mixture of (35-45 % w/w) 1,6-diamino-2,2,4-trimethylhexane and (55-65 % w/w)1,6-diamino-2,4,4-trimethylhexane

Full Material List

N/A

Identifiers

CAS (Single)	25513-64-8
FEMA	N/A
EINECS	N/A
Synonyms	1,6- hexanediamine, 2,2,4(or 2,4,4)-trimethyl- 2,2,4-(or 2,4,4)- trimethyl-1,6-hexanediamine 2,2,4(or 2,4,4)- trimethylhexane-1,6-diamine
JECFA Food Flavoring	N/A
JECFA Food Additive	N/A
DG SANTE Food Flavourings	N/A
DG SANTE Food Contact Materials	mixture of (35-45 % w/w) 1,6-diamino-2,2,4-trimethylhexane and (55-65 % w/w)1,6-diamino-2,4,4-trimethylhexane
FDA UNII	00PFI6E4JT
CoE Number	N/A
XlogP3-AA	N/A
Molecular Weight	N/A
Molecular Formula	N/A

Properties

Food Chemicals Codex Listed	No
Appearance	N/A
Assay	95.00 to 100.00
Specific Gravity	N/A
Lbs/Gal (est)	N/A
Refractive Index	N/A
Melting Point	N/A
Boiling Point	N/A
Flash Point	N/A
Acid Value	N/A
Vapor Pressure	N/A
Vapor Density	N/A
logP (o/w)	N/A
Soluble In	water, 2.851e+004 mg/L @ 25 °C (est)
Occurrence	not found in nature

Organoleptic

Organoleptic Notes	Odor and/or flavor descriptions from others (if found).
Odor	N/A
Flavor	N/A

AInsights BETA

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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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