The provided JSON configuration outlines a detailed prompt for generating a comprehensive entry on a flavor and fragrance material, specifically "tagete oil egypt (CAS: 8016-84-0)," for FlavScents.com. This prompt is designed for a technical research assistant and emphasizes clarity, accuracy, and relevance to formulation and safety contexts. Here’s a breakdown of the key components and requirements:

Key Components:

1. Material Type Handling:

   • The material is identified as a complex natural material (e.g., essential oil), which means it should be treated as a mixture rather than a single compound. This affects how the chemical information is presented, focusing on the material type and source rather than a molecular structure.

2. Depth Requirement:

   • The entry must be comprehensive, with a target length of 1100-1700 words for complex natural materials. Each section should be detailed, with a target of 120-220 words per section.

3. Output Format:

   • The entry should be structured with specific numbered headings, each requiring a "Citation hooks:" line to indicate relevant sources for further reference.

Sections to Include:

1. Identity & Chemical Information:

   • Provide common names, CAS number, and other identifiers. Discuss the material type and source.

2. Sensory Profile:

   • Describe odor and flavor descriptors, including intensity and typical sensory roles.

3. Natural Occurrence & Formation:

   • Detail known natural sources and formation pathways, and discuss relevance to "natural flavor" or "natural fragrance" designations.

4. Use in Flavors:

   • Discuss flavor categories, functional roles, typical use levels, and stability considerations.

5. Use in Fragrances:

   • Describe fragrance families, functional roles, concentration ranges, and volatility.

6. Key Constituents (Typical):

   • List major or character-impact constituents, noting variability in composition.

7. Regulatory Status (Regional Overview):

   • Summarize regulatory treatment across different regions, distinguishing explicit approvals and known uncertainties.

8. Toxicology, Safety & Exposure Considerations:

   • Discuss safety in the context of oral, dermal, and inhalation exposure, addressing differences between food and fragrance applications.

9. Practical Insights for Formulators:

   • Provide expert insights on the material's value, synergies, and common formulation pitfalls.

10. Confidence & Data Quality Notes:

    • Summarize well-established data, industry practices, and known data gaps.

11. QA Check:

    • Confirm all sections are present, citation hooks are included, and specific requirements (e.g., ppm ranges, exposure routes) are met.

Style & Constraints:

• The writing should be for experienced professionals, avoiding marketing language and focusing on interpretive insights rather than encyclopedic repetition.

This structured approach ensures that the entry is not only informative but also practical for professionals in the flavor and fragrance industry, providing them with the necessary insights and data to make informed decisions.

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.

Generated 2026-02-13 18:51:46 GMT (p2)