Introduction to Introduction to Sensory Data Analysis - Camo
Introduction to Introduction to Sensory Data Analysis - Camo
Introduction to Introduction to Sensory Data Analysis - Camo
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4. Principal Component <strong>Analysis</strong>: PCA / a) Theory<br />
Map of variables<br />
High contribution on PC 2<br />
Firmness and Firmness inside are<br />
correlated<br />
Anticorrelated with Meltyness<br />
Not contributing<br />
<strong>to</strong> PC1 & 2<br />
High contribution on PC 1<br />
Toma<strong>to</strong> odor/flavor, Juciness, Sweetness, External color<br />
are anti‐correlated with Mealyness<br />
• Loadings can be<br />
visualized <strong>to</strong> map<br />
which variables<br />
have contributed<br />
<strong>to</strong> the score plot.<br />
• Variables far away<br />
from the center<br />
are well described<br />
and important<br />
• Variables near the<br />
center are less<br />
important.
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