development of a beverage based on sweet potato peels ...€¦ · •sweet potato peels and leaves...
TRANSCRIPT
Development of a beverage based on sweet potato peels:
formula optimization
Ana Anastácio
DATA SCIENTISTS, ACADEMICS & EDUCATION
ANTIOXIDANT AND PHENOLIC COMPOUNDS IN
SWEET POTATO PEELS AND LEAVES:
FOOD APPLICATIONS AND HEALTH BENEFITS
Ana Isabel Mimoso Tomás Coelho Anastácio
Doutoramento em Ciências Biotecnológicas/Biotecnologia Alimentar
Orientadora: Professora Doutora Isabel Maria Marques Saraiva de Carvalho
2
Production 2014 104 Mtonnes
3http://www.talkafrica.co.ke/sweet-potato-scientists-awarded-the-prestigious-world-food-prize/
0,022
1,3
70,7
Portugal
USA
China
Africa; 20,2%
Americas; 3,7%
Asia; 75,3%
Europe; 0,1% Oceania; 0,8%
(Mtonnes)
FAOSTAT
http://blogs.usda.gov/2016/07/14/crop-insurance-continues-to-strengthen-rural-communities/
Sweet potato wastes3 ton/ha
17
29
Manual Abrasion
Peeling (% loss)
4
Objectives
Food prototype
Waste
1
2
PrototypeANTIOX
New food developmentprocess
5
New food development process
Design of experiments
Review Tests Key factors Optimisation
Phenolics
Antioxidant activity in vitro
6
77
New food development process
Tests
8
Process
Extraction conditions
Product
Beverage formulation
Optimisation
Ana Anastácio & Isabel Saraiva de Carvalho (2016): Development of a beveragebenchtop prototype based on sweet potato peels: optimization of antioxidant activityby a mixture design, International Journal of Food Sciences and Nutrition, DOI:10.1080/09637486.2016.1174984.
Product optimisation
Objective
• Optimisation of antioxidant beverageformula
9
9
10
• Sweet potato peels and leaves can be related to health benefits such as oxidative stress reduction.
• Optimized process for antioxidants extraction.
• No patent application on antioxidant beverage with sweet potato peels and leaves.
Previous knowledge
Components
SPPE 50 – 100 %
SPLE 0 – 50 %
HonS 0 – 50 %
• TPC, FRAP, DPPH assays and SS
Extraction
RSM
ANN
Product optimisation
11
• Mixture design
Mixture designProduct optimisation
SPPE
SPLE
Ho
nS
I-optimal randomized
mixture design
24 runs
12
13
Product optimisation Mixture profiler
14
Product optimisation Profiler
RSM OptimalProduct optimisation
15
SPPE
SPLE
Ho
nS
50.0 %
22.1 %
27.9 %
ANNNeuron
Product optimisation
16
inputs output
ANNProduct optimisation
17
Activation: H1 = w1 x1 + w2 x2
Transformation: Y = ao + a1 Tanh(0.5*H1) + a2 Tanh(0.5*H2)
inputs output
x1
x2
YH1
Artifical neuron
w1
w2
Product optimisation
18
Hi =
+ wi1 X1
+ wi2 X2
+ wi3 X3
Y =
+ ao
+ a1*Tanh(0.5*H1)
+ a2*Tanh(0.5*H2)
+ a3*Tanh(0.5*H3)
ANN
SPPE
SPLE
HonS
Product optimisation
19
SPPE
SPLE
Ho
nS
50.0 %
21.5 %
28.5 %
ANN Optimal
Comparison
FJ: fruit juice
VJ: vegetable juice
HI: herbal infusion
T: tea
PPE: plum peel enriched
nectar
CP: cardio protective beverage
NJ: nutraceutical juice
SPPE Bev: sweet potato peel
beverage
Commercial Laboratory
Product optimisation
20
Formula optimisation
JMP
21
• Design
• Data visualisation
• Modelling
• Optimisation
22
21
(in press)
Thank you.
Ana Anastácio
23
DATA SCIENTISTS, ACADEMICS & EDUCATION