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NIR-analysis v1.1.0

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@chenjia5268 chenjia5268 released this 25 Oct 20:32
v1.1.0
8602e53

Archived version

NIR-analysis skill v1.1.0.

  • Existing tag: v1.1.0
  • Commit: 8602e538896172830cda150bce5b06845a791fc9

This release preserves the code at the existing tag; it does not introduce changes to the analytical algorithms.

Software and documentation

The source archive includes:

  • Skill instructions: .agents/skills/nir-analysis/SKILL.md
  • Reusable Python modules: .agents/skills/nir-analysis/scripts/nir_library/
  • Method and validation guidance: .agents/skills/nir-analysis/references/
  • Dependency lists: requirements.txt and requirements-dev.txt
  • Installation instructions and data-access guidance: README.md

Running the skill

  1. Download and extract the source archive for this tag.
  2. Create a Python environment and install the dependencies listed in requirements.txt, following the README.
  3. Install or enable .agents/skills/nir-analysis/ in a compatible agent client.
  4. Specify the input data structure, analytical task, preprocessing candidates, parameter-search ranges, validation design, random seed and required outputs. Review the generated implementation and outputs as described in the skill instructions.

Associated tea-study settings

The study-specific software environment, preprocessing candidates, model-optimisation ranges and validation procedures are described in Section 2.4 of the accompanying manuscript. Representative natural-language task specifications are provided in Supplementary Table S5.

  • Classification: stratified four-fold cross-validation within the calibration set, with random_state=42.
  • Moisture regression: leave-one-product-out cross-validation within the calibration set.
  • Preprocessing selection and hyperparameter optimisation are restricted to calibration data; held-out and external validation use the selected configurations without further optimisation.

Data policy

No experimental tea data, third-party paper datasets, manuscript files, figures, fitted models or prediction outputs are attached to this release. The source archive contains the reusable skill code and documentation only.

Tea-data access follows the Data Availability Statement in the manuscript. Third-party datasets must be obtained from their original sources under the applicable terms; source links are listed in the README.

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