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Ioannis Minas*, Fernando Blanco Cipollone

Non-destructive

assessment of peach fruit

internal quality using NIR

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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

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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

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Development of non-destructive

techniques to estimate internal fruit quality

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• 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

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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

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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)

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Unthinned Commercial

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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

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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

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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

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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

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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)

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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

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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 (

%)

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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

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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

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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

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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

CSU_Pomologyioannis.minas@colostate.edu • David Sterle, CSU

• Bryan Braddy, CSU• Emily Dwody, CSU• Brady Shanahan, CSU• Western Colorado Hort. Society• Colorado Ag. Experiment Station

Acknowledgements