non-destructive assessment of peach fruit internal quality
TRANSCRIPT
TH
E C
OL
LE
GE
of A
GR
ICU
LTU
RA
L SC
IEN
CE
S
Ioannis Minas*, Fernando Blanco Cipollone
Non-destructive
assessment of peach fruit
internal quality using NIR
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Fruit Quality
Definition
• Fruit ‘quality’ is a general notion that includes physical, mechanical (mass, volume, firmness), and sensory properties (appearance, texture, taste and aroma), nutrition value, safety and defects
• All the above contribute to a fruit degree of excellence and economic value that can be interpreted differently by producers, shippers and consumers of fresh fruit products
• Producer: fruit size; Shipper: firmness and color; consumer: appearance, taste, nutrition value
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Fruit Quality
• Harvesting immature or over-ripe fruit has a high impact on fruit eating quality and storage/shipping performance
• Judging fruit maturity by shape and color alone results in varying success
• Internal fruit quality in terms of dry matter, total soluble solids and acidity are important quality parameters that correlate with consumer acceptance
• Traditional quality measurements such as Dry matter content (DMC), Soluble solids concentration (SSC), Fruit Firmness (FF), Titratable acidity (TA) are destructive and work intensive
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Development of non-destructive
techniques to estimate internal fruit quality
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
• Among the non-destructive techniques NIR can be used for determining traditional peach fruit quality traits (non-structural carbohydrates)
• NIR radiation covers the range of the electromagnetic spectrum between 780 and 2500 nm
• The fruit is irradiated with NIR radiation, and the reflected or transmitted radiation is measured
• Spectral characteristics of radiation, that penetrates the product, change through wavelength dependent scattering and absorption processes
• This change depends on the chemical composition of the product
• Advanced multivariate statistical techniques, such as partial least squares regression is applied to develop prediction models for the different traits
Near infrared (NIR) spectroscopy
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
F-750 Produce Quality Meter
Near-Infrared Spectroscopy (NIR)
DA-meter
Vis/NIR
• Spectra: 729-935nm
• Dry Matter Content
• Soluble Solids Content
• Index of Absorbance Difference (IAD)
• IAD=A670nm-A720nm
• Chlorophyll’s Content
• Fruit Maturity
• Good potential as research tools
• Need a lot of work for adaptation from growers (requires R&D)
• Good potential for research on orchard factors affecting fruit quality
Handheld non-destructive sensors to
estimate internal fruit quality and maturity in
the field
Costa et al., 2009
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Modeldevelopmentapproach• To obtain maximum variability among fruit 4 crop load levels on Sierra Rich,
Crest Haven and Red Haven trees were created (Unthinned, Heavy crop load, Commercial, Light)
• Fruit were sampled at 5 developmental stages (100 fruit x 5 = 500 fruit) • Fruit were scanned at 0, 20 and 30 oC and on the scanned areas the
reference value was measured (DMC, SSC, IAD, FF)• Subsequently reference values and scans entered into the manufacturer’s
‘Model Builder’ software to create the models. Once created models were validated with 150 fruit
• DMC and SSC models were created in the spectra range of 729-935 nm
• IAD in the spectra range 600-750 nm and FF in 477-657 nm
• Fruit were harvested at the commercial maturity stage (commercial crop load) and separated based on fruit position within canopy: Low-located
(lower 1.5 m of the canopy) or up-located (upper 1.5 m of the canopy)
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Unthinned Commercial
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Fruit growth and development patterns and yield
and fruit size were affected by crop load
30.00
35.00
40.00
45.00
50.00
55.00
60.00
65.00
70.00
75.00
5/20 5/30 6/9 6/19 6/29 7/9 7/19 7/29 8/8
Dia
met
er (m
m)
Date
Untinned2''6''12''
Sierra Rich
Fruit No/tree
Yield per tree (kg)
Fruit weight (g)
Unthinned 189.8a 16.4a 86.1dHeavy 79.7b 10.9b 136.1c
Commercial 32.8c 5.9c 179.3bLight 16.2d 3.4d 209.9a
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Influence of crop load and position
in the canopy on peach fruit internal quality
-0.015
-0.01
-0.005
0
0.005
0.01
402
435
468
501
534
567
600
633
666
699
732
765
798
831
864
897
930
963
996
1029
Wavelength (nm)
Sierra Rich 6/22/16
Sierra Rich 7/29/16
Sierra Rich 7/22/16
Seco
nd
deri
vati
ve
sp
ectr
a
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Sierra Rich DM, SSC, IAD, FF models validations
5 10 15 20 255
10
15
20
25
Real
Predicted
R2=0.9748y= 1.028x
0.0 0.5 1.0 1.5 2.00.0
0.5
1.0
1.5
2.0
RealPredicted
R2=0.9414y= 1.0115x
5 10 15 205
10
15
20
Real
Predicted
R2=0.94572y= 0.9866x
0 20 40 60 800
20
40
60
80
100
Real
Predicted
R2=0.05061y= 1.0903x
Fruit Firmness (N)SSC (%)DMC (%) IAD
RMSEP=0.39 RMSEP=0.48 RMSEP=0.08 RMSEP=4.4
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Red Haven DM, SSC and IAD models validations
DMC (%)
5 10 15 205
10
15
20
Real
Predicted
R2=0.9442y= 1.035x
0.0 0.5 1.0 1.5 2.0 2.50.0
0.5
1.0
1.5
2.0
2.5
RealPredicted
R2=0.9390y= 0.9775x
8 10 12 14 16
8
10
12
14
16
Real
Predicted
R2=0.9039y= 1.044x
0 20 40 60 8040
50
60
70
80
Real
Predicted
R2=0.13897y= 0.1992x+53.578
SSC (%) IAD Fruit Firmness(N)
RMSEP=0.39 RMSEP=0.62 RMSEP=0.09 RMSEP=13.0
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
8 10 12 14 16 188
10
12
14
16
18
Real
Predicted
R2=0.9487y= 0.9982x
R2=0.9037y=1.0x
8 10 12 14 16 188
10
12
14
16
18
Real
Predicted
R2=0.9366y=1.010x
R2=0.8584y=0.9857x
0.0 0.5 1.0 1.5 2.0 2.50.0
0.5
1.0
1.5
2.0
2.5
RealPredicted
R2=0.9885y=0.9948x
R2=0.9195y=1.049x
Red Haven DMC, SSC, IAD and FF models validation
Dry matter content (%) IADSSC (%)
0 20 40 60 80 1000
20
40
60
80
100
Real
Predicted
R2=0.7952y=9465x
R2=0.5775y=0.9576x
Fruit Firmness (N)
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
DMC and SSC correlation based on predicted
values
Sierra Rich Cresthaven Red Haven
5 10 15 20 255
10
15
20
25
DM (%)
SSC
(%)
R2=0.9911y= 0.9104x
DMC
5 10 15 20 255
10
15
20
25
DM (%)
SSC
(%)
R2=0.9707y= 0.9149x
DMC
8 10 12 14 16 188
10
12
14
16
18
DM (%)
SSC
(%)
R2=0.9546y= 0.9570x
DMC
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Effect of crop load and fruit position in the canopy
on DMC (Sierra Rich)
PredictedReal
Crop load level
Unthinned Heavy Commercial Light0
5
10
15
20UpDown
a a ababbccdd d
Low
Unthinned Heavy Commercial Light0
5
10
15
20UpDown a a abab
bccdd d
Low
DM
C (
%)
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Effect of crop load and fruit position in the canopy
on SSC (Sierra Rich)
PredictedReal
Crop load level
SS
C (
%)
Unthinned Heavy Commercial Light0
5
10
15
20UpDown
ab a abcbc cdd d
Low
Unthinned Heavy Commercial Light0
5
10
15
20UpDown
a a abb bcc c
Low
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Effect of crop load and fruit position in the canopy
on Absorbance difference index (IAD) (Sierra Rich)
Crop load level
PredictedReal
I AD
Unthinned Heavy Commercial Light0.0
0.5
1.0
1.5UpDown
de
aab
cdebc
cdde
e
Low
Unthinned Heavy Commercial Light0.0
0.5
1.0
1.5UpDown
d
aab
cdbc
cdd
d
Low
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Effect of crop load and fruit position in the canopy
on Fruit Firmness (Sierra Rich)
Crop load level
Fru
it f
irm
ness (
N)
PredictedReal
Unthinned Heavy Commercial Light0
20
40
60
80UpDown
aa a aa
b
a
a Low
Unthinned Heavy Commercial Light0
20
40
60
80UpDown
bc
aab ab abc
cddcd
Low
TH
E C
OLLE
GE
of AG
RIC
ULT
UR
AL S
CIE
NC
ES
Conclusions
• Canopy position and crop load affects internal fruit quality and maturity at harvest
• NIR spectroscopy was able to accurately sense DMC, SSC and IAD differences
• Non-destructive techniques using these models will allow measurement of large samples or entire lots
• Two online measurements at the same time
• Understanding of preharvest factors (training, pruning, rootstock etc.) on internal fruit quality
[email protected] • David Sterle, CSU
• Bryan Braddy, CSU• Emily Dwody, CSU• Brady Shanahan, CSU• Western Colorado Hort. Society• Colorado Ag. Experiment Station
Acknowledgements