The provided JSON configuration outlines a detailed prompt for generating a comprehensive entry for a flavor and fragrance material, specifically (E)-2-decenal, for the FlavScents.com database. This prompt is designed for use by a technical research assistant and includes specific instructions on how to structure the entry, what information to include, and how to ensure quality and accuracy. Below is a breakdown of the key components and requirements of the prompt:
Key Components of the Prompt:
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Material Information: The prompt is focused on a single chemical compound, (E)-2-decenal, with its CAS number provided.
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Source Priority: The prompt specifies a hierarchy of sources to consult, prioritizing internal references from FlavScents and authoritative external sources like FEMA, EFSA, IFRA, and others.
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Material Type Handling: Instructions are given on how to handle single compounds versus complex natural materials, with specific guidelines for each.
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Depth Requirement: The prompt enforces a target word count for each section and the overall entry, ensuring comprehensive coverage of the topic.
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Output Format: The entry must follow a specific format with numbered headings and include a "Citation hooks" line under each section.
Sections to Include in the Entry:
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Identity & Chemical Information: Details about the compound, including common names, IUPAC name, CAS number, and other identifiers.
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Sensory Profile: Descriptions of odor and flavor characteristics, including thresholds and typical sensory roles.
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Natural Occurrence & Formation: Information on natural sources and formation pathways, and relevance to natural designations.
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Use in Flavors: Details on flavor applications, functional roles, typical use levels, and stability considerations.
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Use in Fragrances: Information on fragrance applications, functional roles, concentration ranges, and volatility.
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Key Constituents (Typical): Only for complex natural materials, listing major constituents and noting variability.
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Regulatory Status (Regional Overview): Summary of regulatory treatment across different regions, including explicit approvals and known uncertainties.
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Toxicology, Safety & Exposure Considerations: Discussion of safety in the context of oral, dermal, and inhalation exposure.
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Practical Insights for Formulators: Expert insights on the material's value, synergies, and common formulation pitfalls.
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Confidence & Data Quality Notes: Summary of data quality, industry practices, and known data gaps.
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QA Check: A checklist to ensure all required sections are present and meet the specified criteria.
Quality Assurance:
- The prompt includes a QA section to verify that all sections are present, citation hooks are included, and specific requirements for flavor, toxicology, and regulatory sections are met.
Style & Constraints:
- The writing style is tailored for experienced professionals, avoiding marketing language and focusing on interpretive insights.
This structured approach ensures that the entry is both comprehensive and technically accurate, providing valuable information 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 2026-02-07 23:20:08 GMT (p2)