Glen C. Rains

Using insect sniffing devices for detection

Emerging information about the ability of insects to detect and associatively learn has revealed that they could be used within chemical detection systems. Such systems have been developed around free-moving insects, such as honey bees. Alternatively, behavioral changes of contained insects can be interpreted by sampling air pumped over their olfactory organs. These organisms are highly sensitive, flexible, portable and cheap to reproduce, and it is easy to condition them to detect target odorants. However, insect-sensing systems are not widely studied or accepted as proven biological sensors. Further studies are needed to examine additional insect species and to develop better methods of using their olfactory system for detecting odorants of interest.

Associative learning of odor with food- or blood-meal by Culex quinquefasciatus Say (Diptera: Culicidae)

The ability of many insects to learn has been documented. However, a limited number of studies examining associative learning in medically important arthropods has been published. Investigations into the associative learning capabilities of Culex quinquefasciatus Say were conducted by adapting methods commonly used in experiments involving Hymenoptera. Male and female mosquitoes were able to learn a conditioned stimulus that consisted of an odor not normally encountered in nature (synthetic strawberry or vanilla extracts) in association with an unconditioned stimulus consisting of either a sugar (males and females) or blood (females) meal. Such information could lead to a better understanding of the ability of mosquitoes to locate and select host and food resources in nature.

Behavioral Monitoring of Trained Insects for Chemical Detection

A portable, handheld volatile odor detector (“Wasp Hound”) that utilizes a computer vision system and Microplitis croceipes (Cresson) (Hymenoptera: Braconidae), a parasitoid wasp, as the chemical sensor was created. Five wasps were placed in a test cartridge and placed inside the device. Wasps were either untrained or trained by associative learning to detect 3‐octanone, a common fungal volatile chemical. The Wasp Hound sampled air from the headspace of corn samples prepared within the lab and, coupled with Visual Cortex, a software program developed using the LabView graphical programming language, monitored and analyzed wasp behavior. The Wasp Hound, with conditioned wasps, was able to detect 0.5 mg of 3‐octanone within a 240 mL glass container filled with feed corn (≈2.6 × 10−5 mol/L). The Wasp Hound response to the control (corn alone) and a different chemical placed in the corn (0.5 mg of myrcene) was significantly different than the response to the 3‐octanone. Wasp Hound results from untrained wasps were significantly different from trained wasps when comparing the responses to 3‐octanone. The Wasp Hound may provide a unique method for monitoring grains, peanuts, and tree nuts for fungal growth associated with toxin production, as well as detecting chemicals associated with forensic investigations and plant/animal disease.

Influence of handling and conditioning protocol on learning and memory of Microplitis croceipes

Microplitis croceipes (Cresson) (Hymenoptera: Braconidae) learns odors in association with both hosts and food. The food‐associated ‘seeking’ behavior of M. croceipes was investigated under various training protocols utilizing the conditioning odor, 3‐octanone. We investigated the effects of odor training, or its lack, training duration, training frequency, time elapsed after training, wasp hunger state, and training reinforcement, on the food‐seeking responses of M. croceipes females. We found that odor‐trained females show strong food seeking responses, whereas non‐odor‐trained females do not respond to the odor, and that a single 10 s association with the odor whilst feeding on sugar water subsequently conditioned the wasps to exhibiting significant responses to it. Increases in training time to more than 10 s did not improve their responses. Repetition of the food–odor associations increased a wasps recall, as well as its response over time, compared to a single exposure. Repeated exposure to the learned odor in the absence of a food reward decreased the responses of less hungry individuals. However, the level of response increased significantly following a single reinforcement with the food–odor association. Understanding the factors that influence learning in parasitoids can enhance our ability to predict their foraging behavior, and opens up avenues for the development of effective biological detectors.

Comparison of detection ability of learned odors between males and females in the larval parasitoid Microplitis croceipes

Although female parasitic wasps are known to learn to associate odors with hosts and food, the ability of males to learn and detect odors has been neglected. We conducted laboratory experiments to compare the detection ability of learned odors between males and females in the larval parasitoid Microplitis croceipes (Cresson) (Hymenoptera: Braconidae). We first conditioned males and females to associate sucrose water with methyl benzoate, 3‐octanone, or cyclohexanone, and then observed their behavior toward various concentrations (40 ng l−1−4 mg l−1) of the trained odors. Conditioned male wasps responded as well as female wasps to various concentrations of the three odors. Response times by wasps to these three odors were not significantly different between sexes. For the three odors, response times of both sexes were longer at the intermediate concentrations (40–400 µg l−1) than the higher or lower concentrations. The present study suggests that M. croceipes males can learn and respond to the three chemicals tested as well as the females, and conditioned males are as sensitive to learned odors as conditioned females. By using their sensitive learning and odor‐detection capabilities, M. croceipes males could search for food sources as efficiently as females under natural conditions.

Conditioned Microplitis croceipes Cresson (Hymenoptera: Braconidae) detect and respond to 2,4-DNT: development of a biological sensor.

We examined the ability of M. croceipes to learn, detect, and respond to 2,4-DNT, which is a volatile discriminator of trinitrotoluene (TNT). The percentage of conditioned wasps to detect and respond to the various concentrations of 2,4-DNT for > or = 15 sec was measured. Significantly more of the conditioned wasps responded to the concentration of 2,4-DNT used for conditioning than other concentrations examined. Accordingly, percent conditioned wasps to respond > or = 15 sec could be used as a suitable measure to screen air samples and distinguish between samples with or without the target odorant. The data recorded in this study indicate the measured behavior could be used to estimate the concentration of target odorants. Data in this study indicate M. croceipes can detect and respond to this compound, which provide further support for its development as a biological sensor.

A Customized Metal Oxide Semiconductor-Based Gas Sensor Array for Onion Quality Evaluation: System Development and Characterization

A gas sensor array, consisting of seven Metal Oxide Semiconductor (MOS) sensors that are sensitive to a wide range of organic volatile compounds was developed to detect rotten onions during storage. These MOS sensors were enclosed in a specially designed Teflon chamber equipped with a gas delivery system to pump volatiles from the onion samples into the chamber. The electronic circuit mainly comprised a microcontroller, non-volatile memory chip, and trickle-charge real time clock chip, serial communication chip, and parallel LCD panel. User preferences are communicated with the on-board microcontroller through a graphical user interface developed using LabVIEW. The developed gas sensor array was characterized and the discrimination potential was tested by exposing it to three different concentrations of acetone (ketone), acetonitrile (nitrile), ethyl acetate (ester), and ethanol (alcohol). The gas sensor array could differentiate the four chemicals of same concentrations and different concentrations within the chemical with significant difference. Experiment results also showed that the system was able to discriminate two concentrations (196 and 1964 ppm) of methlypropyl sulfide and two concentrations (145 and 1452 ppm) of 2-nonanone, two key volatile compounds emitted by rotten onions. As a proof of concept, the gas sensor array was able to achieve 89% correct classification of sour skin infected onions. The customized low-cost gas sensor array could be a useful tool to detect onion postharvest diseases in storage.

Development of Microplitis croceipes as a biological sensor

Classical conditioning, a form of associative learning, was first described in the vertebrate literature by Pavlov, but has since been documented for a wide variety of insects. Our knowledge of associative learning by insects began with Karl vonFrisch explaining communication among honeybees, Apis mellifera L. (Hymenoptera: Apidae). Since then, the honey bee has provided us with much of what we understand about associative learning in insects and how we relate the theories of learning in vertebrates to insects. Fruit flies, moths, and parasitic wasps are just a few examples of other insects that have been documented with the ability to learn. A novel direction in research on this topic attempts to harness the ability of insects to learn for the development of biological sensors. Parasitic wasps, especially Microplitis croceipes (Cresson) (Hymenoptera: Braconidae), have been conditioned to detect the odors associated with explosives, food toxins, and cadavers. Honeybees and moths have also been associatively conditioned to several volatiles of interest in forensics and national security. In some cases, handheld devices have been developed to harness the insects and observe conditioned behavioral responses to air samples in an attempt to detect target volatiles. Current research on the development of biological sensors with insects is focusing on factors that influence the learning and memory ability of arthropods as well as potential mathematical techniques for improving the interpretation of the behavioral responses to conditioned stimuli. Chemical detection devices using arthropod‐based sensing could be used in situations where trained canines cannot be used (such as toxic environments) or are unavailable, electronic devices are too expensive and/or not of sufficient sensitivity, and when conditioning to target chemicals must be done within minutes of detection. The purpose of this article is to provide a brief review of the development of M. croceipes as a model system for exploring associative learning for the development of biological sensors.

4D crop monitoring: Spatio-temporal reconstruction for agriculture

Autonomous crop monitoring at high spatial and temporal resolution is a critical problem in precision agriculture. While Structure from Motion and Multi-View Stereo algorithms can finely reconstruct the 3D structure of a field with low-cost image sensors, these algorithms fail to capture the dynamic nature of continuously growing crops. In this paper we propose a 4D reconstruction approach to crop monitoring, which employs a spatio-temporal model of dynamic scenes that is useful for precision agriculture applications. Additionally, we provide a robust data association algorithm to address the problem of large appearance changes due to scenes being viewed from different angles at different points in time, which is critical to achieving 4D reconstruction. Finally, we collected a high-quality dataset with ground-truth statistics to evaluate the performance of our method. We demonstrate that our 4D reconstruction approach provides models that are qualitatively correct with respect to visual appearance and quantitatively accurate when measured against the ground truth geometric properties of the monitored crops.

Near-infrared Hyperspectral Reflectance Imaging for Early Detection of Sour Skin Disease in Vidalia Sweet Onions

Sour skin is a major onion disease caused by the bacterium Burkholderia cepacia (B. cepacia). It not only causes substantial economic loss from diseased onions but also could lead to pulmonary infection in humans. It is critical to prevent onions infected by sour skin from entering storage rooms or being shipped to fresh vegetable markets. This paper reports the development of a hyperspectral imaging method for early detection of onions infected by sour skin. In this study, near-infrared hyperspectral reflectance images of 40 Vidalia sweet onions were taken in 2 nm increments from 950 nm to 1650 nm, before and after they were inoculated with B. cepacia. Inoculated onion samples were scanned every day after inoculation for 7 days, while the hyperspectral images scanned before inoculation were used as controls. Spectral signatures of onion hyperspectral images were extracted from selected regions of interest. Based on the principal component analysis conducted on spectral signatures of control and inoculated samples, two optimal spectral bands (1070nm and 1400nm) were selected to construct ratio images, which better revealed the difference between the control and inoculated samples. Mean ratio values at three different areas on the onion surface (flesh body area, root or neck area, and the whole onion area) were calculated from ratio images and used as inputs for classification models. The three spatial features of mean ratio values obtained from band-ratio images were proved to be good indicators of sour skin-infected onions. When comparing two classifiers, The back-propagation neural network (BPNN) models performed better (95% accuracy) than support vector machine (SVM) classifiers (85%-90%) in discriminating control samples and inoculated samples on day 6 after inoculation. Then, the optimal BPNN classifier using three spatial features of band-ratio images was applied to classify hyperspectral images of tested onion samples over the period of 1-7 days after inoculation, respectively. The results of tests showed that the near-infrared hyperspectral reflectance imaging technique could detect sour skin-infected onions effectively from day 4 to day 7 after inoculation by achieving overall classification accuracies of 80%, 85%, 95%, and 100%, respectively.