Using partial bootstrap to evaluate the uncertainty associated with TCATA trajectories

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1 Using partial bootstrap to evaluate the uncertainty associated with TCATA trajectories John C. Castura Compusense Inc. Allison K. Baker Carolyn F. Ross Washington State University

2 Must 25.9 Bx 6 fermenters 4 fermenters 21 Bx Musts 27 Bx

3 21 Bx Musts 27 Bx ~10.5% ethanol v/v Wines ~15.5% ethanol v/v

4 ~10.5% ethanol v/v low ~15.5% ethanol v/v high ~10.5% ethanol v/v ~15.5% ethanol v/v ~15.5% ethanol v/v low low-to-high high

5 3 wine treatments Low Adjusted High ~10.5% ethanol v/v ~15.5% ethanol v/v ~15.5% ethanol v/v low low-to-high high

6 How do flavours evolve in the finish of these wines 3 wine treatments Low Adjusted High ~10.5% ethanol v/v ~15.5% ethanol v/v ~15.5% ethanol v/v low low-to-high high

7 How do flavours evolve in the finish of these wines Temporal Check All That Apply (TCATA)

8 Temporal Check-All-That-Apply (TCATA) Check and uncheck words to track changes in the wine. (Check all that apply. Uncheck all that do not apply.) 0:00 Heat Earthy Astringent Red Fruit Green Sour Other Bitter Spices Dark Fruit

9 Temporal Check-All-That-Apply (TCATA) Check and uncheck words to track changes in the wine. (Check all that apply. Uncheck all that do not apply.) 0:10 Expectorate now Heat Earthy Astringent Red Fruit Green Sour Other Bitter Spices Dark Fruit

10 TCATA raw data Astringent Bitter Dark Fruit Earthy Green Heat Other Red Fruit Sour Spices Time (seconds)

11 60 s 180 s break 180 s Sip 1 evaluation Sip 2 evaluation n = 13 (x4 replicates)

13 High ethanol Sip 1 H1 Sip 2 H2 WineSips Low ethanol Sip 1 Sip 2 L1 L2 Adjusted (Low-to-High) ethanol Sip 1 A1 Sip 2 A2

14 Exploratory data analysis (multivariate) Citation proportions in multi-way array Matrix

15 10.1 s 10.0 s Exploratory data analysis (multivariate) Citation proportions in multi-way array Matrix H1 c Columns: Attributes H2 L1 Rows: WineSips x Times L2 A1 A2 c H1 H2 L1 L2 A1 c A

18 How well do these trajectories represent the evolution of flavours in the WineSips?

19 Data resampling

20 The Real Panel (n=13) Barack Obama Princess Pablo Leia Picasso Shrek Muhammad Ali Adele Jean-Paul Satre Scarlett Johansson Pelé Jane Goodall Zinedine Zidane Hedy Lamarr Oprah Winfrey

21 The Real Panel (n=13) Barack Obama Princess Pablo Leia Picasso Shrek Muhammad Ali Adele Jean-Paul Satre Scarlett Johansson Pelé Jane Goodall Zinedine Zidane Hedy Lamarr Oprah Winfrey Virtual Panel 1 (n=13)

22 Jane Goodall The Real Panel (n=13) Virtual Panel 1 (n=13) Hedy Lamarr Oprah Winfrey Pelé Barack Obama Pelé Jane Goodall Zinedine Zidane Barack Obama Barack Obama Shrek Shrek Pelé Pablo Picasso Shrek Princess Leia Jean-Paul Satre Oprah Winfrey Adele Zinedine Zidane Muhammad Ali Scarlett Johansson Barack Obama Jane Goodall Pelé Hedy Lamarr

23 The Real Panel (n=13) Barack Obama Princess Pablo Leia Picasso Shrek Muhammad Ali Adele Jean-Paul Satre Scarlett Johansson Pelé Jane Goodall Zinedine Zidane Hedy Lamarr Oprah Winfrey Virtual Panel 2 (n=13)

24 The Real Panel (n=13) Barack Obama Princess Pablo Leia Picasso Shrek Muhammad Ali Adele Jean-Paul Satre Scarlett Johansson Pelé Jane Goodall Zinedine Zidane Hedy Lamarr Oprah Winfrey Virtual Panel 2 (n=13) Adele Shrek Adele Princess Shrek Leia Scarlett Scarlett Johansson Johansson Pelé Hedy Lamarr Jean-Paul Pablo Muhammad Satre Pablo Picasso Ali Picasso

25 The Real Panel (n=13) Virtual Panel 3 (n=13) Jean-Paul Satre Oprah Winfrey Jane Goodall Pelé Jean-Paul Satre Oprah Winfrey Princess Leia Shrek Shrek Hedy Lamarr Pelé Oprah Winfrey Princess Leia Pablo Picasso Shrek Princess Leia Jean-Paul Satre Oprah Winfrey Adele Zinedine Zidane Muhammad Ali Scarlett Johansson Barack Obama Jane Goodall Pelé Hedy Lamarr

26 The Real Panel (n=13) Barack Obama Princess Pablo Leia Picasso Shrek Muhammad Ali Adele Jean-Paul Satre Scarlett Johansson Pelé Jane Goodall Zinedine Zidane Hedy Lamarr Oprah Winfrey and so on

27 Partial bootstrap 1 real panel (n=13) virtual panels (n=13) For each panel, obtain and project coordinates for each WineSip Time into the multivariate sensory space

30 Contrails

31 How do flavours evolve in the finish of the WineSips

33 Selected TCATA Publications Castura et al. (2016). Temporal Check-All-That-Apply (TCATA): A Novel Temporal Sensory Method for Characterizing Products. Food Quality and Preference, 47A, Ares et al. (2016). Comparison of TCATA and TDS for dynamic sensory characterization of food products. Food Research International, 78, Boinbaser et al. (2015). Dynamic sensory characterization of cosmetic creams during application using Temporal Check-All-That-Apply (TCATA) questions. Food Quality and Preference, 45, Oliveira et al. (2015). Sugar reduction in probiotic chocolate-flavored milk: Impact on dynamic sensory profile and liking. Food Research International, 75,

34 Thank you for your attention! John C. Castura Allison K. Baker Carolyn F. Ross 14th Agrostat Symposium on Statistical Methods for the Food Industry March Lausanne, Switzerland.

Sensory Approaches and New Methods for Developing Grain-Based Products. Symposia Oglethorpe CC Monday 26 October :40 a.m.

Sensory Approaches and New Methods for Developing Grain-Based Products Symposia Oglethorpe CC Monday 26 October 2016 8:40 a.m. 102-S Perception dynamics of grain-based ready-to-eat cereal products using