Skip to main content
NCBI home page
Food Chemistry: X logoLink to Food Chemistry: X
. 2026 Jul 16;38:104219. doi: 10.1016/j.fochx.2026.104219

From monthly volatile dynamics to cultivar differences in the aroma of jasmine (Jasminum sambac): integrated volatile profiling and proteomic insights

Yuhang Zhang a,b, Weiwei Ren c, Wenjing Gui a, Shanshan Zeng a, Pengjie Wang d, Shan Jin a, Kunbo Wang b, Mengya Gu a,⁎, Qin Luo e,⁎, Naixing Ye a,⁎
PMCID: PMC13401015  PMID: 42502870

Abstract

Jasminum sambac is widely valued for its aroma, yet the monthly dynamics of volatile compounds and the proteomic basis associated with cultivar differences remain unclear. This study investigated monthly changes in volatile compounds in five jasmine cultivars using HS-SPME-GC–MS, and the total content of detected volatile compounds reached its highest level in August. Integrated volatile profiling and data-independent acquisition (DIA) proteomic analysis were subsequently applied to explore the proteomic basis associated with aroma differentiation. OAV analysis identified 17 potential key SDVs distinguishing jasmine cultivars, including methyl anthranilate, benzyl alcohol, and decyl aldehyde. Proteomic analysis revealed 23 proteins associated with terpenoid metabolism and 26 proteins associated with phenylpropanoid/benzenoid metabolism. Differences in protein abundance may contribute to aroma variation among jasmine cultivars. MYB8 and 4CL19 were identified as candidate proteins associated with benzaldehyde accumulation. These findings provide integrative insights into monthly volatile dynamics and the proteomic basis of aroma variation in jasmine.

Keywords: Jasminum sambac, Proteomics, Aroma, Cultivar differences, Monthly dynamics

Graphical abstract

Unlabelled Image

Open in a new tab

Highlights

  • •

    Monthly volatile dynamics were characterized in five jasmine cultivars.

  • •

    Detected volatile compound content reached its highest level in August.

  • •

    OAV analysis identified 17 potential key SDVs among jasmine cultivars.

  • •

    DIA proteomics revealed candidate proteins associated with aroma variation.

1. Introduction

Jasminum sambac (L.) Aiton (Oleaceae) is a small diploid erect or climbing shrub (Wang et al., 2022a). Due to its unique fragrance, it holds significant economic value and is primarily used in the perfume, cosmetics, and food flavoring industries (Qi et al., 2024). In China, jasmine flowers are regarded as a food. Not only is the plant used as a time-honored traditional Chinese medicinal herb, but it is also employed in the scenting process of jasmine tea (Deng et al., 2016). Its floral aroma is closely associated with the sensory perception, flavor performance, and market value of jasmine-derived products. In addition, flowers of different jasmine cultivars vary in form and are classified into three categories based on the number of corolla layers: single-petal type, double-petal type, and multi-petal type (Zhou et al., 2023). The physiological and biochemical characteristics of different types of jasmine vary considerably. It is worth noting that double-petaled jasmine accounts for over 95% of jasmine production (Zhang et al., 2025b). This phenomenon may limit the diversified utilization of jasmine resources. Differences in aroma characteristics among jasmine cultivars may directly influence their suitability as fresh floral materials for jasmine tea scenting and other food-related applications. This variation is also closely linked to flavor diversification in final products, which is of considerable importance for the development and utilization of jasmine flowers. Accordingly, it is necessary to further characterize different jasmine cultivars, particularly the volatile profiles and potential molecular basis associated with aroma formation among cultivars.

The complex composition and content of volatile organic compounds (VOCs) in jasmine are influenced by numerous endogenous and exogenous factors. Exogenous factors include environmental variations (such as seasonal changes, particularly monthly variations) and cultivar differences. Systematic analysis of seasonal variation in volatile compound profiles of horticultural plants is useful for identifying periods with higher accumulation of aroma associated volatile compounds and provides a theoretical basis for production (Mahanta et al., 2023). However, the monthly variation in volatile compound profiles among different jasmine cultivars remains unclear.

Endogenous factors are also closely associated with jasmine aroma biosynthesis. In jasmine, floral odors mainly originate from endogenous biosynthetic volatile compounds, including volatile terpenes (VTs), volatile phenylpropanoids/benzenoids (VPBs), and fatty acid-derived volatile compounds (Zhou et al., 2022). VTs are synthesized by producing precursor substances via the mevalonate (MVA) and 2-C-methyl-D-erythritol 4-phosphate (MEP) pathways, followed by catalysis by terpenoid synthases (TPS) (Hong et al., 2024). The first step in VPB biosynthesis involves the conversion of L-phenylalanine to t-cinnamic acid catalyzed by phenylalanine ammonia-lyase (PAL), followed by the conversion of t-cinnamic acid into a series of VPB precursors catalyzed by 4-coumarate-CoA ligase (4CL) (Muhlemann et al., 2014). Notably, some VPBs are synthesized directly from L-phenylalanine (Hong et al., 2024). Overall, enzymes such as TPS, PAL, and 4CL are important for the biosynthesis of jasmine VOCs, and differences in their protein abundance may contribute to variation in jasmine aroma profiles. To date, studies on jasmine at the protein level remain limited, and the protein-level regulation of volatile biosynthesis is still poorly understood.

To address the aforementioned issues, this study selected five representative jasmine cultivars, including Jasminum sambac ‘Bijiandanban’ (BJ), Jasminum sambac ‘Fuzhoudanban’ (FD), Jasminum sambac ‘Yuanye’ (YY), Jasminum sambac ‘Shuangban’ (SB) and Jasminum sambac ‘Bijianduoban’ (BD). Volatile compounds in five jasmine cultivars were detected using headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME-GC–MS) across different months from May to September. Through multivariate statistical analysis, dynamic changes in VOC content were characterized across five jasmine cultivars from May to September, and the total content of detected VOCs reached its highest level in August in all cultivars. To further explore the proteomic basis associated with cultivar differences in volatile profiles, jasmine samples collected in August were subjected to DIA proteomic analysis based on the previously published jasmine protein database (Wang et al., 2022b). Through integrated volatile profiling and proteomics, we conducted a systematic investigation of proteins associated with the biosynthesis of VOCs in jasmine. This study aims to provide a basis for understanding monthly changes in volatile compound accumulation and the proteomic basis of aroma variation among jasmine cultivars.

2. Materials and methods

2.1. Materials

The floral organs of Jasminum sambac ‘Bijiandanban’ (BJ), Jasminum sambac ‘Fuzhoudanban’ (FD), Jasminum sambac ‘Yuanye’ (YY), Jasminum sambac ‘Shuangban’ (SB) and Jasminum sambac ‘Bijianduoban’ (BD) were used as experimental materials. All plants were grown in the Jasmine Germplasm Resource Garden of Fujian Agriculture and Forestry University, Cangshan District, Fuzhou, Fujian Province, China (26.082191° N, 119.241295° E). The soil type of the experimental field was sandy loam. The five jasmine cultivars were cultivated in the same field under uniform agronomic management practices. All sampled plants were five-year-old plants.

Floral organs of the five jasmine cultivars were collected on May 11, June 21, July 15, August 11, and September 17, 2024, and these samples were designated as the May, June, July, August, and September jasmine samples, respectively. All samples were collected between 20:00 and 23:00 on each sampling day. To reduce variation caused by flower developmental stage and diurnal rhythm, only flowers at the opening stage with similar morphology and size were collected. Each sample contained three biological replicates. For each biological replicate, three different plants of the same cultivar were selected, and 10 flowers at the opening stage with similar size were collected from each plant. Thus, 30 flowers were pooled as one biological replicate. The collected flowers were immediately frozen in liquid nitrogen, mixed, and ground into powder for subsequent volatile compound analysis and DIA proteomic analysis.

Weather conditions at the sampling site were recorded on each sampling day. The weather was sunny on all five sampling days. To further describe the regional environmental background during sampling, publicly available Fuzhou regional meteorological records were summarized, including the regional daily temperature range, regional station temperature during the sampling period, estimated relative humidity during the sampling period, sunrise and sunset times, and day length. This information is provided in Supplementary Table S1.

2.2. Analysis of volatile compounds in jasmine samples

2.2.1. Instruments

GCMS-QP2020NX Gas Chromatography-Mass Spectrometry System (Shimadzu Corporation, Kyoto City, Japan); SPME extraction needle (120 μm DVB/CAR/PDMS, Agilent, Santa Clara, CA, USA); 30 m × 0.25 mm × 0.25 μm DB-5MS capillary column (Agilent, Santa Clara, CA, USA); SYG-2 Digital Constant Temperature Water Bath (Changzhou Langyue Instrument Manufacturing Co., Ltd., Jiangsu, China); electronic balance (MS105DU, METTLER TOLEDO, Zurich, Switzerland).

2.2.2. Reagents

Ethyl decanoate standard solution diluted with anhydrous ethanol (100 mg/L, Dr. Ehrenstorfer, North Rhine-Westphalia, Germany).

2.2.3. Experimental methods

The SPME extraction head was inserted into the GC–MS injection port and aged at 250 °C for 30 min. 1.50 g of jasmine flower sample was weighed into a sample vial, and 15 μL of ethyl decanoate (100 mg/L) was added. Then quickly the cap was sealed and the vial was placed in a 60 °C water bath for 10 min. After completing the water bath heating, the aged extraction head was inserted into the sample vial and headspace extraction was performed for 30 min. Then the extraction head was removed and inserted into the GC–MS instrument's inlet port. Using a capillary column, high-purity helium gas (99.999%) at a flow rate of 1.2 mL/min was introduced. Then the program was configured according to the four gradients. First gradient: the temperature was increased to 50 °C and maintained for 2 min. Second gradient: The temperature was increased to 120 °C at a rate of 10 °C/min. Third gradient: The temperature was raised to 150 °C at a rate of 1 °C/min. Fourth gradient: the temperature was raised to 300 °C at a rate of 30 °C/min and held for 1 min. The acquired GC–MS raw data were searched against the NIST mass spectrometry library, with volatile compounds screened based on a match score ≥ 80%.

2.2.4. Quantitative analysis of volatile compounds

In this study, the content of volatile compounds in jasmine samples was calculated using Eq. (1).

Xi=Vs×CsM×IiIs, (1)

Xi: Volatile compound content. Vs: Volume of Ethyl decanoate (internal standard). Cs: Concentration of Ethyl decanoate (internal standard). M: Weight of jasmine flower samples. Ii: Peak area of volatile compounds in jasmine flower samples. Is: Peak area of Ethyl decanoate (internal standard).

2.3. Molecular docking procedure

The structural models of the selected volatile compounds were obtained from PubChem. The 3D models of human olfactory receptors were obtained from UniProt. Molecular docking was performed on CB-Dock2. The results were visualized using BIOVIA Discovery Studio 2019 Client.

2.4. Protein extraction and DIA quantitative proteomic analysis

2.4.1. Reagents

Sodium dodecyl sulfate (SDS), Ethylenediaminetetraacetic acid (EDTA), Acetone (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China); Tris, DL-dithiothreitol (DTT) (Solarbio Technology Co., Ltd., Beijing, China); Phenylmethanesulfonyl fluoride (PMSF) (Xiyang Chemical Co., Ltd., Shandong, China).

2.4.2. Protein extraction

Jasmine samples were ground into powder in liquid nitrogen with SDT lysis buffer (4% SDS, 100 mmol/L Tris-HCl, 10 mmol/L DTT, 1 mmol/L PMSF, and 2 mmol/L EDTA) and mixed. After shaking to mix, the mixture was heated in water at 95 °C for 15 min. After 10 min of ultrasonication on ice, the sample was centrifuged to obtain the supernatant. Acetone was added to the supernatant to precipitate the proteins in the supernatant. The precipitate was dissolved in 8 mol/L urea solution. The protein concentration in the solution was determined using the BCA assay kit (Beyotime Biotechnology Co., Ltd., Shanghai, China).

Based on the protein concentration, 100 μg of protein was taken, and the volume was adjusted to 200 μL with 8 mol/L urea. Then DTT (5 mmol/L) was added and the mixture was reduced at 37 °C for 45 min. Then the solution was immediately placed in a dark room for iodoacetamide alkylation. 2 μL of trypsin (V5280, Promega, Wisconsin, USA) was added to 800 μL of ammonium bicarbonate solution (25 mmol/L), and the solution was incubated at 37 °C overnight. The above method drew upon previous research with slight modifications (Pu et al., 2023).

2.4.3. LC-MS/MS detection and database retrieval

Liquid chromatography separation and mass spectrometry analysis were performed using an Orbitrap Exploris 480 mass spectrometer coupled with an EASY-nLC 1200 liquid chromatography-mass spectrometry system (Thermo Fisher Scientific, Bremen, Germany). Mobile phase A was 0.1% formic acid aqueous solution. Mobile phase B was 0.1% formic acid acetonitrile solution (Prepared by adding 0.1% formic acid to pure acetonitrile). The peptide solution containing dissolved formic acid was injected into a C18 reverse-phase analytical column (ID: 75 μm; length: 25 cm; particle size: 1.9 μm). Then an analytical gradient was established using mobile phases A and B, and separation was performed at a flow rate of 300 nL/min. The chromatographically separated samples were subjected to DIA mass spectrometry analysis using an Orbitrap Exploris 480 mass spectrometer (Thermo Scientific, Massachusetts, USA). The detection mode was positive ion mode, the parent ion scan range was 380–980 m/z, the MS1 resolution was 240,000 at 200 m/z, the normalized AGC target was 500%, and the maximum IT was 5 ms. Each biological replicate was analyzed by LC-MS/MS in technical triplicate. Database searches were performed using DIA-NN (v1.8.1), and the jasmine reference proteome database was downloaded for database searching and protein identification (Wang et al., 2022b). Mass spectrometry analysis and quantitative analysis were performed by Wuhan Maiwei Metabolic Biotechnology Co., Ltd. (Hubei, China).

2.5. Data processing and analysis

Origin 2021 was used for creating bar charts and line charts. SIMCA 14.1 was used to calculate variable importance in projection (VIP) and perform orthogonal partial least squares discriminant analysis (OPLS-DA). TBtools was used to visualize Venn diagrams and heatmaps. Analysis of variance (ANOVA) and post hoc significance tests (Duncan's method) were performed using SPSS 19.0. In this study, the screening criteria for significantly differential volatiles (SDVs) were VIP > 1 and p < 0.05. The formula for calculating the Odor Activity Value (OAV) is: OAV = volatile compound content / odor threshold of the volatile compound in water. The odor thresholds were obtained from the literature (Gu et al., 2025; Zhang et al., 2025b). The screening criteria for differentially abundant proteins (DAPs) were set at p < 0.05, with a fold change (FC) ≥ 1.5 or ≤ 0.6667. The parameters for weighted protein co-expression network analysis (WPCNA) were: merge cut height of 0.35, minimum module size of 100.

3. Results and analysis

3.1. Analysis of volatile compounds in five jasmine cultivars

To investigate monthly variations in volatile compounds in BJ, FD, YY, SB and BD, this study employed HS-SPME-GC–MS to detect volatile compounds in jasmine samples collected from May to September and calculated their contents. A total of 98, 105, 97, 101, and 95 volatile compounds were detected in BJ, FD, YY, SB, and BD, respectively, across the different sampling months. Terpenoids, alcohols, aldehydes, hydrocarbons, esters, ketones, heterocycles, and other compounds collectively contributed to the volatile profile of jasmine. Among these, terpenoids, esters, and aldehydes were the three major volatile compound classes in jasmine (Fig. 1C).

Fig. 1.

Fig. 1

Open in a new tab

Volatile compounds in jasmine samples and their monthly dynamics. (A) Total volatile compound content in five jasmine cultivars from May to September. (B) Appearance of the five jasmine cultivars at the opening stage in August. (C) Proportions of volatile compound classes in five jasmine cultivars from May to September. (D) Normalized trend of total volatile compound content in jasmine across different months. Different letters within the same cultivar indicate significant differences among months at p < 0.05. BJ: Jasminum sambac ‘Bijiandanban’; FD: Jasminum sambac ‘Fuzhoudanban’; YY: Jasminum sambac ‘Yuanye’; SB: Jasminum sambac ‘Shuangban’; BD: Jasminum sambac ‘Bijianduoban’.

The sum of the contents of all detected VOCs in jasmine was defined as the total volatile compound content. To understand the dynamic changes in total volatile compound content across months, we calculated this value for the five jasmine cultivars during the sampling period. The results revealed that the total volatile compound content in all samples peaked in August and was significantly higher than in other months. Except for BD, the total volatile compound content of the other four jasmine cultivars showed an initial increase followed by a decrease from May to September (Fig. 1A).

Jasmine flowers are classified based on the number of corolla layers (Zhou et al., 2023). As shown in Fig. 1B, BJ and FD were classified as single-petal jasmine, YY and SB as double-petal jasmine, and BD as multi-petal jasmine. The total volatile compound contents summed across the five sampling months were 304.31–309.85 μg/g, 349.01–358.83 μg/g, 451.28–469.97 μg/g, 626.82–652.51 μg/g, and 253.66–284.35 μg/g for BJ, FD, YY, SB, and BD, respectively. The total volatile compound content of the five jasmine cultivars was ranked as follows: SB > YY > FD > BJ > BD. These results suggested that the total volatile compound content differed among jasmine petal types, with double-petal jasmine showing higher volatile accumulation than single-petal and multi-petal jasmine under the conditions of this study. Previous studies have found that SB contains higher levels of aroma-related volatiles than BJ (Zhang et al., 2024). The findings of this study support this observation and further compare volatile compound contents among multiple jasmine cultivars. These results provide data support for understanding differences in volatile accumulation among jasmine cultivars.

In China, seasons are divided according to the twenty-four solar terms (Qian et al., 2012). Therefore, in this study, the jasmine samples were categorized into three seasonal groups (spring, summer, and autumn) based on their collection time. A normalized trend analysis was performed for the total volatile compound content in jasmine across different months. The results showed that jasmine flowers exhibited the lowest levels of detected volatile compounds in spring, followed by autumn, whereas summer showed the highest levels. Overall, the trend followed a ‘Λ' pattern, rising initially before declining. These results indicated that the total content of detected volatile compounds in jasmine flowers was higher in summer than in spring and autumn, with the highest level observed in August. Because no sensory evaluation or market quality assessment was performed in this study, these results should be interpreted as monthly variation in volatile compound abundance rather than as a direct evaluation of overall aroma quality.

3.2. Monthly dynamics of volatile compounds among five jasmine cultivars

To investigate monthly differences in volatile compounds among the five jasmine cultivars, SDVs were screened in May vs. June, May vs. July, May vs. August, and May vs. September using the criteria of VIP > 1 and p < 0.05. The four comparison groups contained 25, 25, 26, and 18 SDVs, respectively (Fig. 2A). Among these, most SDVs were terpenoids and esters (Fig. 2B). Visualization of the aforementioned SDVs using Venn diagrams revealed volatile compounds shared among the five jasmine cultivars across the sampling months (Fig. 2C). Nine volatile compounds were shared among different jasmine cultivars across the sampling months, including α-farnesene, benzyl acetate, benzeneacet aldehyde, linalool, benzaldehyde, α-cadinene, copaene, (Z)-3-hexenyl benzoate, and methyl salicylate.

Fig. 2.

Fig. 2

Open in a new tab

Shared and differential volatile compounds among jasmine cultivars during monthly variation. (A) Venn diagrams showing SDVs among monthly comparison groups in each jasmine cultivar. (B) Numbers of SDVs belonging to different compound classes in five jasmine cultivars. (C) Venn diagram showing volatile compounds shared among the five jasmine cultivars across sampling months. (D) Chemical structures and odor attributes of nine common characteristic volatiles. (E) Box plots showing the contents of nine common characteristic volatiles across different jasmine cultivars. BJ: Jasminum sambac ‘Bijiandanban’; FD: Jasminum sambac ‘Fuzhoudanban’; YY: Jasminum sambac ‘Yuanye’; SB: Jasminum sambac ‘Shuangban’; BD: Jasminum sambac ‘Bijianduoban’.

α-Farnesene possesses a fruity aroma and plays a crucial role in the formation of the floral and fruity notes in oolong tea (Ming et al., 2025). Benzyl acetate possesses a characteristic jasmine aroma (Qi et al., 2024). Benzeneacet aldehyde possesses sweet and honey-like aromas (Zhang et al., 2025a). Linalool is a common floral volatile in many flowers (An et al., 2022). Copaene has been identified as an aroma-related compound in citrus fruits and exhibits woody notes (Liu et al., 2024). Benzaldehyde has been associated with sweet and fruity aroma notes in tea (Zhang et al., 2025a). Methyl salicylate possesses a minty and wintergreen aroma, contributing to the fragrance of jasmine tea (Gu et al., 2025). (Z)-3-Hexenyl benzoate exhibits a fruity aroma and has been reported to account for 50% of the total volatile compounds in feijoa fruit (Baena-Pedroza et al., 2020). α-Cadinene possesses a fresh aroma and is abundantly present in Zingiberaceae plants (Zhang et al., 2023). Based on their reported odor descriptions, these shared volatile compounds may be associated with sweet, floral, fruity, woody, and minty aroma notes in jasmine. These compounds may contribute to the characteristic volatile profile of jasmine. Therefore, these nine volatile compounds were regarded as common characteristic volatiles in jasmine (Fig. 2D). Furthermore, box plots illustrated the distribution of these SDVs across different jasmine cultivars, revealing that approximately half of the common characteristic volatiles exhibited higher contents in SB (Fig. 2E).

3.3. Screening and analysis of characteristic volatile compounds in different jasmine cultivars

In this study, the total volatile compound content of all jasmine samples reached its highest level in August. To further analyze cultivar differences in volatile profiles based on volatile compound content, PCA was performed using the August samples. PCA showed distinct differences among the five jasmine cultivars (Fig. 3A). Subsequently, pairwise comparisons of volatile compounds among the five jasmine cultivars were performed, and SDVs were screened using the criteria of VIP > 1 and p < 0.05 (Fig. 3B). The analysis revealed a total of 41 SDVs across these comparison groups, comprising 19 terpenoids, 2 alcohols, 7 aldehydes, 12 esters, and 1 other compound (Fig. 3C). These results indicated clear differences in volatile compound profiles among jasmine cultivars. These differences may partly explain the aroma diversity among jasmine cultivars.

Fig. 3.

Fig. 3

Open in a new tab

SDVs among jasmine cultivars in August. (A) PCA score plot of five jasmine cultivars. (B) Scatter plot showing upregulated and downregulated SDVs across pairwise comparison groups. (C) Heatmap showing the relative contents of SDVs among five jasmine cultivars. (D) Bar charts showing the OAVs of 17 volatile compounds with OAV > 1. (E) Cultivar-specific volatile compounds detected exclusively in YY or SB. Different letters within the same compound indicate significant differences among cultivars at p < 0.05. Red dots indicate SDVs with higher contents, whereas blue dots indicate SDVs with lower contents. The color-coded scale from blue to red indicates low to high volatile compound contents. BJ: Jasminum sambac ‘Bijiandanban’; FD: Jasminum sambac ‘Fuzhoudanban’; YY: Jasminum sambac ‘Yuanye’; SB: Jasminum sambac ‘Shuangban’; BD: Jasminum sambac ‘Bijianduoban’. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Because the aroma contribution of volatile compounds depends not only on their contents but also on their odor thresholds, OAV analysis was further used to evaluate the potential aroma contribution of SDVs. Generally, SDVs with OAV > 1 are considered to have greater potential contributions to aroma characteristics (Zhang et al., 2025b). We further screened and analyzed SDVs based on the criterion of OAV > 1 and identified 17 potential key SDVs with distinct cultivar-specific OAV distribution patterns (Fig. 3D). Among these potential key SDVs, 1, 3, 10, and 3 compounds showed the highest OAV values in BJ, YY, SB, and BD, respectively. Specifically, (E)-3-hexenyl acetate showed the highest OAV value in BJ and has been associated with a fragrant note (Gu et al., 2025). Ethyl benzoate, methyl salicylate, and copaene showed the highest OAV values in YY. These compounds have been reported to possess fresh, fruity, minty, wintergreen, and woody notes (Gu et al., 2025; Liu et al., 2024; Xu et al., 2019). The remaining 10 compounds showed the highest OAV values in SB, including (Z)-3-hexenyl benzoate, benzyl acetate, linalool, α-farnesene, methyl anthranilate, benzyl alcohol, geraniol, β-farnesene, (E, Z)-2,6-nonadienal, and (E)-2-hexenal. These compounds were mainly associated with fruity, floral, grape-like, sweet, and fresh notes according to previous reports (An et al., 2022; Baena-Pedroza et al., 2020; Chambers et al., 2013; Liang et al., 2023; Ming et al., 2025; Qi et al., 2024; Sabbatini et al., 2019). In BD, benzeneacet aldehyde, benzaldehyde, and decyl aldehyde showed the highest OAV values and were associated with floral, sweet, fruity, and citrus-like notes (Li et al., 2024; Zhang et al., 2025a). Overall, these 17 potential key SDVs exhibited cultivar-specific OAV distribution patterns and may partly contribute to aroma differences among jasmine cultivars.

In addition to the 17 OAV-based potential key SDVs, several volatile compounds showed cultivar-specific detection patterns (Fig. 3E). Indole, which has a characteristic jasmine scent (Gu et al., 2025), and benzyl benzoate, which has a floral note (Gu et al., 2025), were detected exclusively in YY. In contrast, (1S)–(1)-β-pinene, which has woody and grassy notes (Xu et al., 2023), and guaiene, which has a woody note (Zhang et al., 2021), were detected only in SB. These cultivar-specific volatile compounds may further contribute to the distinct aroma characteristics of YY and SB.

3.4. Molecular docking analysis between potential key volatile compounds and human olfactory receptors

Three compounds with higher OAV values were selected from the 17 potential key SDVs for molecular docking analysis, including linalool, α-farnesene, and decyl aldehyde. OR1A1, OR1G1, OR2W1, OR5M3, OR7D4, and OR8D1 are six common human olfactory receptors (Liang et al., 2024). The molecular docking results between the six human olfactory receptors and the three selected volatile compounds were visualized (Fig. 4). The docking results suggested that linalool, α-farnesene, and decyl aldehyde could interact with the six olfactory receptors. These three volatile compounds were predicted to interact with the olfactory receptors mainly through hydrophobic interactions, including Pi-alkyl, alkyl, and Pi-sigma interactions. Moreover, the three compounds showed the most negative binding energies with OR5M3, with values of −5.6, −6.8, and − 5.3 kcal/mol for linalool, α-farnesene, and decyl aldehyde, respectively.

Fig. 4.

Fig. 4

Open in a new tab

Molecular docking of linalool, α-farnesene, and decyl aldehyde with six human olfactory receptors. Binding affinity values are shown for each receptor-ligand pair.

3.5. Proteomic analysis reveals enrichment of DAPs in volatile terpene and volatile phenylpropanoid/benzenoid biosynthesis pathways

Proteomic analysis was performed on five jasmine cultivars using BJ, FD, YY, SB, and BD samples collected in August as experimental materials. The results indicated that 9161 proteins were identified and quantified. PCA showed that the proteomic profiles of the five jasmine cultivars were clearly separated, with samples from the same cultivar clustering closely (Fig. 5A). This indicated distinct differences in proteomic profiles among the five jasmine cultivars.

Fig. 5.

Fig. 5

Open in a new tab

Proteomic analysis of five jasmine cultivars in August and screening of DAPs. (A) PCA score plot of proteomic profiles from five jasmine cultivars. (B) Venn diagram and bar chart showing DAPs among different comparison groups. (C) Bar chart showing the numbers of upregulated and downregulated DAPs across different comparison groups. (D) KEGG enrichment analysis of DAPs. (E) Proteins associated with VT biosynthesis in the MVA and MEP pathways. (F) Heatmap showing the abundance of proteins associated with VT biosynthesis. (G) Proteins associated with VPB biosynthesis and their abundance patterns. Different colors of the vertical lines beside the heatmaps represent different protein families. The color-coded scale from blue to red indicates low to high protein abundance. BJ: Jasminum sambac ‘Bijiandanban’; FD: Jasminum sambac ‘Fuzhoudanban’; YY: Jasminum sambac ‘Yuanye’; SB: Jasminum sambac ‘Shuangban’; BD: Jasminum sambac ‘Bijianduoban’. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Currently, SB is the predominant cultivated jasmine cultivar in China, accounting for over 95% of jasmine production (Zhang et al., 2025b). Therefore, four comparison groups centered on SB were established for DAP screening: BJ vs. SB, FD vs. SB, YY vs. SB, and BD vs. SB. A total of 4713 DAPs were identified, with 1976, 2503, 1596, and 2217 DAPs identified in the four comparison groups, respectively. This indicated that the abundance of many proteins differed significantly among different jasmine cultivars (Fig. 5B). Moreover, we found that the number of significantly downregulated DAPs in these comparison groups exceeded that of upregulated DAPs (Fig. 5C).

To further characterize the biological functions of DAPs, KEGG enrichment analysis was performed on the aforementioned DAPs (Fig. 5D). Among the top 20 significantly enriched KEGG pathways, some DAPs were enriched in “Phenylpropanoid biosynthesis”, “Diterpenoid biosynthesis”, “Monoterpenoid biosynthesis”, and “Terpenoid backbone biosynthesis”. DAPs in these pathways were associated with the biosynthesis of VTs and VPBs. Differences in protein abundance may partly contribute to variation in the contents of VTs and VPBs among jasmine cultivars. Therefore, proteins associated with the biosynthetic pathways of VTs and VPBs were further screened from the jasmine proteome. In plants, the mevalonate (MVA) pathway is mainly associated with sesquiterpene biosynthesis, whereas the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway is mainly associated with monoterpene and diterpene biosynthesis. Together, these two pathways constitute the main routes of terpenoid biosynthesis (Vranova et al., 2013; Zhou et al., 2022). A total of 23 proteins associated with VT biosynthesis were identified from the proteomic data, including three mevalonate kinases (MK), two phosphomevalonate kinases (PMK), one farnesyl pyrophosphate synthase (FPPS), two 1-deoxy-D-xylulose 5-phosphate synthases (DXS), one 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR), one 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase (MCT), and 13 terpene synthases (TPS). Based on the proteins identified through the above screening, a terpenoid biosynthesis pathway map was constructed (Fig. 5E), and protein abundance was visualized using a heatmap (Fig. 5F). Most proteins associated with VT biosynthesis showed higher abundance in YY and SB and lower abundance in BD. Additionally, 26 proteins associated with VPB biosynthesis were identified, including four phenylalanine ammonia-lyase (PAL) proteins and 22 4-coumarate-CoA ligase (4CL) proteins. These two protein families are involved in the formation of upstream precursors for certain VPBs, such as benzaldehyde, benzyl acetate, and methyl salicylate. Most proteins associated with VPB biosynthesis exhibited higher abundance in YY and SB. However, some specific abundance patterns were observed, such as the higher abundance of 4CL-6, 4CL-7, and 4CL-8 in BJ (Fig. 5G). The protein abundance patterns described above were generally consistent with the trends in total volatile compound content in jasmine. YY and SB both showed higher abundance of VT- and VPB-biosynthesis-associated proteins and higher total volatile compound content.

3.6. Weighted protein co-expression network analysis

To further explore the potential associations between co-expressed protein clusters and potential key SDVs in jasmine, WPCNA was performed using 4713 DAPs and 17 potential key SDVs. WPCNA identified eight distinct co-expression modules, designated M1-M8, with proteins within the same module showing highly similar abundance patterns. This suggested that proteins within the same module may be functionally related or involved in similar biological processes. Among these modules, M1 contained the highest number of proteins (1657), whereas M8 contained the fewest (27) (Fig. 6A). In the M1 module, the contents of 13 potential key SDVs showed significant positive correlations with the module abundance profile, with eight correlation coefficients exceeding 0.7. In the M6 module, the contents of the other four potential key SDVs showed significant positive correlations with the module abundance profile. Therefore, proteins in the M1 and M6 modules may be closely associated with differences in the accumulation of potential key SDVs among jasmine cultivars. Accordingly, correlation networks were constructed for the M1 and M6 modules (Fig. 6B, C). The network showed that three ARFs were positively correlated with TPS-3, TPS-4, TPS-8, PMK-2, DXS-2, and 4CL-15. ERF-1 exhibited a positive correlation with TPS-4. Four MYBs were positively correlated with TPS-2, TPS-8, PAL-4, and 4CL-19. Previous studies have identified transcription factor families associated with volatile biosynthesis in jasmine, including MYB, ARF, ERF, bHLH, bZIP, and WRKY (Wang et al., 2022a; Zhou et al., 2022; Hong et al., 2024). Candidate transcription factors in the M1 and M6 modules were screened, including 5 ARFs, 1 ERF, 4 bHLHs, 5 bZIPs, 1 WRKY, and 9 MYBs, and their protein abundance patterns were visualized using a heatmap (Fig. 6D).

Fig. 6.

Fig. 6

Open in a new tab

Correlation analysis between volatile compounds and proteomic profiles of different jasmine cultivars in August. (A) Module correlation analysis between protein co-expression modules and 17 potential key SDVs. (B) Protein abundance trend, heatmap, and correlation network of the M1 module. (C) Protein abundance trend, heatmap, and correlation network of the M6 module. (D) Bubble heatmap showing the protein abundance patterns of candidate transcription factors in the M1 and M6 modules. Red lines indicate correlation coefficients greater than 0.90 between two nodes. The color-coded scale from blue to red indicates low to high protein abundance. BJ: Jasminum sambac ‘Bijiandanban’; FD: Jasminum sambac ‘Fuzhoudanban’; YY: Jasminum sambac ‘Yuanye’; SB: Jasminum sambac ‘Shuangban’; BD: Jasminum sambac ‘Bijianduoban’. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

Jasmine, a world-renowned ornamental plant, is widely used in the food industry due to its fresh and elegant aroma (Wang et al., 2022a). In production practice, the perceived aroma intensity of jasmine flowers has often been used as an important reference for evaluating their suitability for use. Summer harvests are often regarded in production practice as being associated with stronger aroma perception. In this study, the monthly ranking of total volatile compound content in jasmine was as follows: August > September > July > June > May. This finding provides data support for enhancing the utilization efficiency of jasmine. Although jasmine flowers were collected at a similar opening stage and within the same time window during sampling, volatile compound accumulation in flowers may still be affected by monthly environmental factors, such as temperature, light, rainfall, and humidity. Because this study was conducted in a single year and did not include multi-year or multi-location sampling, the observed monthly differences should be interpreted as monthly variation in volatile compound abundance under the conditions of this study. Future studies integrating multi-year sampling and detailed environmental factor modeling will be needed to further clarify the contribution of environmental variables to jasmine volatile accumulation. In addition to monthly variation, the aroma characteristics of different jasmine cultivars differ considerably (Wang et al., 2022a; Hong et al., 2024). In recent years, many researchers have broadened their perspective in jasmine studies beyond a single cultivar (Wang et al., 2022a; Hong et al., 2024; Fan et al., 2025). However, most studies have compared only two jasmine cultivars, making it difficult to determine shared volatile features among different jasmine cultivars. This study found that esters, terpenoids, and aldehydes were the major volatile compound classes in the five jasmine cultivars. Previous studies have also observed similar results in BJ, FD, and SB (Hong et al., 2024; Zhang et al., 2024; Zhou et al., 2022). These findings suggest that the predominance of these compound classes may be a common feature of jasmine volatile profiles. The volatile profiles of most jasmine cultivars may therefore be mainly characterized by these three compound classes. Furthermore, esters, terpenoids and aldehydes are also major components of the volatile compounds in many jasmine teas (Gu et al., 2025; Zhang et al., 2025b). This result indirectly supports the important contribution of jasmine flower-derived volatiles to the aroma of jasmine tea.

Traditionally, jasmine tea production has mainly relied on double-petal jasmine as the floral material. In recent years, jasmine tea products scented with different jasmine cultivars, such as single-petal, round-leaf, and multi-petal jasmine, have emerged (Chen et al., 2025; Gu et al., 2025; Zhang et al., 2025b). The development of these products is closely related to the exploration of distinct aroma characteristics among different jasmine cultivars. Therefore, investigating differences in aroma characteristics among jasmine cultivars is important for the diversified utilization of jasmine floral resources. In this study, OAV analysis identified 17 potential key SDVs with cultivar-specific distribution patterns. (E)-3-Hexenyl acetate, ethyl benzoate, methyl salicylate and other characteristic volatile compounds have also been identified as typical volatile compounds of jasmine in other studies (Wang et al., 2022a; Hong et al., 2024). These results suggest that variation in the contents and OAVs of these potential key SDVs may partly contribute to differences in aroma characteristics among jasmine cultivars. Future research should employ aroma reconstruction and aroma omission techniques to further validate the contribution of these potential key SDVs to the aroma profiles of different jasmine cultivars.

In recent years, the combined analysis of metabolomics with other omics disciplines such as transcriptomics and proteomics has substantially accelerated progress in the study of plant metabolic pathways (Singh et al., 2022). Previous investigations into the metabolic pathways of jasmine flowers have primarily focused on the genomic and transcriptomic levels (Wang et al., 2022a; Zhou et al., 2023; Hong et al., 2024). Although gene expression is regulated at the transcriptional level, transcript abundance does not always directly correspond to protein abundance because of translation and post-translational regulation (Wang et al., 2024). Therefore, integrated proteomic and metabolomic analysis may provide more direct insights into plant metabolic pathways. This research method has proven valuable in a variety of horticultural plants (Gonulalan et al., 2020; Olmedo et al., 2023). This study identified 23 proteins associated with VT biosynthesis through integrated volatile profiling and proteomic analysis. MK, PMK, and FPPS belong to the MVA pathway, while DXS, DXR, and MCT belong to the MEP pathway. Previous studies have suggested that increased abundance of upstream proteins in VT biosynthesis may enhance the accumulation of terpenoid precursors (Webb et al., 2013; Zhou et al., 2022). However, TPSs are key downstream enzymes that directly convert prenyl diphosphate substrates into diverse terpene products. PlTPS1 in Paeonia lactiflora is positively correlated with geraniol content (Zhao et al., 2025). In Freesia × hybrida, FhTPS1 has been confirmed as a single-product enzyme, and its expression level is positively correlated with linalool content (Gao et al., 2018). Numerous studies have confirmed that multiple members within the TPS family possess the ability to generate various VT derivatives from a single prenyl pyrophosphate substrate, or that the same VT can be synthesized by different TPS enzymes (Degenhardt et al., 2009; Green et al., 2012; Nieuwenhuizen et al., 2013). This study identified 13 TPSs, most of which showed higher abundance in FD, YY, and SB. In particular, TPS2, TPS3, TPS4, and TPS8 were assigned to the M1 module in WPCNA and showed positive correlations with α-farnesene, linalool, β-farnesene, and geraniol, with all correlation coefficients exceeding 0.75. This suggests that VT accumulation may be associated with the abundance of specific TPS proteins in jasmine flowers. These TPS proteins may represent candidate proteins involved in VT biosynthesis in jasmine, and their functions should be validated in future studies. Additionally, this study identified 7 VPBs among the 17 potential key SDVs in jasmine, including benzyl acetate, benzeneacet aldehyde, methyl anthranilate, benzyl alcohol, benzaldehyde, methyl salicylate and ethyl benzoate. Some studies suggest that VPBs may be synthesized directly from L-phenylalanine or t-cinnamic acid (Muhlemann et al., 2014; Zhou et al., 2022). In this study, 4 PALs and 22 4CLs were identified. The abundance patterns of the 4 PALs were highly similar, with all showing the highest abundance in YY, whereas the abundance patterns of the 22 4CLs varied considerably. 4CL plays an important role in VPB biosynthesis (Kutty et al., 2021). These 4CLs with distinct abundance patterns may partly contribute to variation in the contents of VPBs among different jasmine cultivars. Overall, differences in the abundance of proteins associated with VT and VPB biosynthesis may be one of the factors contributing to variation in aroma characteristics among different jasmine cultivars.

In horticultural flowers, MYB transcription factors have been widely reported to regulate the biosynthesis of volatile compounds. Silencing certain TFs in the MYB family significantly reduced the content of volatile compounds in Hedychium coronarium (Abbas et al., 2021). In Jasminum sambac, MYB4 plays a potential role in the biosynthesis of VPBs (Hong et al., 2024). Based on WPCNA, we identified co-expression modules associated with potential key SDVs in different jasmine cultivars, which included several MYB transcription factors. These MYB transcription factors may be involved in the regulation of volatile compound differences among jasmine cultivars. Interestingly, within the M6 module, GWHPBFHJ001173 (MYB8) and GWHPBFHJ016435 (4CL19) showed a strong correlation, and their protein abundance patterns were consistent with the trend in benzaldehyde content. Therefore, MYB8 and 4CL19 may represent candidate proteins associated with benzaldehyde accumulation or biosynthesis in jasmine.

5. Conclusion

This study employed HS-SPME-GC–MS to identify volatile compounds from five jasmine cultivars. Aldehydes, esters, and terpenoids were the major volatile compound classes in jasmine. The total volatile compound content of all five jasmine cultivars reached its highest level in August. A total of 41 SDVs were identified among jasmine cultivars using August samples. Among these, 17 potential key SDVs, including (E)-3-hexenyl acetate, ethyl benzoate, and methyl salicylate, may partly contribute to aroma differences among jasmine cultivars. DIA proteomic analysis identified and quantified 9161 proteins from the five jasmine cultivars. KEGG enrichment analysis showed that numerous DAPs were enriched in pathways associated with VT and VPB biosynthesis. Variations in the abundance of these DAPs may partly contribute to differences in the contents of VTs and VPBs, thereby providing clues to the proteomic basis of aroma variation among jasmine cultivars. Additionally, MYB, ERF, and ARF transcription factor families may be associated with the regulation of volatile compound accumulation in jasmine. Notably, MYB8 and 4CL19 may represent candidate proteins associated with benzaldehyde accumulation or biosynthesis. This study provides a basis for understanding monthly changes in volatile compound accumulation and the proteomic basis of aroma variation among jasmine cultivars.

CRediT authorship contribution statement

Yuhang Zhang: Writing – original draft, Visualization, Data curation. Weiwei Ren: Formal analysis. Wenjing Gui: Validation. Shanshan Zeng: Validation. Pengjie Wang: Methodology. Shan Jin: Methodology. Kunbo Wang: Methodology. Mengya Gu: Investigation, Conceptualization. Qin Luo: Validation, Conceptualization. Naixing Ye: Writing – review & editing, Project administration, Methodology.

Ethics statement

This study did not involve human participants or animals.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was supported by the National Key Research and Development Program of China (No. 2022YFD2101102), the Fuzhou Jasmine Tea Technology and Globally Important Agricultural Cultural Heritage Joint Research Center Special Project (KH1501920), and the Special Fund for Science and Technology Innovation of Fujian Zhang Tianfu Tea Development Foundation (FJZTF01).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochx.2026.104219.

Appendix A. Supplementary data

Supplementary material Supplementary Table S1. Sampling dates and regional meteorological background of jasmine samples collected in 2024.

mmc1.csv (10.1KB, csv)

Data availability

Data will be made available on request.

References

  1. Abbas F., Ke Y.G., Zhou Y.W., Yu Y.Y., Waseem M., Ashraf U.…Fan Y.P. Genome-wide analysis reveals the potential role of MYB transcription factors in floral scent formation in Hedychium coronarium. Frontiers in Plant Science. 2021;12 doi: 10.3389/fpls.2021.623742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. An H.M., Ou X.C., Zhang Y.B., Li S., Xiong Y.F., Li Q.…Liu Z. Study on the key volatile compounds and aroma quality of jasmine tea with different scenting technology. Food Chemistry. 2022;385(Aug.15) doi: 10.1016/j.foodchem.2022.132718. [DOI] [PubMed] [Google Scholar]
  3. Baena-Pedroza A., Londono-Giraldo L.M., Taborda-Ocampo G. Volatilome study of the feijoa fruit [Acca sellowiana (O. Berg) Burret.] with headspace solid phase microextraction and gas chromatography coupled with mass spectrometry. Food Chemistry. 2020;328 doi: 10.1016/j.foodchem.2020.127109. [DOI] [PubMed] [Google Scholar]
  4. Chambers A.H., Evans S.A., Folta K.M. Methyl anthranilate and γ-decalactone inhibit strawberry pathogen growth and achene germination. Journal of Agricultural and Food Chemistry. 2013;61(51):12625–12633. doi: 10.1021/jf404255a. [DOI] [PubMed] [Google Scholar]
  5. Chen Y., An H.M., Huang Y.W., Jiang Y.C., Ying J.Q., Wang S.R.…Huang J.A. Characterization of key aroma components of jasmine tea with diverse aroma types: Focus on jasmine tea scented with multipetal jasmine. Food Chemistry: X. 2025;29 doi: 10.1016/j.fochx.2025.102734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Degenhardt J., Koellner T.G., Gershenzon J. Monoterpene and sesquiterpene synthases and the origin of terpene skeletal diversity in plants. Phytochemistry. 2009;70(15–16):1621–1637. doi: 10.1016/j.phytochem.2009.07.030. [DOI] [PubMed] [Google Scholar]
  7. Deng Y.M., Jia X.P., Liang L.J., Gu C.S., Sun X.B. Morphological anatomy, sporogenesis and gametogenesis in flowering process of jasmine (Jasminum sambac Aiton) Scientia Horticulturae. 2016;198:257–266. doi: 10.1016/j.scienta.2015.11.036. [DOI] [Google Scholar]
  8. Fan W.M., Liao Z.Y., Gu M.Y., Zhang Y.H., Lei W.L., Zhang Y.A.…Wang P.J. Pan-genome of Jasminum sambac reveals the genetic diversity of different petal morphology and aroma-related genes. Molecular Ecology Resources. 2025;25(7) doi: 10.1111/1755-0998.70013. [DOI] [PubMed] [Google Scholar]
  9. Gao F.Z., Liu B.F., Li M., Gao X.Y., Fang Q., Liu C.…Gao X. Identification and characterization of terpene synthase genes accounting for volatile terpene emissions in flowers of Freesia × hybrida. Journal of Experimental Botany. 2018;69(18):4249–4265. doi: 10.1093/jxb/ery224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gonulalan E.M., Nemutlu E., Bayazeid O., Kocak E., Yalcin F.N., Demirezer L.O. Metabolomics and proteomics profiles of some medicinal plants and correlation with BDNF activity. Phytomedicine. 2020;74 doi: 10.1016/j.phymed.2019.152920. [DOI] [PubMed] [Google Scholar]
  11. Green S.A., Chen X.Y., Nieuwenhuizen N.J., Matich A.J., Wang M.Y., Bunn B.J.…Atkinson R.G. Identification, functional characterization, and regulation of the enzyme responsible for floral (E)-nerolidol biosynthesis in kiwifruit (Actinidia chinensis) Journal of Experimental Botany. 2012;63(5):1951–1967. doi: 10.1093/jxb/err393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Gu M.Y., Zhang Y.H., Weng Q.H., Weng W.F., Ren W.W., Jin S.…Ye N.X. Metabolomics analysis reveals dynamic changes of volatile and non-volatile metabolites during the scenting process of jasmine tea. Food Chemistry: X. 2025;28 doi: 10.1016/j.fochx.2025.102617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hong Y.P., Gu M.Y., Zhang Y.H., Lin H.Z., Gao T., Jin S.…Ye N.X. Aroma formation in single- and double-petal jasmines (Jasminum sambac) during flowering via volatile metabolome and transcriptome. Scientia Horticulturae. 2024;333 doi: 10.1016/j.scienta.2024.113289. [DOI] [Google Scholar]
  14. Kutty N.N., Ghissing U., Mitra A. Revealing floral metabolite network in tuberose that underpins scent volatiles synthesis, storage and emission. Plant Molecular Biology. 2021;106(6):533–554. doi: 10.1007/s11103-021-01171-7. [DOI] [PubMed] [Google Scholar]
  15. Li W.W., Qin Y., Gong J.H., Zhang B.B., Zhang W.W., Yao D.J.…Huang R. Comparative analysis of physicochemical properties, flavor compounds, and metabolites among three major cultivated walnut (Juglans sigillata Dode) varieties in China Yunnan. LWT - Food Science and Technology. 2024;212 doi: 10.1016/j.lwt.2024.117003. [DOI] [Google Scholar]
  16. Liang S., Wang F., Granato D., Zhong X.Y., Xiao A.F., Ye Q.…Xu Y.Q. Effect of β-glucosidase on the aroma of liquid-fermented black tea juice as an ingredient for tea-based beverages. Food Chemistry. 2023;402(Feb.15) doi: 10.1016/j.foodchem.2022.134201. [DOI] [PubMed] [Google Scholar]
  17. Liang Y.L., Wang Z.H., Zhang L.Z., Dai H.M., Wu W.W., Zheng Z.Q.…Sun W.J. Characterization of volatile compounds and identification of key aroma compounds in different aroma types of Rougui Wuyi rock tea. Food Chemistry. 2024;455 doi: 10.1016/j.foodchem.2024.139931. [DOI] [PubMed] [Google Scholar]
  18. Liu D.J., Shen Q.M., Lin K.W., Wang F., Bu Z.B., Peng J.…Ma L.K. The aroma profiles of dried gonggans: Characterization of volatile compounds in oven-dried and freeze-dried gonggan. Food Research International. 2024;191 doi: 10.1016/j.foodres.2024.114716. [DOI] [PubMed] [Google Scholar]
  19. Mahanta B.P., Lahon D., Kalita D., Lal M., Haldar S. Study on aroma-profile, key odorants and ontogenetic variability of black turmeric (Curcuma caesia Roxb.) essential oil: An aroma perspective. Industrial Crops and Products. 2023;193 doi: 10.1016/j.indcrop.2022.116115. [DOI] [Google Scholar]
  20. Ming Q.J., Liao N.K., Lin H.Z., Hu Y.F., Hao Z.L., Lin J.K.…Wu L.Y. The role of stem and leaves in shaping the integrated aroma of oolong tea. Food Chemistry. 2025;488 doi: 10.1016/j.foodchem.2025.144808. [DOI] [PubMed] [Google Scholar]
  21. Muhlemann J.K., Klempien A., Dudareva N. Floral volatiles: From biosynthesis to function. Plant, Cell & Environment. 2014;37(8):1936–1949. doi: 10.1111/pce.12314. [DOI] [PubMed] [Google Scholar]
  22. Nieuwenhuizen N.J., Green S.A., Chen X., Bailleul E.J.D., Matich A.J., Wang M.Y., Atkinson R.G. Functional genomics reveals that a compact terpene synthase gene family can account for terpene volatile production in apple. Plant Physiology. 2013;161(2):787–804. doi: 10.1104/pp.112.208249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Olmedo P., Nunez-Lillo G., Vidal J., Leiva C., Rojas B., Sagredo K.…Campos-Vargas R. Proteomic and metabolomic integration reveals the effects of pre-flowering cytokinin applications on central carbon metabolism in table grape berries. Food Chemistry. 2023;411 doi: 10.1016/j.foodchem.2023.135498. [DOI] [PubMed] [Google Scholar]
  24. Pu J., Zhao B.Y., Liu X., Li S.G., Wang B.B., Wu D.…Geng F. Quantitative proteomic analysis of chicken egg white and its components. Food Research International. 2023;170 doi: 10.1016/j.foodres.2023.113019. [DOI] [PubMed] [Google Scholar]
  25. Qi X.Y., Wang H.D., Liu S.Y., Chen S.S., Feng J., Chen H.J.…Deng Y. The chromosome-level genome of double-petal phenotype jasmine provides insights into the biosynthesis of floral scent. Horticultural Plant Journal. 2024;10(1):259–272. doi: 10.1016/j.hpj.2023.03.006. [DOI] [Google Scholar]
  26. Qian C., Yan Z.W., Fu C.B. Climatic changes in the twenty-four solar terms during 1960-2008. Chinese Science Bulletin. 2012;57(2–3):276–286. doi: 10.1007/s11434-011-4724-4. [DOI] [Google Scholar]
  27. Sabbatini A., Jurnatan Y., Fraatz M.A., Govori S., Haziri A., Millaku F.…Zhang Y.Y. Aroma characterization of a wild plant (Sanguisorba albanica) from Kosovo using multiple headspace solid phase microextraction combined with gas chromatography-mass spectrometry-olfactometry. Food Research International. 2019;120(Jun.):514–522. doi: 10.1016/j.foodres.2018.10.093. [DOI] [PubMed] [Google Scholar]
  28. Singh K.S., van der Hooft J.J.J., van Wees S.C.M., Medema M.H. Integrative omics approaches for biosynthetic pathway discovery in plants. Natural Product Reports. 2022;39(9):1876–1896. doi: 10.1039/d2np00032f. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Vranova E., Coman D., Gruissem W. Network analysis of the MVA and MEP pathways for isoprenoid synthesis. Annual Review of Plant Biology. 2013;64:665–700. doi: 10.1146/annurev-arplant-050312-120116. [DOI] [PubMed] [Google Scholar]
  30. Wang P.J., Fang J.P., Lin H.Z., Yang W.W., Yu J.X., Hong Y.P.…Ye N. Genomes of single- and double-petal jasmines (Jasminum sambac) provide insights into their divergence time and structural variations. Plant Biotechnology Journal. 2022;20(7):1232–1234. doi: 10.1111/pbi.13820. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Wang P.J., Gu M.Y., Yang W.W., Hong Y.P., Jiang M.W., Lin H.Z.…Ye N.X. High-resolution transcriptome and volatile assays provide insights into flower development and aroma formation in single- and double-petal jasmines (Jasminum sambac) Industrial Crops and Products. 2022;189 doi: 10.1016/j.indcrop.2022.115846. [DOI] [Google Scholar]
  32. Wang S.Y., Sun S., Du Z.H., Gao F.Q., Li Y.Y., Han W.B.…Yu X.M. Characterization of CsUGT73AC15 as a multifunctional glycosyltransferase impacting flavonol triglycoside biosynthesis in tea plants. Journal of Agricultural and Food Chemistry. 2024;72(23):13328–13340. doi: 10.1021/acs.jafc.4c03824. [DOI] [PubMed] [Google Scholar]
  33. Webb H., Lanfear R., Hamill J., Foley W.J., Kuelheim C. The yield of essential oils in Melaleuca alternifolia (Myrtaceae) is regulated through transcript abundance of genes in the MEP pathway. PLoS One. 2013;8(3) doi: 10.1371/journal.pone.0060631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Xu C.C., Li Q.L., Wang N., Liu D.K., Guo C.X. Identifying and discriminating aroma attribute and bioactive components of five commercial essential oils of celery (Apium graveolens L.) seeds using E-nose, HS-GC-IMS, and GC-MS. LWT - Food Science and Technology. 2023;184 doi: 10.1016/j.lwt.2023.115094. [DOI] [Google Scholar]
  35. Xu J.N., Qi Y.M., Zhang J., Liu M.M., Wei X.Y., Fan M.T. Effect of reduced glutathione on the quality characteristics of apple wine during alcoholic fermentation. Food Chemistry. 2019;300 doi: 10.1016/j.foodchem.2019.125130. [DOI] [PubMed] [Google Scholar]
  36. Zhang H., Wang J.J., Zhang D.D., Zeng L., Liu Y.A., Zhu W.…Huang Y.Y. Aged fragrance formed during the post-fermentation process of dark tea at an industrial scale. Food Chemistry. 2021;342 doi: 10.1016/j.foodchem.2020.128175. [DOI] [PubMed] [Google Scholar]
  37. Zhang P., Wu W.W., Ma B.X., Zheng C.K., Lin Q., Zhao X.Y.…Sun W.J. Revealing the characteristic honey-like aroma formation in honey-scented black tea via molecular sensory analysis. Food Chemistry. 2025;493(Pt 4):146023. doi: 10.1016/j.foodchem.2025.146023. [DOI] [PubMed] [Google Scholar]
  38. Zhang Y.H., Gu M.Y., Xu M.T., Lin H.Z., Hong Y.P., Yang W.W.…Ye N.X. Differential analysis of aroma composition of Jasminum sambac ‘Bijian danban-moli’ flowers at different opening stages based on HS-SPME-GC-MS. Food Science. 2024;45(19):94–103. https://link.cnki.net/urlid/11.2206.TS.20240308.1133.028 [Google Scholar]
  39. Zhang Y.H., Gu M.Y., Yang S.W., Fan W.M., Lin H.Z., Jin S.…Ye N.X. Dynamic aroma characteristics of jasmine tea scented with single-petal jasmine “Bijian”: A comparative study with traditional double-petal jasmine. Food Chemistry. 2025;464(Pt 2) doi: 10.1016/j.foodchem.2024.141735. [DOI] [PubMed] [Google Scholar]
  40. Zhang Y.J., Su R.X., Yuan H.L., Zhou H.H., Jiangfang Y.D., Liu X.Q., Luo J. Widely targeted Volatilomics and metabolomics analysis reveal the metabolic composition and diversity of Zingiberaceae plants. Metabolites. 2023;13(6):700. doi: 10.3390/metabo13060700. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Zhao Q., Li Y.Q., Gu L.N., Li S.T., He D., Dong S.R.…Zhang Y.L. Identification and characterization of terpene synthase genes accounting for volatile terpene emissions in the flower of Paeonia lactiflora. Postharvest Biology and Technology. 2025;219 doi: 10.1016/j.postharvbio.2024.113231. [DOI] [Google Scholar]
  42. Zhou C.Z., Zhu C., Tian C.Y., Xie S.Y., Xu K., Huang L.J.…Guo Y.Q. The chromosome-scale genome assembly of Jasminum sambac var. unifoliatum provides insights into the formation of floral fragrance. Horticultural Plant Journal. 2023;9(6):1131–1148. doi: 10.1016/j.hpj.2023.03.003. [DOI] [Google Scholar]
  43. Zhou C.Z., Zhu C., Tian C.Y., Xu K., Huang L.J., Shi B.Y.…Guo Y. Integrated volatile metabolome, multi-flux full-length sequencing, and transcriptome analyses provide insights into the aroma formation of postharvest jasmine (Jasminum sambac) during flowering. Postharvest Biology and Technology. 2022;183 doi: 10.1016/j.postharvbio.2021.111726. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material Supplementary Table S1. Sampling dates and regional meteorological background of jasmine samples collected in 2024.

mmc1.csv (10.1KB, csv)

Data Availability Statement

Data will be made available on request.

Articles from Food Chemistry: X are provided here courtesy of Elsevier

RESOURCES