Determining Juice Quality in Relation to HLB Infection

M Irey, G Thelwell, H Hou, D VanStrijp, P Sun – SGC/USSC

L Baldwin, A Plotto, W Zhao, S Raithore, J Bai, J Manthey – USDA ARS

Quick Update on the Southern Gardens GMO TrialsM Irey, R Kress, G Thelwell, J Kurimai, J Snively, R Terra, B Ingram

Determining Juice Quality in Relation to HLB Infection

M Irey, G Thelwell, H Hou, D VanStrijp, P Sun – SGC/USSC

L Baldwin, A Plotto, W Zhao, S Raithore, J Bai, J Manthey – USDA ARS

What do we know (or what do we think we know…)?

Juice Quality in Relation to HLB

• What we know……

– Off flavors are worse in Hamlin

than in Valencia

• Subtle but there

• More so early in the season

– The levels of the bitter

compounds nomilin and limonin

are related to perceived off-

flavors

• Most likely related to, but not directly

responsible for the off flavors

– Brix tends to go down as a result

of HLB infection

– Trend towards higher acid and

lower ratio

• What we don’t know…..

– What compound(s) cause(s) the

off flavors

• As a result, we don’t know what to

measure

– For what we do measure, we

don’t know the critical thresholds

that parallel the perceived off

flavors

• Complex matrix

• Metabolites change during season

– The ranges overlap

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5Baldwin/Plotto

Managing Quality

• With respect to HLB, off flavors are correlated to the amount of

symptomatic fruit in the mix

– More symptomatic = more off flavors

• Controlled studies have shown that it is possible to blend out the

off-flavors (25:75 symptomatic to healthy, above that, trained

panelists can tell a difference)

– But where do you get the healthy fruit?

• All groves are infected to some degree, many groves up to 100% infected

• We need a way to measure the potential to produce off flavors

– It would be nice to have a real time assay

• If we had a way to measure the potential we may be able to

manage the harvest or processing to minimize the negative quality

effects of HLB

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Possible Parameters to Measure as Indicators

• Fruit

size

• Limonin and Nomilin

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• PCR on orange juice – Patent pending test to measure DNA in

orange juice

Use of qPCR to predict quality of

orange juice affected by HLB

Elizabeth Baldwin, Wei Zhao, Jinhe Bai, Anne Plotto,

John Manthey, Smita Raithore and Mike Irey

US Horticultural Research Laboratory , Ft. Pierce, FL

Corr Li/HLB Corr LJ/HLB

-0.650221308 -0.788697173

Corr Li/Bitter Corr LJ/Bitter

-0.665778254 -0.679466371

Corr Li/metallic Corr LJ/metallic

-0.679974119 -0.704863099

Corr LI/sweet Corr LJ/sweet

0.61662541 0.712966687

Corr LI/sour Coor LJ/sour

-0.194126855 -0.097221665

CorrLI/stale Corr LJ stale

-0.495466167 -0.688194752

Corr grape/LI Corr grape/LJ

-0.605449742 -0.621003765

fruity non cit/LI Fruity non cit/LJ

0.565460735 0.681861145

Li\orange LJ/orange

0.527810667 0.705207427

Li /green LJ/green

-0.54042317 -0.693573863

Li/orange peel LJ/orange peel

-0.548996016 -0.438095888

Li/oxid oil LJ/Oxid oil

-0.645452635 -0.724286668

Li/umami LJ/umami

-0.645510022 -0.771602072

Li/body LJ/body

0.445981827 0.595169395

Li/tingly LJ/tingly

-0.600287878 -0.555725211

Li/astringent LJ/astringent

-0.625057855 -0.606409874

Li/burning LJ/burning

-0.450891717 -0.679466371

Li/aft bitter LJ/aft bitter

-0.665778254 -0.679466371

Li/aft astring LJ/after astring

-0.639675817 -0.640706474

Li/aft burn LJ/aft burn

-0.47504825 -0.425505407

Li/Sum aft LJ/sum aft

-0.648777106 -0.638515573

LI/sum pos LJ/sum pos

0.588213854 0.727153998

Li/sum neg LJ sum neg

-0.696179869 -0.791868269

Corr LI/sour

Ct: Bigger number = better quality

Smaller number = lower quality

So what did we do for 2014-2015 Crop?

• Once a week collected random juice samples from the loads

coming to the plant

– Total of 778 samples

• 305 E/M samples

• 455 Valencia samples

• Analyses

– Limonin/Nomilin by HPLC

– Ct value by real time PCR (qPCR)

– Brix, acid, ratio, pieces per box: plant data

– Electronic tongue

– Week of crop (December 1 used as starting point for week calculation)

• Principal Component Analysis

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Electronic tongue

• 7 Electronic sensors

• Sensors map to sensory

descriptors

– Sourness

– Metallic

– Saltiness

– Umami

– Spiciness

– Sweetness

– Bitterness

• Can be calibrated to standards

(if available)

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Principal Component Analysis

• Principal component analysis (PCA) is a statistical procedure that uses an

orthogonal transformation to convert a set of observations of possibly

correlated variables into a set of values of linearly uncorrelated variables

called principal components.

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Principal Component Analysis

• Principal component analysis (PCA) is a technique used to emphasize

variation and bring out strong patterns in a dataset. It's often used to make

data easy to explore and visualize.

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– Is a variable reduction technique

– Is used when variables are highly correlated

– Reduces the number of observed variables to a smaller number of

principal components which account for most of the variance of the

observed variables

• The first principal component accounts for most of the variance in the data.

• The second component accounts for the second largest amount of variance in the

data and is uncorrelated with the first principal component, and so on.

• In PCA, observed variables are standardized, e.g. mean = 0

Correlation Matrix – E/M

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Variables SRS GPS STS UMS SPS SWS BRS Acid Brix Ratio Pcs/Box Limonin Nomilin Ct Week

SRS 1 -0.045 0.780 -0.523 0.811 -0.130 0.190 -0.516 0.062 0.447 -0.225 -0.457 -0.466 0.297 0.605

GPS -0.045 1 -0.456 -0.053 -0.093 0.972 0.885 0.118 -0.013 -0.122 0.083 0.002 0.009 -0.142 0.136

STS 0.780 -0.456 1 -0.445 0.895 -0.556 -0.213 -0.277 0.252 0.418 -0.143 -0.452 -0.438 0.382 0.551

UMS -0.523 -0.053 -0.445 1 -0.520 -0.005 -0.110 0.105 -0.229 -0.258 0.044 0.259 0.335 -0.177 -0.442

SPS 0.811 -0.093 0.895 -0.520 1 -0.215 0.163 -0.191 0.307 0.387 -0.068 -0.502 -0.483 0.363 0.627

SWS -0.130 0.972 -0.556 -0.005 -0.215 1 0.808 0.131 -0.064 -0.173 0.098 0.047 0.058 -0.185 0.044

BRS 0.190 0.885 -0.213 -0.110 0.163 0.808 1 0.003 0.046 0.013 -0.004 -0.145 -0.109 0.006 0.233

Acid -0.516 0.118 -0.277 0.105 -0.191 0.131 0.003 1 0.174 -0.646 0.410 0.223 0.147 -0.183 -0.118

Brix 0.062 -0.013 0.252 -0.229 0.307 -0.064 0.046 0.174 1 0.629 0.100 -0.620 -0.409 0.302 0.361

Ratio 0.447 -0.122 0.418 -0.258 0.387 -0.173 0.013 -0.646 0.629 1 -0.241 -0.637 -0.420 0.368 0.359

Pcs/Box -0.225 0.083 -0.143 0.044 -0.068 0.098 -0.004 0.410 0.100 -0.241 1 0.176 0.021 -0.204 -0.066

Limonin -0.457 0.002 -0.452 0.259 -0.502 0.047 -0.145 0.223 -0.620 -0.637 0.176 1 0.707 -0.407 -0.586

Nomilin -0.466 0.009 -0.438 0.335 -0.483 0.058 -0.109 0.147 -0.409 -0.420 0.021 0.707 1 -0.303 -0.499

Ct 0.297 -0.142 0.382 -0.177 0.363 -0.185 0.006 -0.183 0.302 0.368 -0.204 -0.407 -0.303 1 0.287

Week 0.605 0.136 0.551 -0.442 0.627 0.044 0.233 -0.118 0.361 0.359 -0.066 -0.586 -0.499 0.287 1

Values in bold are different from 0 with a significance level alpha=0.05

• Principal component analysis (PCA) is a technique used to emphasize

variation and bring out strong patterns in a dataset. It's often used to make

data easy to explore and visualize.

Principal Component Analysis

• Principal component analysis (PCA) is a technique used to emphasize

variation and bring out strong patterns in a dataset. It's often used to make

data easy to explore and visualize.

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Week of Crop – Early Mids

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12/9/2014 – 2/5/2015

Early Mids – All weeks

• Initial PCA looked at all

parameters and then re-ran

with only those parameters

that were most important in the

first two components

• Used a threshold of 0 on the

first component and grouped in

to two categories

– Worse (high lim/nom, low Ct)

– Better (low lim/nom, high Ct)

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High Lim/Nom

Low Ct

“Worse”

Low Lim/Nom

High Ct

“Better”

Why are the “Worse” E/M’s skewed to the front of the crop

• If you had two blocks, one with excessive drop and one without,

which would you pick first?

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December 2, 2014, 3 replications, 3 tree plots, 5 treatments: Healthy tree, Healthy shaken left on tree, healthy shaken on ground, infected shaken on tree, infected shaken on ground

December 2, 2014, 3 replications, 3 tree plots, 5 treatments: Healthy tree, Healthy shaken left on tree, healthy shaken on ground, infected shaken on tree, infected shaken on ground

Healthy

Unshaken Healthy Tree

Healthy Ground

HLB Tree HLB Ground P-value

Orange 4.4 a 4.5 a 3.8 b 3.1 c 2.1 d < 0.0001

Grapefruit 2.9 c 2.5 c 2.5 c 4.4 b 5.9 a < 0.0001

Fruity-non-citrus 1.8 a 1.7 ab 1.4 bc 1.1 c 1.1 c 0.0009

Orange peel 2.3 b 2.1 b 2.0 b 2.5 ab 2.9 a 0.027

Green 2.4 b 2.4 b 2.4 b 2.8 ab 3.1 a 0.052

Stale 2.4 b 2.6 b 2.6 b 3.3 ab 4.1 a 0.009

Oxidized oil 1.6 b 1.6 b 1.5 b 2.2 ab 2.7 a 0.012

Typical HLB 4.1 c 4.0 c 4.3 c 7.6 b 10.2 a <0.0001

Sweetness 5.5 a 5.1 ab 4.6 b 4.1 c 3.2 d <0.0001

Sourness 5.0 ab 4.6 b 4.8 b 5.2 ab 5.6 a 0.04

Umami 2.3 b 2.2 b 2.4 b 2.8 ab 3.4 a 0.006

Bitterness 4.1 c 3.1 c 3.4 c 7.1 b 9.3 a <0.0001

Metallic 2.4 c 2.0 c 2.1 c 3.1 b 4.3 a <0.0001

Body 4.9 a 4.5 ab 4.3 b 4.4 ab 4.7 ab 0.166

Tingling 1.8 bc 1.6 c 1.5 c 2.3 ab 2.7 a 0.0001

Astringent 2.3 b 1.8 b 2.1 b 3.4 a 3.9 a <0.0001

Burning 1.4 b 1.2 b 1.2 b 2.1 a 2.5 a 0.0003

AfterBitter 2.0 c 1.6 c 2.0 c 4.5 b 6.0 a <0.0001

AfterAstringent 1.3 b 1.3 b 1.1 b 2.6 a 3.1 a <0.0001

AfterBurning 0.8 bc 0.6 c 0.8 bc 1.3 ab 1.7 a 0.001

December 2, 2014, 3 replications, 3 tree plots, 5 treatments: Healthy tree, Healthy shaken left on tree, healthy shaken on ground, infected shaken on tree, infected shaken on ground

Plotto/Baldwin

Valencia – All Weeks

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High Lim/Nom

Low Ct

“Worse”

Low Lim/Nom

High Ct

“Better”

March 2,2015 – May 5, 2015

So how can this approach be used to manage the harvest?

• Used the same basic PCA approach but basically did it in 2-3

weeks where possible

– Where not possible to group, looked at individual weeks

• Used the same basic “Better” / “Worse” threshold approach for the

component that explained the Ct and Lim/Nom variability

– Group 1 = Better

– Group 2 = Worse

• Averaged the “Group” rating by grower

• 3 Categories

– Better (<1.4) = Green

– OK (>1.4 <1.6) = Yellow

– Worse (>1.6) = Red

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Early/Mids

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Valencia

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Combined

• Quality (as defined by the PCA

analysis using Ct and Lim/Nom

as the criteria for grouping)

appears to vary by grower

– Consistent during the season

– Consistent between varieties

• Going forward

– Need to go down another level

(grove) to see if there are

consistent differences there

– Need to go compare a couple of

years to see if the trends are

consistent

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Conclusions

• Although no one parameter by itself is sufficient to measure

quality, PCA using a combination of wet chemistry, electronic

tongue (as a proxy for sensory), and molecular technologies

appears promising as a means of evaluating quality

– Ct may be a rough indicator

• Quality (as it relates to HLB associated off flavors) appears to vary

by grower

• Harvesting patterns could potentially affect quality

– Incentives are opposite……

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Thank You / Questions?

Quick Update on the Southern Gardens GMO TrialsM Irey, R Kress, G Thelwell, J Kurimai, J Snively, R Terra, B Ingram

Upon the discovery of HLB, Southern Gardens decided to:

• Use the opinions of the best experts available

– 3 pronged approach

• Open our diagnostic lab to the industry at no cost

– Now partially funded by CRDF

• Work with Federal, State and private researchers to develop

solutions

• Establish our own projects to develop a long term solution to HLB

– Multiple partners

– Multiple projects

– Multiple approaches

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Southern Gardens Research

• Texas A & M University

–Disease resistant plants

• Integrated Plant Genetics

–Disease resistant plants

• Cornell University

– Insect resistant plants

• AgroMed LLC

– Identification of synthetic resistance genes

• University of Florida

–Gene delivery system

• USDA

–Screening of potential genes, juice quality

Transgenic Trees

• Stable integration-long term

• Multiple projects

– Antimicrobial peptides

• Integrated Plant Genetics

–Gene from a bacteriophage

• Agromed

–Synthetics

•Texas A & M

–Spinach defensins (multiple)

– Resistance to ACP

• Cornell

–Hirsutellin

–Lectins

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Transgenic Trees

• Ideally, looking for immunity

• Process involves screening many lines (200+)

• Testing

– Greenhouse

– Field

• Field testing is done under USDA-APHIS-BRS permits

– 3 years

– Defined conditions

• EPA and FDA not involved in permitting (unless EUP is required)

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Non-transgenic Rohde Red Val

Transgenic Rohde Red Val

2 2

5 5

7 7

Texas Project

• Spinach defensins

– Has been a learning curve

• Generations 1, 2, 3, 4, 5, …..

–Multiple genes

–With and without signal peptides

–Different codon optimizations schemes

–Corrected sequences

–Alone or stacked

– All trees created in Texas at Texas A & M, Weslaco by Dr. Erik Mirkov

• Hamlin, Valencia, Lemons, Mexican Lime, Grapefruit, rootstocks

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Texas Project

• FDACS Regulatory Process

– Since trees were created in Texas, they were subject to CGIP

• Issues:

–Time delay

–Overloading of the system

» Would not be a good use of FDACS resources given that the majority of the lines would not make it

(200+ lines and more still coming)

– Over time and multiple iterations, a system was developed that worked

• Protected the Florida industry

• Allowed us to move material to Florida

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So where are we with the deregulation process -EUP

• EUP- allows for greater than 10ac of trials

• For the transgenic tree defensin project, we have an EUP that

allows:

– 400 ac in Florida

– 200 ac in Texas

– Two defensins

– 4 Plant lines

• 2 Sweet Oranges

• 1 Grapefruit

• 1 Lemon

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Temporary Tolerance Exemption

• Temporary tolerances are granted by the EPA to allow holders of

an EUP to use the products that come off the trials in the EUP

• A petition for a temporary tolerance or an exemption must be

submitted with the application for the EUP (if not a crop destruct)

• In the case of spinach defensins in transgenic trees, a tolerance

exemption was granted

– Based on the data that was submitted, there were no toxicity issues

– Already in the food chain

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Going forward…..

• We have many more lines being tested and it is likely that we may

have lines better than those in the current EUP

• The EUP process is:

– Tedious

– Costly

– Takes time

– Is restrictive

• Specific lines

• Specific constructs

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Going forward…..

• Have a new EUP amendment submission

– Multiple events (i.e. not restricted to 4 lines) as long as they were restricted to

certain constructs

• First step towards a “by construct” approach

• This will be necessary going forward

• In the process of:

– EUP for spinach defensin using viral vector

– Temporary tolerance for 3 spinach defensins in CTV

– Temporary tolerance for all spinach defensins

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Other efforts

• Global approval

– Have started the process to determine the landscape for approval of a

transgenic tree-based commercialization of spinach defensins

• 5 key markets in addition to the US

–Canada

–Japan

–Korea

– Israel

–EU

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Other efforts

• Also working on:

– Consumer acceptance

• Proactively to improve general acceptance

–Consumers

–Retailers

• Legal preparation for the eventual law suits

– Freedom to operate

– Stewardship issues

• “Defensins plus”

• Second mode of action

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Thank you (again..) / Questions