The provided JSON configuration outlines a detailed prompt for generating a comprehensive entry on a specific flavor or fragrance material, in this case, (E)-tiglic acid (CAS: 80-59-1). The prompt is designed for use by a technical research assistant contributing to FlavScents.com, a specialized resource for professionals in the flavor and fragrance industry. The entry must be technically accurate, prioritize clarity, and provide context on safety and formulation relevance.
Key Elements of the Prompt:
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Material Type Handling:
- If the material is a single compound, the entry should focus on its chemical identity and properties.
- If it's a complex natural material, the entry should treat it as a mixture, detailing its key constituents and noting variability in composition.
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Depth Requirement:
- The entry must be comprehensive, with a target length of 900-1400 words for single compounds and 1100-1700 words for complex materials.
- Each section must be substantive, with a target length of 120-220 words, even if specific data is missing.
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Output Format:
- The entry should follow a structured format with numbered headings, covering sections such as Identity & Chemical Information, Sensory Profile, Natural Occurrence & Formation, Use in Flavors, Use in Fragrances, Regulatory Status, Toxicology, Safety & Exposure Considerations, Practical Insights for Formulators, and Confidence & Data Quality Notes.
- Each section must include a "Citation hooks:" line listing relevant sources.
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Quality Assurance:
- A final "QA Check" section is required to confirm that all sections are present, citation hooks are included, and specific requirements (e.g., ppm ranges in the flavor section, routes in toxicology, regions in regulatory) are met.
Sections Overview:
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Identity & Chemical Information: Details the chemical identity, including common names, IUPAC name, CAS number, molecular formula, and relevant identifiers.
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Sensory Profile: Describes the odor and flavor characteristics, including intensity and typical sensory roles.
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Natural Occurrence & Formation: Lists natural sources and formation pathways, discussing relevance to "natural" designations.
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Use in Flavors: Covers flavor applications, functional roles, typical use levels, and stability considerations.
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Use in Fragrances: Describes fragrance applications, functional roles, concentration ranges, and volatility.
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Regulatory Status: Summarizes regulatory treatment across different regions, highlighting approvals and uncertainties.
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Toxicology, Safety & Exposure Considerations: Discusses safety in terms of oral, dermal, and inhalation exposure, addressing risk profiles for food and fragrance applications.
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Practical Insights for Formulators: Provides expert insights on the material's value, synergies, and common formulation pitfalls.
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Confidence & Data Quality Notes: Summarizes well-established data, industry practices, and known data gaps.
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QA Check: Confirms the presence and completeness of all required sections and adherence to specific requirements.
This structured approach ensures that the entry is thorough, well-researched, and useful for professionals in the flavor and fragrance industry.
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 2026-02-05 11:45:02 GMT (p2)