On-line quality control in polymer processing using hyperspectral imaging

L’industrie du plastique se tourne de plus en plus vers les matériaux composites afin d’économiser de la matière et/ou d’utiliser des matières premières à moindres coûts, tout en conservant de bonnes propriétés. L’impressionnante adaptabilité des matériaux composites provient du fait que le manufacturier peut modifier le choix des matériaux utilisés, la proportion selon laquelle ils sont mélangés, ainsi que la méthode de mise en œuvre utilisée. La principale difficulté associée au développement de ces matériaux est l’hétérogénéité de composition ou de structure, qui entraîne généralement des défaillances mécaniques. La qualité des prototypes est normalement mesurée en laboratoire, à partir de tests destructifs et de méthodes nécessitant la préparation des échantillons. La mesure en-ligne de la qualité permettrait une rétroaction quasi-immédiate sur les conditions d’opération des équipements, en plus d’être directement utilisable pour le contrôle de la qualité dans une situation de production industrielle. L’objectif de la recherche proposée consiste à développer un outil de contrôle de qualité pour la qualité des matériaux plastiques de tout genre. Quelques sondes de type proche infrarouge ou ultrasons existent présentement pour la mesure de la composition en-ligne, mais celles-ci ne fournissent qu’une valeur ponctuelle à chaque acquisition. Ce type de méthode est donc mal adapté pour identifier la distribution des caractéristiques de surface de la pièce (i.e. homogénéité, orientation, dispersion). Afin d’atteindre cet objectif, un système d’imagerie hyperspectrale est proposé. À l’aide de cet appareil, il est possible de balayer la surface de la pièce et d’obtenir une image hyperspectrale, c’est-à-dire une image formée de l’intensité lumineuse à des centaines de longueurs d’onde et ce, pour chaque pixel de l’image. L’application de méthodes chimiométriques permettent ensuite d’extraire les caractéristiques spatiales et spectrales de l’échantillon présentes dans ces images. Finalement, les méthodes de régression multivariée permettent d’établir un modèle liant les caractéristiques identifiées aux propriétés de la pièce. La construction d’un modèle mathématique forme donc l’outil d’analyse en-ligne de la qualité des pièces qui peut également prédire et optimiser les conditions de fabrication.The use of plastic composite materials has been increasing in recent years in order to reduce the amount of material used and/or use more economic materials, all of which without compromising the properties. The impressive adaptability of these composite materials comes from the fact that the manufacturer can choose the raw materials, the proportion in which they are blended as well as the processing conditions. However, these materials tend to suffer from heterogeneous compositions and structures, which lead to mechanical weaknesses. Product quality is generally measured in the laboratory, using destructive tests often requiring extensive sample preparation. On-line quality control would allow near-immediate feedback on the operating conditions and may be transferrable to an industrial production context. The proposed research consists of developing an on-line quality control tool adaptable to plastic materials of all types. A number of infrared and ultrasound probes presently exist for on-line composition estimation, but only provide single-point values at each acquisition. These methods are therefore less adapted for identifying the spatial distribution of a sample’s surface characteristics (e.g. homogeneity, orientation, dispersion). In order to achieve this objective, a hyperspectral imaging system is proposed. Using this tool, it is possible to scan the surface of a sample and obtain a hyperspectral image, that is to say an image in which each pixel captures the light intensity at hundreds of wavelengths. Chemometrics methods can then be applied to this image in order to extract the relevant spatial and spectral features. Finally, multivariate regression methods are used to build a model between these features and the properties of the sample. This mathematical model forms the backbone of an on-line quality assessment tool used to predict and optimize the operating conditions under which the samples are processed

Liquid crystal hyperspectral imager

Hyperspectral imaging is the collection, processing and analysis of spectral data in numerous contiguous wavelength bands while also providing spatial context. Some of the commonly used instruments for hyperspectral imaging are pushbroom scanning imaging systems, grating based imaging spectrometers and more recently electronically tunable filters. Electronically tunable filters offer the advantages of compactness and absence of mechanically movable parts. Electronically tunable filters have the ability to rapidly switch between wavelengths and provide spatial and spectral information over a large wavelength range. They involve the use of materials whose response to light can be altered in the presence of an external stimulus. While these filters offer some unique advantages, they also present some equally unique challenges. This research work involves the design and development of a multichannel imaging system using electronically tunable Liquid Crystal Fabry-Perot etalons. This instrument is called the Liquid Crystal Hyperspectral Imager (LiCHI). LiCHI images four spectral regions simultaneously and presents a trade-off between spatial and spectral domains. This simultaneity of measurements in multiple wavelengths can be exploited for dynamic and ephemeral events. LiCHI was initially designed for multispectral imaging of space plasmas but its versatility was demonstrated by testing in the field for multiple applications including landscape analysis and anomaly detection. The results obtained after testing of this instrument and analysis of the images are promising and demonstrate LiCHI as a good candidate for hyperspectral imaging. The challenges posed by LiCHI for each of these applications have also been explored

Development of an optical sensor for real-time weed detection using laser based spectroscopy

The management of weeds in agriculture is a time consuming and expensive activity, including in Australia where the predominant strategy is blanket spraying of herbicides. This approach wastes herbicide by applying it in areas where there are no weeds. Discrimination of different plant species can be performed based on the spectral reflectance of the leaves. This thesis describes the development of a sensor for automatic spot spraying of weeds within crop rows. The sensor records the relative intensity of reflected light in three narrow wavebands using lasers as an illumination source. A prototype weed sensor which had been previously developed was evaluated and redesigned to improve its plant discrimination performance. A line scan image sensor replacement was chosen which reduced the noise in the recorded spectral reflectance properties. The switching speed of the laser sources was increased by replacing the laser drivers. The optical properties of the light source were improved to provide a more uniform illumination across the viewing area of the sensor. A new opto-mechanical system was designed and constructed with the required robustness to operate the weed sensor in outdoor conditions. Independent operation of the sensor was made possible by the development of hardware and software for an embedded controller which operated the opto-electronic components and performed plant discrimination. The first revised prototype was capable of detecting plants at a speed of 10 km/h in outdoor conditions with the sensor attached to a quad bike. However, it was not capable of discriminating different plants. The final prototype included a line scan sensor with increased dynamic range and pixel resolution as well as improved stability of the output laser power. These changes improved the measurement of spectral reflectance properties of plants and provided reliable discrimination of three different broadleaved plants using only three narrow wavelength bands. A field trial with the final prototype demonstrated successful discrimination of these three different plants at 5 km/h when a shroud was used to block ambient light. A survey of spectral reflectance of four crops (sugarcane, cotton, wheat and sorghum) and the weeds growing amongst these crops was conducted to determine the potential for use of the prototype weed sensor to control spot-spraying of herbicides. Visible reflectance spectra were recorded from individual leaves using a fibre spectrometer throughout the growing season for each crop. A discriminant analysis was conducted based on six narrow wavebands extracted from leaf level spectral reflectance measured with a spectrometer. The analysis showed the potential to discriminate cotton and sugarcane fro

Snapshot hyperspectral imaging : near-infrared image replicating imaging spectrometer and achromatisation of Wollaston prisms

Conventional hyperspectral imaging (HSI) techniques are time-sequential and rely on temporal scanning to capture hyperspectral images. This temporal constraint can limit the application of HSI to static scenes and platforms, where transient and dynamic events are not expected during data capture. The Near-Infrared Image Replicating Imaging Spectrometer (N-IRIS) sensor described in this thesis enables snapshot HSI in the short-wave infrared (SWIR), without the requirement for scanning and operates without rejection in polarised light. It operates in eight wavebands from 1.1μm to 1.7μm with a 2.0° diagonal field-of-view. N-IRIS produces spectral images directly, without the need for prior topographic or image reconstruction. Additional benefits include compactness, robustness, static operation, lower processing overheads, higher signal-to-noise ratio and higher optical throughput with respect to other HSI snapshot sensors generally. This thesis covers the IRIS design process from theoretical concepts to quantitative modelling, culminating in the N-IRIS prototype designed for SWIR imaging. This effort formed the logical step in advancing from peer efforts, which focussed upon the visible wavelengths. After acceptance testing to verify optical parameters, empirical laboratory trials were carried out. This testing focussed on discriminating between common materials within a controlled environment as proof-of-concept. Significance tests were used to provide an initial test of N-IRIS capability in distinguishing materials with respect to using a conventional SWIR broadband sensor. Motivated by the design and assembly of a cost-effective visible IRIS, an innovative solution was developed for the problem of chromatic variation in the splitting angle (CVSA) of Wollaston prisms. CVSA introduces spectral blurring of images. Analytical theory is presented and is illustrated with an example N-IRIS application where a sixfold reduction in dispersion is achieved for wavelengths in the region 400nm to 1.7μm, although the principle is applicable from ultraviolet to thermal-IR wavelengths. Experimental proof of concept is demonstrated and the spectral smearing of an achromatised N-IRIS is shown to be reduced by an order of magnitude. These achromatised prisms can provide benefits to areas beyond hyperspectral imaging, such as microscopy, laser pulse control and spectrometry

Hyperspectral analysis of selected fabrics submerged in the Indian Ocean: An innovative way to aid in the estimation of the time human remains have spent in water

Estimating the time since death (minimum Post-Mortem Interval, minPMI) is crucial in forensic investigations. In an aquatic environment, this process is particularly challenging because of the complexity of a corpse’s decomposition process and the many factors related to the environment. Furthermore, there is a general paucity of research in this field. Recently, the use of the clothing discovered alongside a corpse has come under scrutiny: clothing has a high chance to be present, and their colonisation rate by aquatic organisms could be used to estimate the victim’s minimum Post-Mortem Submersion Interval (minPMSI). Besides a biological/zoological-based estimation, no other avenues to age clothing in an underwater context have been tested. This research is the first to focus on the use of Hyperspectral Imaging (HSI) to age fabrics, considering the modification of their optical properties as a result of exposure to a marine environment. Cotton, neoprene, satin, and velvet were submerged underwater over a period of six months off the coast of Perth, Western Australia. In a pilot study, the fabrics were analysed using two different light scenarios (VIS-NIR and VIS-NIR + VIS-H) to identify which one would provide the best reflectance profiles. Results demonstrated that the additional halogen illumination (VIS-NIR + VIS-H) did not provide any extra information with respect to VIS-NIR. In the main study, the fabric’s spectral profiles were therefore captured using only VIS-NIR lighting. Profiles were generated for all submerged samples as well as controls (N=112), and the resulting data were compared within and between fabrics. The most significant differences were observed for the cotton and satin, with a strong negative regression observed between the months spent submerged and the profiles generated. These fabrics showed a significant change of the colour, texture, and structure, as marine organisms were highly attracted to them. Neoprene and velvet, instead, showed minimal significant changes, with the first few months showing similar profiles to the controls and differences toward the end of the experiment. As opposed to cotton and satin, neoprene and velvet were less affected by the marine organisms. Overall, in a forensic context, when investigated via HSI technology, thin and natural fabrics can provide the most information to the investigators. This study is the first to provide data to support the estimation of minPMSI based on the use of remote sensing, HSI, on different fabric types placed in Western Australian marine waters, providing the potential for a new tool in estimation of the minPMSI for forensic investigation

Synthesis, Application and Protein Nanomaterial Interactions of Selected Nanofiber, Nanoparticle and Nanoarray

Nanomaterials have been a hot research topic for past decades due to their unique optical, electronic, catalytic and mechanical properties. This dissertation aims to investigate selected aspects of nanomaterial synthesis, application and protein nanomaterial interactions. We target to improve nanomaterials synthesis, explore their novel applications and study their potential hazardous. Chapter 1 describes new hydrothermal synthesis of carbon nanofibers from cellulose nanocrystals. The described hydrothermal synthesis from cellulose is an environmentally friendly method that has commercial potential for inexpensive production of carbon nanofibers. Chapter 2 describes the application of poly(methyl methacrylate) (PMMA) stabilized 2D AgNP array for measuring changes of bulk refractive index and sensing of selected volatile organic compound (VOC). The PMMA stabilized 2D AgNP array gives linear response to bulk refractive index changes and can be re-used after rinsing with water. Responsive polymer films were spin-coated on PMMA stabilized 2D AgNP array to fabricate the nanocomposite films. These nanocomposite films exhibit sharp coherent plasmon coupling, spectra position of which is affected by the changes of local dielectric environment when interacting with VOC vapors. Chapter 3 describes studies related to the interaction of AgNP and AuNP with cytoskeleton protein (actin and tubulin), immune system protein (complementary component 3) and plasma protein (albumin and fibronegen). The nanoparticle protein interaction is influenced by both nanoparticle and protein sizes. The work presented here establishes basic knowledge related to nanomaterial synthesis and their advanced applications

A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

Collecting plant phenotypic data with sufficient resolution (in both space and time) and accuracy represents a long standing challenge in plant science research, and has been a major limiting factor for the effective use of genomic data for crop improvement. This is particularly true in plant breeding where collecting large-scale field-based plant phenotypes can be very labor intensive and costly. In this paper we reported a multi-sensor system for high throughput phenotyping in plant breeding. The system comprised five sensor modules (ultrasonic distance sensors, thermal infrared radiometers, NDVI sensors, portable spectrometers, and RGB web cameras) to measure crop canopy traits from field plots. A GPS was used to geo-reference the sensor measurements. Two environmental sensors (a solar radiation sensor and air temperature/relative humidity sensor) were also integrated into the system to collect simultaneous environmental data. A LabVIEW program was developed to control and synchronize measurements from all sensor modules and stored sensor readings in the host computer. Canopy reflectance spectra (by portable spectrometers) were post processed to extract NDVI and red-edge NDVI spectral indices; and RGB images were post processed to extract canopy green pixel fraction (as a proxy for biomass). The sensor system was tested in a soybean and wheat field trial. The results showed strong correlations among the sensor-based plant traits at both early and late growing season. Significant correlations were also found between the sensor-based traits and final grain yield at the early season (Pearson’s correlation coefficient r ranged from 0.41 to 0.55) and late season (r from 0.55 to 0.70), suggesting the potential use of the sensor system to assist in phenotypic selection for plant breeding. The sensor system performed satisfactorily and robustly in the field tests. It was concluded that the sensor system could be a powerful tool for plant breeders to collect field-based, high throughput plant phenotyping data

Perspective and Potential of Smart Optical Materials

The increasing requirements of hyperspectral imaging optics, electro/photo-chromic materials, negative refractive index metamaterial optics, and miniaturized optical components from microscale to quantum-scale optics have all contributed to new features and advancements in optics technology. Development of multifunctional capable optics has pushed the boundaries of optics into new fields that require new disciplines and materials to maximize the potential benefits. The purpose of this study is to understand and show the fundamental materials and fabrication technology for field-controlled spectrally active optics (referred to as smart optics) that are essential for future industrial, scientific, military, and space applications, such as membrane optics, light detection and ranging (LIDAR) filters, windows for sensors and probes, telescopes, spectroscopes, cameras, light valves, light switches, and flat-panel displays. The proposed smart optics are based on the Stark and Zeeman effects in materials tailored with quantum dot arrays and thin films made from readily polarizable materials via ferroelectricity or ferromagnetism. Bound excitonic states of organic crystals are also capable of optical adaptability, tunability, and reconfigurability. To show the benefits of smart optics, this paper reviews spectral characteristics of smart optical materials and device technology. Experiments testing the quantum-confined Stark effect, arising from rare earth element doping effects in semiconductors, and applied electric field effects on spectral and refractive index are discussed. Other bulk and dopant materials were also discovered to have the same aspect of shifts in spectrum and refractive index. Other efforts focus on materials for creating field-controlled spectrally smart active optics (FCSAO) on a selected spectral range. Surface plasmon polariton transmission of light through apertures is also discussed, along with potential applications. New breakthroughs in micro scale multiple zone plate optics as a micro convex lens are reviewed, along with the newly discovered pseudo-focal point not predicted with conventional optics modeling. Micron-sized solid state beam scanner chips for laser waveguides are reviewed as well

Functional Hyperspectral Imaging by High-Related Vegetation Indices to Track the Wide-Spectrum Trichoderma Biocontrol Activity Against Soil-Borne Diseases of Baby-Leaf Vegetables

Research has been increasingly focusing on the selection of novel and effective biological control agents (BCAs) against soil-borne plant pathogens. The large-scale application of BCAs requires fast and robust screening methods for the evaluation of the efficacy of high numbers of candidates. In this context, the digital technologies can be applied not only for early disease detection but also for rapid performance analyses of BCAs. The present study investigates the ability of different Trichoderma spp. to contain the development of main baby-leaf vegetable pathogens and applies functional plant imaging to select the best performing antagonists against multiple pathosystems. Specifically, sixteen different Trichoderma spp. strains were characterized both in vivo and in vitro for their ability to contain R. solani, S. sclerotiorum and S. rolfsii development. All Trichoderma spp. showed, in vitro significant radial growth inhibition of the target phytopathogens. Furthermore, biocontrol trials were performed on wild rocket, green and red baby lettuces infected, respectively, with R. solani, S. sclerotiorum and S. rolfsii. The plant status was monitored by using hyperspectral imaging. Two strains, Tl35 and Ta56, belonging to T. longibrachiatum and T. atroviride species, significantly reduced disease incidence and severity (DI and DSI) in the three pathosystems. Vegetation indices, calculated on the hyperspectral data extracted from the images of plant-Trichoderma-pathogen interaction, proved to be suitable to refer about the plant health status. Four of them (OSAVI, SAVI, TSAVI and TVI) were found informative for all the pathosystems analyzed, resulting closely correlated to DSI according to significant changes in the spectral signatures among health, infected and bio-protected plants. Findings clearly indicate the possibility to promote sustainable disease management of crops by applying digital plant imaging as large-scale screening method of BCAs' effectiveness and precision biological control support