Introduction to Principal Component Analysis (PCA)
Why PCA in Food Science?
Food analysis often generates high-dimensional data — for example, a single NIR spectrum may contain hundreds or thousands of wavelength measurements. PCA helps by:
- Reducing dimensionality while preserving the most important patterns
- Visualizing sample groupings and detecting outliers
- Identifying the most informative wavelengths for further analysis
How PCA Works
- Standardize the data matrix
- Compute the covariance matrix
- Extract eigenvalues and eigenvectors
- Select the top k principal components
- Project the data onto the new subspace
Applications in Food Industry
- Classification of olive oil origins
- Detection of food adulteration
- Quality grading of wheat and grains
- Monitoring fermentation processes