ARTICLE   Open Access    

Revealing the flavor profile of citrus Pu-erh tea through GC-MS-O and untargeted metabolomics

  • # These authors contributed equally: Jiaqing Sun, Weitong Cai

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  • Chinese citrus Pu-erh tea is recognized for its unique flavor, which is composed of key aroma-active compounds and affected by taste-impact metabolites. In this study, the whole citrus Pu-erh tea (CP), its out-layer fruit (OF) container and inside tea (IT) powder, were analyzed by solvent-assisted flavor evaporation (SAFE) coupled with GC-MS-O and UHPLC-MS/MS. As a result, 47 important volatiles were identified, including 27 (IT), 30 (OF) and 27 (CP) volatiles that were screened out based on their odor activity value (OAV) and aroma character impact value (ACI), and further validated by aroma omission/recombination experiment. Combined with the sensory evaluation and PLSR model, the aroma profile of CP was characterized with the following ten flavor attributes: sweet (vanillin); floral (β-ionone); fruity (methyl anthranilate, methyl methanthranilate, citronellal); roasted (thymol); musty (p-cymene), woody (perillaldehyde); herbal (linalool, α-terpineol); phenolic (2,4-di-tert-butylphenol, p-cresol); minty (dihydrocarvone); and fatty (octanoic acid) volatiles. As for the non-volatile taste-impact chemicals, the most prominent metabolites were identified as flavonoids that mainly contributed to the taste of bitter (catechin, epicatechin, gallocatechin), astringency (leucopelargonidin) and sweet (neohesperidin). This novel finding has provided an insight and better understanding of the aroma profile of citrus Pu-erh tea and some guidance for flavor pairing and taste improvement.
  • Salvia rosmarinus L. (old name Rosmarinus officinalis), common name Rosemary thrives well in dry regions, hills and low mountains, calcareous, shale, clay, and rocky substrates[1]. Salvia rosmarinus used since ancient times in traditional medicine is justified by its antiseptic, antimicrobial, anti-inflammatory, antioxidant, and antitumorigenic activity[1,2]. The main objective of the study is to evaluate the antimicrobial activity of different extracts of Salvia rosmarinus in vitro, and its compounds related to in silico targeting of enzymes involved in cervical cancer. Since the start of the 20th century, some studies have shown that microbial infections can cause cervical cancers worldwide, infections are linked to about 15% to 20% of cancers[3]. More recently, infections with certain viruses like Human papillomaviruses (HPV) and Human immunodeficiency virus (HIV), bacteria like Chlamydia trachomatis, and parasites like schistosomiasis have been recognized as risk factors for cancer in humans[3]. Then again, cancer cells are a group of diseases characterized by uncontrolled growth and spread of abnormal cells. Many things are known to increase the risk of cancer, including dietary factors, certain infections, lack of physical activity, obesity, and environmental pollutants[4]. Some studies have found that unbalanced common flora Lactobacillus bacteria around the reproductive organ of females increases the growth of yeast species (like Candida albicans) and some studies have found that women whose blood tests showed past or current Chlamydia trachomatis infection may be at greater risk of cervical cancer. It could therefore be that human papillomavirus (HPV) promotes cervical cancer growth[3]. Salvia rosmarinus is traditionally a healer chosen as a muscle relaxant and treatment for cutaneous allergy, tumors, increases digestion, and the ability to treat depressive behavior; mothers wash their bodies to remove bacterial and fungal infections, promote hair growth, and fight bad smells[5] .

    The study of plant-based chemicals, known as phytochemicals, in medicinal plants is gaining popularity due to their numerous pharmacological effects[6] against drug resistance pathogens and cancers. The causes of drug resistance to bacteria, fungi, and cancer are diverse, complex, and only partially understood. The factors may act together to initiate or promote infections and carcinogenesis in the human body is the leading cause of death[7]. Antimicrobial medicines are the cornerstone of modern medicine. The emergence and spread of drug-resistant pathogens like bacteria and fungi threaten our ability to treat common infections and to perform life-saving procedures including cancer chemotherapy and cesarean sections, hip replacements, organ transplantation, and other surgeries[7]. On the other hand, information about the current magnitude of the burden of bacterial and fungal drug resistance, trends in different parts of the world, and the leading pathogen–drug combinations contributing to the microbial burden is crucial. If left unchecked, the spread of drug resistance could make many microbial pathogens much more lethal in the future than they are today. In addition to these, cancers can affect almost any part of the body and have many anatomies and molecular subtypes that each require specific management strategies to avoid or inhibit them. There are more than 200 different types of cancer that have been detected. The world's most common cancers affecting men are lung, prostate, colorectal, stomach, and liver cancers[8]. While breast, cervix, colorectal, lung, and stomach cancers are the most commonly diagnosed among women[8]. Although some cancers said to be preventable they seem to still be one of the causes of death to humans, for example cervical cancer. The need to fill the gap to overcome the problem of searching for antimicrobials and anticancers from one source of Salvia rosmarinus is of importance.

    Cervical cancer is a common cancer in women and a prominent cause of death[9]. In Ethiopia, cervical cancer is a big deal for women aged 15 to 44, coming in as the second most common cancer[9]. Globally, it's the fourth most common prevalent disease for women[10]. Aberrant methylation of tumor-suppressor genes' promoters can shut down their important functions and play a big role in causing cervical tumors[10]. There are various cervical cancer repressor genes (proteins turn off or reduce gene expression from the affected gene), such as CCNA1, CHF, HIT, PAX1, PTEN, SFRP4, and TSC1. The genes play a crucial role in causing cervical cancer by regulating transcription and expression through promoter hypermethylation, leading to precursor lesions during cervical development and malignant transformation[11]. The process of DNA methylation is primarily carried out by a group of enzymes known as DNA methyltransferases (DNMT1). It has been reported that DNMT1 (PDB ID: 4WXX), a protein responsible for DNA methylation can contribute to the development of cervical cancer. DNMT1 inhibits the transcription of tumor suppressor genes, facilitating tumorigenesis, which finally develops into cervical cancer. Tumor suppressor gene transcription is inhibited by DNMT1, which helps cancer grow and eventually leads to cervical cancer. Repressive genes' hypermethylation may be decreased, their expression can be increased, and the phenotype of malignant tumors can be reversed by inhibiting the DNMT1 enzyme.

    On the other hand, infection by the human papilloma virus (HPV) phenotype 16, enzyme 6 (PDB ID: 4XR8) has been correlated with a greatly increased risk of cervical cancer worldwide[12]. Based on variations in the nucleotide sequences of the virus genome, over 100 distinct varieties of the human papilloma virus (HPV) have been identified (e.g. type 1, 2 etc.). Genital warts can result from certain types 6 and 11 of sexually transmitted HPVs. Other HPV strains, still, that can infect the genitalia, do not show any symptoms of infection[8]. Persistent infection with a subset of approximately 13 so-called 'high-risk' sexually transmitted HPVs, including such as types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, and 68 different from the ones that cause warts may lead to the development of cervical intraepithelial neoplasia (CIN), vulvar intraepithelial neoplasia (VIN), penile intraepithelial neoplasia (PIN), and/or anal intraepithelial neoplasia (AIN). These are precancerous lesions and can progress to invasive cancer. Almost all occurrences of cervical cancer have HPV infection as a required component[13]. Superfluous infection by HPV type 16 E6 (PDB ID: 4XR8) has been correlated with a greatly increased genital risk of precursor cervical cancer worldwide[11]. Scholars more defined in major biochemical and biological activities of HPV type 16 E6 (PDB ID: 4XR8) in high-risk HPV oncogenes and how they may work together in the development of cervical disease and cancer[13].

    One potential approach to treat cervical cancer is to inhibit the activity of the DNMT1 and HPV type 16 E6 enzymes specifically[1316]. Over 50% of clinical drug forms worldwide originate from plant compounds[17]. In the past, developing new drugs was a lengthy and costly process. However, with the emergence of bioinformatics, the use of computer-based tools and methods have become increasingly important in drug discovery. One such method is molecular docking and ADMET profiling which involves using the structure of a drug to screen for potential candidates. This approach is known as structure-based drug design and can save both time and resources during the research process[15]. Structural-based drug designing addresses ligand binding sites with a known protein structure[15]. Using free binding energies, a computational method known as docking examines a large number of molecules and suggests structural theories for impeding the target molecule[17]. Nowadays, due to increasing antibiotic resistance like bacteria, fungi, and cancer cells, natural products remain an important source for discovering antimicrobial compounds and novel drugs for anti-cancers like cervical cancers. Therefore, the purpose of this research is to assess the antimicrobial activity of extracts, molecular docking, ADMET profiling in anticancer properties of compounds isolated from Salvia rosmarinus, on a targeting DNMT1 and HPV type 16 E6 in human cervical cancer. In the present study, various solvent crude extracts obtained from Salvia rosmarinus were used for antimicrobial activity and the isolated compounds 1 and 2 were submitted for in silico study to target the DNMT1 and HPV type 16 E6 enzymes to inhibit the growth of human cervical cancer cells.

    Healthy Salvia rosmarinus leaves were collected in Bacho district, Southwest Showa, Oromia, Ethiopia, during the dry season of November 2022. The plant materials were authenticated by Melaku Wondafrish, Natural Science Department, Addis Ababa University and deposited with a voucher number 3/2-2/MD003-80/8060/15 in Addis Ababa University's National Herbarium.

    The most common organic solvent used in extractions of medicinal plants is 2.5 L of petroleum ether, chloroform/methanol (1:1), and methanol. The test culture medium for microbes was used and performed in sterile Petri dishes (100 mm diameter) containing sterile Muller–Hinton Agar medium (25 mL, pH 7) and Sabouraud Dextrose Agar (SDA) for bacteria and fungi, respectively. A sterile Whatman filter paper (No. 1) disc of 6 mm diameter was used to determine which antibiotics an infective organism is sensitive to prescribed by a minimum zone of inhibition (MZI). Ciprofloxacin antibiotic reference (manufactured by Wellona Pharma Ciprofloxacin tablet made in India) and Ketoconazole 2% (made in Bangladesh) were used as a positive controls for antibacterial and antifungal, respectively and Dimethyl sulfoxide (DMSO) 98.9% was used as a negative control for antimicrobial tests. In the present study, the height of the column was 650 mm and the width was 80 mm. Several studies by previous researchers showed the acceptable efficiency of column chromatography (up to 43.0% w/w recovery) in the fractionation and separation of phenolic compounds from plant samples[18]. In column chromatography, the ideal stationary phase used silica gel 60 (0.200 mm) particles. The 1H-NMR spectrums of the compounds were analyzed using a 600 MHz NMR machine and 150 MHz for 13C NMR. The compounds were dissolved in MeOD for compound 1 and in DMSO for compound 2 for NMR analysis. On the other hand, UV spectroscopy (made in China) used 570 nm ultraviolet light to determine the absorbency of flavonoids (mg·g−1) phytochemicals.

    The samples (extracts) were analyzed to detect the presence of certain chemical compounds such as alkaloids (tested using Wagner's reagents), saponins (tested using the froth test), steroids (tested with Liebermann Burchard's tests), terpenoids (tested with Lidaebermann Burchard's tests), quinones, and flavonoids (tested using Shinoda tests)[19].

    The leaves of Salvia rosmarinus (500 g) were successively extracted using maceration using petroleum ether, chloroform/methanol (1:1), and methanol, every one 2.5 L for 72 h to afford 3.6, 6, and 53 g crude extracts, respectively. The methanol/chloroform (1:1) extract (6 g) was loaded to silica gel (150 g) column chromatography using the increasing polarity of petroleum ether, methanol/chloroform (1:1) solvent system to afford 80 fractions (100 mL each). The fraction obtained from chloroform/methanol 1:1 (3:2) after repeated column chromatography yielded compound 1 (18 mg). Fractions 56-65, eluted with chloroform/methanol (1:1) were combined and purified with column chromatography to give compound 2 (10 mg).

    The microorganisms were obtained from the Ethiopia Biodiversity Institution (EBI). Two gram-positive bacteria namely Staphylococcus aureus serotype (ATCC 25923) and Streptococcus epidermidis (ATCC14990); and three gram-negative bacteria, namely Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 5702), and Klebsiella pneumonia (ATCC e13883) were inoculated overnight at 37 °C in Muller–Hinton Agar/MHA culture medium and two fungus strains of Candida albicans (ATCC 16404) and Aspergillus niger (ATCC 11414) were inoculated overnight at 27−30 °C in Sabouraud Dextrose Agar/SDA culture medium[20].

    The antibacterial and antifungal activities of different crude extracts obtained from Salvia rosmarinus plant leaves were evaluated by the disk diffusion method (in accordance with the 13th edition of the CLSI M02 document on hardydiagnostics.com/disk-diffusion). Briefly, the test was performed in sterile Petri dishes (100 mm diameter) containing solid and sterile Muller–Hinton Agar medium (25 mL, pH 7) and Sabouraud Dextrose Agar (SDA) for bacteria and fungi, respectively. The extracts were placed on the surface of the media that had previously been injected with a sterile microbial suspension (one microbe per petri dish) after being adsorbed on sterile paper discs (5 μL per Whatman disc of 6 mm diameter). To prevent test samples from eventually evaporating, all Petri dishes were sealed with sterile laboratory films. They were then incubated at 37 °C for 24 h, and the zone diameter of the inhibition was measured and represented in millimeters. Ciprofloxacin antibiotic reference (manufactured by Wellona Pharma Ciprofloxacin tablet, India) was used as a positive control and DMSO was used as a negative control for antibacterial activity test while Ketoconazole 2% (Bangladesh) was used as a positive control and 10 μL of 0.2% agar as a negative control for antifungal activity tests[20]. The term 'inhibitory concentration' refers to the minimum sample concentration required to kill 99.9% of the microorganisms present[21]. Three repetitions of the crude extract sample were used to precisely measure the inhibitory halo diameter (in mm), which was then expressed as mean ± standard deviation to assess the anti-microbial activity.

    Cervical cancer-causing protein was identified through relevant literature. The protein molecule structure of DNA (cytosine-5)-methyltransferase 1 (DNMT1) (PDB ID: 4WXX)[21] and HPV type 16 E6 (PDB ID: 4XR8)[21] - a protein known to cause cervical cancer - were downloaded from the Protein Data Bank[22]. The stability of the protein molecule was assessed using Rampage[23].

    Phytochemical constituents of Salvia rosmarinus plant leaves were used to select a source of secondary metabolites (ligands). Ligand molecules were obtained through plant extraction, and isolation, and realized with PubChem (https://pubchem.ncbi.nlm.nih.gov/). The ligands were downloaded in Silver diamine fluoride format (SDF) and then converted to PDB format using an online SMILES translator (https://cactus.nci.nih.gov/translate/). The downloaded files were in PDB format, which was utilized for running various tools and software[24].

    The Biovia Discovery Studio Visualizer software was used to analyze the protein molecule. The protein molecule was converted into PDB format and its hierarchy was analyzed by selecting ligands and water molecules. Both the protein molecule and the water molecules lost their attached ligands during the analysis. Finally, the protein's crystal structure was saved in a PDB file[25].

    PyRx software was utilized to screen secondary metabolites and identify those ligands with the lowest binding energy to the protein target. The ligands with the lowest binding energy were further screened for their drug-likeliness property through analysis. It is worth noting that PyRx runs on PDBQT format. To begin using PyRx, it needs to load a protein molecule. This molecule should be converted from PDB to the protein data bank, partial charge (Q), and Atom Type (PDBQT) format. Once the protein molecule is loaded, it can import ligands from a specific folder in Silver diamine fluoride format. The ligand energy was minimized and changed to PDBQT format. The protein was docked with the ligand and screened based on minimum binding energy (https://cactus.nci.nih.gov/translate/).

    The optimal ligand was selected for final docking using AutoDock Vina and Biovia by modifying the reference of Discovery Studio Client 2021 (https://cactus.nci.nih.gov/translate/).

    The protein target from the Protein Data Bank (PDB) was loaded onto the graphical interface of AutoDock Vina. To prepare the protein for docking, water molecules were removed, hydrogen polar atoms were added, and Kollman charges were assigned to the protein molecule. Ultimately, PDBQT format was used to store the protein. After being imported in PDB format, the Ligand molecule was transformed to PDBQT format. Next, a grid box was chosen to represent the docked region. The command prompt was used to run AutoDock Vina and the outcomes were examined (https://cactus.nci.nih.gov/translate/).

    Docking the ligand with the protein target DNMT1(PDB ID: 4WXX)[22] and HPV type 16 E6 (PDB ID: 4XR8)[21] enzymes were performed using Biovia Discovery Studio Client 2021 by loading the protein target first followed by the ligand in PDB format. The charges were attached to the protein molecule, and the energy was minimized for the ligands. Both the protein and ligand molecules were prepared for docking. Once the docking process was complete, the results were analyzed based on several parameters, including absolute energy, clean energy, conf number, mol number, relative energy, and pose number. The interaction between the protein and ligand was analyzed using structure visualization tools, such as Biovia Discovery Studio Visualizer and PyMol (https://cactus.nci.nih.gov/translate/).

    The process of visualizing the structure was carried out using the PyMol tool. PyMol is a freely available software. Firstly, the protein molecule in PDBQT form was loaded on the PyMol graphical screen. Then, the output PDBQT file was added. The docked structure was visualized and the 'molecule' option was changed to 'molecular surface' under the 'shown as' menu (https://cactus.nci.nih.gov/translate/).

    Drug likeliness properties of the screened ligands were evaluated using the SwissADME online server. SMILE notations were obtained from PubChem and submitted to the SwissADME web server for analysis. The drugs were subjected to Lipinski's rule of five[20] for analysis. Lipinski's rules of five were selected for final docking through AutoDock Vina and Biovia Discovery Studio Client 2021. Ligands 1 and 2 were analyzed using Lipinski's rule of five for docking with AutoDock Vina and Biovia Discovery Studio Client 2021.

    The antimicrobial analysis data generated by triplicate measurements reported as mean ± standard deviation, and a bar graph also generated by GraphPad Prism version 8.0.1 (244) for Windows were used to perform the analysis. GraphPad Prism was used and combined with scientific graphing, comprehensive bar graph fitting (nonlinear regression), understandable statistics, and data organization. Prism allows the performance and modification of basic statistical tests commonly used and determined through the statistical applications in microbiology labs (https://graphpad-prism.software.informer.com/8.0/).

    Phytochemical screening of the different extracts for the presence (+) and absence (−) of alkaloids, steroids, glycosides, coumarins, terpenoids, flavonoids, carbohydrates, tannins, and saponins were done. The present study showed that alkaloids, terpenoids, flavonoids, and tannins tests in S. rosmarinus leaves of petroleum ether, chloroform/methanol (1:1), and methanol extracts were high whereas glycoside, coumarins, and carbohydrates had a moderate presence. The extract of S. rosmarinus leaves contain commonly bioactive constituents such as alkaloids, steroids, terpenoids, flavonoids, tannins, and saponins. These bioactive chemicals have active medicinal properties. Phytochemical compounds found in S. rosmarinus leaves have the potential to treat cancer cells and pathogens. The study also found that these flavonoids are related to natural phenolic compounds with anticancer and antimicrobial properties in the human diet (Table 1).

    Table 1.  Phytochemical screening tests result of petroleum ether, chloroform/methanol (1:1) and methanol extracts of Salvia rosmarinus leaves.
    Botanical name Phytochemicals Phytochemical screening tests Different extracts
    Petroleum ether Chloroform/methanol (1:1) Mehanol
    Salvia rosmarinus Alkaloids Wagner's test ++ ++ ++
    Steroids Libermann Burchard test ++ + ++
    Glycoside Keller-Killiani test +
    Coumarins Appirade test + +
    Terpenoids Libermann Burchard test ++ ++ ++
    Flavonoids Shinoda test ++ ++ ++
    Carbohydrate Fehling's test ++ ++
    Tannins Lead acetate test ++ ++ ++
    Saponins Foam test + + +
    + indicates moderate presence, ++ indicates highly present, − indicates absence.
     | Show Table
    DownLoad: CSV

    Two compounds were isolated and characterized using NMR spectroscopic methods (Fig. 1 & Supplementary Fig. S1ac). Compound 1 (10 mg) was isolated as yellow crystals from the methanol/chloroform (1:1) leaf extract of Salvia rosmarinus. The TLC profile showed a spot at Rf 0.42 with methanol/chloroform (3:2) as a mobile phase. The 1H-NMR spectrum (600 MHz, MeOD, Table 2, Supplementary Fig. S1a) of compound 1 showed the presence of one olefinic proton signal at δ 5.3 (t, J = 3.7 Hz, 1H), two deshielded protons at δ 4.7 (m, 1H), and 4.1 (m, 1H) associated with the C-30 exocyclic methylene group, and one O-bearing methine proton at δH 3.2 (m, 1H), and six methyl protons at δ 1.14 (s, 3H), 1.03 (d, J = 6.3 Hz, 3H), 1.00 (s, 3H), 0.98 (s, 3H), 0.87 (s, 3H), and 0.80 (s, 3H). A proton signal at δ 2.22 (d, J = 13.5 Hz, 1H) was attributed to methine proton for H-18. Other proton signals integrate for 20 protons were observed in the range δ 2.2 to 1.2. The proton decoupled 13C-NMR and DEPT-135 spectra (151 MHz, MeOD, Supplementary Fig. S1b & c) of compound 1 revealed the presence of 30 well-resolved carbon signals, suggesting a triterpene skeleton. The analysis of the 13C NMR spectrum displayed signals corresponding to six methyl, nine methylene, seven methine, and eight quaternary carbons. Among them, the signal observed at δ 125.5 (C-12) belongs to olefinic carbons. The methylene carbon showed signals at δC 39.9, 28.5, 18.1, 36.7, 23.9, 30.4, 26.5, 32.9, and 38.6. The quaternary carbons showed a signal at δC 39.4, 41.9, 38.4, 138.2, 41.8, and 47.8. The signals of exocyclic methylene carbon signals appeared at δ 153.1 and 103.9. The spectrum also showed sp3 oxygenated methine carbon at δ 78.3 and carboxyl carbon at δ 180.2. The spectrum revealed signals due to methyl groups at δC 27.4, 16.3, 15.0, 20.2, 22.7, and 16.4. The remaining carbon signals for aliphatic methines were shown at δC 55.3, 55.2, 53.0, and 37.1. The NMR spectral data of compound 1 is in good agreement with data reported for micromeric acid, previously reported from the same species by Abdel-Monem et al.[26]. (Fig. 1, Table 2).

    Figure 1.  Structure of isolated compounds from the leaves of Salvia rosmarinus.
    Table 2.  Comparison of the 13C-NMR spectral data of compound 1 and micromeric acid (MeOD, δ in ppm).
    Position NMR data of compound 1 Abdel-Monem
    et al.[26]
    1H-NMR 13C-NMR 13C-NMR
    1 38.60 39.9
    2 27.8 28.5
    3 3.2 (m, 1H) 78.3 80.3
    4 39.4 39.9
    5 55.3 56.7
    6 18.1 18.3
    7 36.7 34.2
    8 41.9 40.7
    9 53 48.8
    10 38.4 38.2
    11 23.9 24.6
    12 5.3 (t, J = 3.7 Hz, 1H) 125.5 127.7
    13 138.2 138
    14 41.8 43.3
    15 30.4 29.1
    16 26.5 25.6
    17 47.8 48
    18 δ 2.22 (d, J = 13.5 Hz, 1H) 55.2 56.1
    19 37.1 38.7
    20 153.1 152.8
    21 32.9 33.5
    22 39.0 40.1
    23 27.4 29.4
    24 16.3 16.9
    25 15.0 16.6
    26 20.2 18.3
    27 22.7 24.6
    28 180.2 177.8
    29 16.4 17.3
    30 4.7 (m, 1H), and 4.1 (m, 1H) 103.9 106.5
     | Show Table
    DownLoad: CSV

    Compound 2 (18 mg) was obtained as a white amorphous isolated from 40% methanol/chloroform (1:1) in petroleum ether fraction with an Rf value of 0.49. The 1H NMR (600 MHz, DMSO, Supplementary Fig. S2a) spectral-data showed two doublets at 7.79 (d, J = 8.7 Hz, 2H), and 6.90 (d, J = 8.7 Hz, 2H) which are evident for the presence of 1,4-disubstituted aromatic group. The oxygenated methylene and terminal methyl protons were shown at δ 4.25 (q, J = 7.1 Hz, 2H) and 1.29 (t, J = 7.1 Hz, 3H), respectively. The13C-NMR spectrum, with the aid of DEPT-135 (151 MHz, DMSO, Table 3, Supplementary Fig. S2b & c) spectra of compound 2 confirmed the presence of well-resolved seven carbon peaks corresponding to nine carbons including threee quaternary carbons, one oxygenated methylene carbon, one terminal methyl carbon, and two symmetrical aromatic methine carbons. The presence of quaternary carbon signals was shown at δ 120.9 (C-1), 148.2 (C-4), and ester carbonyl at δ 166.0 (C-7). The symmetry aromatic carbons signal was observed at δ 131.4 (C-2, 6), and 116.8 (C-3, 5). The oxygenated methylene and terminal methyl carbons appeared at δC 60.4 (C-8) and 14.7 (C-9), respectively. The spectral results provided above were in good agreement with those for benzocaine in the study by Alotaibi et al.[27]. Accordingly, compound 2 was elucidated to be benzocaine (4-Aminobenzoic acid-ethyl ester) (Table 3, Fig. 1, Supplementary Fig. S2ac), this compound has never been reported before from the leaves of Salvia rosmarinus.

    Table 3.  Comparison of the 1H-NMR, and 13C-NMR spectral data of compound 2 and benzocaine (DMSO, δ in ppm).
    Position NMR data of compound 2 Alotaibi et al.[27]
    1H-NMR 13C-NMR 1H-NMR 13C-NMR
    1 120.9 119
    2 7.79 (d, J = 8.7 Hz, 2H) 131.4 7.86 (d, J = 7.6 Hz) 132
    3 6.90 (d, J = 8.7 Hz, 2H) 116.8 6.83 (d, J = 7.6 Hz) 114
    4 148.2 151
    5 6.90 (d, J = 8.7 Hz, 2H) 116.8 6.83 (d, J = 7.6 Hz) 114
    6 7.79 (d, J = 8.7 Hz, 2H) 131.4 7.86 (d, J = 7.6 Hz) 132
    7 166.0 169
    8 4.3 (q, J = 7.1 Hz, 2H) 60.4 4.3 (q, J = 7.0 Hz) 61
    9 1.3 (t, J = 7.1 Hz, 3H) 14.7 1.36 (t, J = 7.0 Hz) 15
     | Show Table
    DownLoad: CSV

    The extracts and isolated compounds from Salvia rosmarinus were evaluated in vitro against microbes from gram-positive bacteria (S. aureus and S. epidermidis), gram-negative bacteria (E. coli, P. aeruginosa, and K. pneumoniae) and fungi (C. albicans and A. Niger) (Table 4). The petroleum ether extracts exhibited significant activity against all the present study-tested microbes at 100 μg·mL−1, resulting in an inhibition zone ranging from 7 to 21 mm. Chloroform/methanol (1:1) and methanol extracts demonstrated significant activity against all the present study-tested microbes at 100 μg·mL−1 exhibiting inhibition zones from 6 to 14 mm and 6 to 13 mm, respectively (Table 4). The chloroform/methanol (1:1) extracts were significantly active against bacteria of E. coli and K. pneumonia, and A. Niger fungi at 100 μg·mL−1. On the other hand, chloroform/methanol (1:1) extracts were significantly inactive against the S. rosmarinus and P. aeruginosa of bacteria and C. albicans of fungi, and again chloroform/methanol (1:1) extracts overall significantly active produced an inhibition zone of 12 to 14 mm (Table 4). Methanol extracts exhibited significant activity against S. aureus, E. coli bacteria, and A. Niger fungi at 100 μg·mL−1. The inhibition zone was recorded to be 11 to 13 mm. However, methanol extracts exhibited significant inactivity against K. pneumoniae (Table 4). The overall result of our studies shows that Salvia rosmarinus was extracted and evaluated in vitro, exhibiting significant antibacterial and antifungal activity, with inhibition zones recorded between 6 to 21 mm for bacteria and 5 to 21 mm for fungi. In our study, the positive control for ciprofloxacin exhibited antibacterial activity measured at 21.33 ± 1.15 mm, 15.00 ± 0.00 mm, and 14.20 ± 0.50 mm for petroleum ether, chloroform/methanol (1:1), and methanol extracts, respectively. Similarly, the positive control for ketoconazole demonstrated antifungal activity of 22.00 ± 1.00 mm, 13.67 ± 0.58 mm, and 15.00 ± 0.58 mm for petroleum ether, chloroform/methanol (1:1), and methanol extracts, respectively. Additionally, our findings indicated that the mean values of flavonoids (mg/g) tested were 92.2%, 90.4%, and 94.0% for petroleum ether, chloroform/methanol (1:1), and methanol extracts, respectively. This suggests that the groups of phenolic compounds evaluated play a significant role in antimicrobial activities, particularly against antibiotic-resistant strains.

    Table 4.  Comparison of mean zone of inhibition (MZI) leaf extracts of Salvia rosmarinus.
    Type of specimen, and standard antibiotics for
    each sample
    Concentration (μg·mL−1) of extract
    in 99.8% DMSO
    Average values of the zone of inhibition (mm)
    Gram-positive (+) bacteria Gram-negative (−) bacteria Fungai
    S. aurous S. epidermidis E. coli P. aeruginosa K. pneumoniae C. albicans A. niger
    Petroleum ether extracts
    S. rosmarinus 50 18.50 ± 0.50 15.33 ± 0.58 0.00 ± 0.00 0.00 ± 0.00 10.00 ± 0.00 15.93 ± 0.12 4.47 ± 0.50
    75 19.87 ± 0.06 17.00 ± 0.00 9.33 ± 0.29 10.53 ± 0.50 10.93 ± 0.12 18.87 ± 0.23 5.47 ± 0.50
    100 21.37 ± 0.78 17.50 ± 0.50 11.47 ± 0.50 13.17 ± 0.29 12.43 ± 0.51 20.83 ± 0.76 6.70 ± 0.10
    Standard antibiotics Cipro. 21.33 ± 1.15 18.33 ± 0.58 9.33 ± 0.58 12.30 ± 0.52 15.00 ± 0.00
    Ketocon. 22.00 ± 1.00 10.67 ± 0.58
    Chloroform/methanol (1:1) extracts
    50 5.47 ± 0.42 0.00 ± 0.00 10.33 ± 0.00 0.00 ± 0.00 9.70 ± 0.00 0.00 ± 0.12 8.47 ± 0.50
    S. rosmarinus
    75 5.93 ± 0.06 0.00 ± 0.00 11.33 ± 0.29 0.00 ± 0.50 12.50 ± 0.12 0.00 ± 0.23 10.67 ± 0.50
    100 6.47 ± 0.06 0.00 ± 0.00 14.17 ± 0.50 7.33 ± 0.29 14.17 ± 0.51 0.00 ± 0.76 12.67 ± 0.10
    Standard antibiotics Cipro. 15.00 ± 0.00 11.00 ± 1.00 11.33 ± 0.58 10.00 ± 0.52 12.67 ± 0.00
    Ketocon. 7.00 ± 1.00 13.67 ± 0.58
    Methanol extracts
    50 9.17 ± 0.29 5.50 ± 0.50 0.00 ± 0.00 7.50 ± 0.00 0.00 ± 0.00 6.57 ± 0.12 0.00 ± 0.50
    S. rosmarinus
    75 9.90 ± 0.10 6.93 ± 0.12 9.33 ± 0.29 8.50 ± 0.50 0.00 ± 0.00 8.70 ± 0.23 0.00 ± 0.50
    100 11.63 ± 0.55 7.97 ± 0.06 11.47 ± 0.50 9.90 ± 0.10 0.00 ± 0.00 10.83 ± 0.76 13.13 ± 0.10
    Standard antibiotics Cipro. 13.00 ± 0.00 11.50 ± 0.50 14.20 ± 0.58 13.33 ± 0.29 10.00 ± 0.00
    Ketocon. 12.00 ± 1.00 15.00 ± 0.58
    Mean values of flavonoids (mg·g−1) by 570 nm
    S. rosmarinus
    Petroleum ether extracts Chloroform/methanol (1:1) extracts Methanol extracts
    50 0.736 0.797 0.862
    75 0.902 0.881 0.890
    100 0.922 0.904 0.940
    Samples: Antibiotics: Cipro., Ciprofloxacin; Ketocon., ketoconazole (Nizoral); DMSO 99.8%, Dimethyl sulfoxide.
     | Show Table
    DownLoad: CSV

    Determining the three solvent extracts in S. rosmarinus plants resulted in relatively high comparable with positive (+) control. Especially, the S. rosmarinus petroleum ether leaf extracts against drug resistance human pathogenic bacteria S. aureus, S. epidermidis, E. coli, P. aeruginosa, and K. pneumoniae were minimum zone of inhibition (MZI) recorded that 21.37 ± 0.78, 17.50 ± 0.50, 11.47 ± 0.50, 13.17 ± 0.29, and 12.43 ± 0.51 mm, respectively and against human pathogenic fungi C. albicans and A. niger were minimum zone of inhibition (MZI) recorded that 20.83 ± 0.76 and 6.70 ± 0.10 mm, respectively which was used from bacteria against S. aureus MZI recorded that 21.37 ± 0.78 mm higher than the positive control (21.33 ± 1.15 mm). The S. rosmarinus of chloroform/methanol (1:1) extracts were found to be against E. coli (14.17 ± 0.50 mm) and K. pneumoniae (14.17 ± 0.51 mm) higher than the positive control 11.33 ± 0.58 and 12.67 ± 0.00 mm, respectively. The methanol extracts of leaves in the present study plants were found to have overall MZI recorded less than the positive control. The Salvia rosmarinus crude extracts showed better antifungal activities than the gram-negative (−) bacteria (Table 4, Fig 2, Supplementary Fig. S3). Therefore, the three extracts, using various solvents of different polarity indexes, have been attributed to specific biological activities. For example, the antimicrobial activities of Salvia rosmarinus extracts may be due to the presence of alkaloids, terpenoids, flavonoids, tannins, and saponins in natural products (Table 1).

    Figure 2.  Microbes' resistance with drugs relative to standard antibiotics in extracts of Salvia rosmarinus. The figures represent understudy of three extracts derived from Salvia rosmarinus. (a) Petroleum ether, (b) chloroform/methanol (1:1), and (c) methanol extracts tested in Salvia rosmarinus.

    Compounds 1 and 2 were isolated from chloroform/methanol (1:1) extract of Salvia rosmarinus (Fig. 1, Tables 2 & 3). The plant extract exhibited highest antibacterial results recorded a mean inhibition with diameters of 21 and 14 mm at a concentration of 100 mg·mL−1 against S. aureus and E. coli/K. pneumoniae, respectively. After testing, overall it was found that the highly active petroleum ether extract of Salvia rosmarinus was able to inhibit the growth of S. aureus and C. albicans, with inhibition zones of 21 and 20 mm, respectively. The petroleum ether extracts showed good efficacy against all tested microbes, particularly gram-positive bacteria and fungi (Table 4). This is noteworthy because gram-negative bacteria generally exhibit greater resistance to antimicrobial agents. Petroleum ether and chloroform/methanol (1:1) extracts of the leaves were used at a concentration of 100 mg·mL−1, resulting in impressive inhibition zone diameters of 11 and 14 mm for E. coli, 13 and 7 mm for P. aeruginosa, and 12 and 14 mm for K. pneumoniae, respectively.

    The present study found that at a concentration of 50 μg·mL−1, petroleum ether, chloroform/methanol (1:1), and MeOH extracts did not display any significant inhibition zone effects against the tested microbes. This implies that the samples have a dose-dependent inhibitory effect on the pathogens. The leaves of Salvia rosmarinus have been found to possess remarkable antimicrobial properties against gram-negative bacteria in different extracts such as E. coli, P. aeruginosa, and K. pneumoniae with 14.17 ± 0.50 in chloroform/methanol (1:1), 13.17 ± 0.29 in petroleum ether and 14.17 ± 0.51 in chloroform/methanol (1:1), respectively. However, in the present study, Salvia rosmarinus was found to possess remarkable high zones of inhibition with diameters of 21.37 ± 0.78 and 17.50 ± 0.50 mm antimicrobial properties against S. aureus, and S. epidermidis of gram-positive bacteria, respectively (Supplementary Fig. S3). The results are summarized in Fig. 2ac.

    The crystal structure of human DNMT1 (351-1600), classification transferase, resolution: 2.62 Å, PDB ID: 4WXX. Active site dimensions were set as grid size of center X = −12.800500 Å, center Y = 34.654981 Å, center Z = −24.870231 Å (XYZ axis) and radius 59.081291. A study was conducted to investigate the binding interaction of the isolated compounds 1 and 2 of the leaves of Salvia rosmarinus with the binding sites of the DNMT1 enzyme in human cervical cancer (PDB ID: 4WXX), using molecular docking analysis.

    The study also compared the results with those of standard anti-cancer agents Jaceosidin (Table 5 & Fig. 3). The compounds isolated had a final fixing energy extending from −5.3 to −8.4 kcal·mol−1, as shown in Table 4. It was compared to jaceosidin (–7.8 kcal·mol−1). The results of the molecular docking analysis showed that, compound 1 (−8.4 kcal·mol−1) showed the highest binding energy values compared with the standard drugs jaceosidin (–7.8 kcal·mol−1). Compound 2 has shown lower docking affinity (–5.3 kcal·mol−1) but good matching amino acid residue interactions compared to jaceosidin. After analyzing the results, it was found that the isolated compounds had similar residual interactions and docking scores with jaceosidin.

    Table 5.  Molecular docking results of ligand compounds 1 and 2 against DNMT1 enzyme (PDB ID: 4WXX).
    Ligands Binding affinity

    ( kcal·mol−1)
    H-bond Residual interactions
    Hydrophobic/electrostatic Van der Waals
    1 −8.4 ARG778 (2.85249), ARG778 (2.97417), VAL894 (2.42832) Lys-889, Pro-879, Tyr-865, His-795, Cys-893, Gly-760, Val-759, Phe-892, Phe-890, Pro-884, Lys-749
    2 −5.3 ARG596 (2.73996), ALA597 (1.84126), ILE422 (2.99493), THR424 (2.1965), ILE422 (2.93653) Electrostatic Pi-Cation-ARG595 (3.56619), Hydrophobic Alkyl-ARG595 (4.15839), Hydrophobic Pi-Alkyl-ARG595 (5.14967) Asp-423, Glu-428, Gly-425, Ile-427, Trp-464, Phe-556, Gln-560, Gln-594, Glu-559, Gln-598, Ser-563
    Jaceosidin −7.8 ASP571 (2.93566), GLN573 (2.02126), GLU562 (2.42376), GLN573 (3.49555), GLU562 (3.46629) Hydrophobic Alkyl-PRO574 (4.59409), Hydrophobic Alkyl-ARG690 (5.09748), Hydrophobic Pi-Alkyl-PHE576 (5.1314), Hydrophobic Pi-Alkyl-PRO574 (4.97072), Hydrophobic Pi-Alkyl-ARG690 (5.07356) Glu-698, Cys-691, Ala-695, Pro-692, Val-658, Glu-566, Asp-565
     | Show Table
    DownLoad: CSV
    Figure 3.  The 2D and 3D binding interactions of compounds against DNMT1 enzyme (PDB ID: 4WXX). The 2D and 3D binding interactions of compound 1 and 2 represent against DNMT1 enzyme, and jaceosidin (standard) against DNMT1 enzyme.

    Hence, compound 1 might have potential anti-cancer agents. However, anti-cancer in vitro analysis has not yet been performed. Promising in silico results indicate that further research could be beneficial. The 2D and 3D binding interactions of compounds 1 and 2 against human cervical cancer of DNMT1 enzyme (PDB ID: 4WXX) are presented in Fig. 3. The binding interactions between the DNMT1 enzyme (PDB ID: 4WXX), and compound 1 (Fig. 3) and compound 2 (Fig. 3) were displayed in 3D. Compounds and amino acids are connected by hydrogen bonds (green dash lines) and hydrophobic interactions (non-green lines).

    Crystal structure of the HPV16 E6/E6AP/p53 ternary complex at 2.25 Å resolution, classification viral protein, PDB ID: 4XR8. Active site dimensions were set as grid size of center X = −43.202782 Å, center Y = −39.085513 Å, center Z = −29.194115 Å (XYZ axis), R-value observed 0.196, and Radius 65.584122. A study was conducted to investigate the binding interaction of the isolated compounds 1 and 2 of the leaves of Salvia rosmarinus with the binding sites of the enzyme of human papilloma virus (HPV) type 16 E6 (PDB ID: 4XR8), using molecular docking analysis software. The study also compared the results with those of standard anti-cancer agents jaceosidin (Table 6 & Fig. 4). The compounds isolated had a bottom most fixing energy extending from −6.3 to −10.1 kcal·mol−1, as shown in Table 6. It was compared to jaceosidin (–8.8 kcal·mol−1). The results of the molecular docking analysis showed that, compound 1 (−10.1 kcal·mol−1) showed the highest binding energy values compared with the standard drugs jaceosidin (–8.8 kcal·mol−1). Compound 2 has shown lower docking affinity (–6.3 kcal·mol−1) but good matching amino acid residue interactions compared to jaceosidin. After analyzing the results, it was found that the isolated compounds had similar residual interactions and docking scores with jaceosidin.

    Table 6.  Molecular docking results of ligand compounds 1 and 2 against HPV type 16 E6 (PDB ID: 4XR8).
    Ligands Binding affinity
    (kcal·mol−1)
    H-bond Residual interactions
    Hydrophobic/electrostatic Van der Waals
    1 −10.1 ASN101 (2.25622), ASP228 (2.88341) Asp-148, Lys-176, Lys-180, Asp-178, Ile-179, Tyr-177, Ile-334, Glu-382, Gln-336, Pro-335, Gln-73, Arg-383, Tyr-100
    2 −6.5 TRP63 (1.90011), ARG67 (2.16075), ARG67 (2.8181) Hydrophobic Pi-Sigma-TRP341 (3.76182), Hydrophobic Pi-Pi Stacked-TYR156 (4.36581), Hydrophobic Pi-Pi T-shaped-TRP63 (5.16561), Hydrophobic Pi-Pi T-shaped-TRP63 (5.44632), Hydrophobic Alkyl-PRO155 (4.34691), Hydrophobic Pi-Alkyl-TRP341 (4.11391), Hydrophobic Pi-Alkyl-ALA64 (4.61525) Glu-154, Arg-345, Asp-66, Met-331, Glu-112, Lys-16, Trp-231
    Jaceosidin −8.8 ARG146 (2.06941), GLY70 (3.49991), GLN73 (3.38801) Electrostatic Pi-Cation-ARG67 (3.93442), Hydrophobic Pi-Alkyl-PRO49 (5.40012) Tyr-342, Tyr-79, Ser-338, Arg-129, Pro-335, Leu-76, Tyr-81, Ser-74, Tyr-71, Ser-80, Glu-46
     | Show Table
    DownLoad: CSV
    Figure 4.  The 2D and 3D binding interactions of compounds against HPV type 16 E6 (PDB ID: 4XR8). The 2D and 3D binding interactions of compound 1 and 2 represent against HPV type 16 E6 enzyme, and jaceosidin (standard) against HPV type 16 E6 enzyme.

    Hence, compounds 1 and 2 might have potential anti-cancer agents of HPV as good inhibitors. However, anti-cancer in vitro analysis has not been performed yet on HPV that causes cervical cancer agents. Promising in silico results indicate that further research could be beneficial. The 2D and 3D binding interactions of compounds 1 and 2 against human papilloma virus (HPV) type 16 E6 enzyme (PDB ID: 4XR8) are presented in Fig. 4. The binding interactions between the HPV type 16 E6 enzyme (PDB ID: 4XR8) and compound 1 (Fig. 4) and compound 2 (Fig. 4) were displayed in 3D. Compounds and amino acids are connected by hydrogen bonds (magenta lines) and hydrophobic interactions (non-green lines).

    In silico bioactivities of a drug, including drug-likeness and toxicity, predict its oral activity based on the document of Lipinski's Rule[25] was stated and the results of the current study showed that the compounds displayed conform to Lipinski's rule of five (Table 7). Therefore, both compounds 1 and 2 should undergo further investigation as potential anti-cancer agents. Table 8 shows the acute toxicity predictions, such as LD50 values and toxicity class classification (ranging from 1 for toxic, to 6 for non-toxic), for each ligand, revealing that none of them were acutely toxic. Furthermore, they were found to be similar to standard drugs. Isolated compound 1 has shown toxicity class classification 4 (harmful if swallowed), while 2 showed even better toxicity prediction giving results of endpoints such as hepatotoxicity, mutagenicity, cytotoxicity, and irritant (Table 8). All the isolated compounds were predicted to be non-hepatotoxic, non-irritant, and non-cytotoxic. However, compound 1 has shown carcinogenicity and immunotoxicity (Table 9). Hence, based on ADMET prediction analysis, none of the compounds have shown acute toxicity, so they might be proven as good drug candidates.

    Table 7.  Drug-likeness predictions of compounds computed by Swiss ADME.
    Ligands Formula Mol. Wt. (g·mol−1) NRB NHA NHD TPSA (A°2) Log P (iLOGP) Log S (ESOL) Lipinski's rule of five
    1 C30H46O3 454.68 1 3 2 57.53 3.56 −6.21 1
    2 C 9H11NO2 165.19 3 2 1 52.32 1.89 −2.21 0
    Jaceosidin C17H14O7 330.3 3 7 3 105 1.7 1 0
    NHD, number of hydrogen donors; NHA, number of hydrogen acceptors; NRB, number of rotatable bonds; TPSA, total polar surface area; and log P, octanol-water partition coefficients; Log S, turbid metric of solubility.
     | Show Table
    DownLoad: CSV
    Table 8.  Pre ADMET predictions of compounds, computed by Swiss ADME.
    Ligands Formula Skin permeation value
    (logKp - cm·s−1)
    GI
    absorption
    Inhibitor interaction
    BBB permeability Pgp substrate CYP1A2 inhibitor CYP2C19 inhibitor CYP2C9 inhibitor CYP2D6 inhibitor
    1 C30H46O3 −4.44 Low No No No No No No
    2 C 9H11NO2 −5.99 High Yes No No No No No
    Jaceosidin C17H14O7 −6.13 High No No Yes No Yes Yes
    GI, gastrointestinal; BBB, blood brain barrier; Pgp, P-glycoprotein; and CYP, cytochrome-P.
     | Show Table
    DownLoad: CSV
    Table 9.  Toxicity prediction of compounds, computed by ProTox-II and OSIRIS property explorer.
    Ligands Formula LD50
    (mg·kg−1)
    Toxicity
    class
    Organ toxicity
    Hepatotoxicity Carcinogenicity Immunotoxicity Mutagenicity Cytotoxicity Irritant
    1 C30H46O3 2,000 4 Inactive Active Active Inactive Inactive Inactive
    2 C 9H11NO2 NA NA Inactive Inactive Inactive Inactive Inactive Inactive
    Jaceosidin C17H14O7 69 3 Inactive Inactive Inactive Inactive Inactive Inactive
    NA, not available.
     | Show Table
    DownLoad: CSV

    Rosemary is an evergreen perennial plant that belongs to the family Lamiaceae, previously known as Rosmarinus officinalis. Recently, the genus Rosmarinus was combined with the genus Salvia in a phylogenetic study and became known as Salvia rosmarinus[28,29] and it has been used since ancient times for various medicinal, culinary, and ornamental purposes. In the field of food science, rosemary is well known as its essential oil is used as a food preservative, thanks to its antimicrobial and antioxidant properties, rosemary has many other food applications such as cooking, medicinal, and pharmacology uses[30]. According to the study, certain phytochemical compounds found in Salvia rosmarinus leaves have the potential to halt the growth of cancer cells, and pathogens or even kill them[31]. In literature, alkaloids are found mostly in fungi and are known for their strong antimicrobial properties, which make them valuable in traditional medicine[32,33]. However, in the present study, S. rosmarinus species have been shown to possess alkaloids. Most alkaloids have a bitter taste and are used to protect against antimalarial, antiasthma, anticancer, antiarrhythmic, analgesic, and antibacterial[33] also some alkaloids containing nitrogen such as vincristine, are used to treat cancer.

    Steroids occur naturally in the human body. They are hormones that help regulate our body's reaction to infection or injury, the speed of metabolism, and more. On the other hand, steroids are reported to have various biological activities such as chronic obstructive pulmonary disease (COPD), multiple sclerosis, and imitate male sex hormones[34]. It is a natural steroid compound occurring both in plants and animals[35]. Thus, were found in the present study. Terpenoids are derived from mevalonic acid (MVA) which is composed of a plurality of isoprene (C5) structural units. Terpenoids, like mono-terpenes and sesquiterpenes, are widely found in nature and more than 50,000 have been found in plants that reduce tumors and cancers. Many volatile terpenoids, such as menthol and perillyl alcohol, are used as raw materials for spices, flavorings, and cosmetics[36]. In the present study, high levels of these compounds were found in Salvia rosmarinus leaves.

    Flavonoids are a class of phenolic compounds commonly found in fruits and vegetables and are considered excellent antioxidants[37]. Similarly, the results of this study revealed that S. rosmarinus contain flavonoids. According to the literature, these flavonoids, terpenoids, and steroid activities include anti-diabetic, anti-inflammatory, anti-cancer, anti-bacterial, hepatic-protective, and antioxidant effects[36]. Tannins are commonly found in most terrestrial plants[38] and have the potential to treat cancer, and HIV/AIDS as well as to treat inflamed or ulcerated tissues. Similarly, in the present study, tannins were highly found in the presented plant. On the other hand, due to a sudden rise in the number of contagious diseases and the development of antimicrobial resistance against current drugs, drug development studies are vital to discovering novel medicinal compounds[30] and add to these cancer is a complex multi-gene disease[39] as in various cervical cancer repressor genes[11] that by proteins turn off or reduce gene expression from the affected gene to cause cervical cancer by regulating transcription and expression through promoter hypermethylation (DNMT1), leading to precursor lesions during cervical development and malignant transformation.

    In a previous study[40], a good antibacterial result was recorded at a median concentration (65 μg·mL−1). Methanol extract showed a maximum and minimum zone antibacterial result against negative bacteria E. coli 14 + 0.71 and most of the petroleum ether tests show null zone of inhibition. However, in the present study at a concentration of 100 μg·mL−1, the methanol extract demonstrated both maximum and minimum antibacterial zones against E. coli 11.47 ± 0.50. Conversely, the test conducted with petroleum ether exhibited a good zone of inhibition by increasing concentration. Further research may be necessary to determine the optimal concentration for this extract to maximize its efficacy. The results obtained in gram-negative bacteria such as E. coli, P. aeruginosa, and K. pneumoniae are consistent with previous research findings[41]. However, in the present study, Salvia rosmarinus has been found to possess high zones of inhibition with diameters of 21.37 ± 0.78 and 17.50 ± 0.50 mm antimicrobial properties against S. aureus, and S.epidermidis of gram-positive bacteria, respectively (Table 4 & Fig. 2, Supplementary Fig. S3). According to a previous study[42], the ethanolic leaf extract of Salvia rosmarinus did exhibit activity against C. albicans strains. In the present study, the antifungal activity of petroleum ether extracts from Salvia rosmarinus were evaluated against two human pathogenic fungi, namely C. albicans and A. niger. The findings showed that at a concentration of 100 μg·mL−1, the extracts were able to inhibit the growth of C. albicans 20.83 ± 0.76 resulting in a minimum zone of inhibition.

    Antimicrobial agents can be divided into groups based on the mechanism of antimicrobial activity. The main groups are: agents that inhibit cell wall synthesis, depolarize the cell membrane, inhibit protein synthesis, inhibit nucleic acid synthesis, and inhibit metabolic pathways in bacteria. On the other hand, antimicrobial resistance mechanisms fall into four main categories: limiting the uptake of a drug; modifying a drug target; inactivating a drug; and active drug efflux. Because of differences in structure, etc., there is a variation in the types of mechanisms used by gram-negative bacteria vs gram-positive bacteria. Gram-negative bacteria make use of all four main mechanisms, whereas gram-positive bacteria less commonly use limiting the uptake of a drug[43]. The present findings showed similar activity in chloroform/methanol (1:1) and methanol extracts of leaves of Salvia rosmarinus than gram-negative bacteria like P. aeruginosa and Klebsiella pneumoniae. However, Staphylococcus epidermidis of gram-positive bacteria under chloroform/methanol (1:1) extracts have similarly shown antimicrobial résistance. This occurred due to intrinsic resistance that may make use of limiting uptake, drug inactivation, and drug efflux that need further study. The structure of the cell wall thickness and thinners of gram-negative and gram-positive bacteria cells, respectively when exposed to an antimicrobial agent, there happen two main scenarios may occur regarding resistance and persistence. In the first scenario, resistant cells survive after non-resistant ones are killed. When these resistant cells regrow, the culture consists entirely of resistant bacteria. In the second scenario, dormant persistent cells survive. While the non-persistent cells are killed, the persistent cells remain. When regrown, any active cells from this group will still be susceptible to the antimicrobial agent.

    Ferreira et al.[44] explained that with molecular docking, the interaction energy of small molecular weight compounds with macromolecules such as target protein (enzymes), and hydrophobic interactions and hydrogen bonds at the atomic level can be calculated as energy. Several studies have been conducted showing natural products such as epigallocatechin-3-gallate-3-gallate (EGCG), curcumin, and genistein can be used as an inhibitor of DNMT1[4547] . In the literature micromenic (1) is used for antimicrobial activities and for antibiotic-resistance like methicillin-resistant Staphylococcus aureus (MRSA)[48], and benzocaine (2) is used to relieve pain and itching caused by conditions such as sunburn or other minor burns, insect bites or stings, poison ivy, poison oak, poison sumac, minor cuts, or scratches[49]. However, in the present study, Salvia rosmarinus was used as a source of secondary metabolites (ligands) by using chloroform/methanol (1:1) extract of the plant leaves yielded to isolate micromeric (1) and benzocaine (2) in design structure as a candidate for drugs as inhibitors of the DNMT1 enzyme by inhibiting the activity of DNMT1 that prevent the formation of cervical cancer cells.

    Cervical cancer is one of the most dangerous and deadly cancers in women caused by Human papillomaviruses (HPV). Some sexually transmitted HPVs (type 6 owner of E6) may cause genital warts. There are several options for the treatment of early-stage cervical cancer such as surgery, nonspecific chemotherapy, radiation therapy, laser therapy, hormonal therapy, targeted therapy, and immunotherapy, but there is no effective cure for an ongoing HPV infection. In the present study, Salvia rosmarinus leaves extracted and isolated compounds 1 and 2 are one of the therapeutic drugs design structure as a candidate drug for inhibiting HPV type 16 E6 enzyme. Similarly, numerous researchers have conducted studies on the impact of plant metabolites on the treatment of cervical cancer. Their research has demonstrated that several compounds such as jaceosidin, resveratrol, berberin, gingerol, and silymarin may be active in treating the growth of cells[47].

    Small-molecule drugs are still most commonly used in the treatment of cancer[50]. Molecular docking in in silico looks for novel small-molecule (ligands) interacting with genes or DNA or protein structure agents which are still in demand, newly designed compounds are required to have a specific even multi-targeted mechanism of action to anticancer and good selectivity over normal cells. In addition to these, in the literature, anti-cancer drugs are not easily classified into different groups[51]. Thus, drugs have been grouped according to their chemical structure, presumed mechanism of action, and cytotoxic activity related to cell cycle arrest, transcription regulation, modulating autophagy, inhibition of signaling pathways, suppression of metabolic enzymes, and membrane disruption[52]. Another problem for grouping anticancers often encountered is the resistance that may emerge after a brief period of a positive reaction to the therapy or may even occur in drug-naïve patients[50]. In recent years, many studies have investigated the molecular mechanism of compounds affecting cancer cells and results suggest that compounds exert their anticancer effects by providing free electron charge inhibiting some of the signaling pathways that are effective in the progression of cancer cells[53] and numerous studies have shown that plant-based compounds such as phenolic acids and sesquiterpene act as anticancer agents by affecting a wide range of molecular mechanisms related to cancer[53]. The present investigations may similarly support molecular mechanisms provided for the suppression of metabolic enzymes of cervical cancer.

    The main aim of the study was to evaluate the antimicrobial activity of different extracts of Salvia rosmarinus in vitro, and its compounds related to in silico targeting of enzymes involved in cervical cancer. The phytochemical screening tests indicated the presence of phytochemicals such as alkaloids, terpenoids, flavonoids, and tannins in its extracts. The plant also exhibited high antimicrobial activity, with varying efficacy in inhibiting pathogens in a dose-dependent manner (50−100 μg·mL−1). However, this extract exhibited a comparatively high inhibition zone in gram-positive and gram-negative bacteria had lower inhibition zones against E. coli, P. aeruginosa, and K. pneumoniae, respectively, and stronger antifungal activity 20.83 ± 0.76 mm inhibition zone against C. albicans fungi. Molecular docking is a promising approach to developing effective drugs through a structure-based drug design process. Based on the docking results, the in silico study predicts the best interaction between the ligand molecule and the protein target DNMT1 and HPV type 16 E6. Compound 1 (–8.3 kcal·mol−1) and 2 (–5.3 kcal·mol−1) interacted with DNMT1 (PDB ID: 4WXX) and the same compound 1 (–10.1 kcal·mol−1) and 2 (–6.5 kcal·mol−1) interacted with HPV type 16 E6 (PDB ID: 4XR8). Compounds 1 and 2 may have potential as a medicine for treating agents of cancer by inhibiting enzymes DNMT1 and HPV type 16 E6 sites, as well as for antimicrobial activities. None of the compounds exhibited acute toxicity in ADMET prediction analysis, indicating their potential as drug candidates. Further studies are required using the in silico approach to generate a potential drug through a structure-based drug-designing approach.

  • The authors confirm contribution to the paper as follows: all authors designed and comprehended the research work; plant materials collection, experiments performing, data evaluation and manuscript draft: Dejene M; research supervision and manuscript revision: Dekebo A, Jemal K; NMR results generation: Tufa LT; NMR data analysis: Dekebo A, Tegegn G; molecular docking analysis: Aliye M. All authors reviewed the results and approved the final version of the manuscript.

  • All data generated or analyzed during this study are included in this published article.

  • This work was partially supported by Adama Science and Technology University under Grant (ASTU/SP-R/171/2022). We are grateful for the fellowship support from Adama Science and Technology University (ASTU), the identification of plants by Mr. Melaku Wendafrash, and pathogenic strain support from the Ethiopian Biodiversity Institute (EBI). We also thank the technical assistants of the Applied Biology and Chemistry departments of Haramaya University (HU) for their help.

  • The authors declare that they have no conflict of interest.

  • Supplemental Table S1 Mean intensity values of the ten attributes of the Citrus Pu-erh tea samples during sensory evaluation.
    Supplemental Table S2 Mean intensity values of the taste of the Citrus Pu-erh tea samples using electronic tongue.
    Supplemental Table S3 Standard curves of aroma-active compounds of Citrus Pu-erh tea samples.
    Supplemental Fig. S1 Results of multivariate statistical analysis of untargeted metabolomics.
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  • Cite this article

    Sun J, Cai W, Feng T, Chen D, Lu J, et al. 2024. Revealing the flavor profile of citrus Pu-erh tea through GC-MS-O and untargeted metabolomics. Beverage Plant Research 4: e009 doi: 10.48130/bpr-0024-0001
    Sun J, Cai W, Feng T, Chen D, Lu J, et al. 2024. Revealing the flavor profile of citrus Pu-erh tea through GC-MS-O and untargeted metabolomics. Beverage Plant Research 4: e009 doi: 10.48130/bpr-0024-0001

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Revealing the flavor profile of citrus Pu-erh tea through GC-MS-O and untargeted metabolomics

Beverage Plant Research  4 Article number: e009  (2024)  |  Cite this article

Abstract: Chinese citrus Pu-erh tea is recognized for its unique flavor, which is composed of key aroma-active compounds and affected by taste-impact metabolites. In this study, the whole citrus Pu-erh tea (CP), its out-layer fruit (OF) container and inside tea (IT) powder, were analyzed by solvent-assisted flavor evaporation (SAFE) coupled with GC-MS-O and UHPLC-MS/MS. As a result, 47 important volatiles were identified, including 27 (IT), 30 (OF) and 27 (CP) volatiles that were screened out based on their odor activity value (OAV) and aroma character impact value (ACI), and further validated by aroma omission/recombination experiment. Combined with the sensory evaluation and PLSR model, the aroma profile of CP was characterized with the following ten flavor attributes: sweet (vanillin); floral (β-ionone); fruity (methyl anthranilate, methyl methanthranilate, citronellal); roasted (thymol); musty (p-cymene), woody (perillaldehyde); herbal (linalool, α-terpineol); phenolic (2,4-di-tert-butylphenol, p-cresol); minty (dihydrocarvone); and fatty (octanoic acid) volatiles. As for the non-volatile taste-impact chemicals, the most prominent metabolites were identified as flavonoids that mainly contributed to the taste of bitter (catechin, epicatechin, gallocatechin), astringency (leucopelargonidin) and sweet (neohesperidin). This novel finding has provided an insight and better understanding of the aroma profile of citrus Pu-erh tea and some guidance for flavor pairing and taste improvement.

    • Citrus Pu-erh tea (CPT), also known as Ganpu tea, is a novel type of citrus blend-tea that is co-fermented with citrus peel (Citrus reticulata Blanco cv. Chachiensis) and Pu-erh tea (Camellia sinensis var. assamica)[1]. The earliest record of tea processed with citrus peel appeared 1,400 years ago in the Tang Dynasty[2]. Nowadays, thousands of enterprises have been involved in the production of citrus tea which was tailored to satisfy consumers' flavor desires along with the emergence of food-pairing hypothesis[3]. The citrus Pu-erh tea which was made by peel produced between August and September in Xinhui District of Guangdong, China, and the ripened Pu-erh tea produced in Yunnan, China so-called 'Xiao Qing Gan' is the most popular, and is familiar with most consumers[4,5]. However, few systematic studies have been conducted on its flavor.

      The mellow taste and hedonistic aroma of citrus Pu-erh tea were generated during stuffing fermented tea to citrus pericarps and then redrying them together[2,6]. During the sun-drying and fermentation process, numerous enzymes and compounds were catalyzed to transform in the citrus peels, producing specific compounds with a fruity aroma[4]. For example, flavonoid glycosides such as hesperidin and phenolic acids were usually considered to be the critical flavor contributors[2,7]. Meanwhile, the degradation, oxidation, glycosylation and other reactions that occur in Pu-erh tea under conditions of high humidity and temperature with microorganisms also lead to the generation of volatile compounds[7]. The previous results have investigated the impact of different citrus species on CPT and showed that the fundamental odorants associated with the aroma of citrus blend black teas were mainly the outstanding combination of heptanal, limonene, linalool, and trans-β-ionone[8,9]. Wang et al. also demonstrated that an interaction of various volatiles originating from white tea and citrus occurred and significantly changed the properties of their olfactory properties[10]. Thus, the tangy aroma of the citrus peel is a perfect match for the mellow earthiness of Pu-erh tea, resulting in its attractive aroma.

      Flavor wheel had a strong advantage for revealing the flavor characteristics of samples. Flavor wheel is a quantitative analysis tool that standardizes the quality of a sample, and flavor descriptions are organized according to categories and arranged in a disc-shaped frame that is systematic yet concise and clear[11,12]. Flavor is affected by both aroma (volatiles) and taste (non-volatiles)[13]. In 1987, Suffet et al. created the first drinking water flavor wheel containing both olfactory and gustatory descriptions to represent the diversity of odor and aroma qualities[14], while the Specialty Coffee Association of America (SCAA) developed the first flavor wheel in 1995, which has now been updated to a more detailed and comprehensive flavor wheel with three levels and nine categories[15]. The establishment and updating of flavor wheel can enable professional sensory evaluation groups or amateur consumers to have more standards and bases for judging when conducting sensory evaluations and favoritism tests[16,17], which shows that flavor wheel is instructive for the industry in new product research and development, while for academic research, it has a great advantage in revealing the composition of aroma and odor characteristics. In addition, with the flavor wheel providing the basic framework for the formation of the aroma and odor of that sample, and a rough grasp of the compounds corresponding to each kind of flavor, it can shed some light on food cooking and even flavor pairing[18,19].

      Based on this, in this paper, the solvent-assisted flavor evaporation (SAFE) with high recovery rate was used to extract the three different parts of the CPT (CP, OF, IT), which was combined with the GC-MS-O method to analyze the volatile chemical components. And then the untargeted metabolomics by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) is to study its non-volatile compounds, in order to identify the different metabolic pathways and metabolites affecting the flavor of our CPT. After obtaining the key volatile compounds and non-volatile compounds, a more complete flavor wheel was drawn by combining both of them with the sensory evaluation results for co-analysis, so as to reveal the crucial compounds affecting the flavor of CPT brewing and the differences among the three parts of CPT. This study will significantly increase the knowledge of CPT infusion flavors and provide insights into manufacturing techniques for CPT qualities that will enable companies to improve the quality of blended tea in the future to meet the marketing standards of the products in question. It will also provide data for future flavor matching practices.

    • Citrus Pu-erh tea samples which were processed by full-sunlight withering and have been stored for five years were purchased from local tea plantations in Jiangmen City (Guangdong, China) in October 2021. In these samples, the citrus peel of Xiao Qing Gan refers to the citrus peel produced in Xinhui, Guangdong province, while Pu-erh tea refers to the sun dried green raw tea of Yunnan large leaf tea. The purchased samples were vacuum-packed and stored at room temperature until usage. Before the chemical analyses, the sample was divided into three parts: out-layer fruit (OF) peel, inside tea (IT) powder, and whole citrus Pu-erh tea (CP). Samples were ground into small particles and passed through 30 mesh filter. The infusion was prepared by the methods reported earlier[20]. Each sample was taken by 5 g and mixed with 100 mL of boiling water for 5 min to take the 1st round of extract, the residues were repeated by mixing with 75 mL of boiling water at two time intervals (3 min and 2 min respectively). Then, the three rounds of each infusion (extract) were combined after filtration and cooled in an ice bath for subsequent instrumental analysis and sensory evaluation.

      The preparation of metabolites was as follows: Each powdered samples (i.e., CP, IT, OF) in 0.05 g constant weight was respectively mixed with 400 μL of methanol-acetonitrile (1:1) (v:v) solvent in a 2 mL centrifuge tube. After the mixture was ground for 6 min (−10 °C, 50 kHz) and extracted using low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz), the extract was placed at −20 °C for 30 min and then centrifuged for 15 min (13,000 g, 4 °C) to precipitate the dissolvable residues. Finally, the supernatant was taken for analysis.

    • The following authentic standards were commercially purchased, including (+)-limonene (≥ 99%), γ-terpinene (≥ 95%), (+)-dihydrocarvone (98%), (E)-p-mentha-2,8-dien-1-ol (98%), (−)-pinocarveol (98%), (+)-carvone (≥ 98%), perillaldehyde (98%) from Sigma-Aldrich (Shanghai, China). 2,3-dimethyl pyrazine (98%), nonanal (95%), phenol (≥ 99%), 2-pyrrolaldehyde (98%), carvacrol (99%), methyl anthranilate (99%), 2,4-di-tert-butylphenol (97%) from TCI (Shanghai, China). 2,4-dimethyl styrene (98%), piperitone (> 94%), p-cymene (99%), prenol (≥ 99.5%), decanal (97%) and limonene glycol (98%) from Aladdin (Shanghai, China). (Z)-carveol (97%), p-cymenol (99%), linalool (≥ 98%), perillalcohol (≥ 98%) from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). 2-ethylhexanol (99%), β-pinene (95%), methyl methanthranilate (98%), palmitic acid (99%), benzophenone (99.5%), dimethyl sulfone (99%) and 1,2-dimethoxybenzene (98%) from Merck (Darmstadt, Germany). β-ionone (98%), citronellal (≥ 98%) and vanillin (99.5%) from Yuanye Bio-Technology Co., Ltd (Shanghai, China). 4-methoxyacetophenone (99%), styrene (> 99.5%), o-Cresol (≥ 99.7%), p-Cresol (≥ 99.7%), terpinen-4-ol (≥ 98%), (−)-carveol (97%) and α-terpineol (≥ 96%) from Macklin (Shanghai, China). Octanoic acid (≥ 99.9%), thymol (> 99%), lauric acid (98%), benzaldehyde (> 99%), benzyl alcohol (≥ 99%) and phenylethyl alcohol (≥ 99%) from Boer (Shanghai, China).

      C7−C30 (n-alkanes) and 1,2-dichlorobenzene (internal standard) were purchased from Sigma-Aldrich (Shanghai, China). Dichloromethane, anhydrous sodium sulfate, acetone and sodium chloride were obtained from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). All reagents were of analytical grade.

    • Isolation of the volatiles was performed according to a typically adopted method[21]. For each sample, 100 mL of infusion and 100 μL of 1,2-dichlorobenzene (100 mg/kg, solved in acetone) were mixed and extracted with dichloromethane (3 × 100 mL) at 200 rpm using a magnetic stirrer (85-1, Shanghai Meiyingpu Instrument Manufacturing Co., Ltd., Shanghai, China) at room temperature for 3 h (3 × 1 h). Afterwards, all of the organic phase extract was combined, filtered, and concentrated to 150 mL using a rotary evaporator (RE 52-86A, Shanghai Yarong Biochemical Instrument Factory, Shanghai, China), then poured into a 500 mL distillation flask of SAFE apparatus (Glasbläserei Bahr, Manching, Germany) under a 40 °C water bath and 5 × 10−5 mbar vacuum to concentrate the organic phase, and separate the volatiles from the nonvolatile fraction. Each extract of SAFE was further concentrated to 5 mL by a rotary evaporator under 40 °C and finally concentrated to 1 mL by a nitrogen stream. The concentrate was immediately analyzed in Dr Feng's lab.

    • A gas chromatograph 6890A equipped with a 5975C mass selective detector (Agilent Technologies, Santa Clara, CA, USA) and an ODP-3 olfactory detection port (Gerstel, Mühlheim an der Ruhr, Germany) was used for flavor analyses. Separation of the volatiles was performed using two fused silica capillaries: HP-INNOWAX and HP-5 (both 60 m × 0.25 mm i.d., 0.25 μm film thickness; Agilent Technologies, USA) at a flow rate of 1.0 mL/min. The flow split ratio at the end of the column was 1:1 between the detector and olfactory port. The SAFE extract (2 μL) was injected into the injection port at 250 °C in a splitless mode. The oven temperature was programmed at 40 °C (held for 3 min), ramped at 5 °C /min to 100 °C (held for 1 min), then ramped to 180 °C at a rate of 3 °C /min, and ramped to 230 °C at a rate of 4 °C /min and held at 230 °C for 5 min. Mass spectrometer condition was set at electron ionization (EI) mode with the ionization energy of 70 eV and the ion source temperature of 230 °C. The scan range was 30−450 m/z in full-scan mode.

      Five trained panelists (two males and three females, aged 23−42 years, nonsmokers) were selected for GC-O analysis. The retention times (RTs), odor attributes smelled from the sniffing port, and aroma intensities (AIs) were recorded. The AIs were evaluated on a five-point intensity scale: 0 (none), 3 (moderate) and 5 (strong). Each sniff was performed in triplicate and consisted of two evaluation sessions for the compounds that were eluted between 0−30 min and 31−60 min to minimize nasal discomfort and fatigue[22].

    • An UHPLC-Q Exactive HF-X system was used to separate and analyze the metabolites. An ACQUITY HSS T3 (100 mm × 2.1 mm i.d., × 1.8 μm; Waters, Milford, USA) column was used for chromatographic separation of the metabolites. Mobile phase A was composed of 95% water and 5% acetonitrile (containing 0.1% formic acid), and mobile phase B was composed of 47.5% acetonitrile, 47.5% isopropanol and 5% water (containing 0.1% formic acid). The injection volume was 3 μL, and the column temperature was set at 40 °C. The details of the gradient elution procedure and the experimental parameters were the same as a previous study[23].

    • Identification of the volatiles was based on mass spectra compared with NIST Mass Spectral Library 11 Vision; standard chemicals; odor descriptions of authentic, and retention indices (RI) with reference values (https://webbook.nist.gov/chemistry/). The retention indices (RIx) of detected chemicals was calculated as below: RIx = 100Z + 100[(lg tx − lg tz)/(lg tz+1 − lg tz)], where Z is the number of carbon atoms of n-alkane which appears in front of the identified compound in the same GC condition; tx, tz and tz+1 are the retention time of the identified compounds, the lower alkane, and upper alkanes, respectively[24].

      Quantification of the volatiles was calculated according to the standard curves. Firstly, three infusion samples were extracted by dichloromethane until achieving each odorless matrix. The standard chemicals were then dissolved and diluted with the artificial odorless matrix at a concentration ranging from 50 to 30,000 mg/L for six levels (1:5, 1:25, 1:100, 1:250, 1:500 and 1:1,000). Each standard chemical matrix (100 mL) with 100 μL of 1,2-dichlorobenzene was extracted by the SAFE procedure and finally analyzed by GC-MS (As described above). Calibration curves were constructed by the following formula[24]: Ax/Ai = a(Cx/Ci) + b. A and C represent the peak area and the concentration, while x and i represent the authentic compound and internal standard, respectively. The concentration of each volatile compound was calculated based on the calibration equation above. The result was an average of three replicates. The limit of detection (LOD) and the limit of quantitation (LOQ) was defined as the concentration of a standard compound whose signal-to-noise (S/N) ratio was 3 and 10, respectively[25].

    • Each concentrated original solution of SAFE was stepwise diluted with dichloromethane for proportions of 1:2; 1:4; 1:8 … 1:2n and submitted to GC-O analysis under the same GC conditions as described above using an HP-INNOWAX column. The maximum dilution factor of a sample (2n) in which no odorant could be detected by GC-O was defined as the flavor dilution factor (FD)[21]. The larger FD values indicate the greater contribution of the aroma compound to the overall aroma.

    • Odor activity value (OAV) was calculated as the ratio of the concentration to the odor detection threshold in water. Aroma character impact value (ACI) is the fraction of the sum of OAV for individual compounds in a mixture, which can further estimate the aroma contribution of individual components[26]. It is calculated as the following formula: ACI% = (Pi/Ti)/(∑Pk/Tk), where ∑P is the sum of concentration percentage of all compounds, T is the odor threshold of the compounds in the water[27].

    • The sensory evaluation of the three infusion samples was performed using quantitative descriptive analysis. The sensory evaluation procedures were carried out according to Wang et al. with slight modification[28]. Thirty-five healthy and non-smoking assessors were recruited from the students and staff members of the School of Perfume and Aroma Technology (Shanghai Institute of Technology, Shanghai, China). A panel of 10 well-trained panelists (five males and five females with age ranging from 20−42 years) were selected for their familiarity with three infusion samples based on the enforced triangle test. Before sensory evaluation, all panelists were trained about the characteristics of infusion samples and the sensory evaluation requirements (such as the definition of quality attributes and the method of scoring) for more than 2 h per day and lasted a week to familiarize them with the descriptive terms of the infusion. Thereafter, the vocabulary of CPT infusion samples' sensory attributes was generated. In addition, the panelists were trained to reach consensus on rating the intensity of the ten defined aroma attributes, including 'sweet', 'minty', 'fruity', 'woody', 'fatty', 'phenolic', 'roasted', 'floral', 'herbal', and 'musty' which were identified using reference compounds of vanillin, dihydrocarvone, prenol, perillaldehyde, octanoic acid, 2,4-dimethyl styrene, 2-pyrrolaldehyde, benzophenone, 2-ethylhexanol, and p-cymene, respectively. Each sensory attribute was taken on a 10-point intensity scale (0−3, weak; 4−6, middle; 7−9, strong). To validate the reliability of the intensity scale, the recorded data of repeated panel performances were compared using different means of the analysis of variance (ANOVA).

      The sensory analysis was performed at room temperature under daylight with individual booths. Before sensory evaluation, the infusion samples were presented in plastic cups labeled with randomly selected three-digit numbers. The assessors were asked to take three short sniffs to sense the aroma of the samples first and to rinse their mouths with pure water to minimize any residual effect. Each sample was evaluated in triplicate and carefully scored after sensory judgment.

    • The E-tongue (TS-5000Z, Insent Inc., Japan) comprises lipid membrane sensors of basic tastes (umami, sourness, sweetness, saltiness, bitterness, astringency) and corresponding aftertaste (aftertaste-astringency, aftertaste-bitterness and richness) was used. The sensors were conditioned by a conditioning, calibration and diagnostic process before the analysis. Reference solution (30 mM KCl and 0.3 mM tartaric acid aqueous) and washing solution (30% ethanol adding 100 mM hydrochloric acid for the negatively charged sensors; 100 mM potassium chloride and 10 mM potassium hydroxide for the positively charged sensors.) were prepared[29]. Three measurement phases were performed as follow: sample detection (120 s), aftertaste detection (40 s), and washing (10 s). The average taste strength value from 110 to 120 s during sample detection was calculated to be the final result. Each sample was measured in triplicate, and each tea infusion was measured four times[30].

    • The statistical data from GC-MS was analyzed by Microsoft Excel 2019 (Microsoft, Redmond, WA, USA) and expressed as mean ± standard deviation (SD). The descriptive analysis data was evaluated by SPSS version 26.0 (SPSS Inc., Chicago, IL, USA) and performed one-way analysis of variance (ANOVA). The significant differences (p ≤ 0.05) among individual samples for each aroma attribute were identified by the SNK test. Other figures were made by Origin Pro 2021 (OriginLab Corporation, Northampton, MA, USA). The correlations between sensory attributes and volatile compounds were analyzed by PLSR using the Simca 14.1 (Umetrics, Sweden). The identification of metabolites was based on biochemical databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) (www.genome.jp/kegg). The data were analyzed on the online platform of Majorbio Cloud Platform. Heatmap and bubble diagram were employed using the scipy (Version 1.0.0) Python on the Majorbio Cloud Platform (https://cloud.majorbio.com).

    • The sensory evaluation of the three samples (CP, IT, OF) showed significantly different results (p ≤ 0.01 or p ≤ 0.001) (Supplemental Table S1). As shown in Fig. 1a, the CP sample had more prominent aromas in characteristics of phenolic (7.75), fatty (6.00) and minty (4.75) flavors, the IT sample had more roasted (7.25), musty (8.00) and herbal (7.25) aromas, and the OF sample outperformed the other two samples in woody (3.00), fruity (3.25), and floral (7.00) aroma attributes. However, these three samples had similar scores in the aroma of fruity attribute (IT = 2.75, OF = 3.25, CP = 2.25, p ≤ 0.01).

      Figure 1. 

      Sensory spider plot of three CPT infusion samples, (a) sensory evaluation of three CPT samples based on ten aroma attributes, (b) taste profiles of three CPT samples by E-tongue. Note: *, ** and *** significant at p ≤ 0.05, p ≤ 0.01 and p ≤ 0.001.

      Figure 1b showed the significant difference (p ≤ 0.01) in the taste attributes of three CPT infusion samples according to the data of Supplemental Table S2 analyzed by E-tongue. Except for the sweetness and sourness, all the other seven attributes showed the highest scores in the IT sample and the lowest scores in the OF sample. This indicates that the taste attributes' intensity of CP sample was moderated by the IT and OF. For the score of sweetness attribute, it seems there is a synergistic effect between the OF and IT, making the CP samples with the highest score in sweetness. Overall, the differences in the scores of the aromas and taste attributes among the three CPT samples were obvious, which could be clearly distinguished even after years of co-fermentation. Thus, the differences in their specific substances deserve further study.

    • Based on the four identification methods (Table 1), 47 volatile compounds were identified, including six alkenes, six phenols, 14 alcohols, seven aldehydes, six ketones, two esters, three acids, and three others. The aroma descriptions and FD factors of aroma-active compounds determined by AEDA were also listed (Table 1). Compounds with low FD factors are assumed to be less or not important for odor impressions[22]. The results showed that the FD factor of one compound varied greatly from sample to sample. For example, 2,4-dimethyl styrene (FD = 2,048), α-terpineol (FD = 1,024), p-cymenol (FD = 128), 2-ethylhexanol (FD = 2,048), and phenylethyl alcohol (FD = 16) were determined in the IT sample, but those compounds had no or much lower FD factors in the CP and OF samples. This indicates that these compounds contributed more to the aroma profile of the IT samples. Similarly, benzyl alcohol (FD = 2,048), nonanal (FD = 64), and dihydrocarvone (FD = 16) showed higher FD values in the OF sample than in IT and CP samples. Nevertheless, some compounds with larger FD values in CP, such as p-cymene (1,024), carvacrol (1,024), thymol (1,024), linalool (8), (+)-carvone (1,024), methyl methanthranilate (2,048), and methyl anthranilate (1,024), also showed higher values both in IT and OF samples. The above results were initially obtained by the AEDA sniffing method to screen the key aroma-active compounds of each sample, which are needed for validation.

      Table 1.  Identification analysis of volatile compounds in citrus Pu-erh tea samples.

      No.CompoundsRIaAroma descriptionbFDcIMd
      HP-INNOWAXHP-5MS
      cal.ref.cal.ref.ITOFCP
      Alkenes
      A1styrene12951272890sweet, balsam, floral(acacia)21MS, RI, S, O
      A2β-pinene11351115978woody, pine, hay, green2MS, RI, S, O
      A3(+)-limonene12321001citrus, herbal, sweet444MS, RI, S, O
      A4γ-terpinene127712551064oily, woody, lime, herbal1MS, RI, S, O
      A5p-cymene1305128011031026musty, woody, spice102410241024MS, RI, S, O
      A62,4-dimethyl styrene1459143310761078phenolic, spicy, soil, plastic2048642MS, RI, S, O
      Phenols
      B1phenol20372028981phenolic, plastic, rubber24MS, RI, S, O
      B2o-cresol199820101060musty, phenolic, herbal, leathery11MS, RI, S, O
      B3p-cresol2950207910971098phenolic, floral(narcissus)8MS, RI, S, O
      B4carvacrol2226222512961307spice, woody, phenolic204810241024MS, RI, S, O
      B5thymol2239217212921297herbal, phenolic, roasted102410241024MS, RI, S, O
      B62,4-di-tert-butylphenol2323233015121513phenolic20482048MS, RI, S, O
      Alcohols
      C1prenol13401323778fruity, green, floral(lavender)24MS, RI, S, O
      C2linalool1553154911011104herbal, green, floral(rose),1688MS, RI, S, O
      C3α-terpineol1714117011901191pine, citrus, woody, floral(lilac)102451232MS, RI, S, O
      C4Z-carveol1842186912201220vegetable,green, caraway1684MS, RI, S, O
      C5p-cymenol1868185111851188sweet, fruity(cherry), camphor12884MS, RI, S, O
      C6(-)-carveol1884184612311225minty, green, herbal, spicy44MS, RI, S, O
      C7limonene glycol2298232513451342minty, roasted,211MS, RI, S, O
      C8perillalcohol201820211300spicy(cardamom), floral(violet)121MS, RI, S, O
      C9benzyl alcohol190318851034floral(rose), phenolic20482MS, RI, S, O
      C102-ethylhexanol149614901026citrus, fresh, floral, oil, sweet20484MS, RI, S, O
      C11terpinen-4-ol160216361177herbal, woody, earthy, musty43264MS, RI, S, O
      C12(-)-pinocarveol1679166611371140warm, woody, fennel, cereal42MS, RI, S, O
      C13phenylethyl alcohol194019231121floral(rose)16MS, RI, S, O
      C14(E)-p-mentha-2,8-dien-1-ol1645164111201121fatty, popcorn, minty441024MS, RI, S, O
      Aldehydes
      D1vanillin2615255013971394sweet(chocolate), creamy14MS, RI, S, O
      D2nonanal141013961102waxy, fatty, orange64MS, RI, S, O
      D3decanal151315041195sweet, waxy, citrus(orange), floral4MS, RI, S, O
      D4citronellal202314881158sweet, floral, herbal, waxy, citrus4MS, RI, S, O
      D5benzaldehyde15561529961almond, fruity(cherry)168MS, RI, S, O
      D6perillaldehyde1814180712761279woody, pine, sweet(balsam), minty3216MS, RI, S, O
      D72-pyrrolaldehyde2065204810131015musty, beefy, burnt, roasted, smoky10241024MS, RI, S, O
      Ketones
      E1dihydrocarvone1636164511981200herbal, minty, rubber, rice168MS, RI, S, O
      E2β-ionone1964195314851490powdery, floral(orris), woody8MS, RI, S, O
      E3piperitone1758174312661268woody, minty, camphor84MS, RI, S, O
      E4(+)-carvone1764174412441245minty, fruity, spice102420481024MS, RI, S, O
      E5benzophenone253325051625floral (rose, geranium)442MS, RI, S, O
      E64-methoxyacetophenone2106212013411345fatty, sweet, anisic22MS, RI, S, O
      Esters
      F1methyl methanthranilate2100206814081402fruity, musty, sweet204820482048MS, RI, S, O
      F2methyl anthranilate228322571338floral (orange flower), fruity(grape)102420481024MS, RI, S, O
      Acids
      G1octanoic acid203320701191fatty, waxy, rancid, oily, green, cheesy214MS, RI, S, O
      G2lauric acid248925021570fatty, fruity(coconut), oily22MS, RI, S, O
      G3palmitic acid2512289019681964phenolic, waxy, fatty24MS, RI, S, O
      Others
      H1dimethyl sulfone19441912915roasted, sulfurous, burnt222MS, RI, S, O
      H22,3-dimethyl pyrazine13731352911nutty, butter, coffee, caramel, roasted4MS, RI, S, O
      H31,2-dimethoxybenzene1743174011451143musty, creamy, phenolic, sweet21MS, RI, S, O
      a Retention index of compounds on an HP-INNOWAX column and HP-5MS column. Cal means the RI value calculated by the formula. Ref means the RI value confirmed by comparison retention index to reference standards in the same condition (https://webbook.nist.gov/). b Aroma description. The aroma description vocabulary was generated by the GC-O evaluation team by comparing the aroma characteristics at actual concentrations with the literature and spectral library descriptions. c FD factor, flavor dilution factor determined on a HP-INNOWAX column. '−' means not being detected. d Identification method: MS means identified by comparison with the NIST mass spectral library 11 Vision database; RI means confirmed by comparison retention index; S means confirmed by authentic standard chemicals; O means confirmed by aroma descriptor.

      The concentration of each individual compound was determined by its standard curve (Supplemental Table S3) and listed in Table 2. Figure 2a presents the chemical profiles of the 47 compounds. There was a same trend in categories of alcohols, esters, ketones and alkenes, with the lowest percentage levels in the CP samples among the three samples. Alcohols, including straight-chain and branched alcohols derived from the reduction of Strecker aldehydes or the hydrolysis of glycoside precursors were regarded as the third largest group of volatiles detected in teas[13]. Under the influence of citrus peel, it occupies the largest proportion among the detected compounds in the three samples, with a percentage of 45% (IT), 44% (OF), and 34% (CP). Alkenes are the most important volatile components in citrus fruits[10]. Phenols (27%) and acids (10%) had the highest percentage levels in the CP samples among all three samples. As shown in Fig. 2b, the color coding changed from green to gray, reflecting the chemical concentration decreasing from high to low levels. The concentration of the same compound varied considerably among different samples, which also reflected the differences of the FD factors of compounds in different samples in the qualitative results mentioned above.

      Table 2.  Quantitative analysis of volatile compounds in citrus Pu-erh tea samples.

      No.CompoundsOT (mg/kg)AConcentration (mg/kg)BOAVCACI%D
      ITOFCPITOFCPITOFCP
      A1styrene0.06526.54a8.29b408.26127.490.05260.0041
      A2β-pinene0.144.09a29.240.0038
      A3(+)-limonene0.034233.62a159.49b120.07c6871.154691.003531.550.88520.15130.9724
      A4γ-terpinene137.35a37.350.0012
      A5p-cymene7.294.59a80.77b76.74c13.1411.2210.660.00170.00040.0029
      A62,4-dimethyl styrene0.08516.71c30.69b56.52a196.62361.01664.970.02530.01160.1831
      B1phenol5179.14a150.89b35.8330.180.00460.0083
      B2o-cresol1.419.59b20.07a14.0014.340.00180.0039
      B3p-cresol0.003983.12a21311.635.7137
      B4carvacrol2.291070.68c1413.74b2900.15a467.54617.351266.440.06020.01990.3487
      B5thymol1.71215.72a590.69c1011.75b715.13347.46595.140.09210.01120.1542
      B62,4-di-tert-butylphenol0.5120.60b955.01a241.201910.020.00780.5259
      C1prenol0.251.78b2.88a7.1211.540.00090.0004
      C2linalool0.00022150.37a69.68b58.20c683488.63316718.51264532.6988.048210.215172.8359
      C3α-terpineol1.21139.07a883.02b694.31c949.23735.85578.590.12230.02370.1593
      C4Z-carveol0.251732.26c2991.85a2243.32b6929.0411967.408973.280.89260.38602.4707
      C5p-cymenolND607.73b713.37a445.52c
      C6(-)-carveol0.25285.04a274.93b1140.161099.720.03680.3028
      C7limonene glycolND610.42a148.96c474.19b
      C8perillalcohol1.1169.07b279.38a147.65c153.70253.98134.230.01980.00820.0370
      C9benzyl alcohol2.5469.75a48.40b27.4619.050.00090.0052
      C102-ethylhexanol0.3333.98a42.32b1113.25141.070.14340.0045
      C11terpinen-4-ol1.2282.27a220.39b155.74c235.22183.66129.780.03030.00590.0357
      C12(-)-pinocarveolND93.72a78.28b
      C13phenylethyl alcohol0.086177.55a2064.560.2660
      C14(E)-p-mentha-2,8-dien-1-olND1658.63b1349.01c1829.85a
      D1vanillin0.053402.51a356.40b7594.586724.470.24491.8515
      D2nonanal0.001128.40a25817.100.8327
      D3decanal0.00354.60a18200.572.3446
      D4citronellal0.00658.38a9729.280.3138
      D5benzaldehyde0.7598.19a23.93b130.9231.910.01690.0010
      D6perillaldehyde0.03134.70a75.84b4490.112528.150.14480.6961
      D72-pyrrolaldehyde65225.12a147.44b3.462.270.00040.0006
      E1dihydrocarvone3.2582.27b128.87a25.3139.650.00080.0109
      E2β-ionone0.00000718.17a2595440.1383.7102
      E3piperitone0.6855.93a28.08b82.2541.290.00270.0114
      E4(+)-carvone0.161285.06b1406.29a1215.37c8031.618789.327596.061.03460.28352.0915
      E5benzophenoneND149.77c591.43a157.09b
      E64-methoxyacetophenoneND76.68b94.61a
      F1methyl methanthranilate0.3492611.54b2902.11a2352.80c7482.938315.506741.560.96400.26821.8562
      F2methyl anthranilate0.003116.02b307.63a105.30c38672.96102541.9335101.204.98193.30739.6647
      G1octanoic acid327.78b4.16c64.19a9.261.3921.400.00120.00000.0059
      G2lauric acid10175.08b431.46a17.5143.150.00230.0119
      G3palmitic acid10337.30b1437.30a33.73143.730.00110.0396
      H1dimethyl sulfoneND243.11b145.86c492.16a
      H22,3-dimethyl pyrazine0.820.18a25.230.0033
      H31,2-dimethoxybenzeneND389.68a92.04b
      A The odor detection thresholds in water were obtained from previous studies[12,45] and online database (www.vcf-online.nl/VcfHome.cfm). B Concentration (mg/kg), The concentration of each volatile compound was calculated based on the calibration equation in Supplemental Table S3. C OAV (Odor activity value). D ACI (Aroma character impact value). All results were expressed as mean value (n = 3). Values bearing different lowercase letters (a, b, c) were significantly different (p ≤ 0.05).

      Figure 2. 

      Distribution map of volatile aroma substances, (a) species distribution profile of volatile compounds, (b) concentration distribution of each volatile compounds in three CPT infusion samples.

    • Odor activity value (OAV) and aroma character impact value (ACI) were calculated for quantitative assessment of the contribution of key aroma-active compounds to the overall aroma for a particular sample (Table 2)[27,31]. OAV ≥ 1 were considered to contribute significantly to the overall aroma of the samples[32]. The number of compounds with OAV values greater than one was 27, 30 and 27 for IT, OF and CP, respectively. The top ten key aroma-active compounds in the CP sample were linalool (OAV = 264,532), methyl anthranilate (OAV = 35,101), p-cresol (OAV = 21,311), Z-carveol (OAV = 8,973), (+)-carvone (OAV = 7,596), methyl methanthranilate (OAV = 6,741), vanillin (OAV = 6,724), (+)-limonene (OAV = 3,531), perillaldehyde (OAV = 2,528), 2,4-di-tert-butylphenol (OAV = 1,910). Notably, compounds with high concentration does not necessarily have a high OAV value, which is determined by its odor threshold. Although the content of linalool was far below than others, its olfactory detection threshold of 0.00022 mg/kg made it as the key aroma-active compound in all three samples[8]. This phenomenon was also observed in the IT and OF samples. For example, the extremely low odor thresholds of ionone (0.007 µg/kg), resulted in a high FD (8) and OAV. Three more substances were also ranked in the top ten aroma-impact volatiles in the OF samples based on their OAV values: β-ionone (OAV = 2,595,440), nonanal (OAV = 25,817), and citronellal (OAV = 9,729). Similarly, decanal (OAV = 18,200), phenylethyl alcohol (OAV = 2,064), 2-ethylhexanol (OAV = 1,113) and α-terpineol (OAV = 949) were considered to have played important roles in the aroma contribution in IT. ACI results revealed more information of volatiles in three samples, linalool (88, 10, 72), β-ionone (−, 84, −), methyl anthranilate (5, 3, 10), (+)-carvone (1, 0.3, 2), methyl methanthranilate (1, 0.3, 1.9), (+)-limonene (0.9, 0.1, 1), p-cresol (−, −, 5.7), Z-carveol (0.9, 0.4, 2.5), phenylethyl alcohol (0.3, −, −), perillaldehyde (−, 0.1, 0.7), citronellal (−, 0.3, −), decanal (2, −, −), nonanal (−, 0.8, −), vanillin (−, 0.2, 1.9), α-terpineol (0.1, 0.02, 0.2), 2-ethylhexanol (0.1, 0.005, −), (−)-carveol (−, 0.04, 0.3), 2,4-di-tert-butylphenol (−, 0.01, 0.5), thymol (0.1, 0.01, 0.1), carvacrol (0.1, 0.02, 0.3) and 2,4-dimethyl styrene (0.02, 0.01, 0.2). The aforementioned 21 volatile compounds had high ACI values in the corresponding samples and their corresponding OAV values were also high. They could be categorized as the key aroma-active compounds corresponding to the three samples.

      To further confirm the key aroma-active compounds in the CPT samples, aroma recombination was conducted to initially simulate the aroma of each sample based on quantitative results (Fig. 3)[26]. Statistical analysis revealed a significant difference on one or two odor attributes (p ≤ 0.05). On this basis, further aroma omission experiments were carried out to verify the contribution of a specific group or individual aroma compounds to the overall aroma (Table 3). Significance results were derived from the frequency of sniffing by the evaluators. For the CP samples, the volatile compounds that had a significant effect on the overall aroma were methyl methanthranilate (N = 15, N means the number of being recognized), methyl anthranilate (N = 15), linalool (N = 14), (+)-carvone (N = 13), vanillin (N = 13), Z-carveol (N = 13), 2,4-dimethyl styrene (N = 11), (+)-limonene (N = 9), (−)-carveol (N = 8), terpinen-4-ol (N = 8), 2,4-di-tert-butylphenol (N = 8), and thymol (N = 8). For the OF sample, β-ionone (N = 13), citronellal (N = 12) and nonanal (N = 8) also had significant effects on the overall aroma. In the IT sample, styrene (N = 11), decanal (N = 9) and benzaldehyde (N = 8) were more deficient. The result showed that the key aroma-active compounds of all three CPT samples were captured in the absence experiment, with a more consistent overall aroma but also more distinct individual characteristics. The results of this aroma omission experiment are consistent with the conclusions of the top ranked compounds calculated from the OAV values and ACI values.

      Figure 3. 

      Descriptive sensory analysis radar diagram of recombination model and corresponding CPT samples. Note: The sensorial parameters indicated with * are significantly different between samples (p ≤ 0.05).

      Table 3.  Omission tests of three citrus Pu-erh tea based on aroma recombination model.

      No.Odorants omitted from the complete recombinantNumberaSignificanceb
      ITOFCPITOFCP
      1octanoic acid, lauric acid, palmitic acid6911*****
      1−1octanoic acid565***
      1−2lauric acid525**
      1−3palmitic acid267**
      2thymol, carvacrol, phenol, o-cresol, p-cresol, 2,4-di-tert-butylphenol879*****
      2−1thymol988******
      2−2carvacrol677***
      2−3phenol415**
      2−4o-cresol534**
      2−5p-cresol248**
      2−62,4-di-tert-butylphenol878*****
      3linalool, perillalcohol, p-cymenol, limonene glycol, terpinen-4-ol, prenol, 2-ethylhexanol151515*********
      3−1linalool141514*********
      3-2perillalcohol565***
      3−3p-cymenol232
      3−4limonene glycol123
      3−5terpinen-4-ol898******
      3−6prenol541**
      3−72-ethylhexanol653**
      4phenylethyl alcohol, α-terpineol, (E)-p-mentha-2,8-dien-1-ol, (−)-pinocarveol, benzyl alcohol, (−)-carveol, Z-carveol151415*********
      4−1phenylethyl alcohol612*
      4−2α-terpineol877****
      4−3(E)-p-mentha-2,8-dien-1-ol122
      4−4(−)-pinocarveol223
      4−5benzyl alcohol376**
      4−6(−)-carveol378***
      4−7Z-carveol141313*********
      5p-cymene, β-pinene, styrene, 2,4-dimethyl styrene, (+)-limonene, γ-terpinene, 1,2-dimethoxybenzene131111*******
      5−1p-cymene665***
      5−2β-pinene633*
      5−3styrene1133**
      5−42,4-dimethyl styrene10911******
      5−5(+)-limonene15109*******
      5−6γ-terpinene372*
      5−71,2-dimethoxybenzene321
      6benzaldehyde, 2-pyrrolaldehyde, perillaldehyde, decanal, nonanal, citronellal, vanillin91213********
      6−1benzaldehyde841***
      6−22-pyrrolaldehyde637**
      6−3perillaldehyde276**
      6−4decanal922**
      6−5nonanal384***
      6−6citronellal4125*****
      6−7vanillin51213*******
      72,3-dimethyl pyrazine, methyl methanthranilate, methyl anthranilate, dimethyl sulfone141515*********
      7−12,3-dimethyl pyrazine634**
      7−2methyl methanthranilate141515*********
      7−3methyl anthranilate131415*********
      7−4dimethyl sulfone533*
      8(+)-carvone, dihydrocarvone, piperitone, benzophenone, 4-methoxyacetophenone, β-ionone131415*********
      8−1(+)-carvone121313*********
      8−2dihydrocarvone367**
      8−3piperitone478****
      8−4benzophenone454***
      8−54-methoxyacetophenone234*
      8−6β-ionone4135*****
      a The number of panelists who perceived the aroma difference by means of a triangle test. Fifteen panelists were invited for aroma omission experiment. b Levels of significance, defined based on the number of panelists who were able to determine the difference in aroma omission. −, not significant (0−3, p > 0.05); *, significant (4−7, p ≤ 0.05); **, highly significant (8-11, p ≤ 0.01); ***, very highly significant (12−15, p ≤ 0.001).
    • After confirming the key aroma-active compounds, the correlation between the sensory evaluation scores and the quantitative results of each substance obtained from the instrumental analysis were established using the PLSR model (Fig. 4). Three parallel experiments showed good reproducibility of results, and the same group of samples clustered in similar positions in the results. The ten sensory evaluation descriptors can also be better distinguished from each other. The experimental results have clearly reflected the correlation between the sensory evaluation results and the compounds, according to the correlation coefficient R2, X1 = 0.488, X2 = 0.436, and the summation reach 0.924[2].

      Figure 4. 

      Correlation loadings plot for aroma-active compounds (X-matrix) and sensory attributes (Y-matrix) of three CPT samples.

      The CP samples were mainly located in the upper right corner and associated closely with aroma attributes such as fatty, phenolic and minty flavors. Among them, compound G1 (octanoic acid) has a strong correlation with fatty. Pang et al.[20] also identified octanoic acid in Pu-erh tea and described its flavor as 'sweaty' with a higher concentration. The perception is indeed different depending on the concentration. Phenols were considered a major class of volatiles giving smoky, and phenolic characteristics to Pu-erh tea[13]. Compounds B4 (carvacrol), B6 (2,4-di-tert-butylphenol), and B3 (o-cresol) are more inclined to present phenolic aroma attributes. B5 (thymol) is responsible for phenolic, roasted or woody flavor and was also detected in oolong tea. E1 (dihydrocarone) has a strong association with minty flavor. The OF samples were mainly located in the lower right corner, and close to the four aroma attributes of fruity, floral, woody, and sweet notes. This result is consistent with its higher scoring of those aroma attributes in the sensory evaluation (Fig. 1a). In particular, F1 (methyl methanthranilate, alternate name dimethyl anthranilate) is reported as a volatile marker in citrus peel with a fruity and sweet note[10,33]. F2 (methyl anthranilate) was widely recognized as a grape flavor compound, and has been detected widely in teas deriving from anthranolic acid[10,34,35]. In this experiment, both F1 and F2 were considered as the top five key aroma-active compound of CPT samples with high FD values and high OAV values. E5 (benzophenone) and E2 (β-ionone) have a more significant floral aroma. β-ionone is obviously a significant contributor to the aroma of dark teas formed from carotenoid degradation[36]. D1 (vanillin) has a strong correlation with sweet. In the IT sample located in the lower left corner of Fig. 3, the main aroma characteristics were roasted, musty, and herbal notes. Three sensory attributes are close to each other and the flavor compounds are clustered. A3 ((+)-limonene) was determined to contribute the most to the aroma quality of the corresponding citrus[8,33]. C2 (linalool) is a nearly ubiquitous aroma compound in plants which shows a herbal-like note in the specific concentration in this study[35,36]. Combining the aroma descriptions of single compound standards, it can be concluded that compounds like C11 (terinen-4-ol) and C10 (2-ethylhexanol) are more associated with herbal aroma attributes. Terpinen-4-ol and α-terpineol are isomers, both have a pleasant herbal-like odor similar to lilacs[32]. Compounds like A5 (p-cymene) are the main compounds that cause the samples to produce musty aroma attributes. Wang et al. supposed that p-cymene may come from both citrus and tea leaves, and there might be the simple additive effects between the volatile compounds of pure tea and citrus[8]. The p-cymene content was inversely related to the maturity of the citrus fruit, that is, the lower the citrus maturity, the higher the content of this component[10]. Some compounds located in the upper left corner of Fig. 3, which were less relevant in terms of aroma matching, probably because these compounds were detected in only one sample (IT or CP or OF).

      Among these compounds, combining the FD factors, OAV and ACI values calculated earlier, the key aroma-active compounds can be categorized into the following aroma notes, such as sweet (vanillin); floral (β-ionone); fruity (methyl anthranilate, methyl methanthranilate, citronellal, (+)-carvone, Z-carveol); roasted (thymol); musty (p-cymene), woody (perillaldehyde); herbal (linalool, (+)-limonene, α-terpineol, 2-ethylhexanol); phenolic (2,4-di-tert-butylphenol, p-cresol, carvacrol, 2,4-dimethyl styrene); minty (dihydrocarvone); fatty (octanoic acid).

    • Non-volatile substances in three CPT samples were identified by non-targeted metabolomics. Each sample was repeated by six times, and analyzed by the multivariate statistical analysis (Supplemental Fig. S1), which showed that the three samples had significant differences, and a total of 2,743 metabolites were detected, of which a total of 1,890 were the same metabolites. Tea flavonoids are widely recognized as critical flavor contributors and crucial health-promoting bioactive compounds, and have long been the focus of research worldwide in food science[37]. Figure 5 is a heat map showing the KEGG metabolic pathway enrichment, in which each dot represents a metabolic pathway, and shows the top 20 metabolic pathways participating in the experimental project. Among them, glucosinolate biosynthesis and flavonoid biosynthesis showed significant differences form others (p ≤ 0.05). A total of 29 compounds were involved in the two metabolic pathways, of which 14 compounds contributed to taste (Fig. 6). Four compounds in the CP samples were derived from the citrus peel. For example, neohesperidin is a flavanone glycoside, which has been found in different citrus fruits, is widely used as a natural source to produce neohesperidin dihydrochalcone, a semisynthetic sweetener used in the food industry[38]. Leucopelargonidin belongs to anthocyanidin, compared with leucoanthocyanidin and leucodelphinidin, it often as a minor component was established and contribute both bitterness and astringency[39,40]. Some amino acids, such as Tryptophan, Tyrosine, homomethionine were increased after withering, which contributed to the umami and sweet, mellow taste of tea[41,42]. Most flavan-3-ols (mainly EGCG, ECG, GC, and other catechins) were found to be strongly correlated with the bitter and puckering astringent tastes, different from the mouth-drying or velvety-like astringent taste of flavanols glycosides. Aromadendrin levels decreased significantly as yellowing duration, and the umami and sweetness also decreased at the same time[43]. Chlorogenic acid and desmethylxanthohumol contribute to bitterness[44,45]. Sakuranetin and taxifolin have the potential of bitter-masking, which can enhance 'sweet' and suppressed 'sour', 'bitter', 'astringent' and 'aftertaste'[46, 47]. Since flavan-3-ols are thought to be associated with the bitterness and astringent tastes, Xu et al.[2] supposed that citrus peel could speed up the fermentation of Pu-erh tea so as to contribute to the unique flavor of citrus Pu-erh tea.

      Figure 5. 

      KEGG enrichment analysis of TOP20 metabolic pathways in untargeted metabolomics.

      Figure 6. 

      Non-volatile compounds that significantly contributed to taste of three CPT samples.

    • The 47 flavor substances with their flavor description (35 aroma-active compounds and 12 taste-related compounds) in the CP samples were plotted on the flavor wheel (Fig. 7). Of which, 15 substances in the upper left corner were considered as the main flavor substances provided by IT. O-cresol, p-cresol, and phenol in box A shared similar structures, with cresol as the basic structure which has a methyl group in the para or adjacent position and have phenolic as the main aroma profile. The 13 compounds in the upper right corner were considered to be the flavor substances provided by citrus peel, three of them in box B were structurally similar and provided mainly minty or woody aroma characteristics. The two aldehydes (perillaldehyde and vanillin) in box C contribute to the aroma characteristics of woody and sweet for CP. The substances in the yellow part at the bottom of Fig. 7 can be divided into three main categories. The substances in box F are mainly the sources of musty, roasted and phenolic aroma characteristics. The compounds in box E are mainly the sources of floral, herbal, and fruity aromas, and some correlations are also found in their chemical structural formula, and there is more cis-trans isomerism. The two esters in box D are particularly similar in structure, both with a carboxyl substituent linked to the benzene ring, differing only in the number of carbon atoms attached to the amino substituent of methyl methanthranilate in the benzene ring neighboring substituent, which is considered to be more relevant for floral and fruity aromas (Fig. 3). Flavanol glycosides with low thresholds are important flavor substances in tea leaves[48]. The five compounds in the G box are mainly derived from the inner tea and provide bitterness and astringency.

      Figure 7. 

      Flavor wheel of key flavor compounds of CPT infusion samples. Compounds marked with molecular structure in frames (a)−(g) were specific to the IT, OF or both IT and OF.

      These findings have given some insights on the relationship between structure and aroma characteristics and yielded the main source of flavor compounds of the CP samples. Even after five years of co-fermentation under sunlight, the flavor substances in three samples were still not identical. Most of the aroma substances of the whole fruit sample of citrus tea (CP) were found in both the OF and IT samples, which can be considered to be attributed by both Pu-erh tea and citrus peel. Based on the flavor wheel and the results of the previous screening of key aroma-active compounds, it can be concluded that some of the main characteristics aromas were provided by both IT and OF, and their corresponding key aroma-active compounds were fatty (octanoic acid), roasted (thymol), musty (p-cymene), phenolic (2,4-dimethyl styrene), fruity (methyl methanthrenilate), floral (benzophenone) and herbal (terpinene-4-ol, α-terpinol, (+)-limonene). These detected components can preliminarily explain ingredient-ingredient relation and ingredient-compound relation, which were proposed to support food pairing and further derived flavor pairing[18,49]. For example, compounds in CP that were affected by the OF and IT samples were floral (benzyl alcohol), phenolic (2,4-di-tert-butylphenol), minty (dihydrocarone), woody (perillaldehyde) and sweet (vanillin, neohesperidin) notes. In the OF part, the citrus peel mainly provided fruity (methyl anthranilate) note, floral (β-ionone, benzophene) notes, while in the IT part, Pu-erh tea mainly provided musty(p-cymene) note and herbal (α-terpineol) note and most of the FD values of these aroma components are higher than in the CP part. Generally, the higher FD values, the greater the contribution of the volatile components to the overall aroma. Benzophene provides rose-like or geranium-like floral notes and β-ionone is the representative component of violet aroma that may cooperate with benzyl alcohol so that enhanced faint floral note in CP, musty note and herbal note can also enhance and match the woody (perillaldehyde) note cause their aroma types are close to each other. It can be seen that OF part improved the flavor of Pu-erh tea and gave citrus Pu-erh tea a unique and coordinated flavor.

      In summary, this research studied the flavor profile and revealed taste metabolites of citrus Pu-erh tea, which is expected to provide some useful information for the quality control of the citrus Pu-erh tea.

    • The authors confirm contribution to the paper as follows, original draft writing: Sun J, Cai W; data analysis: Sun J, Cai W; methodology: Yao L, Song S, Wang H; conceptualization: Feng T, Yu C; manuscript review and editing: Feng T, Chen D, Yao L, Lu J, Wang H, Liu Q; partial funds and consultant: Lu J, Feng T . All authors read and approved the final manuscript.

    • All data generated or analyzed during this study are included in this published article and its supplementary information files.

      • The support of the Key Laboratory of cigarette flavoring Technology in Tobacco Industry (TX2018001), Shanghai Gaofeng & Gaoyuan Project for University Academic Program Development (1021GN203004005) and Royal Society of New Zealand Catalyst Seeding Fund (21-AUT-005-CSG).

      • The authors declare that they have no conflict of interest.

      • # These authors contributed equally: Jiaqing Sun, Weitong Cai

      • Supplemental Table S1 Mean intensity values of the ten attributes of the Citrus Pu-erh tea samples during sensory evaluation.
      • Supplemental Table S2 Mean intensity values of the taste of the Citrus Pu-erh tea samples using electronic tongue.
      • Supplemental Table S3 Standard curves of aroma-active compounds of Citrus Pu-erh tea samples.
      • Supplemental Fig. S1 Results of multivariate statistical analysis of untargeted metabolomics.
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (7)  Table (3) References (49)
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    Sun J, Cai W, Feng T, Chen D, Lu J, et al. 2024. Revealing the flavor profile of citrus Pu-erh tea through GC-MS-O and untargeted metabolomics. Beverage Plant Research 4: e009 doi: 10.48130/bpr-0024-0001
    Sun J, Cai W, Feng T, Chen D, Lu J, et al. 2024. Revealing the flavor profile of citrus Pu-erh tea through GC-MS-O and untargeted metabolomics. Beverage Plant Research 4: e009 doi: 10.48130/bpr-0024-0001

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