The Post-harvest Management of Apples, from Hot Water ... van Schaik.pdf · The Post-harvest...
Transcript of The Post-harvest Management of Apples, from Hot Water ... van Schaik.pdf · The Post-harvest...
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The Post-harvest Management of Apples, from
Hot Water Treatment to Decision Support
System.
Alex van Schaik
Coordinator Paolo
Bertolini WP1
Ria Derkx WP2
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Outline
� Non-destructive measurement of quality
� Sustainable prevention of postharvest rot
� Improved storage systems
� Quality prediction
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Post Harvest Chain
Quality determined at harvest!!
- Ripening status
- Size/color/presentation
- Sustainable product
- Biochemical composition
- Potential taste (sugars)
- Sensitivity to defects
- Shelflife
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Initial quality: DA meter
� NIR technique
� Portable
� Quality prediction (harvest and storage)
Research:
Golden Delicious, Red Delicious, Pink Lady
Galaxy, Elstar, Rubens
Harvest 4 Sept Harvest 2 Oct
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IAD
0
0.5
1
1.5
2
2.5
650 750 850 950 1050
Wavelength (nm)
Ab
so
rba
nc
e (
A)
1.1
0.8
0.6
0.4
0.2
� The IAD is calculated as the difference in absorbance at the
wavelengths of 670 (chlorophyll-a absorbance peak) and 720 nm
(background of the spectrum)
� The IAD decreases throughout fruit development and ripening
Workin
g
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DA-meter
� Ethylene production (left) and Starch content (right) in
relation with DA measurement at 3 harvest dates for
Red Delicious apples
0
0.5
1
1.5
2
2.5
1.65-1.55 1.55-1.5 1.5-1.4 1.4-1.3 1.3-1.2 <1.0
IAD
Ethylene emission
ppm h-1 kg-1
0
1
2
3
4
5
6
7
8
9
10
1.65-1.6 1.6-1.55 1.55-1.5 1.5-1.45 1.45-1.4 1.4-1.35 1.35-1 <1
Starch in
dex
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DA meter: results
DA-classes for Gala Apples harvested at three times
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4 Sept
0
5
10
15
20
25
30
35
40
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
DA-index
%
11 Sept
0
5
10
15
20
25
30
35
40
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
DA-index
%
19 Sept
0
5
10
15
20
25
30
35
40
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
DA-index
%
25 Sept
0
5
10
15
20
25
30
35
40
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
DA-index
%
2 Oct
0
5
10
15
20
25
30
35
40
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
DA-index
%
Rubens - DA-index
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
19-9 29-9 9-10 19-10 29-10 8-11 18-11
Date
DA
-index 20
10
4
1
DA distribution at different harvest
dates for Rubens apples
Decline in DA-index during storage
at different temperatures
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Pink Lady: badges of fruit
Elstar individual fruits
Relation DA and Firmness
Correlation between DA-index and
fruit firmness
y = 18.122x + 51.51
R2 = 0.58740
45
50
55
60
65
70
75
80
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
DA-index
Fru
it f
irm
ne
ss
, N
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Tools in the chain: DA meter
� Promising technique for several varieties
� Reduction biological variation: selection at harvest
� Measurement in the orchard possible
� Application for more fruits
� Quality prediction (harvest and storage)
� Relationship with other quality attributes
� Must accepted as a standard tool?!
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Safer Fruit and Environment
Replacing pesticide treatment against Postharvest rot
by:
- HWT Hot Water Treatment
- BCA Biological Control Agents (Antagonists)
- GRAS Generally Recognized Safe Substances
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Hot Water Treatment
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Results of Hot Water Treatment.
Example of results:
� Golden Delicious: 52°C for 40 seconds and 54°C/ 56°C for
20 seconds.
� Braeburn can be safely treated up to 56°C for 40 seconds
and 58°C for 20 seconds.
� Royal Gala can be safely treated up to 52°C for 40
seconds and 54°C for 20 seconds.
� Queen Cox can be safely treated up to 54°C for 20
seconds.
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Results of Hot Water Treatment.
BUT:
� Variation in effects depending to season, orchard, growing
area and variety.
� Good result Nectria and Gloeosporium, not against
Botrytis.
� Longer immersion time and higher temperature: skin
damage.
� Standard protocol for every situation is problematic
� Logistic problems during harvest
� Large scale application is difficult
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Hot Water Treatment on peaches
a
a
E I
84,38%
bE I
100%
b
0
25
50
75
peach "Rich Lady" nectarine "Big Top"
infe
cte
d f
ruit
s %
Control HW 60°C x 20"
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Treatments with GRAS and Antagonists
� Effects of official EU registered Antagonists were
ineffective
� Some GRAS treatments were effective:
- Na-molybdate
- Peroxyacetic acid
- Alcohol ethoxylate
Registration is necessary and is time consuming in EU
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1-MCP is blocking the ripening process
0 50 100 150 2000
200
400
600
800
1000
1200
time [hours]
ethylene production [pmol/kg/s]
receptor
C = C
receptor
C
C - C = C
ethylene
C = C
1-mcp
Storage systems
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Storage techniques: 1-MCP treatment
0
10
20
30
40
50
60
70
80
90
100
0 48 75 103 138 175 0 43 70 98 133 170
storage days storage days
firm
ness [N
/cm
²]
firmness after shelf-life at 18°C
+ MCP - MCP
harvest 1 harvest 2
Kitteman, Bavendorf
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Storage techniques: Dynamic Oxygen Conditions
Relative
respiration
0 % O2 21
ULO
DCS/DCA
Principle
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Dynamic Storage Systems
DCA: Source A. Zanella, Italy
DCS: Source A. van Schaik, Netherlands
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Dynamic Oxygen Systems Influence different technologies on scald Red Delicious
0
10
20
30
40
50
60
70
ULO DPA MCP DCA
Technology
% S
cald
Scald
Zanella,
Laimburg
Skinspots and DCS after 1 Week 18°C
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
11330 10412 10350 10069 avg
Orchard
SV-score (0-3)
ULO
DCS
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Dynamic Control System (DCS)Distribution firmness of apples after 7.5 Month in storage direct en 1 wk shelflife
CA and DCS storage with and without 1-MCP (Elstar)
0
0.1
0.2
0.3
0.4
0.5
0.6
2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8
Firmness class (per 0.5 kg)
am
out of fr
uits (%
)
ULO shelflife Control
ULO shelflife SmartFresh
DCS shelflife Control
DCS shelflife SmartFresh
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Interactive Storage systems
(DCA\DCS\ILOS)� Advantages
� Orchard related storage condition
� Retention of firmness and ground color in the chain
� Reduction of biological variation
� Reduction of physiological disorders
� Replacement of chemical treatments
� Allowed for organic fruits
� Necessary:
� More equipment
� Special sensors
� Gastight rooms
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Peaple
Decision Support System for
Simulation of Postharvest Quality Changesin
Fruit Supply Chains
Pawel Konopacki et all
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Peaple is a prototype Decision Support System to:
simulate quality changes
of apples and peaches
along different supply chains
to meet the demands of consumers
and consequently stimulate the increase of fresh fruit
consumption.
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Peaple contains models for several apple and peach
quality indices:
firmness
acidity
soluble solids content
skin colour
disorders
helpsystem
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1 Cultivars
2 Growth location
3 Harvesting time
4 Transport (conditions)
5 Storage: CA, 1 MCP, DCA, HWT
6 Distribution (conditions)
7 Shelflife
Included in Peaple:
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35
40
45
50
55
60
65
70
0 30 60 90 120 150 180 210 240 270 300
Firmness
Time
RA storage
Transport
Retail
35
40
45
50
55
60
65
70
0 30 60 90 120 150 180 210 240 270 300Firmness
Time
CA storage
Transport
Retail
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www.peaple-dss.eu
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Conclusions
� Improvement Non Destructive measurement of
Quality
� Non Chemical treatments against Post Harvest Rot
in some cases possible.
� New storage systems successful on the market
� Prototype DSS system for quality
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Thanks for your attention !