Scott Myers1, Charles Davidson2, Avishai Ben-David2, & Jeffrey Ballin2

1USDA APHIS Otis Laboratory, Buzzards Bay, MA2Edgewood Chemical Biological Center, APG, MD

We gratefully acknowledge financial support from the Department of Homeland Security.

DHS Science and Technology Directorate funded this work under Contract No. HSHQPM14-14-X-00086 with US Army Edgewood Chemical Biological Center, APG, MD.

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.

Acknowledgements

International trade is one of the major pathways by which exotic species enter the United States.

Khapra Beetle Interceptions in USA

19841986

19881990

19921994

19961998

20002002

20042006

20082010

20122014

0

25

50

75

100

125

150

175

200

225

250

275

300

Frequency of Interception at U.S. Ports of Entry

1996 – New York, NY1998 – Chicago, IL2002 – Jersey City, NJ2004 – Middlesex, NJ 2007 – Staten Island, NY2008 – Worcester, MA 2011 – Bethel, OH

Asian Longhorned Beetle

• Native to China, Korea and Japan.

• Attacks many hardwoods, esp. maple.

Worcester, MA 2010

• Threatens eastern forests, urban trees, wood industry, maple sugar industry. Localized eradications, but spread.

• $$$ losses, detection, control.

Mitigation Efforts to Exclude Exotic Insect Pests

IPPC’s International Standards for Phytosanitary Measures Regulation # 15 (ISPM-15) in 2005: Wood Packing material must be treated with heat or methyl bromide to kill pests

Port Inspections

Cooperative Framework

http://www.cbp.gov/archived/xp/cgov/newsroom/news_releases/archives/2009_news_releases/

Better technology needed to improve our ability to detect exotic organisms at ports of entry

1x1” disposable sensor PVDF or cellulose membrane spotted with an array of colorimetric chemical indicator dyes

Dyes change color in response to chemical stimuli. The color response is subtracted from a baseline to produce unique color “fingerprints.”

Colorimetric Sensor Arrays (CSAs)

(Askim et al. 2013)

Developed by the Suslick lab Univ. of Illinois

Probe reactivity of analytes rather than physical properties

High sensitivity – ppb range

High discriminatory power – coffee, beer, bacterial strains

(Askim et al. 2013. Chem. Soc. Rev., 2013, 42, 8649)

Commercial development in medical diagnoses

Screening blood samples

We will use colorimetry referring to simple three color (RGB) changes, but other possibilities

Colorimetric Sensor Arrays

Objectives

•Evaluate feasibility of pest detection in cargo shipments

using colorimetric sensor arrays (CSAs)

•Develop signature libraries of key exotic insect species.

• Evaluate detection capability of infested versus non-

infested commodities

• Validate developed library against known and unknown

samples

11

Long term Goal: Development of a disposable and inexpensive container-scanning technology to assist

in the screening and monitoring of US imports.

Objectives: Library Development

1. Incubation time required for interpretable response

2. Variability of response: commodity x insect species

3. Feasibility of identification from pattern recognition

- Are there signatures of pest(s)infection regardless of food source?

- Can pest(s) be differentiated?

- Are pest(s) signatures differentiated by food source?

Evaluate CSA color changes in response to quarantine insect pests associated with imported commodities.

(Notional differential VOC response)

13

Assay Design

14

X 3 Reps

Warehouse beetles in cracked wheat (39 replicates)

Lid Design and Assay Setup

Stainless steel lid fits commercially available mason jars and retainer rings (no VOC)

Keyed rackplate to maintain spatial registry and consistent illumination

Two radial 10-32 threaded ports to allow standard Swagelok® fittings

Epson V600 Photoscanner to iSense Software

Initial scan sets baseline.. size and color vary

Scans run every 10 mins over 2-3 days. Image file saved for each CSA x scan = many image files.

MATLAB algorithm finds and references each spot calculates mean RGB values across the area of each spot. (C. Davidson)

Subtract baseline values to measure spot change over time.

Data Collection

RGB Band Analysis: Warehouse Beetle on Cracked Wheat

• Take absolute value of time series RGB band data

• wb = the minimum of two warehouse beetle replicates

• cw = the maximum of two cracked wheat replicates

• We’re interested in wb>cw consistently.

• Sum the value wb-cw for all times and identify positive values as potentially good spots

Unique pattern for gases emitted

D RGB values over time

Strategy for determining potential “good” spots for detecting warehouse beetles

RGB Band Analysis

time step

ba

nd

nu

mb

er

50 100 150 200 250 300 350

20

40

60

80

100

120

140

160

180

200

220

wb -

cw

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

Automated: 3 35 60 61 83 88 106:109 111 113 155 159 162 163 164 171 181 182 183 187 195 197 202 203 205 206 208 209 211 212

Manual: 83 88 89 163 182 183 187 194 202 203 208 211 212

50 100 150 200

-0.04

-0.02

0

0.02

0.04

0.06

band number

me

an

(wb

- c

w)

50 100 150 2000

0.2

0.4

0.6

0.8

1

band number

ab

s(m

ea

n(w

b -

cw

)) >

0.0

1

Band Number

Me

an (

WB

–C

W)

Ban

d N

um

be

r

Time Step

WB

–C

W

Principal component (PC) plots

-0.06 -0.04 -0.02 0 0.02 0.04-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

FS: [ 83 88:89 183 202:203 ]

98% variance explained

-0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

FS: [ 83 88:89 163 182:183 187 194 202:203 208 211:212 ]

91% variance explained

(visually “good” features: 32 bands) (subset of visually “good” features: 13 bands)

H0 = no beetles present in grain

H1 = beetles present in grain

Principal component (PC) plots

-0.06 -0.04 -0.02 0 0.02 0.04-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

FS: [ 83 88:89 183 202:203 ]

98% variance explained

-0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

FS: [ 83 88:89 163 182:183 187 194 202:203 208 211:212 ]

91% variance explained

(visually “good” features: 32 bands) (subset of visually “good” features: 13 bands)

H0 = no beetles present in grain

H1 = beetles present in grain

16:8 L:D photoperiod

Accurate Predictions for Warehouse Beetlein Cracked Wheat

-0.06 -0.04 -0.02 0 0.02 0.04-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

2015-01-05

-0.06 -0.04 -0.02 0 0.02 0.04-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

FS: [ 83 88:89 183 202:203 ]

-0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

2015-01-05

-0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

FS: [ 83 88:89 163 182:183 187 194 202:203 208 211:212 ]

-0.06 -0.04 -0.02 0 0.02 0.04-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

-0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08-0.06

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-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

01-05-15

01-09-15

training set Datasets collected 2015/01/05 & 2015/01/09 show consistency and correctly predict H1 & H0classes

Lights off in chamber

• Calculates nonlinear “decision boundaries” between classes of data as dictated by a subset of support vectors.

•Data can thus be compared against the decision boundary to calculate receiver-operator characteristic (ROC) curves.

Decision Making: Support Vector Machine (SVM) Analysis

PCA 1

PC

A 2

PC

A 2

•Earlier analysis of L:D versus L:D data (2-D PC): Pd (detection) = 79% Pfa (false alarm) = 3%

Training set

Test Data

• The “best” performance is defined as the point on the ROC curve minimizing the sum of probability of a missed detection (false negative) and the probability of false alarm (false positive)

In the case port detection false positive is better than false neg.

Receiver Operating Characteristic (ROC) Curves

Pro

bab

ility

of

De

tect

ion

Probability of False Alarm

•Earlier analysis of L:D versus L:D data (2-D PC): Pd (detection) = 79% Pfa (false alarm) = 3%

SVM detection is working well : (5-D PC analysis is much better)

• Train SVM on 10 replicates

• Test on all January 2015 data (10 replicates)

~80% detection at 1% false alarm!

Even with < 0.5% false alarm rates, detection is ~50 –60%.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.2

0.4

0.6

0.8

1

Probability of false alarm

Pro

babili

ty o

f dete

ction

Warehouse Beetles VOC experiment using selected bands with 5 PCA subsampling=1 Kernel=1

train on 1125(1&2) 1211(1&2) 1216(1&2) 1222(1&2) 1223(1&2) at day & night

test on 0105(1&2) 0109(1&2) 0115(1&2) 0127(1&2) 0127a(1&2) at day & night

10-4

10-3

10-2

10-1

100

0.4

0.5

0.6

0.7

0.8

0.9

1ROC curve in log-scale

Probability of false alarm

Pro

babili

ty o

f dete

ction

Warehouse beetle using selected bands with 5D PCA

Detection of beetles vs cast skins / waste

SVM is much better at detecting larvae (Pd > 95% at false alarm 2%) than detecting larvae skins alone (Pd < 2% at false alarm 2%)

10-3

10-2

10-1

100

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

false alarm

dete

ction

Warehouse Beetles VOC experiment using selected bands with 5 PCA subsampling(train,test)=(1,1) Kernel=1

train on 1125(1&2) 1211(1&2) 1216(1&2) 1222(1&2) 1223(1&2) at day & night

Warehouse Beetles (tested on all January data)

Jan 27 2015 cast-off skin

Pro

bab

ility

of

De

tect

ion

Probability of False Alarm

Warehouse Beetle Detection with CSAs

Data from warehouse beetle on cracked wheat appears reproducible

Data acquired in dark

Automated feature selection (cherry-pick spots)

Better decision boundary analysis (SVM looks robust)

Good discrimination of beetles from exuviae (cast skins)

Warehouse beetle detection on cracked wheat increasingly sensitive

Pd very good at low false alarm rates with existing training set.

Asian Longhorned Beetle Larvae

Clean polystyrene beads ALB w/ polystyrene beads

(no enhancement)

• Same assay design as warehouse beetle• 2 ALB larvae per quart jar• 3 replications X 2 experiments

time step

ba

nd

nu

mb

er

100 200 300 400 500

20

40

60

80

100

120

140

160

180

200

220

wb -

cw

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

50 100 150 200-0.15

-0.1

-0.05

0

0.05

band number

me

an

(wb

- c

w)

50 100 150 2000

0.2

0.4

0.6

0.8

1

band number

ab

s(m

ea

n(w

b -

cw

)) >

0.0

1

1:3 34:36 42 56 60:63 70:71 73 78 82:83 88:89 106 109 114 117 151:152 155:156 159:164 170 181:183 187:188 193 195 197:198 202:203 205:212 216

50 100 150 200

50

100

150

Asian Longhorned Beetle LarvaeB

and

Nu

mb

er

Time Step

ALB

–B

ead

s

Asian Longhorned Beetle: PC Plots

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15All Bands: 93.65% variation explained

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.1555 Bands: 95.92% variation explained

50 100 150 200

-0.4

-0.2

0

0.2PC1

All bands

Subset

50 100 150 200

-0.2

0

0.2

0.4

PC2

All bands

Subset

50 100 150 200

50

100

150

50 100 150 200

50

100

150

50 100 150 200

50

100

150

50 100 150 200

50

100

150

PC1 PC2

55 Bands

All Bands

Trt 3

Trt 2

Trt 1

Control 3

Control 2

Control 1

Trt 3

Trt 2

Trt 1

Control 3

Control 2

Control 1

ALB

Control

55 Bands: 95.2% Variation Explained All 216 Bands: 93.7% Variation Explained

PC

2

PC1PC1

PC

2

Asian Longhorned Beetle: PCA comparison

0 0.2 0.4 0.6 0.8 1

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6Control (Feb)

ALB (Feb)

Control (Jan)

ALB (Jan)

PC1

PC

2

Data Jan experiment Projected onto PC Plot Developed from Feb Data (all bands)

Results show similar and response so far

More experiments required to develop a robust training set.

What’s next…

More analysis

Detection effectiveness versus changing food source

Temperature & RH

Quantify limits of detection

GC / MS

Angela Ervin - DHS

Laurene Levy - USDA APHIS

Mukti Ghimire - USDA APHIS

Hannah Lewis-Rosenblum - USDA APHIS

Sung Lim - iSense

Acknowledgements

Thank you!