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MAP  >  Beverage Plant Research
2022 Volume 2
Article Contents
Abstract
Introduction
Materials and methods
Plant material collection and identification
Materials, reagents, and instrumentation
Qualitative determination of the phytochemical constituents
Extraction and isolation
Anti-bacterial and anti-fungal assays
Identification and selection of target protein
Selection of ligand molecules
Preparation of protein molecule
Virtual screening of ligands
Docking
Autodock vina
Biovia discovery studio client 2021
Structure visualization through pymol
Drug likeliness property analysis
Statistical data analysis
Results
Phytochemical test
Characterization of isolated compounds
Antimicrobial activities assay
Binding mode of isolated compounds docked against dnmt1 enzyme
Binding mode of isolated compounds docked against hpv type 16 e6
Admet profiling in grug-likeness and toxicity analysis
Discussion
Conclusions
Author contributions
Data availability
Acknowledgments
Conflict of interest
Rights and permissions
References
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ARTICLE   Open Access    

Essential aroma substances and release pattern of Xinhui Chenpi

  • 1.

    College of Food Science and Biological Engineering, Tianjin Agricultural University, No. 22, Jinjing Road, Tianjin 300384, PR China

  • 2.

    Key Laboratory of Tea Science of Ministry of Education, Hunan Agricultural University, Changsha, Hunan 410128, PR China

More Information
  • This study focuses on analyzing aqueous solutions of aroma-active compounds in Xinhui Chenpi distilled after being heated with headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC/MS). Feasibility of this method was also tested by comparison with crushed samples. The study also analyzed the aqueous solutions of the aroma-active compounds employing gas chromatography-olfactometry (GC-O) defense, aroma extraction dilution analysis (AEDA) and odor activity value (OAV) as well as their emission patterns. According to the study, there are 24 major aroma-active detected in the aqueous solution. Linalool, d-limonene, 2-methoxy-4-vinylphenol, and α-terpineol with sweet, spicy, and woody aroma contributed the most to the aroma-active compounds and were considered to be the essential aroma substances of Chenpi. When heated, the aroma of aromatic-active compounds rich in Chenpi volatilized rapidly and the release dropped dramatically in a short time. The substantial aroma-active compounds can be collected from the first ten released segments.

  • Introduction

    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[13−16]. 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.

    Materials and methods

    Plant material collection and identification

    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.

    Materials, reagents, and instrumentation

    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.

    Qualitative determination of the phytochemical constituents

    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].

    Extraction and isolation

    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).

    Anti-bacterial and anti-fungal assays

    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.

    Identification and selection of target protein

    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].

    Selection of ligand molecules

    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].

    Preparation of protein molecule

    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].

    Virtual screening of ligands

    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/).

    Docking

    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/).

    AutoDock Vina

    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/).

    Biovia Discovery Studio Client 2021

    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/).

    Structure visualization through PyMol

    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 property analysis

    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.

    Statistical data analysis

    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/).

    Results

    Phytochemical test

    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

    Characterization of isolated compounds

    Two compounds were isolated and characterized using NMR spectroscopic methods (Fig. 1 & Supplementary Fig. S1a−c). 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.
    DownLoad: Full-Size Img PowerPoint
    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. S2a−c), 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

    Antimicrobial activities assay

    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.
    DownLoad: Full-Size Img PowerPoint

    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. 2a−c.

    Binding mode of isolated compounds docked against DNMT1 enzyme

    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.
    DownLoad: Full-Size Img PowerPoint

    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).

    Binding mode of isolated compounds docked against HPV type 16 E6

    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.
    DownLoad: Full-Size Img PowerPoint

    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).

    ADMET profiling in grug-likeness and toxicity analysis

    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

    Discussion

    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[45−47] . 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.

    Conclusions

    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.

    • Acknowledgments

    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.

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  • Cite this article

    Yang D, Wu X, Shi H, Zhang J, Wang C. 2022. Essential aroma substances and release pattern of Xinhui Chenpi. Beverage Plant Research 2:22 doi: 10.48130/BPR-2022-0022
    Yang D, Wu X, Shi H, Zhang J, Wang C. 2022. Essential aroma substances and release pattern of Xinhui Chenpi. Beverage Plant Research 2:22 doi: 10.48130/BPR-2022-0022
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Essential aroma substances and release pattern of Xinhui Chenpi

  • 1.

    College of Food Science and Biological Engineering, Tianjin Agricultural University, No. 22, Jinjing Road, Tianjin 300384, PR China

  • 2.

    Key Laboratory of Tea Science of Ministry of Education, Hunan Agricultural University, Changsha, Hunan 410128, PR China

  • Received: 12 September 2022
  • Accepted: 16 November 2022
  • Published online: 09 December 2022
Beverage Plant Research  2,  Article number: 22  (2022)  |  Cite this article

Abstract: This study focuses on analyzing aqueous solutions of aroma-active compounds in Xinhui Chenpi distilled after being heated with headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC/MS). Feasibility of this method was also tested by comparison with crushed samples. The study also analyzed the aqueous solutions of the aroma-active compounds employing gas chromatography-olfactometry (GC-O) defense, aroma extraction dilution analysis (AEDA) and odor activity value (OAV) as well as their emission patterns. According to the study, there are 24 major aroma-active detected in the aqueous solution. Linalool, d-limonene, 2-methoxy-4-vinylphenol, and α-terpineol with sweet, spicy, and woody aroma contributed the most to the aroma-active compounds and were considered to be the essential aroma substances of Chenpi. When heated, the aroma of aromatic-active compounds rich in Chenpi volatilized rapidly and the release dropped dramatically in a short time. The substantial aroma-active compounds can be collected from the first ten released segments.

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    INTRODUCTION

    • Chenpi (Tangerine peel) is made from ripe peels of citrus (Citrus reticulata Blanco), dried in the sun or low temperatures[1]. The production of dried Chenpi mainly includes fruit picking and washing, peeling, drying in the shade, turning, drying in the sun, storage, turning and drying, and flesh sweeping, as shown in Fig. 1. Chenpi is known for its 'chen' (in Chinese it means a long time), so the best Chenpi comes from being stored for a very long period of time. It should be stored at least for three years. Citrus are usually produced from Guangdong, Fujian, Sichuan, Guangxi and Zhejiang, etc, in China. Multiple kinds of citrus from different places can be made into Chenpi of various qualities. And Xinhui Chenpi is top in its family, Chenpi is usually hard and brittle with a fragrant-pungent, and bitter taste. It plays an important part in promoting digestion and curing-vomiting, coughs, and respiratory diseases[2−8].

      Figure 1. 

      Xinhui Chenpi production process flow chart.

      The unique aroma of Chenpi is among the many criteria used to evaluate its quality. According to available literature, the aroma of food comes mainly from its volatile compounds, also called aroma-active compounds[9]. Much progress has been made in studying aroma-active compounds of Chenpi with the method of GC-MS. Duan et al.[10] used the GC-MS metabolomics approach combined with principal component analysis and orthogonal partial least squares discriminant analysis to show that the method effectively differentiated samples. Finally, 15 compounds such as methyl methanthranilate, α-sinensal, geranyl acetate, thymol were identified as chemical markers of Chenpi samples. With the method of GC-MS, Luo et al.[2] detected 98 compounds in Chenpi volatile oil and their main components were d-limonene, γ-pinene, α-pinene, linalool and myrcene.

      The extraction of volatile compounds of Chenpi is a key technology for its quality analysis. At present, most studies on aroma-active compounds of Chenpi are based on shearing treatment[11] or extraction of volatile compounds with the help of solvent apparatus, and the main extraction methods involved are supercritical fluid extraction (SFE)[12], hydro-distillation (HD)[9], vacuum steam fraction (VSF)[13], solvent extraction (SE)[6,14] and simultaneous distillation extraction (SDE)[15], each of which has its own advantages and disadvantages. The main disadvantages of these methods including the solvent being contaminated and the time consuming nature of the complicated steps, as well as the variety of equipment needed. In this study, water vapor condensation reflux method was used to extract the aroma-active compounds, which is simple and easy to operate. This method resulted in an aqueous solution of aroma-active compounds (hereafter referred to as aroma water), which fully enriched the aroma-active compounds of Chenpi without solvent contamination.

      In addition, GC-O was more often used to screen aroma-active compounds. Analyzing Chenpi oil by GC-O and AEDA, Dharmawan et al.[16] concluded that β-pinene, α-pinene, linalool, and 2-methoxy-3-(2-methylpropyl) pyrazine were characteristic aroma substances. Xiao et al.[17] used GC-O to analyze five citruses and concluded that substances such as nonanal, hexanal, linalool and limonene (OAVs ≥ 1) were the characteristic aroma compounds of orange juice samples.

      This experiment collects Chenpi aroma water from Chenpi volatiles obtained by heating and hydro distillation. Then the aroma water was analyzed by HS-SPME-GC-MS, and its essential aroma substances were selected using the method of GC-O, AEDA results were expressed as flavor dilution factors (FD), and OAV was used to identify the unique contribution of each compound to the characteristic aroma. In addition, the essential aroma-active compounds of Chenpi varied with heating time during the aroma collecting process. Therefore, the release pattern of essential aroma substances of Chenpi was identified.

    MATERIAL AND METHODS

      Materials

    • Xinhui Chenpi sample (WG20210023, WG20210045, and WG20210066) were obtained from Yunnan Tasly Deepure Biological Tea Group Co., Ltd. (Pu’er, Yunnan, China).

      Reagents and chemicals

    • Furfural (PubChem CID: 7362; ≥ 99.5%), octanal (PubChem CID: 454; 99%), myrcene (PubChem CID: 31253; ≥ 90%), γ-terpinene (PubChem CID: 7461; 95%), thymol (PubChem CID: 6989; > 99%), (−)-carvone (PubChem CID: 379; ≥ 95%), cymenol (PubChem CID: 10364; 99%), 2-methoxy-4-vinylphenol (PubChem CID: 332; ≥ 98%), citronellyl acetate (PubChem CID: 9017; ≥ 96%), methyl methanthranilate (PubChem CID: 6826; 98%), geranyl acetate (PubChem CID: 7780; 96%), α-terpinene (PubChem CID: 7462; 95%), octanoic acid (PubChem CID: 379; ≥ 99.5%) and n-decanol (PubChem CID: 8174; 98%) were purchased from McLean Biochemical Technology Co., Ltd (Shanghai, China). Geraniol (PubChem CID: 637566; 99%), 1-nonanol (PubChem CID: 8175; 99.5%), 4-terpineol (PubChem CID: 11230; 98%), α-terpineol (PubChem CID: 17100; > 95%), β-ionone (PubChem CID: 638014; 97%), p-cymene (PubChem CID: 7463; ≥ 99.5%), 1-octanol (PubChem CID: 957; > 99.5%), n-decanoic acid (PubChem CID: 2969; > 99%) and β-ionone (PubChem CID: 638014; 97%) were purchased from Aladdin (Shanghai, China). nonanal (PubChem CID: 31289; ≥ 95%), linalool (PubChem CID: 6549; 98%) and d-limonene (PubChem CID: 440917; > 95%) were purchased from TCI (Shanghai, China). NaCl (analytical purity 99.5%) were purchased from Tianjin Zhiyuan Chemical Reagant Co. n-Alkanes solution (C8-C32) was employed to calculate the retention index (RI) of the detected components.

      Extraction of the volatile compounds

    • Samples were treated using two different methods. Samples were crushed with a universal crusher (Tianjin Teste Instruments Co., Ltd, Tianjin, China) and passed through 30 mesh sieves before the experiment. The second method is as follows: 100 g of sample was weighed in a 2000-mL round bottom flask, 1,500 mL of hot water was added, soaked for 40 min (in order to shorten the extraction time, and the aroma substances are concentrated), then the samples were distilled by heating. The hydro-distillation method was used to recover condensed water (rich in volatile aroma-active components) and saved for HS-SPME pretreatment. The experiments were repeated in triplicate.

      HS-SPME procedure

    • The analytical conditions of HS-SPME, GC-MS, and GC-O were adapted based on the proven method of Liu et al.[11]. The temperature rise procedure, shunt ratio, sample volume, and NaCl addition were optimized to establish the detection method, as shown in Fig. 2. The results showed good peak patterns and were suitable for this study.

      Figure 2. 

      Total ion chromatogram (TIC) of volatile compounds of Chenpi.

      New extraction heads were activated, as follows: the 50/30 μm DVB/CAR/PDMS extraction head was placed on the GC-MS inlet for about 30 min under the inlet temperature of 250 °C.

      TriPlus RSH autosampler (Thermo Fisher Scientific, USA) coupled with 50/30 μm divinylbenzene/carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) (purchased from Supelco) was used for HS-SPME analysis. Respectively, 1.0 g of sample crushing (add 5 mL of ultra-pure water), 5 mL aroma water sample and 1.5 g of NaCl were placed into a 20-mL headspace bottle. Extraction temperature 65 °C, extraction time 30 min, equilibrium temperature 65 °C, equilibrium time 10 min, desorption time 5 min.

      GC-MS analyses and quantitation of aroma-active compounds

    • The equipment used was Rtx-5MS column (30 m × 0.25 mm internal diameter, 0.25 μm film thickness (Restek, Bellefonte, PA, USA), TRACE1300-ISQ gas chromatograph-mass spectrometer (Thermo Fisher Scientific, USA), Thermo Scientific Barnstead water purification system (Thermo Fisher Scientific, USA). The procedures of temperature increase are as follows: initial temperature 40 °C, ramp-up to 70 °C at 10 °C/min, ramp-up to 190 °C at 3 °C/min, ramp-up to 250 °C at 10 °C/min, hold for 3 min; flow rate: 1.0 mL/min; inlet temperature: 250 °C; injection volume: 1.0 μL; split ratio: 70:1; carrier gas: 99.999% high purity helium. EI ion source; ion source temperature: 250 °C; transfer line temperature: 250 °C; ionization energy: 70 eV; scan mode: full scan; mass scan range 40−500 m/z.

      Quantitative analysis of volatile compounds was conducted using a standard curve that obtained for each compound. The stock solution composed of 24 aroma-active compounds was configured and diluted with ethanol to six gradients of 2, 2.5, 10/3, 5, 10 and 20 to produce the standard curve. Meanwhile, the retention indices and aroma characteristics of each aroma-active compound were also used to verify the qualitative results.

      GC-O analyses

    • GC-O analysis was performed using a Thermo Trace 1300 gas chromatograph (Thermo Fisher Scientific Inc., USA) equipped with a flame ionization detector and a sniffing port (ODP2, Gerstel, Inc., Germany), Rtx-5MS column (30 m × 0.25 mm internal diameter, 0.25 µm film thickness; Restek, Bellefonte, PA, USA) was used for the separation, other equipment used was SGH-300 high-purity hydrogen generator, SGK-2L low-noise air pump (Beijing Zhongke Jirui Technology Co. Ltd., Beijing, China).

      Experienced sensory evaluators completed GC-O sniffing experiments, three panelists were selected and trained based on GB/T 16291.1-2012 (Sensory analysis-general guidance for the selection, training, and monitoring of assessors)[18]. The time of odor appearance, end time, and odor description were recorded by the selected panelists.

      Odor activity value (OAV)

    • OAV is often used to evaluate and screen the contribution of aroma-active compounds to the aroma formation of samples[19]. According to the calculation formula, OAV is the ratio of concentration of aroma-active compounds relative to their respective threshold in water, the compound concentration is the absolute concentration corrected by the GC-MS standard curve, and the odor threshold in water is obtained from previous literature[16,20−24].

      Aroma extraction dilution analysis (AEDA)

    • Aroma water was diluted in gradient with water at 2n to obtain dilution multiples of 1:2, 1:4, 1:8, 1:16, etc. GC-O detection, based on sniffing until the aroma-active compound odor disappears, was determined as the FD factor of the compound.

      Aroma-active compound release pattern

    • In order to study the content changes of aroma-active compounds of samples with heating time during the aroma enrichment process, segmented interception of aroma extracts was performed during the extraction of aroma substances, with each 50 mL segment being collected 20 times. The relative quantification was performed by adding 0.01 mL of n-decanol (0.13 mg/kg) to analyze the release pattern of aromatic compounds. The process was repeated three times.

    RESULTS AND DISCUSSION

      Optimization of sample processing method

    • The study analyzed crushed samples, as well as aroma water samples. The number of aroma-active compounds in the samples and their relative content was examined to select the best treatment method. As shown in Fig. 3, 24 aroma-active compounds were identified in the detected values of the two forms of samples, such as d-limonene, linalool, 2-methoxy-4-vinylphenol, furfural, and α-terpinene, etc.; aroma-active compounds specific to the heated enriched aroma-active water such as p-cymene, 1-octanol, 4-terpineol, etc. There were 19 kinds of aroma-active compounds particular to the directly crushed samples such as undecanal, citronellol, neral, etc. There were eight kinds of aromatic compounds.

      Figure 3. 

      Comparison of aroma-active compounds in two forms, (a) concentration and number; (b) the number of compounds.

      The relative content of aroma-active compounds was collected using the normalization method. The sum of the relative content of aroma-active compounds in the water sample was greater than that of the crushed sample, and the aroma-active compounds were also more in the aroma water sample. In conclusion, the experimental feasibility of studying the aroma-active compounds of aroma water met expectations, and the aroma-active compounds of the Xinhui Chenpi samples could be characterized in this way.

      Quantitative analysis of aroma-active compounds

    • The GC-MS qualitative analysis of the volatile compounds contained in the aroma water detected a total of 24 volatile aroma-active compounds with a matching index greater than 80% compared with the database, including six alcohols, four olefins, three aldehydes, two ketones, one acid, three phenols and three esters, as shown in Table 1. The corresponding standard curves were established for each compound with good linearity (equations of standard curves, where y is the area of the peak of an authentic standard, and x is the concentration of the authentic standard). Among them, d-limonene (3,291.64 mg/kg) showed highest in all compounds, followed by linalool (561.39 mg/kg), 4-terpineol (370.81 mg/kg), γ-terpinene (354.97 mg/kg), furfural (274.80 mg/kg), 2-methoxy-4-vinylphenol (253.67 mg/kg) and α-terpineol (250.54 mg/kg). According to the available literature, compounds such as d-limonene, γ-terpinene, linalool, and myrcene are essential components of the aroma composition of Chenpi[25−30], and substances such as 4-terpineol, furfural, 2-methoxy-4-vinylphenol, and p-cymene in the Chenpi aroma-active substances have been rarely reported previously. In the present study, octanal (78.71 mg/kg), thymol (38.18 mg/kg), cymenol (29.62 mg/kg), and myrcene (21.19 mg/kg) were the compounds that contributed significantly to the aroma of Xinhui Chenpi.

      Table 1.  The concentration of volatile compounds detected in aroma water samples.

      NoTime
      (min)      		RICompoundMolecular formulaCalibration curvesR2Linear range
      (mg/kg)      		Content
      (mg/kg)a      		RSD (%)b
      14.12840FurfuralC5H4O2y = 2.08E+09x + 1.76E+06R2 = 0.996592.34-4.62274.8017.64
      27.42983MyrceneC10H16y = 3.55E+11x – 1.25E+07R2 = 0.99391.37-0.0721.1924.52
      37.741002OctanalC8H16Oy = 3.05E+10x – 4.37E+06R2 = 0.9922.34-0.1278.7131.41
      48.191009α-TerpineneC10H16y = 2.73E+11x – 6.85E+06R2 = 0.99190.51-0.033.4727.55
      58.441017p-CymeneC10H14y = 4.27E+11x – 5.02+05R2 = 0.99590.41-0.0212.8513.06
      68.601023d-LimoneneC10H16y = 1.06E+11x + 2.55E+08R2 = 0.9916127.98-6.403291.6428.02
      79.531054γ-TerpineneC10H16y = 1.50E+11x – 3.95E+07R2 = 0.994112.92-0.65354.9726.94
      89.8710711-OctanolC8H18Oy = 1.62E+11x + 6.65E+06R2 = 0.99230.40-0.043.3220.64
      910.911101LinaloolC10H18Oy = 1.37E+11x + 9.32E+08R2 = 0.990233.41-3.34561.3917.00
      1011.081105NonanalC9H18Oy = 1.61E+11x + 4.96E+06R2 = 0.99150.38-0.046.4518.43
      1113.5811681-NonanolC9H20Oy = 1.05E+11x + 3.83E+07R2 = 0.99421.38-0.1410.1021.04
      1213.9011924-TerpineolC10H18Oy = 9.17E+10x + 3.02E+08R2 = 0.990913.14-0.66370.8125.28
      1314.401202α-TerpineolC10H18Oy = 1.08E+11x + 1.66E+08R2 = 0.990819.11-1.91250.5430.68
      1414.701219Octanoic acidC8H16O2y = 2.00E+09x + 1.41E+07R2 = 0.9946--
      1516.521291(-)-CarvoneC10H14Oy = 2.07E+11x + 4.62E+07R2 = 0.9909092-0.096.1824.79
      1616.921304GeraniolC10H18Oy = 2.00E+11x + 4.34E+07R2 = 0.99011.39-0.1416.9928.71
      1718.511358ThymolC10H14Oy = 6.54E+11x + 6.06E+08R2 = 0.99227.88-0.7938.1826.40
      1818.891371CymenolC10H14Oy = 6.14E+11x + 7.10E+07R2 = 0.99160.77-0.0829.6221.95
      1919.4013402-Methoxy-4-vinylphenolC9H10O2y = 3.41E+10x + 2.15E+08R2 = 0.991131.33-3.92253.6730.06
      2021.001360Citronellyl acetateC12H22O2y = 8.53E+11x + 1.20E+07R2 = 0.99070.14-0.011.6826.58
      2121.561380n-Decanoic acidC10H20O2y = 3.55E+11x – 1.42E+07R2 = 0.99240.48-0.057.1731.25
      2222.221385Geranyl acetateC12H20O2y = 1.01E+12x + 6.69E+06R2 = 0.99060.08-0.0080.5430.38
      2323.221453Methyl methanthranilateC9H11NO2y = 1.16E+11x + 7.55E+06R2 = 0.99140.53-0.0515.2010.21
      2426.401486β-IononeC13H20Oy = 5.98E+11x + 3.47E+06R2 = 0.99940.06-0.0071.9423.68
      a: The data of concentration is mean (n = 3 for aroma water sample). b: The RSD is standard deviation (n = 3 for aroma water sample). −: Indicates no detection results.

      GC-O verifies the aroma-active compounds in samples

    • The odor activity of 24 aroma-active compounds detected by GC-MS was characterized by GC-O combined with AEDA and expressed as FD dilution factor, which was used in combination with the OAV value of each aroma-active compounds to verify its contribution to the aroma-active compounds of Xinhui Chenpi[24,31,32]. As shown in Table 2, the FD dilution factors ranged from 2 to 8,192, the higher FD factors, the stronger the odor of the compounds, and the greater the contribution to the aroma of the sample. The compounds with the highest FD factors were linalool (8,192) with sweet, cymenol (8,192) with pungent and refreshing odors, 2-methoxy-4-vinylphenol (8,192) with pungent and flower odors, followed by β-ionone (4,096) with woody odor, 4-terpineol (2,048) with woody and loamy incense odors, α-terpineol (2,048) with woody and flower odors, (−)-carvone (2,048) with mint and spicy odors, geranyl acetate (2,048) with medicinal odor, n-decanoic acid (2,048) with flower odor. The aroma-active compounds of Xinhui Chenpi are mainly alcohols, olefins, esters and aldehydes, and other compounds such as ketones and phenols. Among them, alcohols and olefins accounted for the highest proportion, such as d-limonene and linalool, which mainly show the typical orange and sweet odor of Chenpi, are the most abundant compounds in Xinhui Chenpi, with FD factors of 32 and 8,192, respectively. Aldehydes also contributed to the aroma-active formation of Chenpi, such as octanal, which has a typical orange flavor with an FD factor of 512. Furfural has a nut odor, which has rarely been reported in previous studies of aroma-active compounds of Chenpi.

      Table 2.  The FD factor of aroma-active compounds.

      NoCompoundOdor description*FD**
      1FurfuralNut8
      2MyrcenePungent32
      3OctanalOrange flavor512
      4α-TerpineneWax, orange16
      5p-CymeneRefreshing8
      6d-LimoneneCitrus32
      7γ-TerpineneWoody8
      81-OctanolOily, fruity128
      9LinaloolFlowers, sweet8192
      10NonanalOily, sweet, orange8
      111-NonanolOrange scent2
      124-TerpineolWoody, loamy incense2048
      13α-TerpineolFlowers, woody2048
      14Octanoic acidFruity32
      15(−)-CarvoneMint, spicy2048
      16GeraniolRose512
      17ThymolMedicine1024
      18CymenolPungent, refreshing8192
      192-Methoxy-4-vinylphenolPungent, flowers8192
      20Citronellyl acetateFlowers16
      21n-Decanoic acidFlowers2048
      22Geranyl acetateMedicine2048
      23Methyl methanthranilateOrange, flowers1024
      24β-IononeWoody4096
      * Description of the sniffing results by the sensory evaluator (n = 3 for sensory evaluator).
      ** Maximum dilution of the aroma-active compound.

      To identify the contribution of aroma-active compounds to the aroma of Xinhui Chenpi, the OAV was calculated to verify that each compound's odor threshold was known from the literature. The literature has reported that an aroma-active compound OAV ≥ 1 indicates that the compound contributes to aroma formation[24,33,34]. As shown in Table 3, the compounds have been ranked in order of OAV from largest to smallest. Octanoic acid was not detected in the content; other than that, all 22 aroma-active compounds revealed had OAV > 1 in aroma water. The results showed that d-limonene had the highest OAV (24,027) in all compounds, followed by linalool (20,050), 2-methoxy-4-vinylphenol (13,351), geraniol (1,699), thymol (382), octanal (342), α-terpineol (291), β-ionone (231). The above results show that the most important aroma-active compounds were d-limonene and linalool in Chenpi. Notably, although the contents of geraniol (16.99 mg/kg) and β-ionone (1.94 mg/kg) were not very high, their OAVs were the highest, because the thresholds were low (0.01 and 0.0084 mg/kg).

      Table 3.  The results of OAVs calculation of aroma-active compounds.

      NoCompoundConcentration
      (mg/kg)      		Odor threshold in
      water (mg/kg)      		OAV
      6d-Limonene3291.640.14b24026.58
      9Linalool561.390.03a20049.61
      192-Methoxy-4-vinylphenol253.670.02b13351.10
      16Geraniol16.990.01a1699.27
      17Thymol38.180.10b381.80
      3Octanal78.710.23b342.23
      13α-Terpineol250.540.86b291.32
      24β-Ionone1.940.01c231.49
      18Cymenol29.620.18b164.56
      81-Octanol3.320.02b144.30
      15(-)-Carvone6.180.07a92.26
      1Furfural274.803.00c91.60
      10Nonanal6.450.10a64.50
      124-Terpineol370.816.40a57.94
      21n-Decanoic acid7.170.13b55.19
      23Methyl methanthranilate15.200.35b43.54
      2Myrcene21.190.67a31.63
      111-Nonanol10.101.00a10.10
      7γ-Terpinene354.9755.00c6.45
      22Geranyl acetate0.540.15a3.60
      5p-Cymene12.857.20b1.78
      20Citronellyl acetate1.681.00b1.68
      4α-Terpinene3.472.40b1.45
      14Octanoic acid−0.86b−
      Odor thresholds in water found in the literature. a: Indicates reference[18]. b: Indicates reference[35]. c: Indicates reference[21]. −: Indicates no detection.

      Release pattern of aroma-active compounds

    • The changes of 24 aroma-active compounds with heating time when samples were heated to enrich the aroma-active compounds were averaged over three replicate values. As shown in Table 4 and Fig. 4, a decreasing pattern was seen, with a sharp decrease after the 1st time, and a slight increase from the 2nd to the 3rd time, followed by a slight decrease.

      Table 4.  Changes in the concentration of aroma-active compounds of Chenpi with extraction time.

      Time (min)CompoundConcentration (mg/Kg)
      1234567891011121314151617181920
      4.12Furfural4.072.462.492.482.592.692.712.702.532.412.242.322.632.582.542.662.702.612.512.21
      7.42Myrcene1053.510.370.250.220.310.400.190.510.200.370.350.391.090.440.850.691.200.770.770.98
      7.74Octanal69.184.196.485.876.805.306.975.645.255.565.535.086.294.985.104.965.897.615.994.75
      8.19α-Terpinene114.060.560.590.360.520.510.440.560.500.520.560.420.750.590.780.721.100.970.881.11
      8.44p-Cymene53.161.181.180.891.241.180.881.380.821.161.171.171.341.340.721.711.581.411.331.97
      8.60d-Limonene36762.6620.3020.3321.2420.2921.3218.2344.9220.0032.1830.1533.8045.8236.9142.0830.5020.7921.7929.8426.29
      9.53γ-Terpinene3591.331.631.521.721.811.611.593.731.782.802.763.124.003.053.774.133.674.073.653.33
      9.871-Octanol20.452.862.861.721.871.811.421.081.171.020.881.110.750.580.470.390.450.550.42-
      10.91Linalool1080.19396.41408.52350.55346.02352.47270.33240.68232.30211.12194.25173.38180.08161.11140.28138.62121.05112.0861.1259.41
      11.08Nonanal58.693.083.303.003.723.613.492.962.913.433.593.293.563.283.503.563.543.343.043.39
      13.581-Nonanol16.307.727.627.316.966.806.625.514.713.903.233.293.283.233.293.362.983.002.972.79
      13.904-Terpineol498.61147.36144.59136.17133.69123.46117.43108.56112.45110.63104.22104.54103.89103.29102.11106.1089.2784.6550.5679.04
      14.40α-Terpineol394.39134.60127.01114.39113.81100.9979.6168.7970.1967.8461.1756.7056.0356.1255.6255.0043.9041.6241.0140.69
      14.70Octanoic acid−4.284.11−−−−−−−−−−−−−−−−−
      16.52(−)-Carvone30.1115.9014.4113.3211.479.656.815.314.924.143.342.862.872.101.841.551.040.980.910.78
      16.92Geraniol24.1618.2617.6715.0914.8513.2410.377.848.167.786.635.705.605.284.824.953.853.432.842.66
      18.51Thymol207.17187.75184.75180.91168.43134.25103.7586.0483.9574.1057.6454.9951.6047.0840.0638.6728.1923.1318.9317.40
      18.89Cymenol72.98152.32175.42189.58198.40198.78116.7182.0820.8720.1520.0120.9321.0327.2430.9424.918.897.4914.3213.62
      19.402-Methoxy-4-vinylphenol22.9723.2131.7062.0368.2271.4954.0653.2151.4550.4650.3450.1049.7248.8548.5349.5449.7044.4344.7239.54
      21.00Citronellyl acetate21.935.325.305.205.555.263.653.401.731.521.481.411.731.671.481.441.561.461.862.09
      21.56n-Decanoic acid−12.0514.5214.5215.4516.5812.0510.008.037.427.317.737.307.335.275.735.615.115.725.50
      22.22Geranyl acetate7.131.121.161.291.161.291.201.181.181.161.161.151.161.041.161.171.091.091.181.13
      23.22Methyl methanthranilate−5.126.935.655.024.894.844.823.102.822.522.161.981.681.541.491.431.050.890.73
      26.40β-Ionone7.726.955.234.884.883.823.213.022.852.622.362.232.061.841.721.661.461.090.990.99
      Total concentration44839.781155.541187.931138.381132.861081.40826.57743.90641.10615.12562.88537.87554.55521.93498.49483.52400.94373.74326.45310.42
      - indicates no detection.

      Figure 4. 

      Changes of total concentration of aroma-active components in Chenpi with heating time.

      As shown in Table 4, the nine compounds including furfural, myrcene, octanal, α-terpinene, p-cymene, d-limonene, γ-terpinene, nonanal, and geranyl acetate showed a pattern of rapidly decreasing to a minimum and then being continuously maintained; the 10 compounds including 1-octanol, linalool, 1-nonanol, 4-terpineol, α-terpineol, (−)-carvone, geraniol, thymol, citronellyl acetate, β-ionone showed a gradually decreasing pattern; cymenol, 2-methoxy-4-vinylphenol, n-decanoic acid, and methyl methanthranilate showed a pattern of increasing first, reaching a maximum value, and then gradually decreasing; octanoic acid was detected only in the 2nd and 3rd times.

      The alcohols and ketones in the aroma-active compounds of Xinhui Chenpi mainly showed a gradually decreasing pattern. For example, among the alcohols, linalool (1,080.19 mg/kg), 4-terpineol (498.61 mg/kg), α-terpineol (394.39 mg/kg) are of the highest proportion, linalool decreased from 1,080.19 mg/kg to 59.41 mg/kg; 4-terpineol decreased from 498.61 mg/kg to 79.04 mg/kg; α-terpineol decreased from 394.39 mg/kg to 40.69 mg/kg. This indicates that the alcoholic compounds of Xinhui Chenpi are persistent in aroma and slow in release.

      The olefins and aldehydes mainly showed a pattern of rapidly decreasing at the lowest level and then being continuously maintained. Among the olefins, d-limonene (36,762.66 mg/kg), γ-terpinene (3,591.33 mg/kg), myrcene (1,053.51 mg/kg) and α-terpinene (114.06 mg/kg) are of the highest proportion, and d-limonene decreased from 36,762.66 mg/kg to 20.30 mg/kg in the 2nd and maintained since then; The γ-terpinene decreased from 3,591.33 mg/kg to 1.63 mg/kg in the 2nd and maintained since then; Myrcene decreased from 1,053.51 mg/kg to 0.37 mg/kg in the 2nd and maintained since then; α-terpinene decreased from 114.06 mg/kg to 0.56 mg/kg in the 2nd and maintained at a consequent level after that. It indicates that the olefins of Xinhui Chenpi are easily soluble and released extremely fast.

      Other compounds, such as thymol gradually decreased from 207.17 mg/kg to 17.40 mg/kg; cymenol gradually increased from the initial 72.98 mg/kg, reaching a maximum value of 198.78 mg/kg at the 6th pass, and then started to decrease to 13.62 mg/kg; 2-methoxy-4-vinylphenol gradually increased from the initial 22.97 mg/kg to a maximum of 71.49 mg/kg at the 6th time and then started to decline to 39.54 mg/kg. This indicates that the aroma was persistent and remained in the release phase during the heating extraction.

      Furfural differed from the other substances, with a small amount of 4.07 mg/kg initially, which decreased to 2.46 mg/kg in the 2nd and then continued to be maintained, indicating that Furfural is more difficult to volatilize than the other substances.

    CONCLUSIONS

    • Aroma water obtained by heating and enrichment has obvious advantages. The aroma water of Xinhui Chenpi was comprehensively analyzed using the methods of GC-MS, GC-O and AEDA. It was concluded that linalool, d-limonene, 2-methoxy-4-vinylphenol, and α-terpineol were the four aroma-active compounds with high content and high contribution to the aroma formation, which were identified as the essential aroma substances of Xinhui Chenpi, and mainly presenting orange, sweet, spicy, woody and floral aromas. The heat release pattern was studied, and the results showed that the content of the aroma-active compounds generally showed a decreasing pattern with the heating time in the process of heating enrichment. In particular, the overall content decreased sharply after the 1st time, and it was speculated that this occurred because the Chenpi samples were soaked in heated water before being enriched by heating and distillation. However, this conclusion needs to be further verified. Although the overall content changed significantly after the 1st time, and combined with the respective emission pattern of 24 aroma-active compounds, it is difficult to volatilize all the aroma-active compounds in a short time, so this experiment recommends that the first 10 times of aroma water can be collected to achieve a shorter time and enrichment of the most aroma-active compounds.

      Acknowledgments

      • This work was supported by Youth Top Talents Project in Tianjin Special Support Program, the Science and technology Project of Tianjin (Grant No. 21ZYCGSN00410), and the Science and technology Project of Tianjin (Grant No. 21YDTPJC00910). This work was supported by the National Natural Science Foundation of China [32102004]; the Project funded by China Postdoctoral Science Foundation [2021M701169, 2022T150206], the China Agriculture Research System of MOF and MARA; the Construction of World Big-leaf Tea Technology Innovation Center and Industrialization of Achievements [202102AE090038]; and the Scientific and Technological Talents and Platform Plan (Academician Expert Workstation) [202104AC100001-B01].

      Conflict of interest

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

      Rights and permissions
      • Copyright: © 2023 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 (4)  Table (4) References (35)
  • About this article
    Cite this article
    Yang D, Wu X, Shi H, Zhang J, Wang C. 2022. Essential aroma substances and release pattern of Xinhui Chenpi. Beverage Plant Research 2:22 doi: 10.48130/BPR-2022-0022
    Yang D, Wu X, Shi H, Zhang J, Wang C. 2022. Essential aroma substances and release pattern of Xinhui Chenpi. Beverage Plant Research 2:22 doi: 10.48130/BPR-2022-0022
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  • Introduction
  • Material and methods

  • Materials

  • Reagents and chemicals

  • Extraction of the volatile compounds

  • Hs-spme procedure

  • Gc-ms analyses and quantitation of aroma-active compounds

  • Gc-o analyses

  • Odor activity value (oav)

  • Aroma extraction dilution analysis (aeda)

  • Aroma-active compound release pattern

  • Results and discussion

  • Optimization of sample processing method

  • Quantitative analysis of aroma-active compounds

  • Gc-o verifies the aroma-active compounds in samples

  • Release pattern of aroma-active compounds

  • Conclusions
  • Acknowledgments
  • Conflict of interest
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