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Record W2473860838 · doi:10.21273/hortsci.49.6.785

Essential Oil Yield and Composition of Garden Sage as a Function of Different Steam Distillation Times

2014· article· en· W2473860838 on OpenAlexaff
Valtcho D. Zheljazkov, Tess Astatkie, Santosh Shiwakoti, Shital Poudyal, Thomas Horgan, N. Kovatcheva, Anna Dobreva

Bibliographic record

VenueHortScience · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSAGECampheneSteam distillationMyrceneSalvia officinalisEssential oilLimoneneDistillationSabineneChemistryMonoterpeneComposition (language)BotanySalviaPulp and paper industryHorticultureFood scienceEnvironmental scienceOrganic chemistryBiologyOfficinalisEngineeringArt

Abstract

fetched live from OpenAlex

Garden sage ( Salvia officinalis L.) is a medicinal, culinary, ornamental, and essential oil plant with a wide range of ecological adaptation. Garden sage essential oil traditionally is extracted by steam distillation from the above-ground biomass and has widespread applications as an aromatic agent in the food and pharmaceutical industries as well as in perfumery and cosmetics. The hypothesis of this study was that the steam distillation time (DT) may significantly affect essential oil yield and composition of garden sage and, therefore, DT could be used as a tool to obtain oil with different composition. Therefore, the objective was to evaluate the effect of various steam DTs (1.25, 2.5, 5, 10, 20, 40, 80, and 160 minutes) on garden sage oil yield and composition. Most of the oil in the garden sage dry herbage was extracted in 10-minute DT; extending DT up to 160 minutes did not significantly increase oil yields. Overall, 39 oil constituents were identified in the garden sage essential oil. Fourteen oil constituents with the highest concentration in the oil were selected for statistical analyses. Monoterpenes represented the major percentage (58.2% to 84.1%) of oil composition followed by sesquiterpenes (4.0% to 16.1%) and diterpenes (0.3% to 7.6%). Overall, the monoterpene hydrocarbons (α-pinene, camphene, β-pinene, myrcene, and limonene) were eluted early in the steam distillation process, which resulted in their high concentration in the oil at 5- to 10-minute DT and relatively low concentrations in the oil obtained at 160-minute DT. In general, the concentration of sesquiterpenes (β-caryophyllene, α-humulene, and verdifloral) increased with increasing duration of the DT and reached their respective maximum concentrations in the oil at 160-minute DT. The relative concentrations of major constituents, camphor and cis-thujone, in the oil obtained at 2.5-minute DT were higher than in the oils obtained at longer DT. Therefore, if oil with high concentrations of camphor and cis-thujone is desirable, garden sage dried biomass ought to be steam distilled for 2.5 to 5 minutes and the oil collected. If oil with a high concentration of monoterpene hydrocarbons and a high concentration of oxygenated monoterpenes is desirable, then garden sage should be distilled for 20 minutes. If oil with a high concentration of the diterpene manool is desirable, then garden sage should be steam-distilled for 80 minutes. If oil with a high concentration of sesquiterpenes is desirable, then garden sage should be steam-distilled for 160 minutes. The duration of steam distillation can be used as an economical method to obtain garden sage oil with a different chemical composition. The regression models developed in this study can be used to predict garden sage oil yield and composition distilled for various amounts of time and to compare literature reports in which different durations of DT were used.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.191
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2014
Admission routes1
Has abstractyes

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