Skip to content

Tutorial: Oil vs Chips Matrix Effects

  • Data: examples/data/oil_synthetic.csv, examples/data/chips_synthetic.csv
  • Notebook: examples/notebooks/02_oil_vs_chips_matrix_effects.ipynb
  • Protocol: examples/protocols/Chips_vs_Oil_MatrixEffects_v1.yml
  • Focus: Divergence markers, effect sizes, interpretation of ratios.

Tutorial – Oil vs chips matrix effects

What this tutorial covers

  • Problem: Matrix effects—markers may behave differently in pure oils vs chips.
  • Datasets: examples/data/oils.csv and examples/data/chips.csv (matching peak/ratio columns, metadata).
  • Protocol: examples/protocols/oil_vs_chips.yaml (divergence analysis).

Why it matters (theory)

Matrix components (starch/protein) can alter mean ratios, CV, and heating trends. Identifying matrix-robust vs matrix-sensitive markers is critical for QA. See rq_engine_theory.md.

CLI workflow

foodspec-run-protocol \
  --input examples/data/oils.csv \
  --input examples/data/chips.csv \
  --protocol examples/protocols/oil_vs_chips.yaml \
  --output-dir runs/oil_vs_chips_demo

foodspec-publish runs/oil_vs_chips_demo/<timestamp> --fig-limit 6
Open figures/ for divergence plots and trend charts; tables/ for oil_vs_chips summaries.

Example figure (from run bundle)

Matrix divergence

How to read the results

  • Divergence tables: look for significant differences (post-FDR) in mean, CV, or trends.
  • Effect sizes (e.g., Cohen’s d, slope deltas) quantify practical impact.
  • Interpretation tags (e.g., “stable in oil, unstable in chips”) highlight matrix sensitivity.
  • Prefer matrix-robust markers for cross-matrix QA; avoid markers that flip behavior between oil and chips.