Date post: | 22-Jul-2015 |
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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
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
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
-0.05
-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