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Foundations: Food Spectroscopy Applications

This chapter surveys common applications of Raman, FTIR, and NIR spectroscopy across food matrices. It provides concrete examples that motivate later preprocessing, feature extraction, and modeling choices.

1. Edible oils and fats

  • Questions: What oil type is present? Is it adulterated (e.g., cheap oil mixed into EVOO)? Has heating/oxidation altered quality?
  • Signatures: C=C stretch (~1655 cm⁻¹), ester C=O (~1740–1750 cm⁻¹), CH2/CH3 stretches (2800–3000 cm⁻¹), trans vs cis markers.
  • Typical tasks: Classification (oil_type), ratio monitoring (unsaturation), regression (mixture fractions).
  • See also: Oil authentication workflow, Mixture analysis.

2. Dairy, meat, and protein-rich products

  • Questions: Species/source verification, spoilage/freshness, protein/lipid ratios.
  • Signatures: Amide I/II (≈1650/1550 cm⁻¹), CH stretches, lipid bands; water OH (FTIR).
  • Typical tasks: Classification; QC via one-class models; trend analysis during processing.
  • See also: Domain templates.

3. Microbial identification and contamination

  • Questions: Which species/strain is present? Are there out-of-distribution signals?
  • Signatures: Nucleic acids, proteins (amide), specific polysaccharide/cell-wall features; often subtle and require careful preprocessing.
  • Typical tasks: Multi-class classification; novelty detection; clustering for exploratory analysis.
  • See also: Batch quality control.

4. Spices, grains, and plant materials

  • Questions: Authenticity, adulteration, origin/varietal, moisture content.
  • Signatures: Polysaccharide bands (C–O–C), phenolics/aromatics, carotenoids (Raman), moisture OH (NIR/FTIR).
  • Typical tasks: Classification and regression (moisture/protein), fingerprint similarity.

5. Process and quality monitoring

  • Questions: Real-time tracking of frying/heating, fermentation, drying, or storage effects.
  • Signatures: Time-dependent changes in unsaturation bands, Maillard/oxidation indicators, water/protein shifts.
  • Typical tasks: Ratio trends vs time/temperature; regression/ANOVA; QC alarms.
  • See also: Heating quality monitoring.

6. Hyperspectral imaging

  • Questions: Spatial distribution of components/contaminants in surfaces or slices.
  • Signatures: Same as above, but per pixel; enables mapping and segmentation.
  • Typical tasks: Pixel-wise classification, ratio/intensity maps, cluster maps.
  • See also: Hyperspectral mapping.

Summary

  • Food spectroscopy spans authentication, adulteration, process monitoring, and spatial mapping.
  • Raman/FTIR/NIR modalities highlight different bonds; choose by matrix and question.
  • Later chapters show how preprocessing and models are adapted per application.

Further reading

When Results Cannot Be Trusted

⚠️ Red flags for food spectroscopy applications:

  1. Single-source validation (all "olive oil" from one producer; all "adulterated" from single batch)
  2. Intra-source variability unknown; model may learn source-specific patterns
  3. Generalization to other sources unverified

  4. Fix: Include multiple sources, varieties, origins; validate across independent suppliers

  5. Matrix effects ignored (oil spectra compared to dairy spectra without accounting for matrix differences)

  6. Different food matrices have different spectral baselines, absorption
  7. Direct comparison invalid

  8. Fix: Normalize by matrix; use matrix-matched standards; validate within-matrix first

  9. Aging/storage effects not controlled (samples of different ages compared as if equivalent)

  10. Oxidation, ripening, degradation change spectra over time
  11. Age confounds with treatment

  12. Fix: Control storage time; document sample age; include age as covariate

  13. Adulteration detection without testing at realistic levels (model validated on 50% adulteration, deployed at 1%)

  14. Detection limit unestablished; method sensitivity unknown
  15. May miss realistic adulteration levels

  16. Fix: Test at 0.5%, 1%, 2%, 5%, 10%, 20%; report limit of detection

  17. Spectroscopy method choice not justified (using NIR for C=C bands best detected by Raman)

  18. Different methods have different sensitivities
  19. Mismatch reduces performance

  20. Fix: Match method to analyte; justify choice based on band strengths; benchmark alternatives

  21. Reference method comparisons missing (spectroscopy results not validated by HPLC/GC-MS/wet chemistry)

  22. Spectroscopy is indirect; orthogonal validation essential
  23. Can't confirm chemical interpretation without reference method
  24. Fix: Validate key findings with orthogonal methods; report agreement