Introduction to Introduction to Sensory Data Analysis - Camo

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4. Principal Component Analysis: PCA / a) Theory Number of component to take into account Explained variance in cross‐validation With the explained variance in validation we decide to take into account 5 PCs
4. Principal Component Analysis: PCA / a) Theory Map of samples Average sample • Tomatoes displayed as a score plot. • The purpose is to describe products according to their sensory characteristics. • The relative positions of products reflect their similarities or differences.
Page 1 and 2: Introduction to Sensory Data Analys
Page 3 and 4: Sensory Data Analysis: Course outli
Page 5 and 6: 1. Why sensory data analysis Can we
Page 7 and 8: 1. Why sensory data analysis Sensor
Page 9 and 10: 2. Data collection and experimental
Page 13 and 14: 3. Inspection and preparation of th
Page 31 and 32: 4. Principal Component Analysis: PC
Page 39: 4. Principal Component Analysis: PC
Page 47 and 48: 4.Principal Component Analysis: PCA
Page 49 and 50: 5. PLS regression / a) Theory Regre
Page 51 and 52: 5. PLS regression / a) Theory Examp
Page 53 and 54: 5. PLS regression / a) Theory Do a
Page 55 and 56: 5. PLS regression / a) Theory Sampl
Page 57 and 58: 5. PLS regression / a) Theory Predi
Page 63 and 64: 6. Summary CAMO Products Product Op

Introduction to Introduction to Sensory Data Analysis - Camo

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