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Study Design and Data Requirements

Purpose: Design statistically robust spectroscopy studies with sufficient replication and randomization.
Audience: Researchers planning new experiments or validating existing data.
Time to read: 10–15 minutes.
Prerequisites: Basic understanding of hypothesis testing and ANOVA.

Good statistical practice starts with good design. This chapter summarizes replication, balance, randomization, and instrument considerations for food spectroscopy, and how these affect power and assumptions.

Replication and sample size

  • Multiple spectra per sample and per group capture variability (sample prep, instrument noise).
  • More replicates → lower standard error → higher power. Aim for at least a few replicates per group for ANOVA/t-tests.
  • Avoid pseudo-replication: repeated scans of one aliquot are not independent biological replicates.

Balance vs unbalance

  • Balanced designs (similar n per group) improve ANOVA robustness and power.
  • Unbalanced designs can inflate Type I error or reduce power; use Welch-type tests or carefully interpret ANOVA.

Randomization and blocking

  • Randomize acquisition order to reduce drift/systematic bias.
  • Block by batch/instrument if relevant; include batch as a factor in analysis when possible.

Instrument considerations

  • Calibration and drift: frequent calibration reduces variance; monitor drift over time.
  • Noise: higher noise reduces power; invest in preprocessing (baseline, smoothing, normalization) to stabilize variance.
  • Alignment: ensure wavenumbers are aligned across runs; misalignment can inflate variance and violate assumptions.

Data quality and preprocessing consistency

  • Use consistent preprocessing across all groups (baseline, normalization, cropping).
  • Document instrument settings, laser wavelength, ATR crystal, etc., as they influence comparability.

Design suitability for ANOVA/MANOVA

flowchart LR
  A[Design check] --> B{>= 2-3 reps per group?}
  B -->|No| C[Increase replication or rethink test]
  B -->|Yes| D{Balanced groups?}
  D -->|Yes| E[Proceed with ANOVA/MANOVA]
  D -->|No| F[Consider Welch-type tests / interpret carefully]
  E --> G[Randomize order; include batch if needed]
  F --> G

Reporting

  • State group sizes, replication scheme, randomization/blocking, and any exclusions.
  • Note instrument model/settings and preprocessing applied.
  • Tie design choices to assumptions (normality, homoscedasticity) and power considerations.

When Results Cannot Be Trusted

⚠️ Red flags that invalidate study conclusions:

  1. Insufficient replication (n < 3 per group)
  2. Single measurements are too noisy for food spectroscopy
  3. Natural sample variability alone can exceed your signal

  4. Fix: Increase replicates to ≥3 per group; report confidence intervals, not point estimates

  5. Pseudo-replication (multiple scans of same aliquot)

  6. Repeated scans of identical sample are NOT independent; they're autocorrelated
  7. Statistical tests assume independence; violating this inflates false positives

  8. Fix: Analyze ≥3 distinct aliquots; document which measurements come from same sample

  9. Batch confounding (all treatment A = Day 1, all treatment B = Day 2)

  10. Systematic drift (laser aging, temperature) mimics biological differences
  11. Impossible to know if group difference is real or instrumental drift

  12. Fix: Randomize sample order across batches; use batch-aware CV (GroupKFold); include batch as factor in ANOVA

  13. Gross sample imbalance (n_A = 50, n_B = 3)

  14. Power is limited by smallest group; ANOVA assumptions (homoscedasticity) violated
  15. May mask true effects or generate spurious significance

  16. Fix: Aim for balanced or near-balanced designs; if imbalanced, use Welch's ANOVA or permutation tests

  17. No randomization (samples processed in order: A, A, A, B, B, B)

  18. Systematic bias from drift, operator fatigue, or instrument warmup accumulates within groups
  19. Differences may be temporal, not biological

  20. Fix: Randomize acquisition order; interleave groups across time

  21. Undisclosed preprocessing variations

  22. If baseline correction λ or smoothing window changes between samples, spectra are not comparable
  23. Different preprocessing → different statistics, even on same raw data

  24. Fix: Freeze preprocessing parameters before analysis; document and report all preprocessing details

  25. Ignoring known batch effects (batch effect visible in PCA, not adjusted)

  26. Multi-day or multi-instrument studies require batch correction (ComBat, SVA) or batch-aware CV
  27. Ignoring batch effects inflates variance and reduces power; may create false significance

  28. Fix: Include batch in ANOVA model, or use batch-correction; report residuals after batch adjustment

  29. No control for multiple testing (comparing 100 peaks, reporting p < 0.05 as significant)

  30. Each test has 5% chance of false positive; 100 tests → ~5 false positives expected by chance
  31. Uncorrected p-values are misleading
  32. Fix: Use Bonferroni, Benjamini–Hochberg FDR, or permutation tests; report corrected p-values

Further reading