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Terpenes

Terpenes are a diverse class of organic compounds found in plants, particularly in essential oils.
They are characterized by their distinct aromatic properties and have been the subject of increasing scientific interest due to their potential therapeutic applications.
Terpenes exhibit a wide range of biological activities, including anti-inflammatory, analgesic, and antimicrobial effects.
This MeSH term provides a comprehensive overview of the chemical structure, biosynthesis, and pharmacological properties of terpenes, enabling researchers to explore their role in various areas of medicine and biolodycs.

Most cited protocols related to «Terpenes»

1
Expanded Secondary Metabolite Detection in antiSMASH 2.0
Cited 25 times
In addition to the secondary metabolite cluster types supported in the original release of antiSMASH (type I, II and III polyketides, non-ribosomal peptides, terpenes, lantipeptides, bacteriocins, aminoglycosides/aminocyclitols, β-lactams, aminocoumarins, indoles, butyrolactones, ectoines, siderophores, phosphoglycolipids, melanins and a generic class of clusters encoding unusual secondary metabolite biosynthesis genes), version 2.0 adds support for oligosaccharide antibiotics, phenazines, thiopeptides, homoserine lactones, phosphonates and furans. The cluster detection uses the same pHMM rule-based approach as the initial release (17 (link)): in short, the pHMMs are used to detect signature proteins or protein domains that are characteristic for the respective secondary metabolite biosynthetic pathway. Some pHMMs were obtained from PFAM or TIGRFAM. If no suitable pHMMs were available from these databases, custom pHMMs were constructed based on manually curated seed alignments (Supplementary Table S1). These are composed of protein sequences of experimentally characterized biosynthetic enzymes described in literature, as well as their close homologs found in gene clusters from the same type. The models were curated by manually inspecting the output of searches against the non-redundant (nr) database of protein sequences. The seed alignments are available online at http://antismash.secondarymetabolites.org/download.html#extras. After scanning the genome with the pHMM library, antiSMASH evaluates all hits using a set of rules (Supplementary Table S2) that describe the different cluster types. Unlike the hard-coded rules in the initial release of antiSMASH, the detection rules and profile lists are now located in editable TXT files, making it easy for users to add and modify cluster rules in the stand-alone version, e.g. to accommodate newly discovered or proprietary compound classes without code changes. The results of gene cluster predictions by antiSMASH are continuously checked on new data arising from research performed throughout the natural products community, and pHMMs and their cut-offs are regularly updated when either false positives or false negatives become apparent.
The profile-based detection of secondary metabolite clusters has now been augmented by a tighter integration of the generalized PFAM (22 (link)) domain-based ClusterFinder algorithm (Cimermancic et al., in preparation) already included in version 1.0 of antiSMASH. This algorithm performs probabilistic inference of gene clusters by identifying genomic regions with unusually high frequencies of secondary metabolism-associated PFAM domains, and it was designed to detect ‘classical’ as well as less typical and even novel classes of secondary metabolite gene clusters. While antiSMASH 1.0 only generated the output of this algorithm in a static image, version 2.0 displays these additional putative gene clusters along with the other gene clusters in the HTML output. A key advantage of this is that these putative gene clusters will now also be included in the subsequent (Sub)ClusterBlast analyses.
Blin K., Medema M.H., Kazempour D., Fischbach M.A., Breitling R., Takano E, & Weber T. (2013). antiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers. Nucleic Acids Research, 41(Web Server issue), W204-W212.
Publication 2013
Amino Acid Sequence Aminocoumarins Aminoglycosides Anabolism Antibiotics Bacteriocins Biosynthetic Pathways Childbirth Classes Enzymes Furans Gene Clusters Generic Drugs Genes Genome Genomic Library homoserine lactone Indoles Lactams Melanins Natural Products Oligosaccharides Peptides Phenazines Phosphonates Polyketides Prognosis Protein Domain Proteins Ribosomes Secondary Metabolism Siderophores Terpenes
2
Optimizing BiG-SCAPE Class Weights
Cited 21 times
Tuning of weights for each BiG-SCAPE class was calculated by a brute-force approach, by choosing the weight combination that maximized the correlation between BGC and Compound distances for every pair of BGCs in the same class in a manually curated Compound Group table (Supplementary Dataset). The dataset comprised all BGCs from the MIBiG database (v1.3) that had linked compound SMILES and had at least two predicted domains to filter out minimal gene cluster entries. BGC distances were calculated by moving in steps of 0.01 between the Jaccard, Domain Sequence Similarity, the original Goodman-Kruskal56 , and Adjacency indices, such that JI+DSS+GK+AI=1. The anchorboost parameter of DSS was allowed to change in the range [1,4] with steps of 0.5. For the DSS index, only the original 4 anchor domains were considered (Condensation Domain, PF00668; Beta-ketoacyl synthase N-terminal, PF00109; Beta-ketoacyl synthase C-terminal domain, PF02801, and the Terpene synthase N-terminal, PF01397). Compound distances were calculated only once, between all BGCs in the MIBiG 1.3 that had an annotated SMILES string representing the molecule. Their pairwise distance was calculated by using RDKit (Tanimoto coefficient based on Morgan fingerprinting, radius=4). The nine original curated Compound classes were used to tune the weights of 7 BiG-SCAPE classes (the Terpene BiG-SCAPE class was initially included in the Others Compound class due to a low number of points and was assigned default values of Jw = 0.2, DDSw = 0.75, AIw = 0.05).
Results (Supplementary Figure 40) indicated clear tendencies to favor different indices in each case and corroborated that the proposed Adjacency Index was more informative than the original Goodman-Kruskal synteny metric used in Cimermancic et al3 (link)., which led to the decision of dropping this index from the final distance formula (additional details in Supplementary Note 2 and Supplementary Figure 41).
Navarro-Muñoz J.C., Selem-Mojica N., Mullowney M.W., Kautsar S.A., Tryon J.H., Parkinson E.I., De Los Santos E.L., Yeong M., Cruz-Morales P., Abubucker S., Roeters A., Lokhorst W., Fernandez-Guerra A., Cappelini L.T., Goering A.W., Thomson R.J., Metcalf W.W., Kelleher N.L., Barona-Gomez F, & Medema M.H. (2019). A computational framework to explore large-scale biosynthetic diversity. Nature chemical biology, 16(1), 60-68.
Publication 2019
CSF2RB protein, human Gene Clusters Nitric Oxide Synthase Radius Synteny Terpenes terpene synthase
3
Comprehensive Biosynthetic Gene Cluster Detection
Cited 14 times
Using the HMMer3 tool (http://hmmer.janelia.org/), the amino acid sequence translations of all protein-encoding genes are searched with profile Hidden Markov Models (pHMMs) based on multiple sequence alignments of experimentally characterized signature proteins or protein domains (proteins, protein subtypes or protein domains which are each exclusively present in a certain type of biosynthetic gene clusters). Using both existing pHMMs (5 (link),11–13 ) and new pHMMs from seed alignments, we constructed a library of models specific for type I, II and III PK, NRP, terpene, lantibiotic, bacteriocin, aminoglycoside/aminocyclitol, beta-lactam, aminocoumarin, indole, butyrolactone, ectoine, siderophore, phosphoglycolipid, melanin and aminoglycoside biosynthesis signature genes. Additionally, we constructed a number of pHMMs specific for false positives, such as the different types of fatty acid synthases which show homology to PKSs. The final detection stage operates a filtering logic of negative and positive pHMMs and their cut-offs. The logic is based on knowledge of the minimal core components of each gene cluster type taken from the scientific literature. The cut-offs were determined by manual studies of the pHMM results when run against the NCBI non-redundant (nr) protein sequence database (ftp://ftp.ncbi.nlm.nih.gov/blast/db). All technical details on the pHMM library and the detection rules are available in Supplementary Tables S1 and S2, respectively.
Gene clusters are defined by locating clusters of signature gene pHMM hits spaced within <10 kb mutual distance. To include flanking accessory genes, gene clusters are extended by 5, 10 or 20 kb on each side of the last signature gene pHMM hit, depending on the gene cluster type detected. As a consequence of this greedy methodology, gene clusters that are spaced very closely together may be merged into ‘superclusters’. These gene clusters are indicated in the output as ‘hybrid clusters’; they may either represent a single gene cluster which produces a hybrid compound that combines two or more chemical scaffold types, or they may represent two separate gene clusters which just happen to be spaced very closely together.
Medema M.H., Blin K., Cimermancic P., de Jager V., Zakrzewski P., Fischbach M.A., Weber T., Takano E, & Breitling R. (2011). antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Research, 39(Web Server issue), W339-W346.
Publication 2011
Amino Acid Sequence Aminoglycosides Anabolism Bacteriocins beta-Lactams DNA Library ectoine Gene Clusters Gene Products, Protein Genes Hybrids indole Lantibiotics Melanins Protein Biosynthesis Proteins Siderophores Synthase, Fatty Acid Terpenes
4
Comprehensive Microbial Secondary Metabolite Prediction
Cited 13 times
We used PRISM 4 and antiSMASH 5 to predict the chemical structures of secondary metabolites encoded within 3759 complete bacterial genomes and 6362 metagenome-assembled genomes (MAGs). All bacterial genomes with an assembly level of ‘Complete’ were downloaded from NCBI Genome, and a set of dereplicated genomes as determined by the Genome Taxonomy Database15 (link) were retained to mitigate the impact of highly similar genomes on our analysis. Similarly, a set of 7902 MAGs23 (link) was obtained from NCBI BioProject (accession PRJNA348753) and the subset of dereplicated genomes was retained. Detected BGCs were matched between PRISM and antiSMASH if their nucleotide sequence overlapped over any range. A small number of PRISM BGC types were mapped to more than one antiSMASH BGC type, including aminoglycosides (‘amglyccycl’ and ‘oligosaccharide’), type I polyketides (‘t1pks’ and ‘transatpks’), and RiPPs (‘bottromycin’, ‘cyanobactin’, ‘glycocin’, ‘head_to_tail’, ‘LAP’, ‘lantipeptide’, ‘lassopeptide’, ‘linaridin’, ‘microviridin’, ‘proteusin’, ‘sactipeptide’, and ‘thiopeptide’). The “hybrid” category encompassed all BGCs assigned any combination of two or more cluster types, i.e., it was not limited to hybrid NRPS-PKS BGCs. The “other” category encompassed aryl polyenes, bacteriocins, butyrolactones, ectoines, furans, homoserine lactones, ladderanes, melanins, N-acyl amino acids, NRPS-independent siderophores, phenazines, phosphoglycolipids, resorcinols, stilbenes, terpenes, and type III polyketides. Producing organism taxonomy was based on genome phylogeny and retrieved from the Genome Taxonomy Database15 (link).
Cheminformatic metrics, including molecular weight, number of hydrogen bond donors and acceptors, octanol-water partition coefficients, and Bertz topological complexity, were calculated in RDKit. Both platforms occasionally generated very small, non-specific structure predictions (for example, a single unspecified amino acid or a single malonyl unit) that did not provide actionable information about the chemical structure of the encoded product; to remove these from consideration, we applied a molecular weight filter to remove structures under 100 Da output by either platform. To evaluate the internal structural diversity of each set of predicted structures, we computed the distribution of pairwise Tcs for each set45 , taking the median pairwise Tc instead of the mean as a summary statistic to ensure robustness against outliers. Structural similarity to known natural products was assessed using the RDKit implementation of the ‘natural product-likeness’ score22 (link), and by the median Tc between predicted structures and the known secondary metabolite structures deposited in the NP Atlas database46 (link).
Skinnider M.A., Johnston C.W., Gunabalasingam M., Merwin N.J., Kieliszek A.M., MacLellan R.J., Li H., Ranieri M.R., Webster A.L., Cao M.P., Pfeifle A., Spencer N., To Q.H., Wallace D.P., Dejong C.A, & Magarvey N.A. (2020). Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences. Nature Communications, 11, 6058.
Publication 2020
Amino Acids Aminoglycosides Bacteriocins Base Sequence bottromycin cyanobactins Donors Furans Genome Genome, Bacterial Head homoserine lactone Hybrids Hydrogen Bonds Melanins Metagenome Natural Products Octanols Oligosaccharides Phenazines Polyenes Polyketides prisma Prokaryotic Cells Resorcinols Secondary Metabolism Siderophores Stilbenes Tail Terpenes
5
Phytochemical Analysis of Plant Extracts
Cited 9 times
Qualitative phytochemical analyses of both the extracts were performed by following the protocol of Adetuyi and Popoola [26 ], Trease and Evans [27 ], and Sofowora [28 ].
Tannins. 200 mg of plant material was boiled in 10 mL distilled water and few drops of FeCl3 were added to the filtrate; a blue-black precipitate indicated the presence of Tannins.
Alkaloids. 200 mg plant material was boiled in 10 mL methanol and filtered. 1% HCl was added followed by 6 drops of Dragendorff reagent, and brownish-red precipitate was taken as evidence for the presence of alkaloids.
Saponins (Frothing test). 5 mL distilled water was added to 200 mg plant material. 0.5 mL filtrate was diluted to 5 mL with distilled water and shaken vigorously for 2 minutes. Formation of stable foam indicates the presence of saponins.
Cardiac Glycosides (Keller-Kiliani test). 2 mL filtrate was treated with 1 mL glacial acetic acid containing few drops of FeCl3.Conc. H2SO4 was added to the above mixture giving green-blue colour depicting the positive results for presence of cardiac glycosides.
Steroids (Liebermann-Burchard reaction). 200 mg plant material was added in 10 mL chloroform. Acetic anhydride was added in the ratio of 1 : 1 which resulted into the formation of blue-green ring pointing towards the presence of steroids.
Terpenoids (Salkowski test). To 200 mg plant material 2 mL of chloroform (CHCl3) and 3 mL of concentrated sulphuric acid (H2SO4) were carefully added. A reddish brown colouration signified the presence of terpenoids.
Flavonoids. To the aqueous filtrate 5 mL of dilute ammonia solution was added, followed by concentrated H2SO4. A yellow colouration indicated the presence of flavonoids.
Phlobatannins. The deposition of a red precipitate denoted the presence of phlobatannins when 200 mg of plant material was dissolved in 10 mL of aqueous extract and few drops of 1% HCl were added in the boiling tube.
Anthraquinones. 500 mg of dried plant leaves were boiled in 10% HCl for 5 mins and filtrate was allowed to cool. Equal volume of CHCl3 with few drops of 10% NH3 was added to 2 mL filtrate. The formation of rose-pink colour implies the presence of Anthraquinones.
Reducing Sugars. To the 10 mL of aqueous extract a few drops of Fehling's solution A and B were added; an orange red precipitate suggests the presence of reducing sugars.
Mujeeb F., Bajpai P, & Pathak N. (2014). Phytochemical Evaluation, Antimicrobial Activity, and Determination of Bioactive Components from Leaves of Aegle marmelos. BioMed Research International, 2014, 497606.
Publication 2014
Acetic Acid acetic anhydride Alkaloids Ammonia Anthraquinones Cardiac Glycosides Chloroform Flavonoids Methanol Phytochemicals Plant Leaves Plants Saponins Steroids Sugars Sulfuric Acids Tannins Terpenes

Most recents protocols related to «Terpenes»

1
Dehydrogenation of Terpenes to Cymene
Not available on PMC !

Example 5

Dehydrogenation of terpenes to cymene was evaluated in the fixed bed reactor system depicted in FIG. 2. A mixture of terpenes, primarily rich in Limonene and phellandrene was fed into the reactor at 0.5 mL/min, in the presence of nitrogen at 10 ml/min with a temperature of 300° C. in the reactor. The reactor was packed with 12 g of 5% Palladium on Alumina catalyst. The resulting product show >96% of para cymene.

US11873277B2. Circular economic methods for fragrance ingredients (2024-01-16). International Flavors & Fragrances Inc. [US]. Inventors: Sunitha R. Tadepalli [US], Paul Daniel Jones [US], Geatesh K. Tampy [US], John P. Cherkauskas [US].
Patent 2024
Cymene d-Limonene Nitrogen Oxide, Aluminum Palladium Terpenes
2
Formulation and Stability of Cannabinoid Composition
Not available on PMC !

Example 1

A pharmaceutical composition was prepared as described below. The following products were used in the amounts and concentrations specified:

    • 1. About 20 g cannabinoid distillate
    • 2. About 35 g Ethanol 95%
    • 3. About 40 g maltodextrin/gum acacia mixture
    • The cannabinoid distillate was weighed in a glass beaker. Ethanol 95% was added to the same beaker. The contents of the beaker were allowed to dissolve on a hot plate set to 55° C.

The above solution was combined with the maltodextrin/gum acacia in a planar mixer and was gently mixed until well incorporated.

The above mixture was passed through a granulation screen into a second bowl. This bowl was placed into a vacuum oven at 55° C. for 12 hours. The powder was stirred at least one during this time frame.

The formulation above was tested for potency and stability after 1 year of storage. After this period, no loss of potency was observed (as measured by HPLC), the formulation was visibly stable at room temperature and readily fluid when shaken.

US11857589B2. Water-soluble, powdered cannabinoid and/or terpene extract (2024-01-02). MEDPHARM HOLDINGS, LLC [US]. Inventors: Scott Karolchyk [US].
Patent 2024
Cannabinoids Ethanol Gum Arabic High-Performance Liquid Chromatographies maltodextrin Pharmaceutical Preparations Powder Reading Frames Terpenes Vacuum
3
Metabolomic Analysis of Hormone Treatments
Out of the 91 selected features, 71 were successfully classified (Fig. 5). 45 classes were assigned with 20 belonging to primary metabolism (representing 30 features) and 16 belonging to specialized metabolism (representing 27 features). The remaining eight classes were too broad to be constrained to a specific type of metabolism (representing 14 features). The most detected primary metabolic classes were peptides, amino acids, fatty acids, carboxylic acid derivatives and carbohydrates/carbohydrate conjugates. At the superclass level (Djoumbou Feunang et al., 2016 (link)), the largest groups were the glycosylated compounds, organonitrogen compounds, and the amines. A detailed explanation of the significant feature fluctuations can be found in the Supplementary Information. The functional relations of the selected compounds were determined by calculating a molecular network. The network revealed that the selected compounds related to the hormone treatments were largely involving biochemical pathways of alkaloids, amino acids and peptides, carbohydrates, fatty acids, polyketides, shikimates and phenylpropanoids, and terpenoids (Fig. 6).

Heatmap showing the 91 partly annotated variables selected by the normalized BORUTA. The y-axis displays the clustering of samples and the x-axis displays the clustering of selected features. R2 = 0.75, RMSE = 1.095445, MAE = 0.8. Black boxes were drawn to better differentiate the shifts in the samples. Blue indicates that a feature was produced less than the control and red indicates that a feature was produced more than the control

Molecular network showing the relationships of the selected compounds to pathways and compound classes. More information regarding the selected compounds is available in the Supplementary Material

Blatt-Janmaat K., Neumann S., Schmidt F., Ziegler J., Qu Y, & Peters K. (2023). Impact of in vitro phytohormone treatments on the metabolome of the leafy liverwort Radula complanata (L.) Dumort. Metabolomics, 19(3), 17.
Publication 2023
Alkaloids Amines Amino Acids Carbohydrates Carboxylic Acids derivatives Epistropheus Fatty Acids Hormones Metabolism Peptides Polyketides Terpenes
4
Genotoxicity of Terpenoids on Bar Mutation

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023
Females Terpenes
5
Murine LIM Model for Myopia Induction
A murine LIM model was prepared as previously reported40 (link). We created a mouse eyeglass frame that conformed to the contour of the mouse's head and printed it out using a three-dimensional printer. A negative 30 D lens made of PMMA was created for myopia induction. Myopic induction using the − 30 D lens showed greater myopic shift compared to the form-deprivation myopic model40 (link). With some differences from the LIM model used previously, we used binocular myopic induction instead of monocular induction. The left and right eyes of the glasses were adjusted by the shape of the mouse skull frame and fixed on the stick with a screw, and then glued the Stick to the mouse skull with a self-cure dental adhesive system. This was done under general anesthesia with the combination of midazolam (Sandoz K.K., Minato, Japan), medetomidine (Domitor®, Orion Corporation, Turku, Finland), and butorphanol tartrate (Meiji Seika Pharma Co., Ltd., Tokyo, Japan) (MMB). The dosage for each mouse was 0.01 ml/g.
During the myopia induction phase, mice were given either normal (MF, Oriental Yeast Co., Ltd, Tokyo, Japan) or mixed chow containing the candidate chemical 0.0667 percent GBEs (INDENA JAPAN CO., Tokyo, Japan #9,033,008). 0.0667% GBEs contain 24% of the flavonol glycosides of quercetin, kaempferol, and isorhamnetin and 6% terpene trilactones. The corresponding concentration of GBEs mixed chow was 200 mg/kg/day, which is consistent with the concentration of GBEs that causes the significantly high activity of EGR-1 in vitro experiments. The addition of GBEs and the production of 0.0667% GBEs mixed chow are all produced by chow manufacturing company (Oriental Yeast Co., LTD., Tokyo, Japan).
Hou J., Mori K., Ikeda S.I., Jeong H., Torii H., Negishi K., Kurihara T, & Tsubota K. (2023). Ginkgo biloba extracts improve choroidal circulation leading to suppression of myopia in mice. Scientific Reports, 13, 3772.
Publication 2023
3-methylquercetin Asian Persons Butorphanol Tartrate Cranium Dental Health Services EGR1 protein, human Eyeglasses Flavonols General Anesthesia Glycosides Head kaempferol Lens, Crystalline Medetomidine Midazolam Mus Myopia Polymethyl Methacrylate Quercetin Reading Frames Self Cure adhesive Terpenes Yeast, Dried

Top products related to «Terpenes»

1
Limonene by Merck Group
Sourced in United States, Germany, Italy, United Kingdom, Spain, Mexico, China, Brazil, Switzerland, Canada, Czechia
Limonene is a naturally occurring hydrocarbon found in the rinds of citrus fruits. It is commonly used as a solvent in laboratory settings due to its ability to dissolve a wide range of organic compounds.
2
Linalool by Merck Group
Sourced in United States, Germany, Italy, United Kingdom, China, Spain, France, Brazil, Switzerland, Poland, Australia, Hungary, Belgium, Sao Tome and Principe
Linalool is a naturally occurring terpene alcohol found in various plant species. It is a colorless to pale yellow liquid with a floral, citrus-like aroma. Linalool is commonly used as a fragrance ingredient in personal care products and as a flavoring agent in food and beverages. Its core function is as a chemical precursor and intermediate in the synthesis of other compounds.
3
α pinene by Merck Group
Sourced in United States, Germany, Italy, United Kingdom, Spain, Brazil, Canada, Switzerland, France, Sao Tome and Principe, Japan, Poland, India
α-pinene is a naturally occurring organic compound that is commonly used in laboratory settings. It is a bicyclic monoterpene with the molecular formula C₁₀H₁₆. α-pinene serves as a versatile starting material for various chemical reactions and synthesis processes.
4
Hp 5ms column by Agilent Technologies
Sourced in United States, Spain, Germany, Canada, Japan
The HP-5MS column is a fused silica capillary column used for gas chromatography. It is designed for the separation and analysis of a wide range of organic compounds.
5
Whatman no 1 filter paper by Cytiva
Sourced in United Kingdom, Germany, United States, Switzerland, India, Japan, China, Australia, France, Italy, Brazil
Whatman No. 1 filter paper is a general-purpose cellulose-based filter paper used for a variety of laboratory filtration applications. It is designed to provide reliable and consistent filtration performance.
6
Dimethyl sulfoxide (dmso) by Merck Group
Sourced in United States, Germany, United Kingdom, China, Italy, Sao Tome and Principe, France, Macao, India, Canada, Switzerland, Japan, Australia, Spain, Poland, Belgium, Brazil, Czechia, Portugal, Austria, Denmark, Israel, Sweden, Ireland, Hungary, Mexico, Netherlands, Singapore, Indonesia, Slovakia, Cameroon, Norway, Thailand, Chile, Finland, Malaysia, Latvia, New Zealand, Hong Kong, Pakistan, Uruguay, Bangladesh
DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
7
Ethanol by Merck Group
Sourced in Germany, United States, United Kingdom, Italy, India, France, China, Australia, Spain, Canada, Switzerland, Japan, Brazil, Poland, Sao Tome and Principe, Singapore, Chile, Malaysia, Belgium, Macao, Mexico, Ireland, Sweden, Indonesia, Pakistan, Romania, Czechia, Denmark, Hungary, Egypt, Israel, Portugal, Taiwan, Province of China, Austria, Thailand
Ethanol is a clear, colorless liquid chemical compound commonly used in laboratory settings. It is a key component in various scientific applications, serving as a solvent, disinfectant, and fuel source. Ethanol has a molecular formula of C2H6O and a range of industrial and research uses.
8
Myrcene by Merck Group
Sourced in United States, Germany, Italy, United Kingdom
Myrcene is a lab equipment product manufactured by Merck Group. It is a volatile organic compound commonly used as a reference standard for analytical and research applications. The core function of Myrcene is to serve as a calibration and quality control material for various analytical techniques, such as gas chromatography and mass spectrometry.
9
β pinene by Merck Group
Sourced in United States, Germany, Italy, Brazil, United Kingdom, Japan
β-pinene is a naturally occurring bicyclic monoterpene hydrocarbon found in the essential oils of various plants. It is a colorless liquid with a characteristic pine-like odor. β-pinene is commonly used as a precursor in the synthesis of various organic compounds and as a component in fragrances and flavors.
10
7890a gc system by Agilent Technologies
Sourced in United States, Germany, Spain
The 7890A GC system is a gas chromatograph designed for analytical applications. It is capable of performing separations and quantitative analysis of complex mixtures. The system includes an oven, injector, and detector to facilitate the chromatographic process.

More about "Terpenes"

Terpenes are a diverse class of organic compounds found in plants, particularly in essential oils.
They are characterized by their distinct aromatic properties and have been the subject of increasing scientific interest due to their potential therapeutic applications.
Terpenes exhibit a wide range of biological activities, including anti-inflammatory, analgesic, and antimicrobial effects.
This comprehensive overview covers the chemical structure, biosynthesis, and pharmacological properties of terpenes, enabling researchers to explore their role in various areas of medicine and biology.
Synonyms for terpenes include isoprenoids, terpenoids, and essential oil constituents.
Subtopics related to terpenes include: - Limonene: A monocyclic monoterpene found in citrus fruits with potential anti-cancer and anti-inflammatory properties. - Linalool: A monoterpene alcohol found in many plants, such as lavender, with reported sedative, analgesic, and anti-anxiety effects. - α-Pinene and β-Pinene: Bicyclic monoterpenes with potential antimicrobial, anti-inflammatory, and bronchodilatory activities. - Myrcene: A monoterpene with anti-inflammatory, analgesic, and sedative effects, often found in cannabis and other plants.
Experimental techniques used in terpene research include: - HP-5MS column: A common gas chromatography (GC) column used for the separation and analysis of terpenes and other volatile compounds. - Whatman No. 1 filter paper: A type of filter paper often used in the extraction and purification of terpenes from plant materials. - DMSO and Ethanol: Solvents commonly used in the extraction and solubilization of terpenes for various applications.
By understanding the diverse properties and applications of terpenes, researchers can optimize their experiments and unlock new insights in the fields of medicine, biolodycs, and beyond.