Introduction to Introduction to Sensory Data Analysis - Camo

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5. PLS regression / a) Theory Attributes explaining the preference Not important Important variables Preference is strongly correlated with ”External color” , ”Sweetness” , ”Toma<strong>to</strong> flavor” and ”Juiciness” And strongly anti‐correlated with ”M li ”
5. PLS regression / a) Theory Prediction quality Good R2 good correlation between prediction and measurement Good validation error small error (0.3) when predicting the preference: from 3 <strong>to</strong> 10
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 11 and 12: Sensory Data Analysis: Course outli
Page 13 and 14: 3. Inspection and preparation of th
Page 31 and 32: 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: 5. PLS regression / a) Theory Sampl
Page 63 and 64: 6. Summary CAMO Products Product Op
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Introduction to Introduction to Sensory Data Analysis - Camo

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