A Broadly Tunable Surface Plasmon-Coupled Wavelength Filter for Visible and Near Infrared Hyperspectral Imaging

Hyperspectral imaging is a set of techniques that has contributed to the study of advanced materials, pharmaceuticals, semiconductors, ceramics, polymers, biological specimens, and geological samples. Its use for remote sensing has advanced our understanding of agriculture, forestry, the Earth, environmental science, and the universe. The development of ultra-compact handheld hyperspectral imagers has been impeded by the scarcity of small widefield tunable wavelength filters. The widefield modality is preferred for handheld imaging applications in which image registration can be performed to counter scene shift caused by irregular user motions that would thwart scanning approaches. In the work presented here an electronically tunable widefield wavelength filter has been developed for hyperspectral imaging applications in the visible and near-infrared region. Conventional electronically tunable widefield imaging filter technologies include liquid crystal-based filters, acousto-optic tunable filters, and electronically tuned etalons; each having its own set of advantages and disadvantages. The construction of tunable filters is often complex and requires elaborate optical assemblies and electronic control circuits. I introduce in the work presented here is a novel widefield tunable filter, the surface plasmon coupled tunable filter (SPCTF), for visible and near infrared imaging. The SPCTF is based on surface plasmon coupling and has simple optical design that can be miniaturized without sacrificing performance. The SPCTF provides diffraction limited spatial resolution with a moderately narrow nominal passband (\u3c10 \u3enm) and a large spurious free spectral range (450 nm-1000 nm). The SPCTF employs surface plasmon coupling of the π-polarized component of incident light in metal films separated by a tunable dielectric layer. Acting on the π-polarized component, the device is limited to transmitting 50 percent of unpolarized incident light. This is higher than the throughput of comparable Lyot-based liquid crystal tunable filters that employ a series of linear polarizers. In addition, the SPCTF is not susceptible to the unwanted harmonic bands that lead to spurious diffraction in Bragg-based devices. Hence its spurious free spectral range covers a broad region from the blue through near infrared wavelengths. The compact design and rugged optical assembly make it suitable for hand-held hyperspectral imagers. The underlying theory and SPCTF design are presented along with a comparison of its performance to calculated estimates of transmittance, spectral resolution, and spectral range. In addition, widefield hyperspectral imaging using the SPCTF is demonstrated on model sample

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method

Hydrocarbon Spectra Slope (HYSS): A Spectra Index for Quantifying and Characterizing Hydrocarbon oil on Different Substrates Using Spectra Data

Many sensors in Optical domain allow for detection of hydrocarbons in oil spills study. However, high resolution laboratory and airborne imaging spectrometers have shown potential for quantification and characterization of hydrocarbon. Available methods in literature for quantifying and characterizing  hydrocarbons on these data relies mainly on shapes and positions of hydrocarbon key absorption features, mainly at 1.73 µm and 2.30 µm. Shapes formed by these absorption features are often influenced by spectral features of background substrates, thereby limiting the quality of results. Furthermore, multispectral sensors cannot resolve the shapes of key absorption features, a strong limitation for methods used in previous works. In this study, we present Hydrocarbon Spectra Slope (HYSS), a new spectra index that offers predictive quantification and characterization of common hydrocarbon oils. Slope values for the studied hydrocarbon oils enable clear discrimination for relative quantitative analysis of oil abundance classes and qualitative discrimination for common hydrocarbons on common background substrates. Data from ground-based spectrometers and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are resampled to AVIRIS, Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and LANDSAT 7 Enhanced Thematic Mapper’s (ETM+) Full Width at Half Maximum (FWHM), in order to compute spectra slope values for hydrocarbon abundance /hydrocarbon-substrate characterization. Despite limitations of nonconformity of central wavelengths and/or band widths of multispectral sensors to key hydrocarbon band, statistical significance for both quantitative and qualitative analysis at 95% confidence level (P-value ˂0.01) suggests strong potential of the use of HYSS, multispectral and hyperspectral sensors as emergency response tools for hydrocarbon mapping

Application-Dependent Wavelength Selection For Hyperspectral Imaging Technologies

Hyperspectral imaging has proven to provide benefits in numerous application domains, including agriculture, biomedicine, remote sensing, and food quality management. Unlike standard color imagery composed of these broad wavelength bands, hyperspectral images are collected over numerous (possibly hundreds) of narrow wavelength bands, thereby offering vastly more information content than standard imagery. It is this higher information content which enables improved performance in complex classification and regression tasks. However, this successful technology is not without its disadvantages which include high cost, slow data capture, high data storage requirements, and computational complexity. This research seeks to overcome these disadvantages through the development of algorithms and methods to enable the benefits of hyperspectral imaging in inexpensive portable devices that collect spectral data at only a handful (i.e., 5-7) of wavelengths specifically selected for the application of interest.This dissertation focuses on two applications of practical interest: fish fillet species classification for the prevention of food fraud and tissue oxygenation estimation for wound monitoring. Genetic algorithm, self-organizing map, and simulated annealing approaches for wavelength selection are investigated for the first application, combined with common machine learning classifiers for species classification. The simulated annealing approach for wavelength selection is carried over to the wound monitoring application and is combined with the Extended Modified Lambert-Beer law, a tissue oxygenation method that has proven to be robust to differences in melanin concentrations. Analyses for this second application included spectral convolutions to represent data collection with the envisioned inexpensive portable devices. Results of this research showed that high species classification accuracy (\u3e 90%) and low tissue oxygenation error (\u3c 1%) is achievable with just 5-7 selected wavelengths. Furthermore, the proposed wavelength selection and estimation algorithms for the wound monitoring application were found to be robust to variations in the peak wavelength and relatively wide bandwidths of the types of LEDs that may be featured in the designs of such devices

Compressive Fluorescence Microscopy for Biological and Hyperspectral Imaging

The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices---especially in optics---have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher dimensional signals which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point out to some remaining challenges for CS fluorescence microscopy.Comment: Submitted to Proceedings of the National Academy of Sciences of the United States of Americ

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Recently, the marine habitat has been under pollution threat, which impacts many human activities as well as human life. Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters

Development of innovative analytical methods based on spectroscopic techniques and multivariate statistical analysis for quality control in the food and pharmaceutical fields.

The increasing demand on quality assurance and ever more stringent regulations in food and pharmaceutical fields are promoting the need for analytical techniques enabling to provide reliable and accurate results. However, traditional analytical methods are labor-intensive, time-consuming, expensive and they usually require skilled personnel for performing the analysis. For these reasons, in the last decades, quality control protocols based on the employment of spectroscopic methods have been developed for many different application fields, including pharmaceutical and food ones. Vibrational spectroscopic techniques can be an adequate alternative for acquiring both chemical and physical information related to homogenous and heterogenous matrices of interest. Moreover, the significant development of powerful data-driven methodologies allowed to develop algorithms for the optimal extraction and processing of the complex spectroscopic signals allowing to apply combined approaches for quantitative and qualitative purposes. The present Doctoral Thesis has been focused on the development of ad-hoc analytical strategies based on the application of spectroscopic techniques coupled with multivariate data analysis approaches for providing alternative analytical protocols for quality control in food and pharmaceutical sectors. Regarding applications in food sector, excitation-emission Fluorescence Spectroscopy, Near Infrared Spectroscopy (NIRS) and NIR Hyperspectral Imaging (HSI) have been tested for solving analytical issues of independent case-studies. Unsupervised approaches based on Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC) have been applied on fluorescence data for characterizing green tea samples, while quantitative predictive approaches as Partial Least Squares regression have been used to correlate NIR spectra with quality parameters of extra-virgin olive oil samples. HSI was applied to study dynamic chemical processes which occur during cheese ripening with the aim to map chemical and sensory changes over time. The rapid technical progress in terms of spectroscopic instrumentations has led to have more flexible portable systems suitable for performing measurements directly in the field or in a manufacturing plant. Within this scenario, NIR spectroscopy proved to be one of the most powerful Process Analytical Technologies (PAT) for monitoring and controlling complex manufacturing processes. In this thesis, two applications based on the implementation of miniaturized NIR sensors have been performed for the real-time powder blending monitoring of pharmaceutical and food formulation, respectively. The main challenges in blending monitoring are related to the assessment of the homogeneity of multicomponent formulations, which is crucial to ensure the safety and effectiveness of a solid pharmaceutical formulation or the quality of a food product. In the third chapter of this thesis, tailor made qualitative chemometric strategies for obtaining a global understanding of blending processes and to optimize the endpoint detection are presented

LINEAR UNMIXING PROTOCOL FOR HYPERSPECTRAL IMAGE FUSION ANALYSIS APPLIED TO A CASE STUDY OF VEGETAL TISSUES

Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms

Raman spektroskopi for målinger av matkvalitet i prosesslinjen

A major challenge in the food industry is to effectively handle massive streams of food raw materials and products of different origin and quality. In-line sensor systems for food analysis can potentially measure and collect critical quality and safety parameters throughout the processes. This information can be used for sorting, product differentiation, process optimisation and product control. One emerging technology that shows great promise for future in-line food sensor systems is Raman spectroscopy. The overall goal of this thesis was to elucidate the feasibility of Raman spectroscopy as a tool for detailed quality evaluation of heterogeneous food raw materials, under in-line industrial conditions. To this end, two main application areas were chosen, including A1) in-line measurements of fatty acid features in salmon fillets and A2) in-line characterization of a poultry rest raw material stream. A central element in both application areas was the use of a Wide Area Illumination (WAI) Raman probe to obtain representative measurements of the heterogeneous raw materials and to tackle variations in working distance. Variations in working distance may easily happen in an industrial process line with samples of varying thicknesses and streams of varying production volumes. The limited measurement volume of the WAI probe was increased by scanning over the sample surface. We showed that this strategy was successful with respect to obtaining representative measurements. This was demonstrated through obtaining good performances for EPA+DHA estimation in salmon fillets of varying thickness (± 1 cm) and through characterization (fat, protein, bone and collagen) of poultry rest raw material with larger variations in working distances (± 3 cm). For the latter study, the method was also tested in-line at a real hydrolysis facility with promising results. For the study on salmon fillets, the varying fat deposition across the fillets was shown to have implication for choice of scanning strategy at shorter exposure times due to impact on signal-to-noise ratio (SNR). This illustrates the importance of considering the heterogeneity of the food product in a given application, and of optimizing measurement strategies accordingly. Another main objective was to elucidate the ability of Raman measurements to tackle short exposure times. This is of particular importance for measurements of single samples at a conveyor belt, where exposure time is strictly limited. This was investigated in paper I and II, where we measured single salmon and poultry samples at exposure times down to 1 s. While exposure times around 2-1 s in these cases did give acceptable performances, it was evident that these low exposure times reduced SNR and performance and that SNR was a critical parameter. This indicates that at shorter exposure times, the surface scanning with theWAI Raman probe might be less robust with respect to tackling samples of varying sample sizes or lower analyte concentrations. Therefore, for such single samples, WAI Raman spectroscopy is currently better suited for fast at-line or on-line measurements. However, such measurements could also have high value for the industry, as it represents a frequent quality feedback, which is currently lacking. Overall, it was found that further efforts on calibration development, SNR optimization and practical measurement setup are needed to unlock the full potential for in-line measurements in the two application areas. Still, this thesis has shown that it is feasible to use a WAI Raman probe for detailed characterization of very heterogeneous streams of raw material, at industrially relevant speeds and in presence of moderate variations in working distance and probe tilt. It was shown that WAI Raman spectroscopy is promising, both for measurements of continuous raw material streams and single food products on a conveyor belt. This introduces many new application opportunities for Raman spectroscopy within quality documentation, sorting, process analysis and real-time process control in the food industry.En stor utfordring for matindustrien er å håndtere store strømmer av råvarer og produkter av forskjellig opprinnelse og kvalitet på en effektiv måte. Sensorsystemer som kan brukes direkte på prosesslinjene, såkalt ”in-line”, kan potensielt måle og samle kritisk informasjon om matkvalitet og mattrygghet. Resultatet er verktøy for sortering, produktdifferensiering, prosessoptimering og produktkontroll. Raman spektroskopi er en lovende teknologi under utvikling med stort potensiale som sensorsystem i matindustrien. Målet med dette doktorgradsprosjektet var å undersøke mulighetene for å bruke Raman-spektroskopi som et verktøy for kvalitetsmålinger av heterogene matråvarer direkte i prosesslinjen. For å nå dette målet ble to bruksområder valgt, inkludert A1) in-line målinger av fettsyreprofil i laksefileter og A2) in-line karakterisering av råvarestrømmer fra fjærfe-produksjon. Et sentralt tema for begge bruksområdene var bruken av en Raman-probe med bredt belysningsområde (WAI) for å oppnå representative målinger av heterogene råvarer og for å takle variasjoner i arbeidsavstand. Variasjoner i arbeidsavstand kan fort oppstå i en industriell prosesslinje med prøver av varierende tykkelse og for stømmer med varierende produksjonsvolum. Fokusvolumet til WAI-proben ble økt ved å skanne over prøveoverflaten. Vi viste at denne strategien fungerte godt for å oppnå representative målinger. Dette ble demonstrert ved å oppnå lave prediksjonsfeil for EPA + DHA-estimering i laksefileter med varierende tykkelse (± 1 cm) og for karakterisering (fett, protein, bein og kollagen) av kyllingråstoff med større variasjoner i arbeidsavstand (± 3 cm). For sistnevnte studie ble metoden også testet in-line på et industrielt hydrolyseanlegg, med lovende resultater. For studien på laksefileter ble det vist at det varierende fettinnholdet på filletoverflaten hadde betydning for valg av skannestrategi ved kortere eksponeringstider, grunnet effekten på signal-støy-forholdet. Dette illustrerer hvor viktig det er å gjøre nøye vurderinger av heterogeniteten til et gitt matprodukt, og å optimalisere målestrategien deretter. Et annet hovedmål var å undersøke hvordan Raman-målingene håndterte kortere eksponeringstider. Dette er av spesiell betydning for målinger av enkeltprøver på et transportbelte, der eksponeringstiden er sterkt begrenset. Dette ble undersøkt i artikkel I og II, der vi målte enkeltprøver av laks og fjærfe-restråstoff ved eksponeringstider ned til 1 s. Selv om eksponeringstider rundt 2-1 s i disse tilfellene ga akseptable prediksjonsfeil, var det tydelig at disse lave eksponeringstidene reduserte signal-støy-forholdet og dermed prediksjons-prestasjonen. Signalstøy- forholdet er altså en kritisk faktor, og dette indikerer at skanning med WAI Raman-proben kan være mindre robust når det gjelder å håndtere prøver med varierende prøvestørrelser eller lavere analytt-konsentrasjoner, ved slike korte eksponeringstider. Derfor er Raman-målingene foreløpig bedre egnet for hurtige målinger ved siden av produksjonslinjen (”at-line” eller ”on-line”), for enkeltprøver. Slike målinger kan også ha høy verdi for industrien, da det representerer et system som gir hyppig tilbakemelding på kvalitet, noe som det for øyeblikket ikke finnes løsninger for. En videre innsats innen kalibreringsutvikling, SNR-optimering og utvikling av praktisk måleoppsett er nødvendig for å realisere det fulle potensialet for in-line Raman-målinger i de to applikasjonsområdene. Likevel har dette doktorgradsprosjektet vist at det er mulig å bruke en Raman probe med bredt belysningsområde til detaljert karakterisering av av svært heterogene strømmer med råvaremateriale, ved industrielt relevante eksponeringstider og med moderat variasjon i arbeidsavstand. Det ble vist at WAI Raman spektroskopi er lovende både for målinger på kontinuerlige råvarestrømmer og enkeltprøver på et transportbelte. Dette muliggjør en rekke nye applikasjoner for WAI Raman spektroskopi innen kvalitetsdokumentasjon, sortering, prosessanalyse og sanntids prosesskontroll i matindustrien