An additive manufacturing concept, consisting of 3D photopolymer printing and Ag nanoparticle printing, was investigated for the construction of a microfluidic biosensor based on immobilized cytochrome P450 enzyme. An acylate-type microfluidic chamber composed of two parts, i.e. chamber-housing and chamber-lid was printed with a polyjet 3D printer. A 3-electrode sensor structure was inkjet-printed on the lid using a combination of Ag and graphene printing. The working electrode was covered with carbon nanotubes by drop-casting and immobilized with cytochrome P450 2D6 enzyme. The microfluidic sensor shows a significant response to a test xenobiotic, i.e. dextromethorphan; the cyclic voltammetrical measurements show a corresponding oxidation peak at 0.4 V with around 5 μM detection limit.
This paper presents the development and application of a hyper-spectral imaging system for root phenotyping. For sustainable plant production root systems optimized for growing conditions in the field are required. Therefore, the presented system is used for the research in the field of plant drought resistance. The system is used to acquire spatially resolved near infrared (NIR) spectroscopy data of rhizoboxes. In contrast to using visible light (380 nm-780 nm) the NIR wavelength range (900 nm-1700 nm) allows to discriminate essential features for the root segmentation and water distribution mappings. The increased image contrast in the NIR range allows roots to be segmented from soil and additional information, e.g. basic root-architecture, to be extracted. In addition, the water absorption bands in the NIR wavelength range can be used to determine the water content and to estimate the age of the roots. In this paper the hardware setup of the hyper-spectral root imaging system, the data analysis, the soil water content estimations and the root segmentation using different methods to optimize separation between roots and soil, both constituting complex materials of variable properties, are presented.
This paper presents an approach enabling localization and identification of foreign bodies in polymer materials applying a combined approach of x-ray imaging, imaging microscopy, optical coherence tomography and Raman imaging spectroscopy. The reliable detection of even small foreign bodies in polymer materials and parts designated for use in semiconductor manufacturing and processing machines is essential. Foreign bodies can in particular be metals, burnt particles of the polymer of the work piece, or intact or degenerated foreign polymers. In either case, all surfaces of e.g. a handling equipment that get in contact with the semiconductor material or process solutions have to be free of foreign bodies to ensure the integrity of the manufacturing process. Size, localization and material of the foreign body are main parameters that decide if a work piece has to be rejected. Current inspection systems may enable the localization of the foreign body, but are not capable of identifying the material and structure of the foreign body; many components with inclusions are therefore rejected as a precaution. This work aims towards the development of a combined sensor approach as part of an automatic quality assurance procedure which can be integrated in the fabrication process. X-ray imaging is used to identify metal foreign bodies. Imaging microscopy is used to detect foreign bodies on the surface of the polymer parts. Optical coherence tomography is used to measure the three-dimensional position and size of the foreign bodies. Raman imaging spectroscopy is used to identify the composition of the foreign bodies if they are located on the surface.
Two problems are addressed in this paper (i) the fluorescent marker-based and the (ii) marker-free discrimination between healthy and cancerous human tissues. For both applications the performance of hyper-spectral methods are quantified. Fluorescent marker-based tissue classification uses a number of fluorescent markers to dye specific parts of a human cell. The challenge is that the emission spectra of the fluorescent dyes overlap considerably. They are, furthermore disturbed by the inherent auto-fluorescence of human tissue. This results in ambiguities and decreased image contrast causing difficulties for the treatment decision. The higher spectral resolution introduced by tunable-filter-based spectral imaging in combination with spectral unmixing techniques results in an improvement of the image contrast and therefore more reliable information for the physician to choose the treatment decision. Marker-free tissue classification is based solely on the subtle spectral features of human tissue without the use of artificial markers. The challenge in this case is that the spectral differences between healthy and cancerous tissues are subtle and embedded in intra- and inter-patient variations of these features. The contributions of this paper are (i) the evaluation of hyper-spectral imaging in combination with spectral unmixing techniques for fluorescence marker-based tissue classification, (ii) the evaluation of spectral imaging for marker-free intra surgery tissue classification. Within this paper, we consider real hyper-spectral fluorescence and endoscopy data sets to emphasize the practical capability of the proposed methods. It is shown that the combination of spectral imaging with multivariate statistical methods can improve the sensitivity and specificity of the detection and the staging of cancerous tissues compared to standard procedures.
The recent horse meat scandal in Europe increased the demand for optical sensors that can identify meat type. Micro-Raman spectroscopy is a promising technique for the discrimination of meat types. Here, we present micro-Raman measurements of chicken, pork, turkey, mutton, beef and horse meat test samples. The data was analyzed with different combinations of data normalization and classification approaches. Our results show that Raman spectroscopy can discriminate between different meat types. Red and white meat are easily discriminated, however a sophisticated chemometric model is required to discriminate species within these groups.
KEYWORDS: Sensors, Gold, Electrodes, Gas sensors, Thin films, Temperature metrology, Scanning electron microscopy, Photomicroscopy, Active sensors, Semiconductors
Two types of MOx sensor structures, SnO2 and WO3, of different thicknesses were synthesized on top of the interdigitated Au electrodes and used for the measurements of the ethylene gas. The SEM micrographs revealed inhomogeneities of the WO3 layer and the presence of cracks on the edges of Au electrodes which correlates with the lack of reproducibility of the WO3 sensors. Both sensor structures showed a significant sensitivity to ethylene gas: the sensitivities of both MOx-types were higher at higher temperatures which was more evident in the case of SnO2 structure. The SnO2 layer had approximately 5-times higher sensitivity than the WO3 sensor of the same thickness. The saturation (T10) and desaturation (T90) times were shorter for WO3 sensors at lower temperatures while SnO2 was saturated and desaturated faster at higher temperatures. Sensors with thinner active layer possessed higher sensitivities and shorter T10 and T90 times.
A light field camera acquires the intensity and direction of rays from a scene providing a 4D representation L(x,y,u,v) called the light field. The acquired light field allows to virtually change view point and selectively re-focus regions algorithmically, an important feature for many applications in imaging and microscopy. The combination with hyperspectral imaging provides the additional advantage that small objects (beads, cells, nuclei) can be categorised using their spectroscopic signatures. Using an inverse fluorescence microscope, a LCTF tuneable filter and a light field setup as a test-bed, fluorescence-marked beads have been imaged and reconstructed into a 4D hyper-spectral image cube LHSI(x,y,z,λ). The results demonstrate the advantages of the approach for fluorescence microscopy providing extended depth of focus (DoF) and the fidelity of hyper-spectral imaging.
Terahertz (THz) time-domain spectroscopy has proven to be a promising technology for a wide range of applications, such as inspection of nished products or materials, quality control, biomedical imaging and diagnostics,
counterfeit detection and characterization of semiconductors. This paper investigates the applicability of THz
time-domain spectroscopy for the characterization of silicon solar cell properties such as: conductivity, charge
carrier mobility and density. Moreover, the possibilities for THz spectroscopy and imaging for the defect analysis
in semiconductor and photovoltaic materials are investigated. THz-pump/THz-probe measurements were carried
out on silicon wafers which were illuminated by a halogen light source to inject free charge carriers. Initial results
indicate that THz time-domain spectroscopy is a promising technique for the characterization of silicon wafers
for the photovoltaic industry.
We present a CTIS system that uses an optimized diffractive optical element (DOE) to project the spectral and
spatial information simultaneously onto a CCD. We compare the DOE with and older approach based on glass
gratings and found that the DOE gave an improved spectral response. We argue that a DOE is the most effective
approach for CTIS.
Martin De Biasio, Thomas Arnold, Gerald McGunnigle, Raimund Leitner, Andreas Tortschanoff, Nina Fietz, Lars Weitkämper, Dirk Balthasar, Volker Rehrmann
A Raman mapping system for detecting and discriminating minerals such as dolomite, marble, calcite and pyrite
is demonstrated. The system is built from components that are suitable for industrial conditions. Together
with a signal processing and a classier the system was shown to be capable of discriminating between several
important classes of mineral. The technique is a potential alternative to sensing methods currently used for
mineral sorting.
This paper describes an airborne multi-spectral imaging system which is able to simultaneously capture three
visible (400-670nm at 50% FWHM) and three near infrared channels (670-1000nm at 50% FWHM). The rst
prototype was integrated in a Schiebel CAMCOPTER®S-100 VTOL (Vertical Take-O and Landing) UAV
(Unmanned Aerial Vehicle) for initial test
ights in spring 2010. The UAV was
own over land containing
various types of vegetation. A miniaturized version of the initial multi-spectral imaging system was developed in
2011 to t into a more compact UAV. The imaging system captured six bands with a minimal spatial resolution
of approx. 10cm x 10cm (depending on altitude). Results show that the system is able to resist the high vibration
level during
ight and that the actively stabilized camera gimbal compensates for rapid roll/tilt movements of
the UAV. After image registration the acquired images are stitched together for land cover mapping and
ight
path validation. Moreover the system is able to distinguish between dierent types of vegetation and soil. Future
work will include the use of spectral imaging techniques to identify spectral features that are related to water
stress, nutrient deciency and pest infestation. Once these bands have been identied, narrowband lters will
be incorporated into the airborne system.
Snapshot approaches address various possibilities to acquire the spectral and spatial information of a scene within a
single camera frame. One advantage over the classical push broom or staring imager approaches is that the temporal
inconsistency between consecutive scan lines in first case or between the acquired monochromatic images in the second
case is avoided. However, this has to be paid by some effort to rearrange or reconstruct the explicit spectral cube from
the entangled raw data in the single camera frame. Besides others, the utilization of a diffractive optical element (DOE)
is one such snapshot approach (CTIS - computed tomography imaging spectrometer). The DOE is used to create an
optical transfer function that projects both the spectral and spatial information of a scene onto a sensor array and a
reconstruction algorithm is used that recovers the spectral cube from the dispersed image pattern. The design of the DOE
is crucial for the overall system performance as the absolute transmission efficiency of the zeroth and first order versus
the relative efficiency between the two over the required wavelength range are difficult to optimize if the limited
dynamic range of a real camera is considered. We describe the optimization of such a DOE for the wavelength range
from 400 to 780nm and the required reconstruction algorithm to recover the spectral cube from the entangled snapshot
image. The described snapshot approach has been evaluated using experiments to assess the spatial and spectral
resolution using diffuse reflectance standards. Additionally the results achieved using the described setup for multi-color
in-situ fluorescence hybridized preparations (M-FISH) are discussed.
This paper presents a hyper-spectral video endoscopy system which utilizes a combination of auto-fluorescence
imaging and white-light reflectance spectroscopy for intra-surgery tissue classification. The results of the first
clinical study consisting of 59 cases of otolaryngoscopic examinations and thorax surgeries are discussed in
this paper. The main focus of this application is the detection of tumor tissue, although hyper-spectral video
endoscopy is not limited to cancer detection. The results show that hyper-spectral video endoscopy exhibits a
large potential to become an important imaging technology for medical imaging devices that provide additional
diagnostic information about the tissue under investigation.
Recycling of glass requires the removal of specialist glasses, such as fireproof and mineral glasses, and glass
ceramics, which are regarded as contaminants. The sorting must take place before melting for efficient glass
recycling. Here, we demonstrate the feasibility of a real-time Raman mapping system for detecting and discriminating
a range of industrially relevant glass contaminants in recovered glass streams. The components used are
suitable for industrial conditions and the chemometric model is robust against imaging geometry and excitation
intensity. The proposed approach is a novel alternative to established glass sorting sensors.
Pharmaceutical counterfeiting is a significant issue in the healthcare community as well as for the pharmaceutical
industry worldwide. The use of counterfeit medicines can result in treatment failure or even death. A rapid
screening technique such as near infrared (NIR) spectroscopy could aid in the search for and identification of
counterfeit drugs. This work presents a comparison of two laboratory NIR imaging systems and the chemometric
analysis of the acquired spectroscopic image data. The first imaging system utilizes a NIR liquid crystal tuneable
filter and is designed for the investigation of stationary objects. The second imaging system utilizes a NIR imaging
spectrograph and is designed for the fast analysis of moving objects on a conveyor belt. Several drugs in form of
tablets and capsules were analyzed. Spectral unmixing techniques were applied to the mixed reflectance spectra
to identify constituent parts of the investigated drugs. The results show that NIR spectroscopic imaging can be
used for contact-less detection and identification of a variety of counterfeit drugs.
Spectral imaging measures data that is spatially and spectrally resolved: that is at each point in the image
the spectrum is measured. Classical spectral imaging requires that the sample is scanned either spatially or
spectrally. The main drawback of the classical approaches is that they are sequential. This paper presents a
computed tomographic imaging spectrometer (CTIS) that can image two spatial and one spectral dimension in
one camera frame. Unlike hyper-spectral imaging techniques which provide full spatial and spectral resolution,
with the proposed technique there is a tradeoff between spatial and spectral resolution. The proposed CTIS
system uses two crossed glass gratings that project the spectral and spatial image information to a 2D CCD
camera array. The current system is designed for microscopic applications in pathology and cell imaging as well
as macroscopic material analysis.
There are commercially available industrial systems for identifying and separating polymers, for instance PE
from PP. However, there is a demand for analyzers that can separate within polymer classes: e.g. PE-LD
from PE-HD or different polypropylenes characterised by different melting points. First, the feasibility of a
reliable spectral identification was tested by extracting different PE and PP samples from an industrial recycling
process, and acquiring diffuse reflectance NIR spectra using an FTIR spectrometer. The resulting spectra were
then subjected to a chemometrics analysis. We successfully identified characteristic spectral features; these are
determined by the chemical bonds of the material, and can be correlated to the melting points of the materials.
These features were then adapted for use on a NIR hyper-spectral (HS) system, making it possible to distinguish
not only different polymers, but also different types of one polymer in real-time.
Near-infrared (NIR) spectroscopy is a widely used method for material identification for laboratory and industrial
applications. While standard spectrometers only allow measurements at one sampling point at a time, NIR Spectral
Imaging techniques can measure, in real-time, both the size and shape of an object as well as identify the material the
object is made of.
The online classification and sorting of recovered paper with NIR Spectral Imaging (SI) is used with success in the paper
recycling industry throughout Europe. Recently, the globalisation of the recycling material streams caused that water-based
flexographic-printed newspapers mainly from UK and Italy appear also in central Europe. These flexo-printed
newspapers are not sufficiently de-inkable with the standard de-inking process originally developed for offset-printed
paper. This de-inking process removes the ink from recovered paper and is the fundamental processing step in paper
recycling. Thus, flexo-printed newspapers are a growing problem for recycling as they reduce the quality of the produced
paper if their amount exceeds a certain limit of the recovered paper.
This paper describes the chemometric model development using an NIR LCTF based hyperspectral imaging system for
the detection of flexographic printing inks. The achieved accuracy with the LCTF based system is above 95%.
Subsequently the model was transferred to an industrial spectrograph based sorting prototype evaluating the application
with an instrumentation that is suitable for the recycling industry. Again an accuracy of over 95% on the object level was
achieved for a sorting test including the physical sorting of the objects using an array of pneumatic nozzles.
Monitoring the soil composition of agricultural land is important for maximizing crop-yields. Carinthian Tech
Research, Schiebel GmbH and Quest Innovations B.V. have developed a multi-spectral imaging system that
is able to simultaneously capture three visible and two near infrared channels. The system was mounted on
a Schiebel CAMCOPTER® S-100 UAV for data acquisition. Results show that the system is able to classify
different land types and calculate vegetation indices.
Video endoscopy allows physicians to visually inspect inner regions of the human body using a camera and only
minimal invasive optical instruments. It has become an every-day routine in clinics all over the world. Recently a
technological shift was done to increase the resolution from PAL/NTSC to HDTV. But, despite a vast literature on invivo
and in-vitro experiments with multi-spectral point and imaging instruments that suggest that a wealth of information
for diagnostic overlays is available in the visible spectrum, the technological evolution from colour to hyper-spectral
video endoscopy is overdue. There were two approaches (NBI, OBI) that tried to increase the contrast for a better
visualisation by using more than three wavelengths. But controversial discussions about the real benefit of a contrast
enhancement alone, motivated a more comprehensive approach using the entire spectrum and pattern recognition
algorithms. Up to now the hyper-spectral equipment was too slow to acquire a multi-spectral image stack at reasonable
video rates rendering video endoscopy applications impossible. Recently, the availability of fast and versatile tunable
filters with switching times below 50 microseconds made an instrumentation for hyper-spectral video endoscopes
feasible. This paper describes a demonstrator for hyper-spectral video endoscopy and the results of clinical
measurements using this demonstrator for measurements after otolaryngoscopic investigations and thorax surgeries. The
application investigated here is the detection of dysplastic tissue, although hyper-spectral video endoscopy is of course
not limited to cancer detection. Other applications are the detection of dysplastic tissue or polyps in the colon or the
gastrointestinal tract.
Spectral imaging is the combination of spectroscopy and imaging. These fields are well developed and are used
intensively in many application fields including industry and the life sciences. The classical approach to acquire
hyper-spectral data is to sequentially scan a sample in space or wavelength. These acquisition methods are
time consuming because only two spatial dimensions, or one spatial and the spectral dimension, can be acquired
simultaneously. With a computed tomography imaging spectrometer (CTIS) it is possible to acquire two spatial
dimensions and a spectral dimension during a single integration time, without scanning either spatial or spectral
dimensions. This makes it possible to acquire dynamic image scenes without spatial registration of the hyperspectral
data. This is advantageous compared to tunable filter based systems which need sophisticated image
registration techniques. While tunable filters provide full spatial and spectral resolution, for CTIS systems there
is always a tradeoff between spatial and spectral resolution as the spatial and spectral information corresponding
to an image cube is squeezed onto a 2D image. The presented CTIS system uses a spectral-dispersion element to
project the spectral and spatial image information onto a 2D CCD camera array. The system presented in this
paper is designed for a microscopy application for the analysis of fixed specimens in pathology and cytogenetics,
cell imaging and material analysis. However, the CTIS approach is not limited to microscopy applications, thus
it would be possible to implement it in a hand-held device for e.g. real-time, intra-surgery tissue classification.
The detection of flame retardants is critical for the recycling of polymers. To investigate the possibility of reliable purefraction
sorting, a sample set containing a wide range of relevant polymers and polymer blends containing various
practically relevant flame-retardant additives was produced and investigated. NIR point spectra were acquired with an
FTIR laboratory spectrometer and hyper-spectral NIR images were obtained using a spectrograph-based hyper-spectral
imaging system. The laboratory spectrometer measurements were used to assign spectral features to the corresponding
chemical compounds and derive a chemometric model that can be used to detect flame-retardant additives. The hyperspectral
NIR images were used to adapt the chemometric model to the spectral features present in the hyper-spectral
image data for the real-time detection.
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