Introduction to Introduction to Sensory Data Analysis - Camo

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4. Principal Component Analysis: PCA / a) Theory Bi‐Plot with Varimax rotation
4. Principal Component Analysis: PCA / a) Theory • Some variables are correlated : Conclusions on PCA – ”Firm” and ”Firm inside” //// ”Meltiness” – ”Tomato odor”, ”Tomato flavor”, ”Juiciness”, ”Sweetness”, ”External color” //// ”Mealyness” Some of those variables can be selected if we want to save on time of sensory analyses • Some variables are not descriminative: ”Acidity” and ”Skin width” They don’t have to be tested in the future. • Some tomato varieties are presenting similar characteristics: – F and K are ”Firm” ” – G and H are ”Firm inside” – A, O and C are ”Juicy” – Q and D are ”Melty” Some can be dropped in a consumer study
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
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Page 43: 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
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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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