| CAS (Single) | 138-86-3 |
| FEMA | N/A |
| EINECS | 205-341-0 |
| Synonyms |
|
| JECFA Food Flavoring | N/A |
| JECFA Food Additive | N/A |
| DG SANTE Food Flavourings | 01.001 limonene |
| DG SANTE Food Contact Materials | N/A |
| FDA UNII | 9MC3I34447 |
| CoE Number | 491 |
| XlogP3-AA | N/A |
| Molecular Weight | 136.23752 |
| Molecular Formula | C10 H16 |
| Food Chemicals Codex Listed | No |
| Appearance | colorless clear liquid (est) |
| Assay | 95.00 to 100.00 sum of isomers |
| Specific Gravity | 0.84300 to 0.85100 @ 20.00 °C. |
| Lbs/Gal (est) | 7.023 to 7.089 |
| Refractive Index | 1.47600 to 1.48200 @ 20.00 °C. |
| Melting Point | N/A |
| Boiling Point | 176.00 °C. @ 760.00 mm Hg |
| Flash Point | 110.00 °F. TCC ( 43.33 °C. ) |
| Acid Value | N/A |
| Vapor Pressure | 1.550000 mmHg @ 25.00 °C. |
| Vapor Density | 4.7 ( Air = 1 ) |
| logP (o/w) | 4.57 |
| Soluble In |
|
| Occurrence |
|
| Organoleptic Notes |
|
| Odor | citrus citrus, herbal, terpenic, camphoreous, |
| Flavor | herbal herbal, citrus, aromatic, terpenic, pine, minty, woody, tropical, |
About FlavScents AInsights (Disclosure)
FlavScents AInsights integrates information from authoritative government, scientific, academic, and industry sources to provide applied, exposure-aware insight into flavor and fragrance materials. Data are drawn from regulatory bodies, expert safety panels, peer-reviewed literature, public chemical databases, and long-standing professional practice within the flavor and fragrance community. Where explicit published values exist, they are reported directly; where gaps remain, AInsights reflects widely accepted industry-typical practice derived from convergent sensory behavior, historical commercial use, regulatory non-objection, and expert consensus. All such information is clearly labeled to distinguish documented data from professional guidance or informed estimation, with the goal of offering transparent, practical, and scientifically responsible context for researchers, formulators, and regulatory specialists. This section is generated using advanced computational language modeling to synthesize and structure information from established scientific and regulatory knowledge bases, with the intent of supporting—not replacing—expert review and judgment.
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