Study on comparison of biochemistry between Trogoderma granarium Everts and Trogoderma variabile Ballion

Stored grains are paramount commodities to be preserved and stocked for future supply to the market according to the requirement. However, one of the major problems during storage is insect pests, of which insects from Trogoderma sp. especially khapra beetle (Trogoderma granarium) is considered the world most dangerous stored grain insect pests. Therefore, it has been listed as quarantine insect pests in many counties. For timely management of quarantine pest, effective and rapid diagnostic methods are required. Until now, diagnostic technology is mainly based on morphology of insects which require trained taxonomists. Recently, diagnostics based on metabolites and hyperspectral imaging coupled with machine learning is gaining importance. However, very little is known about the metabolites in Trogoderma sp. and how the host grain, gender, and geographical distribution affect the metabolomic profiling in these species is still unknown. In this thesis, volatile organic compounds (VOCs) emitted by Trogoderma variabile at different life stages were analysed as biomarkers which can help us to understand the biochemistry and metabolomic. Some compounds were identified from T. variabile different stages, which could be used as diagnostic tool for this insect. Gas chromatography coupled to mass spectrometry (GC–MS) was used as a technique to study the metabolite profile of T. variabile in different host grains. However, there are several factors that affect the volatile organic compounds including extraction time and number of insects. The results indicated that the optimal number of insects required for volatile organic compounds (VOC) extraction at each life stage was 25 and 20 for larvae and adults respectively. Sixteen hours were selected as the optimal extraction time for larvae and adults. Some of the VOCs compounds identified from this insect can be used as biomarkers such as pentanoic acid; diethoxymethyl acetate; 1-decyne; naphthalene, 2-methyl-; n-decanoic acid; dodecane, 1-iodo- and m-camphorene from larvae. While butanoic acid, 2-methyl-; pentanoic acid; heptane, 1,1'-oxybis- 2(3H)-Furanone, 5-ethyldihydro-; pentadecane, 2,6,10-trimethyl-; and 1,14-tetradecanediol VOCs, were found in male, whereas pentadecane; nonanic acid; pentadecane, 2,6,10-trimethyl-; undecanal and hexadecanal were identified from female. Additionaly, direct immersion-solid phase microextraction (DI-SPME) was employed, followed by gas chromatography mass spectrometry analysis (GC-MS) for the collection, separation, and identification of the chemical compounds from T. variabile adults fed on four different host grains. Results showed that insect host grains have a significant difference on the chemical compounds that were identified from female and male. There were 23 compounds identified from adults reared on canola and wheat. However, there were 26 and 28 compounds detected from adults reared on oats and barley respectively. Results showed that 11-methylpentacosane; 13-methylheptacosane; heptacosane; docosane, 1-iodo- and nonacosane were the most significant compounds that identified form T. variabile male reared on different host grains. However, the main compounds identified from female cultured on different host grains include docosane, 1-iodo-; 1-butanamine, N-butyl-; oleic acid; heptacosane; 13-methylheptacosane; hexacosane; nonacosane; 2-methyloctacosane; n-hexadecanoic acid and docosane. A novel diagnostic tool to identify between T. granarium and T. variabile were developed using visible near infrared hyperspectral imaging and deep learning models including Convolutional Neural Networks (CNN) and Capsule Network. Ventral orientation showed a better accuracy over dorsal orientation of the insects for both larvae and adult stages. This technology offers a new approach and possibility of an effective identification of T. granarium and T. variabile. from its body fragments and larvae skins. The results showed high accuracy to identify between T. granarium and T. variabile. The accuracy was 93.4 and 96.2% for adults and larvae respectively, and the accuracies of 91.6, 91.7 and 90.3% were achieved for larvae skin, adult fragments, larvae fragment respectively

Nondestructive Multivariate Classification of Codling Moth Infested Apples Using Machine Learning and Sensor Fusion

Apple is the number one on the list of the most consumed fruits in the United States. The increasing market demand for high quality apples and the need for fast, and effective quality evaluation techniques have prompted research into the development of nondestructive evaluation methods. Codling moth (CM), Cydia pomonella L. (Lepidoptera: Tortricidae), is the most devastating pest of apples. Therefore, this dissertation is focused on the development of nondestructive methods for the detection and classification of CM-infested apples. The objective one in this study was aimed to identify and characterize the source of detectable vibro-acoustic signals coming from CM-infested apples. A novel approach was developed to correlate the larval activities to low-frequency vibro-acoustic signals, by capturing the larval activities using a digital camera while simultaneously registering the signal patterns observed in the contact piezoelectric sensors on apple surface. While the larva crawling was characterized by the low amplitude and higher frequency (around 4 Hz) signals, the chewing signals had greater amplitude and lower frequency (around 1 Hz). In objective two and three, vibro-acoustic and acoustic impulse methods were developed to classify CM-infested and healthy apples. In the first approach, the identified vibro-acoustic patterns from the infested apples were used for the classification of the CM-infested and healthy signal data. The classification accuracy was as high as 95.94% for 5 s signaling time. For the acoustic impulse method, a knocking test was performed to measure the vibration/acoustic response of the infested apple fruit to a pre-defined impulse in comparison to that of a healthy sample. The classification rate obtained was 99% for a short signaling time of 60-80 ms. In objective four, shortwave near infrared hyperspectral imaging (SWNIR HSI) in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for the three apple cultivars reaching an accuracy of up to 97.4%. In objective five, the physicochemical characteristics of apples were predicted using HSI method. The results showed the correlation coefficients of prediction (Rp) up to 0.90, 0.93, 0.97, and 0.91 for SSC, firmness, pH and moisture content, respectively. Furthermore, the effect of long-term storage (20 weeks) at three different storage conditions (0 °C, 4 °C, and 10 °C) on CM infestation and the detectability of the infested apples was studied. At a constant storage temperature the detectability of infested samples remained the same for the first three months then improved in the fourth month followed by a decrease until the end of the storage. Finally, a sensor data fusion method was developed which showed an improvement in the classification performance compared to the individual methods. These findings indicated there is a high potential of acoustic and NIR HSI methods for detecting and classifying CM infestation in different apple cultivars

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

In recent decades, the quality and safety of fruits, vegetables, cereals, meats, milk, and their derivatives from processed foods have become a serious issue for consumers in developed as well as developing countries. Undoubtedly, the traditional methods of inspecting and ensuring quality that depends on the human factor, some mechanical and chemical methods, have proven beyond any doubt their inability to achieve food quality and safety, and thus a failure to achieve food security. With growing attention on human health, the standards of food safety and quality are continuously being improved through advanced technology applications that depend on artificial intelligence tools to monitor the quality and safety of food. One of the most important of these applications is imaging technology. A brief discussion in this chapter on the utilize of multiple imaging systems based on all different bands of the electromagnetic spectrum as a principal source of various imaging systems. As well as methods of analyzing and reading images to build intelligence and non-destructive systems for monitoring and measuring the quality of foods

ReSCon '11, Research Student Conference: Book of Abstracts

The fourth SED Research Student Conference (ReSCon2011) was hosted over three days, 20-22 June 2011, in the Hamilton Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. which focused on current research being conducted within the School of Engineering and Design by postgraduate research students from the School. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

OCM 2017 - Optical Characterization of Materials - conference proceedings

Each material has its own specific spectral signature independent if it is food, plastics, or minerals. During the conference we will discuss new trends and developments in material characterization. You also will be informed about latest highlights to identify spectral footprints and their realizations in industry

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer

OCM 2013 - Optical Characterization of Materials - conference proceedings

The state of the art in optical characterization of materials is advancing rapidly. New insights into the theoretical foundations of this research field have been gained and exciting practical developments have taken place, both driven by novel applications that are constantly emerging. This book presents latest research results in the domain of Characterization of Materials by spectral characteristics of UV (240 nm) to IR (14 µm), multispectral image analysis, X-Ray, polarimetry and microscopy

OCM 2013 - 1st International Conference on Optical Characterization of Materials: March 6th - 7th, 2013, Karlsruhe, Germany

The state of the art in optical characterization of materials is advancing rapidly. New insights into the theoretical foundations of this research field have been gained and exciting practical developments have taken place, both driven by novel applications that are constantly emerging. This book presents latest research results in the domain of Characterization of Materials by spectral characteristics of UV (240 nm) to IR (14 µm), multispectral image analysis, X-Ray, polarimetry and microscopy

Amorphous, porous silicon materials and related methods

Provided herein are Si-based materials, methods of making the Si-based materials, and methods for using the Si-based materials. In embodiments, a silicon-based material comprises an aggregate of particles, the particles comprising an ordered array of nanostructures, the nanostructures comprising amorphous silicon, wherein at least some pairs of adjacent nanostructures are connected by one or more bridges comprising amorphous silicon, the one or more bridges extending from the surface of one nanostructure of the pair to the surface of the other nanostructure in the pair