Infrared Imaging Spectroscopy Research Articles

Prediction of WHO grade and methylation class of aggressive meningiomas: Extraction of diagnostic information from infrared spectroscopic data.

Infrared (IR) spectroscopy allows intraoperative, optical brain tumor diagnosis. Here, we explored it as a translational technology for the identification of aggressive meningioma types according to both, the WHO CNS grading system and the methylation classes (MC). Frozen sections of 47 meningioma were examined by IR spectroscopic imaging and different classification approaches were compared to discern samples according to WHO grade or MC. IR spectroscopic differences were more pronounced between WHO grade 2 and 3 than between MC intermediate and MC malignant, although similar spectral ranges were affected. Aggressive types of meningioma exhibited reduced bands of carbohydrates (at 1024cm-1) and nucleic acids (at 1080cm-1), along with increased bands of phospholipids (at 1240 and 1450cm-1). While linear discriminant analysis was able to discern spectra of WHO grade 2 and 3 meningiomas (AUC 0.89), it failed for MC (AUC 0.66). However, neural network classifiers were effective for classification according to both WHO grade (AUC 0.91) and MC (AUC 0.83), resulting in the correct classification of 20/23 meningiomas of the test set. IR spectroscopy proved capable of extracting information about the malignancy of meningiomas, not only according to the WHO grade, but also for a diagnostic system based on molecular tumor characteristics. In future clinical use, physicians could assess the goodness of the classification by considering classification probabilities and cross-measurement validation. This might enhance the overall accuracy and clinical utility, reinforcing the potential of IR spectroscopy in advancing precision medicine for meningioma characterization.

Imaging spectroscopy for monitoring the crop status of tomato plants

The increasing complexity of greenhouse production systems requires the step to a more data-driven approach that is based on robust, intelligent sensors. Whereas climate sensors are widely available in greenhouse systems, control based on crop data is still in its infancy. There is a clear need among growers to monitor the status of the crop in the greenhouse in real time and non-destructively compared to current practice of cumbersome, expensive and slow laboratory measurements. This project aimed to use visible and near infrared imaging spectroscopy to determine the contents of sugars and starch, dry matter percentage, chlorophyll, and nutrient composition in both leaves and fruits of tomato plants. The results were promising, with Q2 values up to 0.97 for dry matter content in leaves, 0.86 for sugar and 0.99 for chlorophyll-a. The results also showed that pigments have a better correlation to the visible spectrum, while water content, sugars and nutrients showed a better correlation in the near infrared part of the spectrum.

Phasor Representation Approach for Rapid Exploratory Analysis of Large Infrared Spectroscopic Imaging Data Sets.

Infrared (IR) spectroscopic imaging is potentially useful for digital histopathology as it provides spatially resolved molecular absorption spectra, which can subsequently yield useful information by powerful artificial intelligence methods. A typical analysis pipeline in using IR imaging data for chemical pathology often involves iterative processes of segmentation, evaluation, and analysis that necessitate rapid data exploration. Here, we present a fast, reliable, and intuitive method based on a phasor representation of spectra and discuss its unique applicability for IR imaging data. We simulate different features extant in IR spectra and discuss their influence on the phasor waveforms; similarly, we undertake IR image analysis in the transform space to understand spectral similarity and variance. We demonstrate the potential of phasor analysis for biomedical tissue imaging using a variety of samples, using fresh frozen surgical prostate resections and formalin-fixed paraffin-embedded breast cancer tissue microarray samples as model systems that span common histopathology practice. To demonstrate further generalizability of this approach, we apply the method to data from different experimental conditions─including standard (5.5 μm × 5.5 μm pixel size) and high-definition (1.1 μm × 1.1 μm pixel size) Fourier transform IR (FTIR) spectroscopic imaging using transmission and transflection modes. Quantitative segmentation results from our approach are compared to previous studies, showing good agreement and quick visualization. The presented method is rapid, easy to use, and highly capable of deciphering compositional differences, presenting a convenient tool for exploratory analysis of IR imaging data.

Uterine leiomyoma as useful model to unveil morphometric and macromolecular collagen state and impairment in fibrotic diseases: An ex-vivo human study

Collagen is one of the main components of the extracellular matrix (ECM), involved, among all, in the maintenance of the structural support of tissues. In fibrotic diseases, collagen is overexpressed, and its production determines the formation of a significantly stiffer ECM. The cross-linking of high-resolution analytical tools, able to investigate both the tridimensional organization and the secondary structure of collagen in fibrotic diseases, could be useful to identify defined markers correlating the status of this protein with specific pathological conditions. To this purpose, an innovative multidisciplinary approach based on Phase-Contrast MicroComputed Tomography, Transmission Electron Microscopy, and Fourier Transform Infrared Imaging Spectroscopy was exploited on leiomyoma samples and adjacent myometrium to characterize microstructural collagen features. Uterine leiomyoma is a common gynecological disorder affecting women in fertile age. It is characterized by a massive collagen production due to the repairing processes occurring at myometrium level, and, hence, it represents a valuable model to investigate collagen self-organization in a pathological condition. Moreover, to evaluate the sensitivity of this multidisciplinary approach, the effects of eicosapentaenoic (EPA) and docosahexaenoic (DHA) omega-3 fatty acids in collagen reduction were also investigated.

DROACOR®-THERMAL: AUTOMATED TEMPERATURE / EMISSIVITY RETRIEVAL FOR DRONE BASED HYPERSPECTRAL IMAGING DATA

Abstract. Thermal remote sensing from unmanned aerial vehicles is a slowly but steadily growing field of application. New hyperspectral systems operating in the thermal infrared are deployable on such systems and are also usable for ground based monitoring, such as in mining applications. Temperature/emissivity retrieval methods have to be adapted for these new situations. This contribution presents an extension of the Drone Atmospheric Correction method (DROACOR®) for thermal infrared imaging spectroscopy. The method includes an implementation of the semi-automatic normalized emissivity mapping (NEM) method for temperature/ emissivity separation. Furthermore, an extension of the method for correction of low emissivity targets, appearing as cold targets in the temperature mapping is introduced. Two examples of DROACOR-thermal processing are presented for a nadir looking drone based and a horizontal ground based data acquisitions are shown. The resulting spectral emissivitiy distributions and temperature mappings are plausible. They are well comparable to spectral library references and allow for the detection of materials only visible in the thermal infrared range.

Simultaneous Characterization of Wildfire Smoke and Surface Properties With Imaging Spectroscopy During the FIREX-AQ Field Campaign.

We introduce and evaluate an approach for the simultaneous retrieval of aerosol and surface properties from Airborne Visible/Infrared Imaging Spectrometer Classic (AVIRIS‐C) data collected during wildfires. The joint National Aeronautics and Space Administration (NASA) National Oceanic and Atmospheric Administration Fire Influence on Regional to Global Environments and Air Quality field campaign took place in August 2019, and involved two aircraft and coordinated ground‐based observations. The AVIRIS‐C instrument acquired data from onboard NASA's high altitude ER‐2 research aircraft, coincident in space and time with aerosol observations obtained from the Aerosol Robotic Network (AERONET) DRAGON mobile platform in the smoke plume downwind of the Williams Flats Fire in northern Washington in August 2019. Observations in this smoke plume were used to assess the capacity of optimal‐estimation based retrievals to simultaneously estimate aerosol optical depth (AOD) and surface reflectance from Visible Shortwave Infrared (VSWIR) imaging spectroscopy. Radiative transfer modeling of the sensitivities in spectral information collected over smoke reveal the potential capacity of high spectral resolution retrievals to distinguish between sulfate and smoke aerosol models, as well as sensitivity to the aerosol size distribution. Comparison with ground‐based AERONET observations demonstrates that AVIRIS‐C retrievals of AOD compare favorably with direct sun AOD measurements. Our analyses suggest that spectral information collected from the full VSWIR spectral interval, not just the shortest wavelengths, enables accurate retrievals. We use this approach to continuously map both aerosols and surface reflectance at high spatial resolution across heterogeneous terrain, even under relatively high AOD conditions associated with wildfire smoke.

Improved tissue preparation for multimodal vibrational imaging of biological tissues

The complementary nature of Infrared (IR) and Raman spectroscopies enables a thorough understanding of biological tissue – so called multimodal vibrational spectroscopic imaging. However, new approaches in terms of sample preparation and data analysis are required to release the full potential of multimodal spectroscopy. Herein, we propose an inexpensive and relatively simple sample preparation technique incorporating mirror-finished stainless-steel slides and polyethylene glycol as an embedding medium that is compatible for both infrared and Raman spectroscopy of tissue sections. K-Means Clustering and Principal Component Analysis (PCA) were used to evaluate the performance of multimodal vibrational spectroscopic imaging compared with IR and Raman spectroscopic imaging individually using a rat kidney as a model. The K-Means cluster maps generated with the multimodal dataset showed the best correlation between different tissue types identified by an adjacent section stained with Masson’s Trichrome compared to either Raman or IR spectroscopy analysed independently. PCA score maps of the multimodal dataset produced a clear separation of individual tissue types along the first three Principal Components. Additionally, PCA permitted the correlation of IR and Raman peaks arising mainly from collagen vibrational modes. Finally, polyethylene glycol embedding is shown as an attractive alternative to paraffin embedding for spectroscopic analyses, due to significantly less fluorescence in Raman measurements and retention of lipids in the tissue, without any retention of the medium within the tissue.

Polymer-Surfactant Driven Interactions and the Resultant Microstructure in Protein-Containing Liquid Crystal Droplets.

Integration of molecular liquid crystals (LCs) with functional proteins can provide new class of materials for potential applications in optical biosensing. However, hydrophobic nematic LCs (length ∼ 1-2 nm) and hydrophilic proteins, size ∼ O (nm), do not intermix without chemical modification of at least one of them. Bioconjugation of proteins with a polyethylene glycol-based polymeric surfactant (PS) can provide a core-shell system that is sequestered within nonaqueous LC (4-cyano-4'-pentylbiphenyl) microdroplets. However, the nature of interactions between the components and detailed understanding of the resultant hybrid microstructure remains unclear. Here, using a combination of isothermal titration calorimetry (ITC), fluorescence microscopy, and infrared-imaging spectroscopy, we show that strong hydrophobic interactions between the LC and PS drives the sequestration of a myoglobin-PS (Mb-PS; dispersed in the aqueous phase) into the LC spherical microdroplets or even into a bulk LC phase. The average values of both, the binding constant and the standard molar enthalpy change, are increased by approximately a factor of 2.5 times when the unmodified Mb is conjugated to the PS. Small-angle X-ray scattering studies reveal that LC molecules act as a solvent for the Mb-PS conjugate; furthermore, the LC long-range order is disturbed due to mixing, as exemplified by the change in its coherence length from 8.9 to 5.7 nm. Detailed all-atomistic molecular dynamic simulations for a three-component PS-water-LC system show a change in interaction energy of -144 kJ mol-1 PS-1 upon the contact of PS chains (initially dispersed in water) with LC and agree with the ITC experiments.

Using mobile surface in situ and remote sensing and airborne remote sensing to derive emissions from a producing central California oil field in complex terrain

In this study, we demonstrate how under certain wind conditions, a mobile surface in situ platform, Truck MObile trace Gas (TMOG) Surveyor, that includes a ceilometer to derive boundary layer height (BL) can derive emissions. Field in situ and airborne thermal infrared imaging spectroscopy data were acquired by the thermal infrared imaging spectrometer, Mako, for three adjacent, central California producing oil fields (Kern River, Kern Front, Poso Creek) on 14 Sept. 2018. Derived methane (CH4) and carbon dioxide (CO2) emissions from TMOG data were 46.6 ± 15 Mg dy−1 (17 ± 5.6 Gg yr−1) and 3.12 ± 1.02 Mg dy−1 (1140 ± 230 Gg yr−1), respectively, with about half the CH4 from near-field point-source plumes and half from a well-mixed far field plume. Uncertainty was 33 % from the Monte Carlo simulations. Mako data confirmed that emissions were in the far field and there were no strong, buoyant plumes. The survey vertically profiled a nearby mountain to measure winds at the boundary layer top and validate BL using in situ data.The CH4 to CO2 emissions ratio for plume matched reservoir composition to better than 10 %. The plume anomaly CH4 to ethane emissions ratio suggested ethane emissions of around 200 kg dy−1 (74 Mg yr−1). Mako data in 2015 and concurrent data in 2018 confirmed that most emissions were from distant plumes in the Poso Creek and Kern Front fields.

The Progress of Label-Free Optical Imaging in Alzheimer's Disease Screening and Diagnosis.

As the major neurodegenerative disease of dementia, Alzheimer’s disease (AD) has caused an enormous social and economic burden on society. Currently, AD has neither clear pathogenesis nor effective treatments. Positron emission tomography (PET) and magnetic resonance imaging (MRI) have been verified as potential tools for diagnosing and monitoring Alzheimer’s disease. However, the high costs, low spatial resolution, and long acquisition time limit their broad clinical utilization. The gold standard of AD diagnosis routinely used in research is imaging AD biomarkers with dyes or other reagents, which are unsuitable for in vivo studies owing to their potential toxicity and prolonged and costly process of the U.S. Food and Drug Administration (FDA) approval for human use. Furthermore, these exogenous reagents might bring unwarranted interference to mechanistic studies, causing unreliable results. Several label-free optical imaging techniques, such as infrared spectroscopic imaging (IRSI), Raman spectroscopic imaging (RSI), optical coherence tomography (OCT), autofluorescence imaging (AFI), optical harmonic generation imaging (OHGI), etc., have been developed to circumvent this issue and made it possible to offer an accurate and detailed analysis of AD biomarkers. In this review, we present the emerging label-free optical imaging techniques and their applications in AD, along with their potential and challenges in AD diagnosis.

Hyperspectral characterization of the MSTO-211H cell spheroid model: A FPA–FTIR imaging approach

Multicellular spheroids are the new frontier for studying how the tumour micro-environment interferes with drug uptake and response, since they can reproduce a three-dimensional cellular organisation mimicking the behaviour of in vivo solid tissues. In this study, we exploited Focal Plane Array - Fourier Transform Infrared Imaging spectroscopy to characterize the biochemical features, in terms of distribution and composition of the meaningful macromolecules (lipids, proteins, sugars and nucleic acids), of malignant pleural mesothelioma spheroid sections, and, as further extent, to investigate the penetrating effects of cisplatin within the spheroid mass. The hyperspectral imaging analysis evidenced, in untreated spheroids, the occurrence of a replicative outer region and a hypoxic inner one, as suggested by the band area ratios related to lipid alkyl chains (2925/2960) and glycogen (1020/1650), which showed the highest values in the inner region. Moreover, the HCA spectroscopic images showed, after cisplatin treatment, an increase of the band area ratio related to lipid carbonyl ester moiety (1740/2925), suggesting the occurrence of lipid peroxidation; furthermore, the band area ratio related to nucleic acids (1240/1220) revealed a DNA fragmentation along all regions of spheroids that may be related to apoptotic mechanisms, whereas a reduction of the band area ratios related to glycogen and carbohydrates (1020/1650 and 1054/1650, respectively) appeared consistent with an inhibition of cell division. The few spectral differences between the outer and the inner regions of cisplatin-treated spheroids pointed out the diffuse penetrating effect of the drug.

Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy

Water management and irrigation practices are persistent challenges for many agricultural systems, exacerbated by changing seasonal and weather patterns. The wild blueberry industry is at heightened susceptibility due to its unique growing conditions and uncultivated nature. Stress detection in agricultural fields can prompt management responses to mitigate detrimental conditions, including drought and disease. We assessed airborne spectral data accompanied by ground sampled water potential over three developmental stages of wild blueberries collected throughout the 2019 summer on two adjacent fields, one irrigated and one non-irrigated. Ground sampled leaves were collected in tandem to the hyperspectral image collection with an unoccupied aerial vehicle (UAV) and then measured for leaf water potential. Using methods in machine learning and statistical analysis, we developed models to determine irrigation status and water potential. Seven models were assessed in this study, with four used to process six hyperspectral cube images for analysis. These images were classified as irrigated or non-irrigated and estimated for water potential levels, resulting in an R2 of 0.62 and verified with a validation dataset. Further investigation relating imaging spectroscopy and water potential will be beneficial in understanding the dynamics between the two for future studies.

Joint Hapke Model and Spatial Adaptive Sparse Representation with Iterative Background Purification for Martian Serpentine Detection

Visible and infrared imaging spectroscopy have greatly revolutionized our understanding of the diversity of minerals on Mars. Characterizing the mineral distribution on Mars is essential for understanding its geologic evolution and past habitability. The traditional handcrafted spectral index could be ambiguous as it may denote broad mineralogical classes, making this method unsuitable for definitive mineral investigation. In this work, the target detection technique is introduced for specific mineral mapping. We have developed a new subpixel mineral detection method by joining the Hapke model and spatially adaptive sparse representation method. Additionally, an iterative background dictionary purification strategy is proposed to obtain robust detection results. Laboratory hyperspectral image containing Mars Global Simulant and serpentine mixtures was used to evaluate and tailor the proposed method. Compared with the conventional target detection algorithms, including constrained energy minimization, matched filter, hierarchical constrained energy minimization, sparse representation for target detection, and spatially adaptive sparse representation method, the proposed algorithm has a significant improvement in accuracy about 30.14%, 29.67%, 29.41%, 9.13%, and 8.17%, respectively. Our algorithm can detect subpixel serpentine with an abundance as low as 2.5% in laboratory data. Then the proposed algorithm was applied to two well-studied Compact Reconnaissance Imaging Spectrometer for Mars images, which contain serpentine outcrops. Our results are not only consistent with the spatial distribution of Fe/Mg phyllosilicates derived by spectral indexes, but also denote what the specific mineral is. Experimental results show that the proposed algorithm enables the search for subpixel, low-abundance, and scientifically valuable mineral deposits.

Identification of cellulose textile fibers.

Distinguishing different textile fibers is important for recycling waste textiles. Most studies on non-destructive optical textile identification have focused on classifying different synthetic and natural fibers but chemical recycling requires more detailed information on fiber composition and polymer properties. Here, we report the use of near infrared imaging spectroscopy and chemometrics for classifying natural and regenerated cellulose fibers. Our classifiers trained on images of consumer textiles showed 100% true positive rates based on model cross-validation and correctly identified on average 8-9 out of 10 test set pixels using images of specifically made cotton, viscose and lyocell samples of known compositions. These results are significant as they indicate the possibility to monitor and control fiber dosing and subsequent dope viscosity during chemical recycling of cellulose fibers. Our results also suggested the possibility to identify fibers purely based on polymer chain length. This finding opens the possibility to indirectly estimate dope viscosity and creates entirely new hypotheses for combining imaging spectroscopy with classification and regression methods within the broader field of cellulose modification.

Label-free spectroscopic imaging of the skin characterizes biochemical changes associated with systemic sclerosis

Systemic sclerosis (SSc) is a chronic multisystem disease associated with collagen accumulation and fibrosis, most prominent in the skin and lungs. There is considerable patient-to-patient, as well as within-patient temporal, heterogeneity in the extent and course of fibrosis. The widely used modified Rodnan skin score (mRSS) employs skin thickening as a surrogate for SSc severity; however, the value of this tool for risk stratification and for prediction of disease progression is limited. Infrared (IR) spectroscopic imaging permits rapid label-free tissue imaging to extract spectral biomarkers for prediction of disease outcome, and shows sensitivity to biochemical changes that can identify sub-clinical complications and pre-histological changes in multiple diseases. In this study, we performed IR spectroscopic imaging of skin biopsies from 15 healthy controls and 20 SSc patients. The results demonstrate unambiguous spectral changes discriminating between healthy and SSc biopsies using multivariate data analysis. Spectral differences in skin biopsies were not correlated with the potential confounding factors age, race or gender or disease duration. In longitudinal analysis, baseline skin biopsies were unable to predict changes in mRSS at 12 months. Together, these results demonstrated the utility of IR spectroscopic imaging for classifying skin biopsies. Further studies are required to determine whether spectral IR imaging can provide clinical-level information for augmenting current approaches to disease classification in SSc.

Single-Pot Method for the Collection and Preparation of Natural Water for Microplastic Analyses: Microplastics in the Mississippi River System during and after Historic Flooding.

We describe a simple single-pot method for collection and preparation of natural water for microplastic analyses. The method prepares samples in the same vessel (mason jars) that they are collected in right up until the microplastics are transferred onto filters or spectroscopic windows for analyses. The method minimized contamination, degradation, and losses, while increasing recoveries and throughput when compared with conventional sieving. We applied it to surface grab samples collected from the Mississippi River and its major tributaries during and after historic flooding in 2019. Microplastics (>~30 µm) were quantified using Nile red fluorescence detection, and a small subset of samples were identified by micro-Fourier transform infrared imaging spectroscopy. Concentrations were lower during the flooding, likely due to dilution. Concentrations ranged from approximately 14 microplastics/L in the Tennessee River during flooding to approximately 83 microplastics/L in the Ohio River during low-flow (summer) conditions. Loads of microplastics tended to increase downriver and ranged from approximately 87 to approximately 129 trillion microplastics/d near New Orleans. Most of the microplastics (>60%) were in the lower size fraction (~30-90 µm) and consisted primarily of fragments (~85%), followed by fibers (~8%) and beads (~7%), with polyester, polyethylene, polypropylene, and polyacrylate as the primary microplastic types. Overall, we demonstrate that the single-pot method is effective and versatile and, because it uses relatively inexpensive and easily assembled materials, can be adapted for microplastic surveys worldwide, especially those involving sample collection by volunteers from the community and schools. Environ Toxicol Chem 2020;39:986-995. © 2020 SETAC.

Fast facial smile detection using convolutional neural network in an intelligent working environment

Infrared facial expression recognition is very important to understand the human emotion and behaviors. Smile detection plays a key role in facial expression recognition. In this paper, we have developed a fast facial smile detection algorithm with convolutional neural network and infrared imaging technology. Due to random noise and band overlap in the infrared imaging spectroscopy, we propose a maximum likelihood (ML) restoration algorithm with spectral-spatial L1-norm constraint for low-resolution infrared spectrum. In this model, we also employ the spectral-spatial L1 norm to contrain the smoothness of the infrared spectrum data. With the aid of spectral-spatial L1-norm minimization as compelling constraints, the proposed method can not only remove noise dramatically but also compute the instrument blurry function simultaneously. The results show that the developed method can recover a noise-free infrared spectrum, and the spectral structure is richer than the compared methods. The high-resolution infrared spectrum could be utilized for fast facial smile detection task in the intelligent learning environment.

An On-Chip Quad-Wavelength Pyroelectric Sensor for Spectroscopic Infrared Sensing.

Merging photonic structures and optoelectronic sensors into a single chip may yield a sensor‐on‐chip spectroscopic device that can measure the spectrum of matter. In this work, an on‐chip concurrent multiwavelength infrared (IR) sensor, which consists of a set of narrowband wavelength‐selective plasmonic perfect absorbers combined with pyroelectric sensors, where the response of each pyroelectric sensor is boosted only at the resonance of the nanostructured absorber, is proposed and realized. The proposed absorber, which is based on Wood's anomaly absorption from a 2D plasmonic square lattice, shows a narrowband polarization‐independent resonance (quality factor – Q of 73) with a nearly perfect absorptivity as high as 0.99 at normal incidence. The fabricated quad‐wavelength IR sensors exhibit four different narrowband spectral responses at normal incidence following the predesigned resonances in the mid‐wavelength infrared region that corresponds to the atmospheric window. The device can be applied for practical spectroscopic applications such as nondispersive IR sensors, IR chemical imaging devices, pyrometers, and spectroscopic thermography imaging.

Neural network radiative transfer for imaging spectroscopy

Abstract. Visible–shortwave infrared imaging spectroscopy provides valuable remote measurements of Earth's surface and atmospheric properties. These measurements generally rely on inversions of computationally intensive radiative transfer models (RTMs). RTMs' computational expense makes them difficult to use with high-volume imaging spectrometers, and forces approximations such as lookup table interpolation and surface–atmosphere decoupling. These compromises limit the accuracy and flexibility of the remote retrieval; dramatic speed improvements in radiative transfer models could significantly improve the utility and interpretability of remote spectroscopy for Earth science. This study demonstrates that nonparametric function approximation with neural networks can replicate radiative transfer calculations and generate accurate radiance spectra at multiple wavelengths over a diverse range of surface and atmosphere state parameters. We also demonstrate such models can act as surrogate forward models for atmospheric correction procedures. Incorporating physical knowledge into the network structure provides improved interpretability and model efficiency. We evaluate the approach in atmospheric correction of data from the PRISM airborne imaging spectrometer, and demonstrate accurate emulation of radiative transfer calculations, which run several orders of magnitude faster than first-principles models. These results are particularly amenable to iterative spectrum fitting approaches, providing analytical benefits including statistically rigorous treatment of uncertainty and the potential to recover information on spectrally broad signals.

Preprocessing Ground-Based Visible/Near Infrared Imaging Spectroscopy Data Affected by Smile Effects.

A data set of very high-resolution visible/near infrared hyperspectral images of young Pinus contorta trees was recorded to study the effects of herbicides on this invasive species. The camera was fixed on a frame while the potted trees were moved underneath on a conveyor belt. To account for changing illumination conditions, a white reference bar was included at the edge of each image line. Conventional preprocessing of the images, i.e., dividing measured values by values from the white reference bar in the same image line, failed and resulted in bad quality spectra with oscillation patterns that are most likely due to wavelength shifts across the sensor’s field of view (smile effect). An additional hyperspectral data set of a Spectralon white reference panel could be used to characterize and correct the oscillations introduced by the division, resulting in a high quality spectra that document the effects of herbicides on the reflectance characteristics of coniferous trees. While the spectra of untreated trees remained constant over time, there were clear temporal changes in the spectra of trees treated with both herbicides. One herbicide worked within days, the other one within weeks. Ground-based imaging spectroscopy with meaningful preprocessing proved to be an appropriate tool for monitoring the effects of herbicides on potted plants.