites). Figure 8 As shown, there are 1308 different metabolites between essential oil-SPME and essential oil-liquid samples (755 metabolites were upregulated and 553 metabolites were downregulated in the essential oil-SPME sample compared to the essential oil-liquid sample). Figure 6 , Figure 7 and Figure 8 The chemical classification of different metabolites in the three paired comparison groups was shown. Chemical classification analysis revealed significant differences in metabolites between rose essential oil and rose hydrosol. Different pretreatment methods (SPME and liquid injection) also significantly affected the detection results of essential oil metabolites, suggesting that the choice of analytical method is crucial for metabolomics research. These findings provide a molecular-level scientific basis for the quality control and process optimization of rose products.

[0064] Each point in the volcano plot represents a metabolite, with green points indicating metabolites with a peak area ratio < 0.5 and red points indicating metabolites with a peak area ratio > 2. The horizontal axis represents the logarithm of the relative abundance difference (log2 FC) between the two sample groups. A larger absolute value on the horizontal axis indicates a more significant difference in the relative abundance of the substance between the two sample groups. Under the FC+P-value screening condition, the vertical axis reflects the significance level of the difference (-log2 FC). 10 The higher the P-value (vertical axis), the more significant the difference, and the more reliable the differentially expressed metabolites are.

[0065] This invention performs a logarithmic transformation (log2N) on the peak area of ​​each metabolite in the dataset, selecting metabolites with log2N>4 to form an initial subset. Subsequently, the peak area ratios of metabolites between different sample groups are calculated, retaining metabolites with ratios >16 (i.e., log2(ratio)>4) or <1 / 16 (i.e., log2(ratio)<-4). Finally, high-confidence differentially expressed metabolites that simultaneously satisfy log2N>4 and peak area ratio>16 are obtained by taking the intersection. Based on this standard, in the comparison between rose essential oil-SPME and hydrosol-SPME, a total of 606 significantly upregulated differentially expressed metabolites (17 subclasses) were identified, such as... Figure 9 As shown, 334 key differential compounds (15 subclasses) were found in the essential oil-SPME sample more than in the hydrosol-SPME sample, including 98 esters (such as terpineol acetate and octyl octanoate), 49 terpenes (such as nerol and trans-nerolidol), 37 hydrocarbons (such as 1-nonene and nonadecane), 36 aromatics (such as p-isopropyltoluene and biphenyl), 30 ketones (such as 2-tetanetanone and 6,10-dimethyl-2-undecane), 25 alcohols (such as 6-undecaneol and 2-undecaneol), 22 other compounds (such as nicotinamide), 10 heterocyclic compounds (such as 2-pentylfuran), 8 ethers (such as ethyl geraniol ether), and 6 aldehydes (such as myristaldehyde). 272 key differentially expressed compounds (16 subclasses) were found in hydrosol-SPME samples more frequently than in essential oil-SPME samples. These included 91 esters (e.g., methyl benzoate, hexyl acetate), 37 hydrocarbons (e.g., nonylcyclopentane, cis-2-pentene), 26 ketones (e.g., 2-methyl-3-hexanone), 20 terpenes (e.g., citronellol, verbenatone, rosinone), 15 alcohols (e.g., 1,10-decanediol), and 16 other compounds (e.g., valeric anhydride). These differentially expressed metabolites are not only of significant taxonomic importance but may also be the key material basis for determining the characteristic quality of rose essential oil and hydrosol products. This screening method, by setting strict statistical thresholds, effectively improved the reliability of differentially expressed metabolite identification and provided a high-quality list of target compounds for subsequent functional studies.

[0066] Comprehensive analysis shows that the differential metabolites between rose essential oil and hydrosol are mainly enriched in five major categories of compounds: esters, terpenes, hydrocarbons, aromatics, and alcohols. Among these, esters dominate in both types of products. Notably, while the quantity of hydrocarbons in essential oil and hydrosol is roughly equal, their specific compositions differ significantly. In contrast, essential oil contains significantly more characteristic aroma components such as terpenes, aromatics, alcohols, and ketones than hydrosol. These differences not only reflect the selective enrichment phenomenon during the preparation of essential oil and hydrosol but also reveal, at the molecular level, the essential differences between the two types of products in terms of aroma characteristics and the types and contents of active ingredients, providing important evidence for the precise development and quality control of rose products.

[0067] 2. Discussion

[0068] 2.1 Differences in trace components between rose essential oil and rose hydrosol

[0069] The quality of rose essential oil and hydrosol is determined by genotype and is also influenced by external conditions (such as geographical location, soil type, climate, harvesting time, storage conditions, and processing methods). The components of rose essential oil, hydrosol, and ethanol extracts differ, resulting in variations in their cytotoxicity, antigenotoxicity, antiviral activity, antibacterial activity, antioxidant activity, and antitumor activity. A total of 606 significantly upregulated differential metabolites were found in rose essential oil-SPME and hydrosol-SPME samples. Among these, 334 key differential compounds were found in higher concentrations in essential oil-SPME samples than in hydrosol-SPME samples, while 272 key differential compounds were found in higher concentrations in hydrosol-SPME samples than in essential oil-SPME samples. This indicates significant differences in metabolites between rose essential oil and hydrosol, primarily attributed to two core factors: differences in extraction principles and differences in biosynthetic mechanisms.

[0070] The differences in composition between essential oils and hydrosols mainly stem from their physicochemical properties (polarity and volatility) and extraction principles. From an extraction mechanism perspective, terpenoids, due to their strong volatility and non-polarity, readily evaporate with steam during distillation and accumulate in the essential oil phase; while highly polar phenols and alcohol glycosides are more readily soluble in the aqueous phase to form hydrosols. This study clearly shows that the types and amounts of terpenoids in essential oils are significantly higher than in hydrosols, a finding perfectly consistent with the aforementioned theory. During distillation, the volatility characteristics of different components differ significantly. Highly volatile components in essential oils evaporate rapidly at lower temperatures, while hydrosol components, although also volatile, evaporate to a lower degree than those in essential oils. This characteristic explains why, under the same distillation conditions, the evaporation rate of essential oils is always higher than that of hydrosols, consistent with the conclusion in this invention that rose essential oil contains more differentially expressed compounds than rose hydrosols. Simultaneously, the selective dissolution effect of water as a key medium cannot be ignored. Highly polar compounds have high solubility in water and are more likely to enter the aqueous phase to form hydrosols. Weakly polar and non-polar compounds have low solubility in water and tend to aggregate to form the essential oil phase. For example, oxygenated monoterpenes are more abundant in hydrosols, while highly hydrophobic monoterpenes and sesquiterpenes are mainly found in essential oils. In summary, rose essential oil and hydrosol have different volatility and solubility characteristics, which may lead to differences in the trace components present in the two.

[0071] Meanwhile, the temperature, duration, and steam flow rate during distillation also affect the components in essential oils and hydrosols. Higher temperatures and steam flow rates may distill out more components, with varying degrees of increase for different types. For example, higher temperatures and increased steam flow rates facilitate the extraction of highly volatile essential oil components. Simultaneously, some hydrosol components that are difficult to volatilize at lower temperatures may also be extracted, thus altering the proportions and amounts of components in essential oils and hydrosols. Extending the distillation time may lead to the extraction of more moderately volatile, water-soluble components from hydrosols, resulting in more pronounced differences in composition between essential oils and hydrosols.

[0072] From a biosynthetic perspective, terpenes, phenols, and nitrogen-containing compounds, as major aroma substances, are synthesized under the regulation of multiple factors. During plant growth and development, different tissues and cells synthesize various compounds through their unique metabolic pathways. Biological or abiotic stresses (temperature, water, salinity, soil nutrients, ultraviolet radiation, climate change) can increase the content of certain compounds in plants. For example, after herbivores consume grass, plants release abundant terpenes (such as 2-PE), mediating direct and indirect plant defense. During rose development, the large amount of terpenes released by the plant attracts pollinators to aid in pollination. The metabolic pathways for the formation of various compounds in essential oils and hydrosols differ, directly leading to differences in the types and amounts of compounds in essential oils and hydrosols. During the synthesis of essential oil components, the terpene metabolic pathway may be relatively active, resulting in a large number of terpene compounds in essential oils. Studies have found that the main components of 'Damask' rose essential oil are 72.73–73.80% phenethyl alcohol, 10.62–11.26% limonol, 2.42–2.47% nerol, and 5.58–5.65% geraniol, while the main compounds of 'Damask' rose hydrosol are 30.74% geraniol, 29.44% limonol, 23.74% phenethyl alcohol, and 16.12% nerol. This difference may stem from the varying activities of key enzymes (such as geraniol synthase) in different synthetic pathways. Simultaneously, the types and contents of compounds in essential oils and hydrosols are also influenced by enzymes, with enzyme activities differing under different environments and conditions. When plants are subjected to drought stress, *R. chinensis* responds by producing a series of osmotic protectants (sugars, amino acids, starch, and lipids) via the trehalose phosphate synthase (TPP1) pathway. The rose gene encoding ethylene response factor 109 (ERF109) and transcription factors of the AP2 / ERF family also regulate the content of rose metabolites under drought stress. Furthermore, drought stress stimulates and increases the content of citronellol, geraniol, and 2-phenylethanol. It is speculated that some enzymes exhibit high activity in the synthesis of essential oil components but relatively low activity in the synthesis of hydrosol components, which may lead to differences in compounds between essential oils and hydrosols.

[0073] 2.2 Differences in compound determination using different metabolomics platforms

[0074] Different metabolomics platforms showed differences in the types of compounds detected in roses. Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) revealed flavonoids, amino acids, phenolic acids, and lipids as the main non-volatile metabolites in roses. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry (GC-Orbitrap / MS) identified terpenes, alcohols, esters, ketones, and aromatic hydrocarbons as the main components of rose aroma. The types and quantities of compounds detected in roses differed among UPLC-MS / MS, GC-MS, and GC-Orbitrap / MS. These differences stem from variations in the separation principles, detection capabilities, sample requirements, and data processing and analysis methods of the three platforms. This resulted in discrepancies in the types and amounts of metabolites detected when analyzing the same sample.

[0075] UPLC-MS / MS platforms are suitable for separating highly polar macromolecular metabolites and can directly analyze liquid samples. Multistage mass spectrometry (MS / MS) provides rich fragment information, which is helpful for metabolite identification, but its resolution is limited. It can be used to detect unstable non-volatile metabolites in samples. GC-MS is suitable for detecting low molecular weight metabolites with low boiling points, moderate polarity, or non-polarity and good thermal stability. The EI (electron impact) source produces characteristic fragment spectra, which can be matched with standard spectral libraries such as NIST, providing reliable qualitative analysis. However, the sample must be volatile and thermally stable. It can detect volatile metabolites in samples. GC-Orbitrap / MS also requires volatile and thermally stable samples, but compared to GC-MS, it has higher resolution, mass accuracy, sensitivity, and selectivity. It greatly enhances the ability to separate and identify metabolites in complex matrices and can accurately distinguish metabolites with very similar mass numbers. It is suitable for detecting trace volatile components.

[0076] Overall, full-spectrum metabolomics techniques (UPLC-MS and GC-MS) detected more compounds in rose hydrosol than gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry (GC-Orbitrap / MS). In terms of the types of compounds specifically detected, high-resolution mass spectrometry is suitable for qualitative detection of terpenes, alcohols, esters, and aldehydes in roses, while high-resolution orbital trap mass spectrometry is suitable for qualitative detection of trace components such as ketones, ethers, aromatics, and hydrocarbons, and can supplement substances not detected by high-resolution mass spectrometry. The aroma components of different rose varieties are mainly composed of terpenes, alcohols, esters, and aldehydes, but the content and quantity of different aroma components vary among different rose varieties, resulting in different aromas. Furthermore, the differences in the types and quantities of trace components in roses may also be an important reason for the differences in aroma among different rose varieties. Based on full-spectrum metabolomics technology, the types and contents of volatile and non-volatile compounds in roses can be preliminarily detected. Combined with gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry system, it can supplement compounds that were not detected and identified in GC-MS, which is more conducive to a comprehensive understanding of the composition of rose metabolites.

[0077] 2.3 Methods for establishing a database of trace active ingredients in rose essential oil and hydrosol (i.e., rose functional ingredient database)

[0078] Trace compounds in essential oils and hydrosols mainly include terpenes, alcohols, esters, ketones, aromatics, and aldehydes. This invention selected 70 compounds from 236 trace compounds in rose essential oil and hydrosol with a ΔRI < 20, and compared 31 compounds with the same screening conditions (1709 compounds initially screened, and compounds with ΔRI < 20) with 2845 compounds determined by UPLC-MS and GC-MS to explore the functions of trace compounds in rose essential oil and hydrosol. Terpenes were the most abundant trace active ingredients in rose essential oil and hydrosol, accounting for 27%. Among them, linalool can be used as a raw material for perfumes, cosmetics, food processing, aromatherapy, and also as a pharmaceutical raw material. A comprehensive review of the bioactivity of linalool in 2021, such as anticancer, antibacterial, anti-anxiety, antidepressant, anti-stress, and anti-inflammatory effects and their potential mechanisms, was conducted. Linalool exerts its anticancer effect by inducing apoptosis in cancer cells through oxidative stress and protecting normal cells. The anti-inflammatory activity of linalool gives it certain effects in protecting the liver, neuroprotection, and kidney protection. The following year, other researchers highlighted the significant potential of linalool as a treatment for depression. Linalool has potential activity in the central nervous system (CNS) and can intervene in neurodegenerative and behavioral disorders (such as depression). Linalool exerts its antidepressant effect through interactions with serotonergic and norepinephrine pathways. Simultaneously, linalool can positively influence behavioral changes and memory consolidation by modulating the serotonergic pathway. When used in aromatherapy, linalool can effectively relieve anxiety and reduce stress, and has certain benefits in treating depression. In vitro and in vivo experiments have fully demonstrated that linalool exerts its anti-inflammatory effect by reducing the levels of various cytokines, such as TNF-α, interleukin-1β (IL-1β), interleukin-1α (IL-1α), and IFN-γ. In addition, linalool also possesses antioxidant, anticonvulsant, anti-anxiety, and sedative activities. The terpenoid α-cucurbitene can protect the liver and has anti-obesity, antibacterial, and anti-leukemia activities. α-cucurbitene can also stimulate plant growth and root development. α-farnesene can be used as a fragrance and flavoring agent, and also has neuroprotective, antioxidant, antibacterial, anticancer, and antidiabetic activities.

[0079] Rose essential oil and hydrosol contain a variety of aromatic compounds and are commonly used in the preparation of fragrances and flavorings. Most of the aromatic components in roses can be used as raw materials for fragrances and flavorings, such as α-farnesene, α-pinene, nonanol, citronellol, ethyl acetate, hexyl butyrate, neraldehyde, phenylacetaldehyde, and geraniic acid. The most frequently discussed efficacy of rose components is their antibacterial, antioxidant, and anti-inflammatory activity. These active ingredients enhance the application of rose essential oil and hydrosol in the cosmetics industry. In recent years, the anti-tumor, anti-cancer, anti-diabetic, and acetylcholinesterase-inhibiting effects of rose components have also attracted increasing attention from researchers, indicating the enormous potential for medicinal applications of roses. Some components of roses possess sedative, antidepressant, and anti-anxiety activities and can be used in aromatherapy in daily life to relieve tension and anxiety. In addition, roses also have antidiarrheal effects, enhance plant immunity, and regulate plant defense mechanisms. Based on the active functional components detected by three metabolomics technologies and referencing literature from Chinese and foreign researchers over the past 15 years, a database of functional components of 'Hanxiang' rose has been established, containing more than 100 effective components, as shown in Table 1, including phenolic acids, terpenes, alcohols, esters, etc., providing more sufficient theoretical support for the application of rose.

[0080] Table 1.1 Database of Functional Components of 'Hanxiang' Rose

[0081]

[0082]

[0083]

[0084] 2.4 Differences in the types of metabolites in roses determined by different metabolomics techniques

[0085] This invention employs a multi-platform combined strategy to systematically analyze the metabolomic characteristics of rose products. Using high-resolution orbital trap mass spectrometry (Orbitrap-MS) coupled with gas chromatography-mass spectrometry (GC-MS), 1709 metabolites were detected in rose essential oil and hydrosol. Through multi-platform data integration and analysis, it was found that GC-Orbitrap / MS and full-spectrum metabolomics (GC-MS+UPLC-MS) jointly detected 103 metabolites, covering 15 classes of compounds including terpenes, alcohols, and esters. Figure 10Compared to the UPLC-MS platform, GC-Orbitrap / MS and UPLC-MS both detected five characteristic metabolites, including octanoic acid, methyl eugenol, and methyl cinnamate. Compared to the GC-MS platform, GC-Orbitrap / MS detected 98 metabolites across 12 major classes, including 49 terpenes (such as 2-phenylethanol, linalool, menthol, geraniol, trans-nerolidol, nerol, and carotene), 8 alcohols (such as lauryl alcohol, nonanol, and benzyl alcohol), 8 ketones (such as 2-heptanone and 2-tetanetanone), 8 aromatics (such as styrene, p-xylene, and 4-isopropyltoluene), 7 esters (such as nerolithate, methyl butyrate, and citronellol butyrate), 6 aldehydes (such as phenylacetaldehyde and heptanal), 3 hydrocarbons (such as nonadecane), 2 acids (such as 2-methylbutyric acid), 2 heterocyclic compounds (such as 2-pentylfuran), 2 phenols, 2 other compounds, and 1 nitrogen-containing compound. These cross-validation results not only confirmed the reliability of the detection method but also revealed the technological complementarity of different analytical platforms. GC-Orbitrap / MS has advantages in the detection of volatile components, while UPLC-MS is better suited for the analysis of non-volatile components. The multi-platform strategy provides an effective solution for the comprehensive characterization of the complex metabolome of rose.

[0086] This invention systematically verified the consistency of detection of characteristic metabolites of rose through multi-platform data integration and analysis. A comparison was made between 236 high-confidence metabolites screened using GC-Orbitrap / MS technology (ΔRI < 20 standard) and 2845 metabolites identified using full-spectrum metabolomics technology (GC-MS + UPLC-MS). The results showed that 75 metabolites (covering 12 major classes including terpenes, aldehydes, and ketones) were co-detected (see...). Figure 10 The 236 screened metabolites were specifically cross-validated with the UPLC-MS platform, revealing 5 marker compounds (e.g., eugenol, methyl cinnamate, and methyl eugenol). Comparison of the 236 screened metabolites with the GC-MS platform identified 70 common metabolites across 9 major classes, including 34 terpenes (e.g., gingerol, terpinene, nerol, carotene, geraniol, menthol, linalool, and 2-phenylethanol), 7 alcohols (e.g., lauryl alcohol, benzyl alcohol, and nonanol), 7 aromatics (e.g., p-xylene and styrene), 7 ketones (e.g., 2-tetranone), 6 aldehydes (e.g., phenylacetaldehyde and heptanal), 5 esters (e.g., nerolithyl acetate, citronellol butyrate, and hexyl butyrate), 2 hydrocarbons (e.g., nonadecane), 1 heterocyclic compound, and 1 acid compound. These findings not only confirm the comparability of data across different analytical platforms but also reveal the unique advantages of GC-Orbitrap / MS technology in the detection of trace volatile components, providing an important reference for establishing standard detection methods for rose metabolites. The key metabolites cross-validated can serve as potential biomarkers for rose product quality control.

[0087] This invention innovatively employs a multi-platform strategy combining GC-MS, UPLC-MS, and GC-Orbitrap / MS to systematically analyze the metabolomic characteristics of rose products. Based on full-spectrum metabolomics technologies (UPLC-MS and GC-MS) and GC-Orbitrap / MS, the metabolites in roses were characterized, revealing that the main components of volatile metabolites in roses are terpenes, alcohols, esters, aldehydes, ketones, and aromatic hydrocarbons. Cross-validation across multiple platforms revealed that the three platforms jointly detected 103 metabolites (15 classes). GC-Orbitrap / MS and UPLC-MS specifically shared 5 biomarkers, and among the 236 high-confidence metabolites rigorously screened, 75 overlapped with the full-spectrum data. Full-spectrum metabolomics technology can preliminarily characterize the composition of metabolites in roses, while GC-Orbitrap / MS technology further provides comprehensive information on key trace aroma components in rose essential oils and hydrosols. Studies have confirmed that GC-Orbitrap / MS has unique advantages in the detection of volatile components, while UPLC-MS is more suitable for the analysis of non-volatile components. These cross-validated key metabolites not only confirm the reliability of the method, but also provide potential biomarkers for the quality control of rose products. Furthermore, by integrating multiple advanced analytical technology platforms (GC-MS, UPLC-MS, and GC-Orbitrap / MS), a systematic methodological framework for rose metabolomics research has been established.

[0088] UPLC-MS / MS (for analyzing the composition of non-volatile metabolites of rose) and GC-MS (for preliminary analysis of volatile metabolites of rose, but due to limitations in quality accuracy and resolution, it cannot comprehensively analyze the composition of volatile components of rose) each have their own limitations. However, GC-Orbitrap / MS, with its quality accuracy and resolution, can detect some trace / ultra-level components that GC-MS cannot detect. This indicates that combining GC-Orbitrap / MS with GC-MS can provide a more systematic and comprehensive analysis of the composition of volatile components of rose. GC-MS and GC-Orbitrap / MS analyze the composition of volatile components of rose, while UPLC-MS / MS analyzes the composition of non-volatile components of rose. The combined use of these three metabolomics methods can comprehensively analyze the composition of rose metabolites. In summary, this invention can provide a comprehensive understanding of the composition of rose aroma components.

[0089] 3. Summary

[0090] This invention is the first to apply GC-Orbitrap / MS technology to the detection of rose compounds. Based on GC-Orbitrap / MS, 1709 compounds were detected in rose essential oil and hydrosol. Using a retention index difference (ΔRI) < 20 as the screening criterion, 236 trace compounds were identified, including terpenes, alcohols, esters, aldehydes, ketones, and aromatics. Through comparative analysis of differentially expressed trace compounds in rose essential oil and hydrosol, this paper found a total of 606 significantly upregulated differential metabolites (comprising 17 subclasses) in both rose essential oil-SPME and hydrosol-SPME samples. Among these, 334 key differentially expressed compounds were higher in essential oil-SPME samples than in hydrosol-SPME samples, including esters, terpenes, aromatics, ketones, and alcohols; and 272 key differentially expressed compounds were higher in hydrosol-SPME samples than in essential oil-SPME samples, including esters, ketones, terpenes, hydrocarbons, ethers, alkenes, aldehydes, and phenols. By combining GC-Orbitrap / MS technology with the previously mentioned full-spectrum metabolomics technology, various compounds contributing to the aroma of roses were successfully screened. Some of these compounds were identified in earlier studies, while others were not. These results demonstrate that GC-Orbitrap / MS can detect a variety of significant trace aroma components, contributing to a comprehensive understanding of the composition of rose aroma components and distinguishing aroma differences between different rose varieties. Research on rose trace components aids in the breeding and improvement of rose varieties. By analyzing and tracking trace components, new rose varieties with more distinctive characteristics and superior aromas can be cultivated. Furthermore, research on rose trace components helps to more accurately replicate and simulate the natural aroma of roses, improve the quality of rose by-products and the realism of rose aromas, and develop more distinctive rose products. To comprehensively explore the substances in roses, especially the composition of aroma components, full-spectrum metabolomics technologies (UPLC-MS and GC-MS) and gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry systems (GC-Orbitrap / MS) can be used to analyze rose samples.

[0091] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A non-targeted analytical method for volatile components in rose essential oil and hydrosol, characterized in that: Includes the following steps: (1) Sample collection, processing and preparation: whole rose flowers were collected and rose essential oil and hydrosol were extracted by steam distillation; the processed samples were detected by gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry system. (2) Statistical analysis: The test results were analyzed, including the identification of unknown trace compounds in rose essential oil and hydrosol, analysis of changes in volatile trace compounds, and determination of differences in the types of metabolites in roses based on different metabolomics methods.

2. The non-targeted analysis method for volatile components in rose essential oil and hydrosol according to claim 1, characterized in that: Statistical analysis was performed using Xcalibur Qual Browser version 4.2 and Compound Discoverer software for data acquisition and analysis of rose essential oil and hydrosol samples. Quan Browser and Qual Browser were used for processing. Peak detection was performed using spectral deconvolution. The built-in deconvolution plugin in the software was used to extract individual peaks from the total ion chromatograms of each EI-MS data file, and candidate metabolites were identified according to the GC-orbitrap flavor compound high-resolution library NIST 2020. Each analysis was performed in duplicate. The deconvolutioned spectra were matched with the standard spectra of metabolites in the NIST 2020 database to determine the molecular formula. Retention indices were obtained by injecting a mixture of C6–C24 n-alkanes under the same chromatographic conditions. Qualitative analysis of metabolites was performed by combining the calculated RI values, mass spectrometry information, and retention indices. The relative abundance of metabolites in the samples was expressed as peak area ratio, and each sample was injected three times. Multivariate analysis was performed on the data, including principal component analysis and hierarchical cluster analysis. The data were visualized using cluster heatmaps and volcano plots.

3. The non-targeted analysis method for volatile components in rose essential oil and hydrosol according to claim 2, characterized in that: The identification of unknown trace compounds in rose essential oil and hydrosol described in step (2) includes the following steps: (A) Overall score and retention index; (B) Chemical ionization is used to identify molecular ion peaks and obtain mass spectra. Information about sample molecules is inferred based on the mass-to-charge ratio of the molecular ion peaks. (C) Detailed comparison of fragment ions to distinguish isomers: By comparing the mass-to-charge ratio of fragment ions in the mass spectrum, the differences between isomers can be found. By comparing the relative abundance of fragment ions and finding and identifying characteristic fragment ions, isomers can be distinguished more precisely. (D) Chemical standard verification: Analyze the standard substance and the sample to be identified under the same chromatographic conditions and compare their chromatographic parameters such as retention time and retention index; If the chromatographic parameters of the sample to be identified are consistent with those of the standard substance, it indicates that they behave similarly in the chromatographic separation process and may be the same metabolite. The search results are comprehensively ranked based on the similarity index SI, high-resolution matching factor HRF value and retention index RI in the spectral library. When the comprehensive score is 90 or above, the SI index is not less than 700, the HRF value is not less than 90 and the ΔRI is 50 or below, the identification result is reliable. Trace components in rose essential oil and hydrosol were analyzed using principal component analysis, volcanic analysis, and partial least squares discriminant analysis.

4. The non-targeted analysis method for volatile components in rose essential oil and hydrosol according to claim 3, characterized in that: In step (2), the analysis of changes in volatile trace compounds uses strict statistical criteria to screen differential metabolites. With a peak area ratio of log2 N>1, for each compound, the peak area ratio between different sample groups is calculated, and then the log2N value is obtained by taking the base-2 logarithm. P<0.05 is used as the significance threshold to identify differential metabolites between different sample groups from the dataset. Differential analysis is performed on the screened metabolites, and the differences between the samples in each comparison group are displayed in the form of a differential metabolite volcano plot. The peak areas of each metabolite in the dataset are logarithmically transformed to base 2, and metabolites with log2N>4 are selected to form an initial subset. Then, the peak area ratio of metabolites between different sample groups is calculated, and metabolites with a ratio>16 or a ratio<1 / 16 are retained. Finally, the high-confidence differential metabolites that simultaneously satisfy log2N>4 and peak area ratio>16 are obtained by taking the intersection.

5. The non-targeted analysis method for volatile components in rose essential oil and hydrosol according to claim 4, characterized in that: In step (2), the differences in the types of metabolites in roses determined by different metabolomics methods were analyzed using a multi-platform strategy to systematically resolve the metabolomics characteristics of rose products. Based on the results obtained from previous full-spectrum metabolomics GC-MS and UPLC-MS, high-resolution orbital trap mass spectrometry coupled with gas chromatography-GC-MS was further used to detect metabolites in rose essential oil and hydrosol. Through multi-platform data integration analysis, the consistency of detection of characteristic metabolites of roses was systematically verified. In the multi-platform data integration analysis method, a number of high-confidence metabolites screened based on the ΔRI<20 standard of the GC-Orbitrap / MS method were compared with a number of metabolites identified by the GC-MS platform and the UPLC-MS platform, and the key metabolite types were obtained through cross-validation.

6. The non-targeted analysis method for volatile components in rose essential oil and hydrosol according to claim 5, characterized in that: The distillation method in step (1) includes the following steps: Rose essential oil and hydrosol are extracted using steam distillation. Based on Dalton's law, the distillation equipment is pre-treated, with separate compartments for flowers and water. First, purified water is added at a 1:1 ratio. This purified water is heated and converted into steam through heating pipes in the jacket. The steam is then piped to the jacket containing the treated roses, where it is used to heat and pressurize the roses for distillation. The distillation pressure is controlled at 0.18-0.2 MPa. The steam passes through a condenser, and the water circulates back into the distillation vessel for further heating and distillation. During distillation, water is continuously added to the jacket containing the purified water to maintain a 4:1 water-to-flower ratio. When collecting rose essential oil, the temperature is controlled at 35℃, and when collecting rose hydrosol, the effervescence temperature is controlled at 25℃. The ratio of rose hydrosol extracted to fresh roses is 1:

1.

7. The non-targeted analysis method for volatile components in rose essential oil and hydrosol according to claim 6, characterized in that: The detection method in step (1) includes the following steps: Accurately transfer 5 mL of rose hydrosol aqueous solution sample to a 20 mL headspace vial, add 3 g of NaCl, and repeat in triplicate, labeling them as Pure-1, Pure-2, and Pure-3. Weigh 5 mL of ultrapure water and add 3 g of NaCl as a blank control. Accurately weigh 0.5 g of rose essential oil sample to a 20 mL headspace vial, tighten the cap, and repeat in triplicate, labeling them as Oil-1, Oil-2, and Oil-3. Use air as a blank control. Accurately weigh 4 mg of rose essential oil sample, add 1 mL of n-hexane to completely dissolve it, and then dilute it by half with n-hexane, repeating in triplicate. Rose essential oil and hydrosol samples were injected and analyzed. Sample vials were sealed with metal caps equipped with PTFE / silicone septa and then placed in a headspace autosampler. Blank analysis was performed strictly following the same procedures as sample pretreatment to identify systematic or background contamination originating from experimental containers, septa, or column wear. Each sample and blank sample was analyzed at least three times in random order using a gas chromatography-electrostatic field orbital trap high-resolution mass spectrometry system equipped with TriPlus. An RSH autosampler, including headspace sampling, was used to analyze volatile metabolites in rose essential oil and hydrosol. The injection port temperature was 240°C. The chromatographic column was a TG-WaxMS capillary column, 60m × 0.25mm × 0.25μm. The chromatographic temperature program was as follows: 40°C for 3 min, then increased to 240°C at a rate of 5°C / min, held for 12 min, for a total of 55 min. The carrier gas was helium, purity: 99.999%, flow rate: 1.2mL / min. Full scan MS acquisition was performed in profile mode using a mass-to-charge ratio range of 41-500m / z. Each sample was analyzed in EI mode, with an electron energy of 70eV, and the mass spectrometry resolution power was set to its maximum at @200m / z, with a full width at half maximum (FWHM) of 60000.

8. The application of the non-targeted analysis method for volatile components in rose essential oil and hydrosol according to any one of claims 1 to 7 in any of the following: (I) Establish a database of functional components of roses; (II) To gain a comprehensive understanding of the composition of rose aroma components and to distinguish the aroma differences between different rose varieties; (III) Selection and improvement of rose varieties: By analyzing and tracking trace components, it is possible to cultivate new rose varieties with more distinctive features and better fragrance. (IV) To more accurately replicate and simulate the natural aroma of roses, improve the quality of rose by-products and the realism of rose aroma, and develop more distinctive rose products; (V) Identification and analysis of rose varieties.

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