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Explosives Detection With Hard-Wired Moths
Tony L. King, Member, IEEE, Frank M. Horine, Kevin C. Daly, and Brian H. Smith
Abstract—Insects, such as moths, can be trained to respond to
explosives odors. A prototype system that can use trained insects
such as moths to detect explosives was designed, assembled, and
tested. It compares the electromyographic signals of insects trained
to respond or not respond to a target explosive vapor in order
to determine whether or not explosive devices, such as bombs or
landmines, are present. The device was designed to be portable by
making it lightweight, battery-powered, and energy efficient. The
prototype performed successfully during testing. This device is a
novel integration of electronics and biology to create a bioinstrument that has not been previously developed.
Index Terms—Bioinstrumentation, electromyography, explosives detection, Pavlovian conditioning.
I. INTRODUCTION
D
ETECTION of explosives is a matter of vital importance
to national security. Science has often turned to nature to
solve such problems. Through evolutionary adaptation, some
species of animals have become exceedingly sensitive to certain odors. For example, dogs have been trained to sniff out and
locate explosives, narcotics, and missing persons.
It has been previously demonstrated that Manduca sexta
moths, shown in Fig. 1, can be trained to respond with a feeding
behavior when exposed to odor signatures from explosives
using Pavlovian conditioning [1]–[3]. Briefly, conditioning is
achieved by repeated (usually six) pairings of the target odor
followed by food, as shown in Fig. 2. This feeding behavior can
be monitored during the acquisition and testing of the learned
response using electromyography (EMG) from the feeding
muscles [1]. Prior to conditioning, presentation of the target
odorant does not yield a feeding response. In fact, it has been
shown that odor alone is insufficient to elicit feeding-related
behaviors in wind tunnel and field experiments [4]. Indeed,
naïve moths need the integration of both olfactory and visual
cues from a flower to elicit orienting and feeding behaviors [4].
However, following Pavlovian conditioning, moths, like
most animals, will readily respond with a feeding response.
This learned response appears to be remembered by the animal
for the duration of its adult lifespan (ca. 1–2 weeks). A typical
feeding response to odor, following training, is shown in the
EMG trace in Fig. 3. These findings are consistent with a large
Manuscript received June 15, 2003; revised April 1, 2004. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the U.S. Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94AL85000. This work was supported by
DARPA-CBS subcontract and NIH-NCRR (RR14166) to B. H. Smith and NIHNIDCD (DC05535) to K. C. Daly.
T. L. King and F. M. Horine are with the Explosive Components Department,
Sandia National Laboratories, Albuquerque, NM 87185-1453 USA (e-mail:
tlking@sandia.gov).
K. C. Daly and B. H. Smith are with the Department of Entomology, The
Ohio State University, Columbus, OH 43210 USA.
Digital Object Identifier 10.1109/TIM.2004.831455
Fig. 1. Moth in holder with attached electrodes and proboscis extended for
food application.
Fig. 2.
Moth training.
body of similar results in other insects, including the honeybee
[5], as well as a variety of other species of moths [6], [7]. With
both the moth and the honeybee, there is evidence of learning
after just one trial. In the moth, this is manifested as a 35%
increase in feeding probability 24 h after a single conditioning
trial (personal observations). The moths are trained individually, with each trial taking about 15 s to perform. Reproduction
aside, they are a fairly nonsocial species and, thus, do not
interact during any aspect of their training or subsequent use in
the detection device.
Once trained, the conditioned feeding response will generalize from the odorant used in conditioning to other odorants
0018-9456/04$20.00 © 2004 IEEE
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Fig. 3. Typical conditioned EMG response to the target odor.
Fig. 4. Probability that a subset of five trained moths will respond to the target odor.
that are structurally related [3]. The specificity of the conditioned response is constrained to odorants that are highly simcarbon
ilar to the trained odorant (i.e., odors that vary by
units on a side chain). Furthermore, the likelihood that a given
odorant will elicit a response systematically decreases as the
structural difference between the conditioned odorant and the
second odorant increases, producing what is known as a generalization gradient [3]. This provides an opportunity to “tune”
the specificity of the animal’s response and, hence, the tuning
characteristics of the detection system.
Tuning the specificity of the moth’s feeding response is
achieved using a differential conditioning technique. In differential conditioning, moths can be trained to respond more
selectively by systematically presenting a target, followed by
food reinforcement and nontarget odors without reinforcement
[2], [3].
Differential conditioning also provides an opportunity
to reduce false positive responses of the system that occur
because of stimulus-driven responses of the feeding muscles.
These false positives may occur from sudden changes in light
intensity or physical disturbances such as bumping or jostling
of the device. To achieve a false positive control, an equal
number of moths were trained to not respond to the target
odorant. Thus, a true positive should only occur when moths
trained to respond to the target respond and moths trained to
not respond do not.
The overall reliability of the system can be defined by the
probability of a feeding response from target-trained moths to
the target odor, typically 60% for a single moth. The theoretical
probability that at least of five trained moths will respond to
, is plotted in Fig. 4. The plot indicates that
the target odor,
,
the probability of at least two of five moths responding,
is 91% and 68% for
. These reliability scores are based on
only six conditioning trials. Additional conditioning, as well as
longer food deprivation periods, can enhance these probabilities [1]. Thus, both specificity and reliability can be adjusted by
modifying the conditioning techniques [1]–[3]. However, these
probabilities are adequate for the purposes of biological signal
acquisition and processing, which is the focus of this paper.
This system uses a behavioral response. Behavior in any animal is variable and dependent on a variety of external and internal conditions. There is currently no data available on how
environmental conditions, such as light and temperature, affect
performance. By placing them in a wind tunnel, many of the external conditions are controlled. However, internal motivational
states driven by, for example, food deprivation and circadian
hormonal cycles can disrupt the probability that any individual
moth responds behaviorally in spite of the fact that the animal
KING et al.: EXPLOSIVES DETECTION WITH HARD-WIRED MOTHS
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Fig. 5. Voting circuit.
detected the target odorant. The effects of internal motivational
states and how they might be used to enhance moth performance
are currently not well understood. It is known, however, that
prior to four days, behavioral performance drops [1]. This is
likely due to excess body fat resulting from a scientifically developed diet [8]. This diet is responsible, in part, for producing
adult moths that are about twice the size of moths of this species
occurring in nature. Furthermore, after conditioning, as moths
continue to age, the probability of feeding behavior increases,
peaking at 90 , 96 h after training (or eight days post eclosion from the chrysalis). It is currently assumed that changing
motivational states, primarily hunger and thirst, are the mediating factors, but this has currently not been substantiated.
Once trained, the moths are confined inside a small wind
tunnel within the detector device where a fan draws ambient air
across the moths’ antennae. Within the wind tunnel, the moths
continue to respond as expected. If the air drawn through the
wind tunnel contains traces of target odorant, trained moths will
exhibit a feeding response whereas the control moths will not.
The EMG electrodes, attached to their feeding muscles, send
the biological indicator of the presence of the target odor to
on-board electronics. The electronics monitor feeding behavior
and tally the number of moths responding at any one time. In
order to account for false positive responses, the circuit subtracts the number of responsive control moths from the number
of responsive moths trained to the target odorant. Again, this
difference calculation corrects for random feeding behavior as
well as environmental stimuli that could elicit feeding muscle
movement from all moths. The differential score is displayed
numerically and is an indication of the extent to which the target
odorant is detected.
II. DESIGN
The electronics for the prototype consist of ten channels divided into two stages: signal conditioning and data processing.
The data processing stage consists of voting, tallying, and display circuits.
In the signal conditioning stage, the EMG signal received
from each moth is amplified and filtered using a DAM50 differential amplifier by World Precision Instruments, Inc. This is
done to eliminate dc offsets and noise and to raise the signal
level, as shown in Fig. 3. The signal peaks, on the order of volts,
indicate increased feeding muscle activity. It is clear from this
trace that the trained moth responded for 12 s when the target
chemical was released. The signal baseline reflects relative inactivity in the feeding muscles. The variation in signal amplitude occurs because there are a number of feeding muscles in
the head cavity [9], all of which provide a valid indicator. In
this example, the EMG signal was ac-coupled and the amplifier gain set to 1000. The bandpass filter was adjusted for a pass
band from 10 to 1000 Hz.
The voting circuit rectifies the ac signal and feeds it into a
comparator, as shown in Fig. 5. The sensitivity of the circuit is
controlled by setting the threshold voltage with a potentiometer.
If the moth responds, the rectified signal exceeds the threshold
voltage and the comparator output goes high. This triggers a
monostable multivibrator that maintains the signal high long
enough to be counted. An LED displays the status of each moth.
The tallying circuit uses a summing amplifier to combine the
outputs of the multivibrators for the five trained moths, as shown
was chosen to produce an amin Fig. 6. The resistance of
plifier output voltage of 2.5 V when all five moths respond. A
second summing amplifier does the same for the five control
moths. The output of each amplifier passes through a potentiometer before entering the differential inputs of a digital panel
voltmeter in the display circuit. The potentiometer is set to divide the voltage by five.
The voltmeter in Fig. 7 displays the difference between the
number of responding trained and control moths. The decimal
point is positioned to display “5.00” when the differential input
is 0.5 V, corresponding to all trained moths and no control moths
responding.
The power circuit uses a voltage regulator powered by a 9-V
battery to provide 5 V to the data processing stage, as shown
in Fig. 8. The signal conditioning stage is also powered by 9-V
batteries.
III. ASSEMBLY
To help preserve the fidelity of the low-level
mV
moth EMG signals, the prototype, shown in Fig. 9, is constructed inside an aluminum enclosure. The electronics are
in the rectangular section on the left. The ten moths are
mounted on removable aluminum carrier tubes and housed in
the cylindrical miniature wind tunnel chamber on the right.
The foreground air-exhaust tube contains a small axial-flow
fan, the rechargeable battery pack for the fan, and an external
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Fig. 6. Tallying circuit for the (a) trained and (b) control moths.
charging jack. The fan motor is the brushless dc type, chosen to
minimize electromagnetic interference on the EMG amplifiers.
No attempt is made to create a laminar-flow air stream over
the moths, as it is felt any benefits would be lost in the spatial
and temporal resolution obtainable from the integrated system.
With the front cover removed, as shown in Fig. 10, the two
stages of the electronics are clearly visible. The signal conditioning stage consists of the ten black boxes on the left. The
data processing stage on the right is breadboarded to save time
and accommodate modifications. CMOS technology is used
wherever possible to maximize battery life. The entire package
weighs 37 lb, and is 8.7 ft in size.
IV. TEST
Fig. 7. Display circuit.
The prototype was demonstrated at the Defense Advanced
Research Projects Agency (DARPA) Bio-Inspired Technologies
Conference on November 7, 2001. It was loaded with ten moths,
KING et al.: EXPLOSIVES DETECTION WITH HARD-WIRED MOTHS
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Fig. 8. Power circuit.
Fig. 10. Prototype system with cover removed.
Fig. 9. Top view of the prototype system.
five of which were trained to respond to cyclohexanone, a chemical found in certain plastic explosives. The electronics successfully displayed the extent to which the moths responded to the
presence of the chemical when released in front of the device.
Specifically, in the absence of odor stimulation, the voltmeter
display maintained a reading at or near 0 V. Variance in this
measure was attributable to spontaneous EMG activity from
individual moths. In the presence of the target odor, the voltmeter displayed a positive number indicating the number of responding moths minus any spontaneous activity from the con-
trol moths. When the system was bumped and jostled, or when
the front of the wind tunnel was removed causing a sudden increase in light intensity, moths in both the trained and control
groups became active, yielding a net reading on the display near
zero.
This species of moth was chosen for demonstration purposes
because of its previously confirmed ability to detect explosives
components [1]–[3]. However, in principle, any species of
insect or small animal could be used in a device such as this.
Depending on the particular target odor and species used,
training is not necessarily a prerequisite. For example, species
of Necrophagous, or carrion beetles, are very sensitive to odors
produced by decaying flesh (another odorant of importance
to the Department of Defense) and are able to detect these
odors in the range of less than 10 ppm. More importantly, these
animals produce an array of instinctive and reflexive orienting
behaviors in response to these odors. This device can readily
exploit such behaviors. The advantage of using instinctive
behaviors of animals such as the carrion beetle is that they are
reflexive, occurring with a high degree of probability.
V. CONCLUSION
A prototype that uses moths to detect explosives was designed, assembled, and tested. It compares the EMG signals of
moths trained to respond to those trained not to respond to the
explosive signature in order to determine the extent to which it
is detected. The device was designed to be portable by making
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it lightweight, battery-powered, and energy efficient. The
prototype performed successfully during a demonstration for
DARPA. Though portable, this prototype is still rather bulky.
At two pounds each, the ten EMG amplifiers constitute the
bulk of the entire system weight. A much smaller and lighter
handheld version of this device could be made by integrating
the signal conditioning and data processing stages, using ASIC
chips, and constructing a one-piece animal exposure chamber
and electronics package.
As designed, the system is only able to detect one or at
most two odorants. Electronic sniffers are also being developed
and deployed for explosives detection. These systems provide
a means of detection and discrimination. Development of
neurophysiological methods for monitoring central processing
of olfactory input would provide a similar means of bypassing
the limitations associated with behavioral measures to provide
a general odorant detector and discriminator [10].
REFERENCES
[1] K. C. Daly and B. H. Smith, “Associative olfactory learning in the moth
Manduca Sexta,” J. Experiment. Biol., vol. 203, pp. 2025–2038, 2000.
[2] K. C. Daly, M. L. Durtschi, and B. H. Smith, “Olfactory-based discrimination learning in the moth, Manduca Sexta,” J. Insect Physiol., vol. 47,
pp. 375–384, 2001.
[3] K. C. Daly, S. Chandra, M. L. Durtschi, and B. H. Smith, “Generalization of olfactory-based conditioned response reveals unique but overlapping odour representations in the moth, Manduca Sexta,” J. Experiment.
Biol., vol. 204, pp. 3085–3095, 2001.
[4] R. A. Raguso and M. A. Willis, “Synergy between visual and olfactory
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Compar. Psychol., vol. 97, pp. 107–119, 1983.
[6] E. Hartlieb, “Olfactory conditioning in the moth Heliothis virescens,”
Naturwissenschaften, vol. 83, pp. 87–88, 1996.
[7] R. Fan, P. Anderson, and B. Hansson, “Behavioral analysis of olfactory conditioning in the moth Spodoptera littoralis (boisd.) (Lepidoptera:
Noctuidae),” J. Experiment. Biol., vol. 200, pp. 2969–2976, 1997.
[8] R. A. Bell and F. G. Joachim, “Techniques for rearing laboratory
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[9] J. L. Eaton, “Morphology of the head and thorax of the adult tobacco
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[10] K. C. Daly, G. A. Wright, and B. H. Smith, “Molecular features of odorants systematically influence slow temporal responses across clusters of
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Tony L. King (M’90) was born in 1961 in
Wilmington, NC. He received the B.S. and M.Eng.
degrees in electrical engineering from Cornell University, Ithaca, NY, in 1983 and 1984, respectively,
and the Ph.D. degree in electrical engineering from
the University of Illinois at Urbana-Champaign in
1994.
From 1984 to 1988, he conducted research on neutral particle beam weapons as an officer in the U.S.
Air Force. In 1994, he joined the faculty of the Electrical and Computer Engineering Department, University of Houston, Houston, TX, and performed research on electronic circuits
and instrumentation. Since 2001, he has been with Sandia National Laboratories, Albuquerque, NM. His research interests include instrumentation and
electro-explosive devices.
Frank M. Horine has pursued a career in experimental electro-mechanics and electrostatics. A few of
his unusual, demonstrated solutions to arcane technical problems include weapon fireball simulation,
a 1-MW magnesium-powered searchlight, and electrical-fire initiation in nuclear reactor control rooms.
He has been involved with missile-launch acceleration-simulation centrifuge designs for remote
environmental-conditioning, arming, and firing of
explosive components and also with experimental
human-body electrostatic-discharge simulators for
explosives initiation research.
Kevin C. Daly received the B.A. degree in biopsychology psychology from Western Washington
University, Bellingham, WA, in 1989, the M.A. degree in evolutionary psychology from the University
of Arizona, Tucson, AZ, in 1996, and the Ph.D.
degree in ethology and evolutionary psychology
from the University of Arizona in 1998.
His research interests are in the exploration of the
neural substrates of olfactory neural coding, learning,
and memory. He is currently a Research Scientist at
The Ohio State University, Columbus.
Brian H. Smith received the Ph.D. degree from the Department of Psychology
at the University of Arizona in 1998. His dissertation topic was non-associative
learning in moths.
Currently, he is a Professor of Entomology at Ohio State University, receiving
his Ph.d. from the His educational background is in learning theory.