‘Forelle’ pear mealiness

Elke Crouch Tavagwisa Muziri, Rudolph Cronjé,

K. Theron, H. Nieuwoudt, P. Verboven, B. Nicolai

CONTRIBUTION TO TOTAL PEAR AREA PER CULTIVAR IN SOUTH AFRICA

Source: HORTGRO Tree Census, 2015

Bi-colour pear

IN ‘FORELLE’ MEALINESS OCCURS ON Post-optimally harvested fruit

Stored less than 12 weeks at -0.5°C and ripened for 7 days.(Carmichael, 2011; Crouch, 2011; De Vries & Moelich, 1995; Hurndall & De Vries, 1993; Martin 2002)

cdcd

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Me

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8 weeks 12 weeks 16 weeks

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0 3 6 9 12 15 18 21

Mealin

ess (

%)

Weeks in storage

0-21 weeks at -0.5°C0,4,7,11 days at 15°C

Ripening required for mealiness development

WHICH FRUIT WILL BECOME MEALY?

SOME FRUIT RIPEN

WITHOUT MEALINESS

Mealy Non-mealy

10 wk -0.5°C

+

7 days 15°C

+

11 days 15°C

3.1 kg / 30.4 N 2.6 kg / 25.5 N

2.3 kg / 22.6 N 2.1 kg / 20.6 N

Crouch, 2011

OBJECTIVES

Establish the potential of NIR spectroscopy as

non-destructive technique for the prediction of

mealiness in ‘Forelle’ pears.

Examine X-ray computed tomography as a

non-destructive technique for detecting mealiness

in intact fruit.

Near Infrared Spectroscopy

• NIR (800 – 2500) spectra were taken of 4 positions using a

Bruker MPA spectrometer on 500 fruit from

La Plaisant, Koelfontein, Fairfied & Oak Valley.

• Physicochemical (maturity) characters and mealiness were

determined after shelf life (8w+7d and 21w+7d).

DATA ANALYSIS / CHEMOMETRICS

• Principal component analysis (PCA) of physicochemical (i.e. maturity) attributes.

• Partial Least Squares (PLS) regression analysis for predicting TSS (OPUS 7.0).

• Partial Least Squares discriminant analysis (PLS-DA) for mealiness prediction (SIMCA version 13.0.3.0)

RELATIONSHIP BETWEENMEALINESS AND PHYSICOCHEMICAL ATTRIBUTES

Calibration Validation

Side Time LV RPD R2 RMSEE R2 RMSEP RPD

Equator blush side 0w+0d 7 2.28 0.81 0.652 0.78 0.691 2.14

8w+0d 8 2.74 0.87 0.577 0.84 0.562 2.49

8w+7d 10 3.95 0.94 0.394 0.80 0.646 2.26

Equator green side 0w+0d 6 2.55 0.85 0.569 0.70 0.825 1.84

8w+0d 6 2.1 0.77 0.701 0.75 0.754 2.01

8w+7d 5 2.06 0.77 0.713 0.76 0.564 2.08

Neck 0w+0d 9 2.32 0.81 0.638 0.79 0.657 2.21

8w+0d 10 2.94 0.88 0.498 0.86 0.562 2.63

8w+7d 9 2.78 0.87 0.5 0.82 0.578 2.38

All sides (averaged) 0w+0d 10 2.26 0.81 0.594 0.74 0.731 1.96

8w+0d 10 2.42 0.83 0.609 0.80 0.665 2.22

8w+7d 10 2.57 0.85 0.552 0.80 0.63 2.18

TSS showed good R2 and predictability

A B

NECK R2 = 84% EQUATORIAL BLUSH SIDE R2 = 88%

ALL SIDES R2 = 72% EQUATORIAL GREEN SIDE R2 =72%

Three classes

Non-mealy Partly mealy Mealy

TSS range <15.2 15.2-16.32 >16.32

TSS prediction

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To

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Mealiness class

Partly-Mealy Non-MealyMealy

TWO-CLASS CLASSIFICATION OF POOLED

(TSS CLASSIFIED)

Sample origin Sensory panel

classification

Model classification Classification

accuracy (%)

Mealy (pooled) Non-mealy OPLS-DA

Equator blush side Mealy 45 1 97.8

Non-mealy 4 42 91.3

Overall 49 43 94.6

Equator green side Mealy 41 5 89.1

Non-mealy 12 33 73.3

Overall 53 38 81.3

Neck (both sides) Mealy 83 9 90.2

Non-mealy 15 77 83.7

Overall 98 86 86.9

Findings

• TSS + Hue angle on the blush side related well to

mealiness.

• NIR spectroscopy could predict TSS on all sides and on

all farms

• Prediction of mealiness was shown to be possible using

PLS-DA.

Practical implications

NIR can classify mealiness non-destructively.

Possible at farm and packhouse level

More data collection for developing a universal model.

Detection of mealiness using X-ray

Computed Tomography

Methodology• Study 1 :

• 72 fruit from Koelfontein and La Plaisante were used.

• Tomographic acquisitions were made using the General

Electric Phonix V │Tome│ X L240 (240 kV) at SU.

• Study 2:

• 16 pears were scanned using Tomohawk RAD.

• Microscans were also made on 16 fruit using the high

resolution Skyscan 1172 system.

• For both studies, scans were done at 8w+0d and at 8W+7d.

• Image reconstruction was done using VG Studio Max

Release 2.1.4 and NRecon version 1.6.6.0 programme.

• Images analysis was done using Image J, CT Analiser, Avizo Fire 7.1 and Avizo fire 8.2

JUICE AREA AND WEIGHT OF MEALY CLASSIFIED FRUIT

• Significant mealiness x side interaction (P=0.0035).

• Mealy fruit higher density of darker voxels between cells.

• Largest % defects was observed in the neck of mealy fruit.

RESULTS

Grey-scale images

PERCENTAGEPOROUS DEFECTS

after STORAGE

(DAY 1)

afterRIPENING(DAY 7)

PERCENTAGE POROUS DEFECTS

Before ripening

8 weeks at -0.5 °C

+ 1 day at 15 °C

After ripening

8 weeks at -0.5 °C

+ 7 days at 15 °C

POROSITY (µCT)

Individual cell segmentation (µCT)

Mealy Non- Mealy

Large ellipsoidal cells

Both lysigenous and schizogenous

intercellular porosity.

High porosity

Smaller, rounded cells

Only schizogenous

intercellular porosity.

Relatively less porous

Findings (CT)

Macro CT

Porosity was higher in mealy than in non mealy fruit,

at 8w+0d and after shelf life (8w+7d)

Micro CT Mealy fruit had larger pore spaces, comprising of

intercellular spaces and damaged cells.

Cells of mealy fruit were relatively larger, more ellipsoidal

than non-mealy fruit cells which were spherical.

Practical implications:

Macro CT = predict mealiness non-destructively

before ripening.

Mealiness porosity threshold needs to be established

for what can be considered mealy.

Macro CT already commercially used

-To detect micro cracks in metal parts

-Food processing, sorting and quality assurance.

CT provided an important clue:

• Mealiness is there even before ripening begins.

• Storage does not cause mealiness.

• Pre-harvest factors influencing mealiness to be studied.

SUMMARY OF FINDINGS

• Mealiness associated with larger, softer fruit with higher TSS.

• TSS + Hue angle on the blush side related well to mealiness.

• NIR spectroscopy could predict TSS on all sides of the fruit

and on all farms.

• Prediction of mealiness was shown to be possible using

OPLS-DA.

• Macro CT has potential to predict mealiness non-

destructively before ripening.

Rudolph Cronje

MSc(Agric) Horticulture student (Stellenbosch University)

Postharvest ‘Forelle’ pear mealiness

influenced by canopy position, ripening

rates and pollination

Project leader: Elke CrouchCollaborators:

Taaibos Human (ARC);

Tavagwisa Muziri (Midlands State University);

Wiehann Steyn; Karen Theron; Michael Schmeisser (SU);

Bart Nicolai; Peter Verboven (KU Leuven).

Introduction

A closer understanding of the association between:

- Fruit position, microclimate, fruit anatomy and

susceptibility to develop a mealy texture

WHYare some ‘Forelle’ fruit,

on the same tree

predisposed to mealiness

?

Effect of fruit canopy position on mealiness score,

irradiance (%), max irradiance (%),

fruit surface temperature (FST) (ºC), max FST (ºC)

after 8 weeks at - 0.5 ºC plus 11 days at 20 ºC.

Fruit position

Mealiness

score

(0,1,2)

Irradiance %Max

irradiance %

Fruit surface

temperature

(FST) (°C)

Max FST (°C)

Inside 0,3 c 2 e 12,54 c 24,8 d 33,72 d

Middle West 0,3 c 14,1 d 90,22 b 25,9c 40,8 b

Outside West 0,7 b 32,9 b 98,17 a 26,8b 42,5 a

Middle East 0,4 c 23,1 c 90,58 b 26,9b 35,86 c

Outside East 1,1 a 45,9 a 98,38 a 29a 39,18 b

Source of variation Pr>F

Fruit position 0.00001 0.00001 0.00001 0.00001

2016 WORK

X-ray CT studies on mealiness detection.

Influence of position in canopy on mealiness

predisposition.

Further CT work is being done to establish if the

different colour groups differ in cell sizes and

mealiness.

Acknowledgements

Farm owners and managers of following farms:

La Plaisante & Koelfontein, Fairfield & Oak valley.

Henk Griessel (Tru-Cape), Margaret Reinecke, Con Louw,

Aileen Zulch and Stefanie De Puysseleir for their assistance in

exporting trail cartons of ‘Forelle’ pear to Flushing Netherlands

for Micro X-Ray CT Scanning.

South African Apple and Pear Producer’s Association