Dendrobium candidum free amino acid prediction failure diagnosis method
By establishing a unified diagnostic evidence intermediate quantity E and an active perturbation script, the problems of matrix interference, link anomalies, and domain offset in the detection of free amino acids in Dendrobium officinale were solved. This achieved consistent data recording and reliable prediction output throughout the entire detection process, improving the accuracy and stability of the detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INST OF MEDICINAL PLANTS YUNNAN ACAD OF AGRI SCI
- Filing Date
- 2026-05-15
- Publication Date
- 2026-07-14
AI Technical Summary
Existing technologies for detecting free amino acids in Dendrobium officinale suffer from problems such as sensitivity to matrix interference, lack of effective identification and diagnosis of abnormalities in the detection chain, lack of sensitive detection of domain offset, and scattered records of process data and intermediate results, resulting in insufficient prediction accuracy and stability.
A unified diagnostic evidence intermediate quantity E is established, including a version number field, a version chain verification field, and an execution license identifier field. Through process meta-data, isotope internal standard response baseline, link health, dual-channel dynamic response, structured state evidence, active perturbation response signature, domain offset fingerprint, etc., the complete recording of data and intermediate results of the entire detection task process and cross-stage consistency constraints are realized. Combined with active perturbation scripts and electrochemical response acquisition, structured state evidence is generated and consistency verification and gating judgment are performed.
It achieves complete recording of data and intermediate results throughout the entire detection task process and cross-stage consistency constraints, improves the traceability and verifiability of prediction output, enhances sensitivity to link health, domain offset and abnormal patterns, reduces the risk of misjudgment, and improves the credibility and robustness of prediction values.
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