The Chemosensory Basis of Flavor In Table Grapes (VV)

Itay MaozTatiana Kaplunov, Eyal Raban, Irina Dynkin,

Oded Degani, Efraim Lewinsohn

Amnon Lichter

The Volcani Center

Tyagi & Maoz, under review

Girdling

Flavor in the berry & cluster

Maoz et al. 2016

Postharvest/controlled atmosphere

Maoz et al. 2019

Variability Vineyard vs.

maturity

Maoz et al. 2018a

Amino acid metabolism within

the berry

Maoz et al. 2018b

Enhancement of flavor

Tyagi & Maoz et al, 2020

Cytokinin &Astringency

Maoz et al. 2014

➢The basic taste – Sugars & acids and their ratio

➢Good texture and low astringency are mandatory

➢Unique flavors are derive from volatile compound(s)

Terpenoid pathway

Fatty acid degradation

Fermentation aerobic & anaerobic

Respiration

Amino acid degradation

Norisoprenoids(β-damascenone, β-ionone)

Mono- and sesquiterpenes(linalool, hotrienol)

C6 and C9

(hexanal, nonanal)

Aromatic(2-phenylethyl alcohol,

benzyl alcohol)

Branched chain(3-methyl-butanol)

ethanol, ethyl acetate

Muscat Green/fruity Flowery, off-flavors Alcoholic, off-flavors

Hypothesis Analysis of large data sets can provide insights unattainable by analysis of individual cultivars

To examine our hypothesis we collected and analyzed over 100 breeding lines and cultivars

The pipeline

Data analysis

and integration

❖ Sweetness❖ Sourness❖ Astringency❖ Seed remains ❖ Skin remains❖ Crunchiness❖ Flavor preference

Volatile compounds(SPME-GC/MS)

Cultivars (3 harvests)

R2 Adj 0.484

Prob <0.0001

Sweetness (1-10)

Sw

eetn

ess

(1-1

0)

TSS (Brix) TSS (Brix)

16.4±1.7 6.7±0.7

TSS and TA correlation to sweetness and sourness

R2 Adj 0.284

Prob<0.0001

R2 Adj 0.530

Prob<0.0001

Sweetness (1-10)TSS (Brix)

Berr

y p

refe

ren

ce (

1-1

0)

There is a better correlation of sweetness then TSS to berry preference

Berr

y p

refe

ren

ce (

1-1

0)

Flavor impression has high correlation to berry preference

Source FDR PValue

flavor impression 2.90E-13

Astringency 6.92E-10

Firmness 1.11E-06

TSS 2.53E-04

Effect summary

Source FDR PValue

Astringency 2.86E-13

TSS 1.45E-12

Firmness 1.62E-04

Effect summary

R2 Adj 0.692

RMSE 0.473

R2 Adj 0.846

RMSE 0.335

Berr

y p

refe

ren

ce

Berry preference predicted Berry preference predicted

Modeling factors that matter (most)

R2 Adj 0.765

RMSE 0.337

Berr

y p

refe

ren

ce

Berry preference predicted

Testing the model

4 new tentative compounds for table grapes • Geranyl formate• α-Bergamotene• Aristolene• α-panasinsen

114 volatile compounds identified by GC-MS

36monoterpenes

32 fatty acid volatiles

18Sesquiterpenes

10 amino acid volatiles

7apocarotenoids

11 others

Core volatiles > 90% of cultivars

Unique volatiles < 10% of cultivars

Negative correlationsPositive correlations

Correlation networks between assayed parameters

TSS

(Bri

x)

17.6

14.8

0

9

18

TOP 10 LOW 10

55

38

0

30

60

TOP 10 LOW 10

# o

f vo

lati

les 0.17

0.05

0.0

0.1

0.2

TOP 10 LOW 10

Sesq

uit

erp

ene

s (t

)(n

g/g

)

0.017

0.00

0.01

0.02

TOP 10 LOW 10

δ-c

adin

ene

(ng

/g)

linal

oo

l (n

g/g

) 16.2

0.40

10

20

TOP 10 LOW 10

0.76

0.0

0.2

0.4

0.6

0.8

TOP 10 LOW 10

met

hyl

ger

anat

e(n

g/g

)

Differences between top 10 preference and low 10 preference cultivars

“Neutral” cultivars (defined by what they don’t have)Low in total number of volatilesLow in monoterepnes

“Muscat”-type cultivars (defined by what they have)High in total number of volatiles High in monoterpenes

“Fruity” cultivars (defined by OR)No negative taste attributes (sugar, acid, texture and astringency) EITHER high in total number of volatiles OR medium in monoterpenesOR high in sesquiterpenes

The dilution conflict 4.4 gr

Muscat cultivars

5.6 gr“Fruity” cultivars

6.8 grNeutral cultivars

Breeding

PGRs, girdling

FlavorTaste

Remarks

• We hope that we extended the knowledge on the flavor of table grapes

• We identified several markers that can be useful in future breeding of cultivars with improved flavor

• The strength and weakness of our data is in the fact that it is arbitrary. It can be biased by the presence of limited genetic resources.

• The data can be further improved by integrating it with other datasets

Special TODA to Tali Grapes -Moshav LachisGuy Rozenfeld, Shahaf Dudai and all the growers for being excellent research partners along the years