This PDF file contains the front matter associated with SPIE Proceedings Volume 9482 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Chemical speciation is defined as the determination of the concentration of an analyte (e.g., Chromium or Cr) in the oxidation state in which it exists in the environment (e.g., sea water). Determinations of the concentration of different Cr-species is important due to toxicity differences of the different oxidation states of Cr. For example, Cr(III) is regarded as generally non-toxic and is considered as an essential micro-nutrient. But Cr(VI) is considered as carcinogenic. In this paper, speciation methods for Cr in sea water samples using large scale plasmas, such as an ICP (Inductively Coupled Plasma) and steps taken toward using a microplasma are described.

We will describe the history and background of portable and handheld spectrometry at Thermo Fisher Scientific. The handheld instruments range from elemental detections based on energy-dispersive XRF to molecular identifications using Raman, NIR and FTIR and their combining techniques. We will also discuss our recent progresses and their applications.

A handheld LIBS instrument has been designed that includes most features found in large bench-top systems including variable gating, argon purge, high resolution, wide spectral range, sample rastering, and video targeting. In this presentation we will discuss the feature selection, trade-off decisions and new developments that made this kind of size reduction possible. Early results will be presented for elemental presence detection and quantification with specific emphasis on defense and security.

Multivariate optical computing (MOC), an optical sensing technique for analog calculation, allows direct and robust measurement of chemical and physical properties of complex fluid samples in high-pressure/high-temperature (HP/HT) downhole environments. The core of this MOC technology is the integrated computational element (ICE), an optical element with a wavelength-dependent transmission spectrum designed to allow the detector to respond sensitively and specifically to the analytes of interest. A key differentiator of this technology is it uses all of the information present in the broadband optical spectrum to determine the proportion of the analyte present in a complex fluid mixture. The detection methodology is photometric in nature; therefore, this technology does not require a spectrometer to measure and record a spectrum or a computer to perform calculations on the recorded optical spectrum. The integrated computational element is a thin-film optical element with a specific optical response function designed for each analyte. The optical response function is achieved by fabricating alternating layers of high-index (a-Si) and low-index (SiO2) thin films onto a transparent substrate (BK7 glass) using traditional thin-film manufacturing processes (e.g., ion-assisted e-beam vacuum deposition). A proprietary software and process are used to control the thickness and material properties, including the optical constants of the materials during deposition to achieve the desired optical response function. The ion-assisted deposition is useful for controlling the densification of the film, stoichiometry, and material optical constants as well as to achieve high deposition growth rates and moisture-stable films. However, the ion-source can induce undesirable absorption in the film; and subsequently, modify the optical constants of the material during the ramp-up and stabilization period of the e-gun and ion-source, respectively. This paper characterizes the unwanted absorption in the a-Si thin-film using advanced thin-film metrology methods, including spectroscopic ellipsometry and Fourier transform infrared (FTIR) spectroscopy. The resulting analysis identifies a fundamental mechanism contributing to this absorption and a method for minimizing and accounting for the unwanted absorption in the thin-film such that the exact optical response function can be achieved.

Different modifications of quartz enhanced photoacoustic spectroscopy (QEPAS) are discussed. An analysis of the signal-to-noise ratio for the generated photoacoustic signal gives hints for an optimized geometry of tuning fork and acoustic resonator. Furthermore, simultaneous or alternate photoacoustic and electrical driving is discussed, leading the way to new detection schemes that are capable of measuring changes in molecular relaxation dynamics.

Designing the next generation of embedded compact, portable near-infrared (NIR) spectrometers while meeting aggressive cost and form factor targets requires novel technologies and creative system designs. New miniature spectrometer architectures are enabled by Texas Instruments DLP® technology. The ability to provide programmable spectral filters using high speed, accurate light modulation with a micro-electro mechanical systems (MEMS) based architecture enables systems with features and sampling techniques that were previously not possible. System design considerations and the latest developments in DLP spectrometer architectures will be presented.

The prevalence of counterfeit and substandard medicines has been growing rapidly over the past decade, and fast, nondestructive techniques for their detection are urgently needed to counter this trend. In this study, energy-dispersive X-ray diffraction (EDXRD) combined with chemometrics was assessed for its effectiveness in quantitative analysis of compressed powder mixtures. Although EDXRD produces lower-resolution diffraction patterns than angular-dispersive X-ray diffraction (ADXRD), it is of interest for this application as it carries the advantage of allowing the analysis of tablets within their packaging, due to the higher energy X-rays used. A series of caffeine, paracetamol and microcrystalline cellulose mixtures were prepared with compositions between 0 - 100 weight% in 20 weight% steps (22 samples in total, including a centroid mixture), and were pressed into tablets. EDXRD spectra were collected in triplicate, and a principal component analysis (PCA) separated these into their correct positions in the ternary mixture design. A partial least-squares (PLS) regression model calibrated using this training set was validated using both segmented cross-validation, and with a test set of six samples (mixtures in 8:1:1 and 5⅓:2⅓:2⅓ ratios) – the latter giving a root-mean square error of prediction (RMSEP) of 1.30, 2.25 and 2.03 weight% for caffeine, paracetamol and cellulose respectively. These initial results are promising, with RMSEP values on a par with those reported in the ADXRD literature.

We report long-wave infrared (LWIR, 5-15 μm) and mid-wave infrared (MWIR, 2.5 – 5 μm) differential absorption spectra of different nerve agent simulants and common solutes sorbed to poly(methyldi(1,1,1-trifluoro-2-trifluoromethyl- 2-hydroxypent-4-enyl)silane, HCSFA2, an NRL developed hypersorbent polymer. HCSFA2 is a strong hydrogen-bond acidic polymer which exhibits large gas-polymer partitions for a variety of hazardous chemicals with hydrogen-bond basic properties such as the phosphonate ester G-nerve agents or their simulants. The measured ATR-FTIR differential absorption spectra show complex fingerprint signal changes in the resonances for the sorbent material itself, as well as new resonances arising from chemical bonding between the solute or analyte and the sorbent or the solute itself being present in the sorbent.

We present a new type of handheld laser-induced breakdown spectroscopy (LIBS) spectrometer for developing mobile atomic spectroscopy solutions for real world applications. A micro diode-pumped passive Q-switched solid-state laser with high repetition rate of well above 1 kHz in comparison to 1-10 Hz as used in a traditional LIBS instrument is employed to produce a train of laser pulses. The laser beam is further fast scanned over a pre-defined area, hence generating several hundreds of micro-plasmas per second at different locations. Synchronized miniature CCD array spectrometer modules collect the LIBS signal and generate LIBS spectra. By adjusting the integration time of the spectrometer to cover a plurality of periods of the laser pulse train, the spectrometer integrates the LIBS signal produced by this plurality of laser pulses. Hence the intensity of the obtained LIBS spectrum can be greatly improved to increase the signal-to-noise ratio (SNR). This unique feature of the high repetition rate laser based LIBS system allows it to measure elements at trace levels, hence reducing the limit of detection (LOD). The increased signal intensity also lessens the sensitivity requirement for the optical spectrometer. In addition, the energy of the individual laser pulse can be reduced in comparison to traditional LIBS system to obtain the same signal level, making the laser pulse less invasive to the sample. The typical measurement time is within 1 second. Several examples of real world applications will be presented.

There is growing interest in rapid analysis of rare earth elements (REEs) both due to the need to find new natural sources to satisfy increased demand in their use in various electronic devices, as well as the fact that they are used to estimate actinide masses for nuclear safeguards and nonproliferation. Laser-Induced Breakdown Spectroscopy (LIBS) appears to be a particularly well-suited spectroscopy-based technology to rapidly and accurately analyze the REEs in various matrices at low concentration levels (parts-per-million). Although LIBS spectra of REEs have been reported for a number of years, further work is still necessary in order to be able to quantify the concentrations of various REEs in realworld complex samples. LIBS offers advantages over conventional solution-based radiochemistry in terms of cost, analytical turnaround, waste generation, personnel dose, and contamination risk. Rare earth elements of commercial interest are found in the following three matrix groups: 1) raw ores and unrefined materials, 2) as components in refined products such as magnets, lighting phosphors, consumer electronics (which are mostly magnets and phosphors), catalysts, batteries, etc., and 3) waste/recyclable materials (aka e-waste). LIBS spectra for REEs such as Gd, Nd, and Sm found in rare earth magnets are presented.

Some uses of portable spectrometers require the same quality as laboratory instruments. Such quality is challenging because of temperature and humidity variation, dust, and vibration. Typically, one chooses materials and mechanical layout to minimize the influence of these noise and background sources. Mechanical stability is constrained by limits on instrument mass and ergonomics. An alternative approach is to make minimally adequate hardware, compensating for variability in software. We describe an instrument developed specifically to use software to compensate for marginal hardware. An initial instantiation of the instrument is limited to 430 – 700 nm. Simple changes will allow expansion to cover 315 – 1000 nm. Outside this range, costs are likely to increase significantly. Inherent wavelength calibration comes from knowing the peak emission wavelength of an LED light source, and fitting of instrument dispersion to a model of order placement with each measurement. Dynamic range is determined by the product of camera response and intentionally wide throughput variation among hundreds of diffraction orders. Resolution degrades gracefully at low light levels, but is limited to ~ 2 nm at high light levels as initially fabricated and ~ 1 nm in principle. Stray light may be measured in real-time. Diffuse stray light can be employed for turbidimetry fluorimetry, and to aid compensation of working curve nonlinearity. While unsuitable for, Raman spectroscopy, the instrument shows promise for absorption, fluorescence, reflectance, and surface plasmon resonance spectrometries. To aid non-expert users, real-time training, measurement sequencing, and outcome interpretation are programmed with QR codes or web-linked instructions.

Here we demonstrate three principle modalities for a smartphone-based spectrometer: absorption, fluorescence, and photonic crystal (PC)-based label-free detection. When combined with some simple optical components, the rear-facing CMOS camera in a mobile device can provide spectrometric data that rivals that of laboratory instruments, but at a fraction of the cost. The use of a smartphone-based platform poses significant advantages based upon the rise of smartphone apps, which allow for user-interface and data-processing algorithms to be packaged and distributed within environments that are externally maintained with potential for integration with services such as cloud storage, GIS-tagging, and remote expert analysis. We demonstrate the absorption modality of our device by performing an enzyme-linked immunosorbent assay (ELISA) on both a cancer biomarker and a peanut allergen, demonstrating clinically relevant limits of detection (LOD). Second, we demonstrate the success of a molecular beacon (MB)-based assay on the smartphone platform, achieving an LOD of 1.3 pM for a specific RNA sequence, less than that of a commercial benchtop instrument. Finally, we use a PC biosensor to perform label-free detection of a representative biological interaction: Protein A and human immunoglobulin G (IgG) in the nanomolar regime. Our work represents the first demonstration of smartphone-based spectroscopy for biological assays, and the first mobile-device-enabled detection instrument that serves to measure three distinct sensing modalities (label-free biosensing, absorption spectroscopy, and fluorescence spectroscopy). The smartphone platform has the potential to expand the use of spectrometric analysis to environments assay from the laboratory, which may include rural or remote locations, low-resource settings, and consumer markets.

Smartphones and other mobile devices are emerging as promising analytical platforms for point-of-care diagnostics, particularly when combined with nanotechnology. For example, we have shown that the optical properties of semiconductor quantum dots (QDs) are well suited to photoluminescence (PL) detection with a smartphone camera. However, this previous work has utilized an external excitation source for interrogation of QD PL. In this proceeding, we demonstrate that the white-light LED photographic flashes built into smartphones can be optically filtered to yield blue light suitable for excitation of QD PL. Measurements were made by recording video with filtered flash illumination and averaging the frames of the video to obtain images with good signal-to-background ratios. These images permitted detection of green-emitting and red-emitting QDs at levels comparable to those possible with excitation using an external long-wave UV lamp. The optical properties of QDs proved to be uniquely suited to smartphone PL imaging, exhibiting emission that was 1–2 orders magnitude brighter than that of common fluorescent dyes under the same conditions. Excitation with the smartphone flash was also suitable for imaging of FRET between green-emitting QD donors and Alexa Fluor 555 (A555) fluorescent dye acceptors. No significant difference in FRET imaging capability was observed between excitation with the smartphone flash and a long-wave UV lamp. Although the smartphone flash did have some disadvantages compared to an external UV lamp, these disadvantages are potentially offset by the benefit of having excitation and detection integrated into the smartphone.

We demonstrate a highly sensitive mobile phone based spectrometer that has potential to detect cancerous skin lesions in a rapid, non-invasive manner. Earlier reports of low cost spectrometers utilize the camera of the mobile phone to image the field after moving through a diffraction grating. These approaches are inherently limited by the closed nature of mobile phone image sensors and built in optical elements. The system presented uses a novel integrated grating and sensor that is compact, accurate and calibrated. Resolutions of about 10 nm can be achieved. Additionally, UV and visible LED excitation sources are built into the device. Data collection and analysis is simplified using the wireless interfaces and logical control on the smart phone. Furthermore, by utilizing an external sensor, the mobile phone camera can be used in conjunction with spectral measurements. We are exploring ways to use this device to measure endogenous fluorescence of skin in order to distinguish cancerous from non-cancerous lesions with a mobile phone based dermatoscope.

The use of portable hand held computing devices for the acquisition of spectrochemical data is briefly discussed using examples from the author’s laboratory. Several network topologies are evaluated. At present, one topology that involves a portable computing device for data acquisition and spectrometer control and that has wireless access to the internet at one end and communicates with a smart phone at the other end appears to be better suited for “taking part of the lab to the sample” types of applications. Thus, spectrometric data can be accessed from anywhere in the world.

VTT Technical Research Centre of Finland has developed a miniaturized optical sensor for gas detection in a cell phone. The sensor is based on a microelectromechanical (MEMS) Fabry-Perot interferometer, which is a structure with two highly reflective surfaces separated by a tunable air gap. The MEMS FPI is a monolithic device, i.e. it is made entirely on one substrate in a batch process, without assembling separate pieces together. The gap is adjusted by moving the upper mirror with electrostatic force, so there are no actual moving parts.

VTT has designed and manufactured a MEMS FPI based carbon dioxide sensor demonstrator which is integrated to a cell phone shield cover. The demonstrator contains light source, gas cell, MEMS FPI, detector, control electronics and two coin cell batteries as a power source. It is connected to the cell phone by Bluetooth. By adjusting the wavelength range and customizing the MEMS FPI structure, it is possible to selectively sense multiple gases.

With a strong industrial trend towards using thin silicon in semiconductor devices, process legacy-induced stresses are matter of increasing practical importance. A key problem here is a lack of suitable metrology equipment for measuring inherent substrate material stresses in the manufacturing line. To overcome this, the use of Raman microspectrometry as a tool for measuring stress levels and distributions quantitatively on entire productive wafers was researched. Combining model cases, theoretical considerations and real-world samples, it could be shown that Raman can provide the necessary analytical accuracy and reliability, allowing to relate ensuing stress states e.g. to different wafer thinning process parameters.

The modern energy production base in the U.S. is increasingly incorporating opportunity fuels such as biogas, coalbed methane, coal syngas, solar-derived hydrogen, and others. In many cases, suppliers operate turbine-based generation systems to efficiently utilize these diverse fuels. Unfortunately, turbine engines are difficult to control given the varying energy content of these fuels, combined with the need for a backup natural gas supply to provide continuous operation. Here, we study the use of a specially designed Raman Gas Analyzer based on capillary waveguide technology with sub-second response time for turbine control applications. The NETL Raman Gas Analyzer utilizes a low-power visible pump laser, and a capillary waveguide gas-cell to integrate large spontaneous Raman signals, and fast gas-transfer piping to facilitate quick measurements of fuel-gas components. A U.S. Department of Energy turbine facility known as HYPER (hybrid performance system) serves as a platform for apriori fuel composition measurements for turbine speed or power control. A fuel-dilution system is used to simulate a compositional upset while simultaneously measuring the resultant fuel composition and turbine response functions in real-time. The feasibility and efficacy of system control using the spontaneous Raman-based measurement system is then explored with the goal of illustrating the ability to control a turbine system using available fuel composition as an input process variable.

OPTRA is currently developing a modular, reconfigurable matched spectral filter (RMSF) spectrometer for the monitoring of greenhouse gases. The heart of this spectrometer will be the RMSF core, which is a dispersive spectrometer that images the sample spectrum from 2000 – 3333 cm^-1 onto a digital micro-mirror device (DMD) such that different columns correspond to different wavebands. By applying masks to this DMD, a matched spectral filter can be applied in hardware. The core can then be paired with different fore-optics or detector modules to achieve active in situ or passive remote detection of the chemicals of interest. This results in a highly flexible system that can address a wide variety of chemicals by updating the DMD masks and a wide variety of applications by swapping out fore-optic and detector modules. In either configuration, the signal on the detector is effectively a dot-product between the applied mask and the sample spectrum that can be used to make detection and quantification determinations. Using this approach significantly reduces the required data bandwidth of the sensor without reducing the information content, therefore making it ideal for remote, unattended systems. This paper will focus on the design of the RMSF core.

Presented is a new hyperspectral imager design based on multiple slit scanning. This represents an innovation in the classic trade-off between speed and resolution. This LWIR design has been able to produce data-cubes at 3 times the rate of conventional single slit scan devices. The instrument has a built-in radiometric and spectral calibrator.

There is a need for compact, hand-held, spectrometers for the measurement of spectral signatures of chemicals or objects. To achieve this goal, a new concept of Fourier-transform interferometer (FTIR) directly integrated on the infrared focal plane array (FPA) has been developed at ONERA. The fundamental properties of this key element called MICROSPOC will be recalled and we will see how those properties can be exploited to get a snapshot, compact and cryogenic MWIR spectrometer. These design rules have been applied to develop a very compact device that combines the metrological properties of a FTIR-FPA of quantum HgCdTe technology with the radiometric performances of a last generation Sofradir detection block (Infrared Detector Dewar Cooler Assembly – IDDCA). The experimental performances of the prototype will be presented, in terms of spectral resolution, acquisition rate, dynamic range and noise equivalent spectral radiance. We will discuss at the end the potential of this technology to meet the requirements of different applications.

Hyperspectral imaging (HSI) is a technology that is rapidly transitioning from laboratory research and field demonstration to real-world deployment for a variety of applications. These applications include precision agriculture, manufacturing process monitoring, mineral and petroleum exploration, environmental management, disaster mitigation, defense intelligence/surveillance/reconnaissance for threat detection and identification, as well as a host of applications within the bio-medical field. Application-specific algorithms are continuously being developed to support the world-wide expanding use of HSI.

A new, compact and portable spectral imaging camera, employing a MEMs-based encoded imaging approach, has been built and demonstrated for detection of hazardous contaminants including gaseous effluents and solid-liquid residues on surfaces. The camera is called the Thermal infrared Reconfigurable Analysis Camera for Effluents and Residues (TRACER). TRACER operates in the long wave infrared and has the potential to detect a wide variety of materials with characteristic spectral signatures in that region. The 30 lb. camera is tripod mounted and battery powered. A touch screen control panel provides a simple user interface for most operations. The MEMS spatial light modulator is a Texas Instruments Digital Microarray Array with custom electronics and firmware control. Simultaneous 1D-spatial and 1Dspectral dimensions are collected, with the second spatial dimension obtained by scanning the internal spectrometer slit. The sensor can be configured to collect data in several modes including full hyperspectral imagery using Hadamard multiplexing, panchromatic thermal imagery, and chemical-specific contrast imagery, switched with simple user commands. Matched filters and other analog filters can be generated internally on-the-fly and applied in hardware, substantially reducing detection time and improving SNR over HSI software processing, while reducing storage requirements. Results of preliminary instrument evaluation and measurements of flame exhaust are presented.

We demonstrate a remote sensing method, based on an imaging polarimetric spectrometer, to determine the complex refractive index of materials. The approach represents an adaptation of a technique used in semiconductor ellipsometry. Our experimental demonstration setup comprises a Sagnac-type LWIR spatial interferometer (8 - 12 micron) designed for hyperspectral imaging in emission and reflection. The method facilitates direct measurement of the complex refractive index and the thickness of a layer. Presented work focuses on SF96, a liquid surrogate for toxic chemicals, but the method is generally applicable to solids, including soils, and to any substrate with a general bidirectional reflectance distribution function.

Physical Sciences Inc. (PSI) is developing a longwave infrared (LWIR) compressive sensing hyperspectral imager (CS HSI) based on a single pixel architecture for standoff vapor phase plume detection. The sensor employs novel use of a high throughput stationary interferometer and a digital micromirror device (DMD) converted for LWIR operation in place of the traditional cooled LWIR focal plane array. The CS HSI represents a substantial cost reduction over the state of the art in LWIR HSI instruments. Radiometric improvements for using the DMD in the LWIR spectral range have been identified and implemented. In addition, CS measurement and sparsity bases specifically tailored to the CS HSI instrument and chemical plume imaging have been developed and validated using LWIR hyperspectral image streams of chemical plumes. These bases enable comparable statistics to detection based on uncompressed data. In this paper, we present a system model predicting the overall performance of the CS HSI system. Results from a breadboard build and test validating the system model are reported. In addition, the measurement and sparsity basis work demonstrating the plume detection on compressed hyperspectral images is presented.

Hyperspectral imaging systems are finding broader applications in both the commercial and aerospace markets. It is becoming clear that to optimize the performance of these systems, their instrument transfer function needs to be tailored for each application. Vis-SWIR systems in the full 400nm to 2500nm waveband present particular design and manufacturing challenges. A single blazed grating is inadequate for a system operating in the full vis-SWIR wavelength range. In addition, optical materials and broad band coatings present a challenge for non-reflective systems. An understanding of the application and wavelengths of interest, combined with a judicious choice of a focal plane array, can then lead to an optimized system for the specific application. The ability to tailor the grating and manufacture a wide variety of grating profiles and substrate shapes becomes a significant performance enabler. This paper will discuss how the use of optical, coating, and grating design/analysis software, combined with grating manufacturing techniques assure meeting high performance requirements for different applications.

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.

In the last 10 years various chemometric methods have been developed and used for the analysis of spectra generated by Laser Induced Breakdown Spectroscopy (LIBS). One of the more successful and proven methods is Partial Least Squares Discriminant Analysis (PLS-DA). Recently PLS-DA was utilized for purposes of provenance of spent brass cartridges and achieved correct classification at around 93% with a false alarm rate of around 5%. The LIBS spectra from the cartridge samples are rich in emission lines from numerous mostly metallic elements comprising the brass and the cited results were based on the analysis of the full broadband high resolution spectra. It was observed that some of the lines were clearly saturated in all spectra, while others were sometimes saturated due to pulse-to-pulse variation. The pulse-to-pulse variation was also evident in the intensity variations of the spectra within cartridges and between cartridges. In order to improve on the accuracy of the classification we have developed some preprocessing strategies including the removal of spectral wavelength ranges susceptible to saturation and normalization techniques to diminish the effects of intensity variations in the spectra. The results indicate incremental improvements when applying additional preprocessing steps to the limit of 100% True Positives and 0% False Positives when utilizing selected wavelengths that are normalized and averaged.

Increasing the selectivity of sensors, while at the same time reducing their complexity, size and cost, are challenges to the sensing community. To this end, an area of exploration has been the development of filter-based chemical sensors. We have recently introduced an approach that utilizes multiple, broadband, infrared (IR) filters to enable discrimination of target chemicals, in the presence of potential interferents that have IR spectral signatures in a limited waveband. Our analysis technique, comparative discrimination spectral detection (CDSD), utilizes a set of broad IR transmission filters, to discriminate between a specific target chemical and multiple interferents with strongly overlapping IR spectra. We have demonstrated the ability of this technique to correctly distinguish between chemicals in the carbon – hydrogen stretch region of the IR absorption spectrum (2700 – 3300 cm^-1; 3.0 – 3.7 μm). We present a numerical study exploring the choices of desired optical filter sets, and the resulting overall discrimination by these filter sets. Filter parameter choices, such as the peak transmission position and bandwidth, are fundamental in filter-based chemical sensing discrimination systems. In this paper, we describe a systematic numerical approach used to explore how optical filter properties, and filter overlap affect corresponding discrimination results. We describe the interaction between the overlapping spectra and various filter sets on both target and interferent chemicals. We discuss which filter parameters provide optimum selectivity for specific target chemicals and how this information can be utilized to select filters for future direct-filter sensors based on this methodology.

A novel hyperspectral imaging sensor is demonstrated that can enable breakthrough applications of hyperspectral imaging in domains not previously accessible. Our technology consists of a planar hyperspectral encoder combined with a traditional monochrome image sensor. The encoder adds negligibly to the sensor’s overall size, weight, power requirement, and cost (SWaP-C); therefore, the new imager can be incorporated wherever image sensors are currently used, such as in cell phones and other consumer electronics. In analogy to Fourier spectroscopy, the technique maintains a high optical throughput because narrow-band spectral filters are unnecessary. Unlike conventional Fourier techniques that rely on Michelson interferometry, our hyperspectral encoder is robust to vibration and amenable to planar integration. The device can be viewed within a computational optics paradigm: the hardware is uncomplicated and serves to increase the information content of the acquired data, and the complexity of the system, that is, the decoding of the spectral information, is shifted to computation. Consequently, system tradeoffs, for example, between spectral resolution and imaging speed or spatial resolution, are selectable in software. Our prototype demonstration of the hyperspectral imager is based on a commercially-available silicon CCD. The prototype encoder was inserted within the camera’s ~1 cu. in. housing. The prototype can image about 49 independent spectral bands distributed from 350 nm to 1250 nm, but the technology may be extendable over a wavelength range from ~300 nm to ~10 microns, with suitable choice of detector.

The benefits of multispectral imaging are well established in a variety of applications including remote sensing, authentication, satellite and aerial surveillance, machine vision, biomedical, and other scientific and industrial uses. However, many of the potential solutions require more compact, robust, and cost-effective cameras to realize these benefits. The next generation of multispectral sensors and cameras needs to deliver improvements in size, weight, power, portability, and spectral band customization to support widespread deployment for a variety of purpose-built aerial, unmanned, and scientific applications. A novel implementation uses micro-patterning of dichroic filters¹ into Bayer and custom mosaics, enabling true real-time multispectral imaging with simultaneous multi-band image acquisition. Consistent with color image processing, individual spectral channels are de-mosaiced with each channel providing an image of the field of view. This approach can be implemented across a variety of wavelength ranges and on a variety of detector types including linear, area, silicon, and InGaAs. This dichroic filter array approach can also reduce payloads and increase range for unmanned systems, with the capability to support both handheld and autonomous systems. Recent examples and results of 4 band RGB + NIR dichroic filter arrays in multispectral cameras are discussed. Benefits and tradeoffs of multispectral sensors using dichroic filter arrays are compared with alternative approaches – including their passivity, spectral range, customization options, and scalable production.

Two compact prototype spectropolarimetric imagers operating in the visible-near-infrared (VNIR) and shortwave-infrared (SWIR) regions were developed. Each of these imagers uses a noncollinear acousto-optic tunable filter (AOTF) and collects data with four linear incident polarizations—vertical, horizontal and ±45°. The spectral region of operation for the first imager is from 450 to 800 nm and the second one from 1000 to 1600 nm. These imagers are field-portable and before field test were characterized for their performance in collecting hyperspectral and full linear polarization signatures. Image cubes of scenes with natural objects were collected and analyzed. Here, we present imaging and spectral analysis results for these imagers.

Inverse analysis of transmission spectra for triarylamine dye in acetone is presented. This analysis employs a parametric model of transmission through a sample of finite thickness, where the permittivity function is represented parametrically by a linear combination of Lorentz oscillator models. The results of this analysis provide estimates of the permittivity function for triarylamine dye, which can be adopted as input data to other types of models, such as those for prediction of transmission and reflectivity spectra for composites containing mixtures of dyes and other materials. In addition, the results of this analysis should contribute to a data base of estimated permittivity functions for practical analysis of spectra.

The main objective of classification is to partition the surface materials into non-overlapping regions by using some decision rules. For supervised classification, the hyperspectral imagery (HSI) is compared with the reflectance spectra of the material containing similar spectral characteristic. As being a spectral similarity based classification method, prediction of different level of upper and lower spectral boundaries of all classes spectral signatures across spectral bands constitutes the basic principles of the Multi-Scale Vector Tunnel Algorithm (MS-VTA) classification algorithm. The vector tunnel (VT) scaling parameters obtained from means and standard deviations of the class references are used. In this study, MS-VT method is improved and a spectral similarity based technique referred to as Weighted Chebyshev Distance (WCD) method for the supervised classification of HSI is introduced. This is also shown to be equivalent to the use of the WCD in which the weights are chosen as an inverse power of the standard deviation per spectral band. The use of WCD measures in terms of the inverse power of standard deviations and optimization of power parameter constitute the most important side of the study. The algorithms are trained with the same kinds of training sets, and their performances are calculated for the power of the standard deviation. During these studies, various levels of the power parameters are evaluated based on the efficiency of the algorithms for choosing the best values of the weights.

This paper describes an analysis of the potential of an airborne hyperspectral imaging IR instrument to infer wake vortices via turbine jet exhaust as a proxy. The goal was to determine the requirements for an imaging spectrometer or radiometer to effectively detect the exhaust plume, and by inference, the location of the wake vortices. The effort examines the gas spectroscopy of the various major constituents of turbine jet exhaust and their contributions to the modeled detectable radiance. Initially, a theoretical analysis of wake vortex proxy detection by thermal radiation was realized in a series of simulations. The first stage used the SLAB plume model to simulate turbine jet exhaust plume characteristics, including exhaust gas transport dynamics and concentrations. The second stage used these plume characteristics as input to the Line By Line Radiative Transfer Model (LBLRTM) to simulate responses from both an imaging IR hyperspectral spectrometer or radiometer. These numerical simulations generated thermal imagery that was compared with previously reported wake vortex temperature data. This research is a continuation of an effort to specify the requirements for an imaging IR spectrometer or radiometer to make wake vortex measurements. Results of the two-stage simulation will be reported, including instrument specifications for wake vortex thermal detection. These results will be compared with previously reported results for IR imaging spectrometer performance.

Scientists in the Biospheric Sciences Laboratory at NASA’s Goddard Space Flight Center have undertaken a unique instrument fusion effort for an airborne package that integrates commercial off the shelf LiDAR, Hyperspectral, and Thermal components. G-LiHT is a compact, lightweight and portable system that can be used on a wide range of airborne platforms to support a number of NASA Earth Science research projects and space-based missions. G-LiHT permits simultaneous and complementary measurements of surface reflectance, vegetation structure, and temperature, which provide an analytical framework for the development of new algorithms for mapping plant species composition, plant functional types, biodiversity, biomass, carbon stocks, and plant growth. G-LiHT and its supporting database are designed to give scientists open access to the data that are needed to understand the relationship between ecosystem form and function and to stimulate the advancement of synergistic algorithms. This system will enhance our ability to design new missions and produce data products related to biodiversity and climate change. G-LiHT has been operational since 2011 and has been used to collect data for a number of NASA and USFS sponsored studies, including NASA’s Carbon Monitoring System (CMS) and the American ICESat/GLAS Assessment of Carbon (AMIGA-Carb). These acquisitions target a broad diversity of forest communities and ecoregions across the United States and Mexico. Here, we will discuss the components of G-LiHT, their calibration and performance characteristics, operational implementation, and data processing workflows. We will also provide examples of higher level data products that are currently available.

Traditional airborne environmental monitoring has frequently deployed hyperspectral imaging as a leading tool for characterizing and analyzing a scene’s critical spectrum-based signatures for applications in agriculture genomics and crop health, vegetation and mineral monitoring, and hazardous material detection. As the acceptance of hyperspectral evaluation grows in the airborne community, there has been a dramatic trend in moving the technology from use on midsize aircraft to Unmanned Aerial Systems (UAS). The use of UAS accomplishes a number of goals including the reduction in cost to run multiple seasonal evaluations over smaller but highly valuable land-areas, the ability to use frequent data collections to make rapid decisions on land management, and the improvement of spatial resolution by flying at lower altitudes (<500 ft.). Despite this trend, there are several key parameters affecting the use of traditional hyperspectral instruments in UAS with payloads less than 10 lbs. where size, weight and power (SWAP) are critical to how high and how far a given UAS can fly. Additionally, on many of the light-weight UAS, users are frequently trying to capture data from one or more instruments to augment the hyperspectral data collection, thus reducing the amount of SWAP available to the hyperspectral instrumentation. The following manuscript will provide an analysis on a newly-developed miniaturized hyperspectral imaging platform, the Nano-Hyperspec®, which provides full hyperspectral resolution and traditional hyperspectral capabilities without sacrificing performance to accommodate the decreasing SWAP of smaller and smaller UAS platforms. The analysis will examine the Nano-Hyperspec flown in several UAS airborne environments and the correlation of the systems data with LiDAR and other GIS datasets.

This study aims to develop an effective regional shadow removal algorithm using rich spectral information existing in hyperspectral imagery. The proposed method benefits from spectral similarity of shadow and neighboring nonshadow pixels regardless of the intensity values. Although the shadow area has lower reflectance values due to inadequacy of incident light, it is expected that this area contains similar spectral characteristics with nonshadow area. Using this assumption, the endmembers in both shadowed and nonshadow area are extracted by Vertex Component Analysis (VCA). On the other hand, HySime algorithm overcomes estimating number of endmembers, which is one of the challenging parts in hyperspectral unmixing. Therefore, two sets of endmembers are extracted independently for both shadowed and nonshadow area. The proposed study aims at revealing the relation between these two endmember sets by comparing their pairwise similarities. Finally, reflectance values of shadowed pixels are re-calculated separately for each spectral band of hyperspectral image using this information.

The primary application of hyperspectral imaging is to classify/quantify objects/materials in the scene based on their spectral signatures. The spectral features that are useful can sometimes fall outside the spectral range of a single hyperspectral imager which is usually limited by the spectral response range of the sensor material of the focal plane array within the imager. For these wide spectrum applications, some users are combining data from two (or more) hyperspectral imaging systems. Aside from the optical alignment, size and synchronization issues involved in such a setup, the process of pixel co-registration, i.e. geometrically transforming data from the 2 hyperspectral imaging systems to overlay one another is tedious and complex.

Headwall Photonics has integrated two of their off-the-shelf highly optimized hyperspectral imagers (Vis-NIR and SWIR) in an optically co-boresighted configuration together with a high performance data processor to produce a compact system which is easy to use and outputs wide spectrum pixel co-registered hyperspectral data. The process of pixel co-registration in this system is computationally very cheap enabling real-time wide-spectrum hyperspectral imaging applications.

This paper presents actual imaging and performance data from these systems showing excellent pixel co-registration, sensitivity and spectral resolution.

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.