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Special Issue "Spectral Imaging at the Microscale and Beyond"

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (7 September 2013)

Special Issue Editor

Guest Editor
Dr. Jesse Greener (Website)

Département de Chimie, Université Laval, 1045 Avenue de la Médecine, Québec, QC G1V 0A6, Canada
Fax: +1 418 656 7916
Interests: lab-on-a-chip analytical solutions; environmental and catalytic biomaterials; microfabrication methods; microbial fuel cells

Special Issue Information

Dear Colleagues,

Never before has the demand been so high for information intensive characterization at the microscale and smaller. Traditionally, fields such as microbiology, microfluidics, nanoscience and the materials sciences have relied on optical and electron imaging to collect information on spatial dimensions. However, sustained advances in these and other fields require techniques that generate spatially-resolved chemical, physical and mechanical information. Spectral imaging is an exciting analytical field that is gaining momentum and finding a growing number of applications at small scales. It is a general approach based on light and electromagnetic-fields. By conducting a spatial resolved analysis of both intensity and frequency, one generates maps that can passively report on a broad range of sample properties. Examples of spectroscopic tools utilized for imaging include, but are not limited to, magnetic resonance, infrared, Raman, UV-Vis, X-ray and broad-band fluorescence.

We dedicate this special issue of Sensors to innovative and emerging approaches to spectral imaging at the microscale (less than 1mm) or smaller. We welcome novel, high-quality submissions ranging from demonstrations of new techniques to the utilization of spectral imaging for studies in biology, chemistry, materials, engineering and more. In the case that authors wish to explore the relevancy of their proposed topics, we encourage communication with the guest editor and Sensors staff in advance of submission.

Dr. Jesse Greener
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs).


Keywords

  • spectroscopy
  • broad-band
  • microscopy
  • confocal
  • mapping
  • imaging
  • spectral imaging
  • confocal spectroscopy
  • multispectral imaging
  • chemical imaging
  • imaging spectroscopy
  • multi-band imaging

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

Open AccessEditorial Spectral Imaging at the Microscale and Beyond
Sensors 2014, 14(5), 8162-8166; doi:10.3390/s140508162
Received: 22 April 2014 / Accepted: 29 April 2014 / Published: 6 May 2014
PDF Full-text (94 KB) | HTML Full-text | XML Full-text
Abstract
Here we give context to the special issue “Spectral Imaging at the Microscale and Beyond” in Sensors. We start with an introduction and motivation for the need for spectral imaging and then present important definitions and background concepts. Following this, we [...] Read more.
Here we give context to the special issue “Spectral Imaging at the Microscale and Beyond” in Sensors. We start with an introduction and motivation for the need for spectral imaging and then present important definitions and background concepts. Following this, we review new developments and applications in environmental monitoring, biomaterials, microfluidics, nanomaterials, healthcare, agriculture and food science, with a special focus on the articles published in the special issue. Some concluding remarks put the presented developments in context vis-à-vis the future of spectral imaging. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)

Research

Jump to: Editorial, Review

Open AccessArticle Resolving Mixed Algal Species in Hyperspectral Images
Sensors 2014, 14(1), 1-21; doi:10.3390/s140100001
Received: 20 November 2013 / Revised: 4 December 2013 / Accepted: 17 December 2013 / Published: 19 December 2013
Cited by 4 | PDF Full-text (1066 KB) | HTML Full-text | XML Full-text
Abstract
We investigated a lab-based hyperspectral imaging system’s response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system’s performance. The spectral response to volumetric changes in single and combinations of algal mixtures with [...] Read more.
We investigated a lab-based hyperspectral imaging system’s response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system’s performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert’s law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
Open AccessArticle A Microfluidic Bioreactor with in Situ SERS Imaging for the Study of Controlled Flow Patterns of Biofilm Precursor Materials
Sensors 2013, 13(11), 14714-14727; doi:10.3390/s131114714
Received: 7 September 2013 / Revised: 19 October 2013 / Accepted: 22 October 2013 / Published: 29 October 2013
Cited by 8 | PDF Full-text (938 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A microfluidic bioreactor with an easy to fabricate nano-plasmonic surface is demonstrated for studies of biofilms and their precursor materials via Surface Enhanced Raman Spectroscopy (SERS). The system uses a novel design to induce sheath flow confinement of a sodium citrate biofilm [...] Read more.
A microfluidic bioreactor with an easy to fabricate nano-plasmonic surface is demonstrated for studies of biofilms and their precursor materials via Surface Enhanced Raman Spectroscopy (SERS). The system uses a novel design to induce sheath flow confinement of a sodium citrate biofilm precursor stream against the SERS imaging surface to measure spatial variations in the concentration profile. The unoptimised SERS enhancement was approximately 2.5 × 104, thereby improving data acquisition time, reducing laser power requirements and enabling a citrate detection limit of 0.1 mM, which was well below the concentrations used in biofilm nutrient solutions. The flow confinement was observed by both optical microscopy and SERS imaging with good complementarity. We demonstrate the new bioreactor by growing flow-templated biofilms on the microchannel wall. This work opens the way for in situ spectral imaging of biofilms and their biochemical environment under dynamic flow conditions. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
Figures

Open AccessArticle A Wide Spectral Range Reflectance and Luminescence Imaging System
Sensors 2013, 13(11), 14500-14510; doi:10.3390/s131114500
Received: 21 August 2013 / Revised: 17 October 2013 / Accepted: 18 October 2013 / Published: 25 October 2013
Cited by 7 | PDF Full-text (2566 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we introduce a wide spectral range (200–2500 nm) imaging system with a 250 μm minimum spatial resolution, which can be freely modified for a wide range of resolutions and measurement geometries. The system has been tested for reflectance and [...] Read more.
In this study, we introduce a wide spectral range (200–2500 nm) imaging system with a 250 μm minimum spatial resolution, which can be freely modified for a wide range of resolutions and measurement geometries. The system has been tested for reflectance and luminescence measurements, but can also be customized for transmittance measurements. This study includes the performance results of the developed system, as well as examples of spectral images. Discussion of the system relates it to existing systems and methods. The wide range spectral imaging system that has been developed is however highly customizable and has great potential in many practical applications. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
Figures

Open AccessArticle Living Matter Observations with a Novel Hyperspectral Supercontinuum Confocal Microscope for VIS to Near-IR Reflectance Spectroscopy
Sensors 2013, 13(11), 14523-14542; doi:10.3390/s131114523
Received: 9 August 2013 / Revised: 15 October 2013 / Accepted: 16 October 2013 / Published: 25 October 2013
Cited by 4 | PDF Full-text (1107 KB) | HTML Full-text | XML Full-text
Abstract
A broad range hyper-spectroscopic microscope fed by a supercontinuum laser source and equipped with an almost achromatic optical layout is illustrated with detailed explanations of the design, implementation and data. The real novelty of this instrument, a confocal spectroscopic microscope capable of [...] Read more.
A broad range hyper-spectroscopic microscope fed by a supercontinuum laser source and equipped with an almost achromatic optical layout is illustrated with detailed explanations of the design, implementation and data. The real novelty of this instrument, a confocal spectroscopic microscope capable of recording high resolution reflectance data in the VIS-IR spectral range from about 500 nm to 2.5 μm wavelengths, is the possibility of acquiring spectral data at every physical point as defined by lateral coordinates, X and Y, as well as at a depth coordinate, Z, as obtained by the confocal optical sectioning advantage. With this apparatus we collect each single scanning point as a whole spectrum by combining two linear spectral detector arrays, one CCD for the visible range, and one InGaAs infrared array, simultaneously available at the sensor output channel of the home made instrument. This microscope has been developed for biomedical analysis of human skin and other similar applications. Results are shown illustrating the technical performances of the instrument and the capability in extracting information about the composition and the structure of different parts or compartments in biological samples as well as in solid statematter. A complete spectroscopic fingerprinting of samples at microscopic level is shown possible by using statistical analysis on raw data or analytical reflectance models based on Abelés matrix transfer methods. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
Open AccessArticle A Fast Smoothing Algorithm for Post-Processing of Surface Reflectance Spectra Retrieved from Airborne Imaging Spectrometer Data
Sensors 2013, 13(10), 13879-13891; doi:10.3390/s131013879
Received: 20 August 2013 / Revised: 26 September 2013 / Accepted: 29 September 2013 / Published: 14 October 2013
Cited by 5 | PDF Full-text (769 KB) | HTML Full-text | XML Full-text
Abstract
Surface reflectance spectra retrieved from remotely sensed hyperspectral imaging data using radiative transfer models often contain residual atmospheric absorption and scattering effects. The reflectance spectra may also contain minor artifacts due to errors in radiometric and spectral calibrations. We have developed a [...] Read more.
Surface reflectance spectra retrieved from remotely sensed hyperspectral imaging data using radiative transfer models often contain residual atmospheric absorption and scattering effects. The reflectance spectra may also contain minor artifacts due to errors in radiometric and spectral calibrations. We have developed a fast smoothing technique for post-processing of retrieved surface reflectance spectra. In the present spectral smoothing technique, model-derived reflectance spectra are first fit using moving filters derived with a cubic spline smoothing algorithm. A common gain curve, which contains minor artifacts in the model-derived reflectance spectra, is then derived. This gain curve is finally applied to all of the reflectance spectra in a scene to obtain the spectrally smoothed surface reflectance spectra. Results from analysis of hyperspectral imaging data collected with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data are given. Comparisons between the smoothed spectra and those derived with the empirical line method are also presented. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
Open AccessArticle Tip-Enhanced Raman Imaging and Nano Spectroscopy of Etched Silicon Nanowires
Sensors 2013, 13(10), 12744-12759; doi:10.3390/s131012744
Received: 2 August 2013 / Accepted: 12 September 2013 / Published: 25 September 2013
Cited by 6 | PDF Full-text (485 KB) | HTML Full-text | XML Full-text
Abstract
Tip-enhanced Raman spectroscopy (TERS) is used to investigate the influence of strains in isolated and overlapping silicon nanowires prepared by chemical etching of a (100) silicon wafer. An atomic force microscopy tip made of nanocrystalline diamond coated with a thin layer of [...] Read more.
Tip-enhanced Raman spectroscopy (TERS) is used to investigate the influence of strains in isolated and overlapping silicon nanowires prepared by chemical etching of a (100) silicon wafer. An atomic force microscopy tip made of nanocrystalline diamond coated with a thin layer of silver is used in conjunction with an excitation wavelength of 532 nm in order to probe the first order optical phonon mode of the [100] silicon nanowires. The frequency shift and the broadening of the silicon first order phonon are analyzed and compared to the topographical measurements for distinct configuration of nanowires that are disposed in straight, bent or overlapping configuration over a microscope coverslip. The TERS spatial resolution is close to the topography provided by the nanocrystalline diamond tip and subtle spectral changes are observed for different nanowire configurations. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
Figures

Open AccessArticle An Approach for Characterizing and Comparing Hyperspectral Microscopy Systems
Sensors 2013, 13(7), 9267-9293; doi:10.3390/s130709267
Received: 27 May 2013 / Revised: 8 July 2013 / Accepted: 15 July 2013 / Published: 19 July 2013
Cited by 4 | PDF Full-text (869 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Hyperspectral imaging and analysis approaches offer accurate detection and quantification of fluorescently-labeled proteins and cells in highly autofluorescent tissues. However, selecting optimum acquisition settings for hyperspectral imaging is often a daunting task. In this study, we compared two hyperspectral systems—a widefield system [...] Read more.
Hyperspectral imaging and analysis approaches offer accurate detection and quantification of fluorescently-labeled proteins and cells in highly autofluorescent tissues. However, selecting optimum acquisition settings for hyperspectral imaging is often a daunting task. In this study, we compared two hyperspectral systems—a widefield system with acoustic optical tunable filter (AOTF) and charge coupled device (CCD) camera, and a confocal system with diffraction gratings and photomultiplier tube (PMT) array. We measured the effects of system parameters on hyperspectral image quality and linear unmixing results. Parameters that were assessed for the confocal system included pinhole diameter, laser power, PMT gain and for the widefield system included arc lamp intensity, and camera gain. The signal-to-noise ratio (SNR) and the root-mean-square error (RMS error) were measured to assess system performance. Photobleaching dynamics were studied. Finally, theoretical sensitivity studies were performed to estimate the incremental response (sensitivity) and false-positive detection rates (specificity). Results indicate that hyperspectral imaging assays are highly dependent on system parameters and experimental conditions. For detection of green fluorescent protein (GFP)-expressing cells in fixed lung tissues, a confocal pinhole of five airy disk units, high excitation intensity and low detector gain were optimal. The theoretical sensitivity studies revealed that widefield hyperspectral microscopy was able to detect GFP with fewer false positive occurrences than confocal microscopy, even though confocal microscopy offered improved signal and noise characteristics. These studies provide a framework for optimization that can be applied to a variety of hyperspectral imaging systems. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
Open AccessArticle Rice Seed Cultivar Identification Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis
Sensors 2013, 13(7), 8916-8927; doi:10.3390/s130708916
Received: 21 May 2013 / Revised: 26 June 2013 / Accepted: 4 July 2013 / Published: 12 July 2013
Cited by 20 | PDF Full-text (530 KB) | HTML Full-text | XML Full-text
Abstract
A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft [...] Read more.
A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
Open AccessArticle Estimation of Melanin and Hemoglobin Using Spectral Reflectance Images Reconstructed from a Digital RGB Image by the Wiener Estimation Method
Sensors 2013, 13(6), 7902-7915; doi:10.3390/s130607902
Received: 30 April 2013 / Revised: 12 June 2013 / Accepted: 17 June 2013 / Published: 19 June 2013
Cited by 10 | PDF Full-text (668 KB) | HTML Full-text | XML Full-text
Abstract
A multi-spectral diffuse reflectance imaging method based on a single snap shot of Red-Green-Blue images acquired with the exposure time of 65 ms (15 fps) was investigated for estimating melanin concentration, blood concentration, and oxygen saturation in human skin tissue. The technique [...] Read more.
A multi-spectral diffuse reflectance imaging method based on a single snap shot of Red-Green-Blue images acquired with the exposure time of 65 ms (15 fps) was investigated for estimating melanin concentration, blood concentration, and oxygen saturation in human skin tissue. The technique utilizes the Wiener estimation method to deduce spectral reflectance images instantaneously from an RGB image. Using the resultant absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are numerically deduced in advance by the Monte Carlo simulations for light transport in skin. Oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments on fingers during upper limb occlusion demonstrated the ability of the method to evaluate physiological reactions of human skin. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)

Review

Jump to: Editorial, Research

Open AccessReview Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety
Sensors 2014, 14(4), 7248-7276; doi:10.3390/s140407248
Received: 25 September 2013 / Revised: 7 April 2014 / Accepted: 8 April 2014 / Published: 22 April 2014
Cited by 26 | PDF Full-text (617 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment. Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects [...] Read more.
Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment. Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects being studied. This paper provides a comprehensive review on the recent development of hyperspectral imaging applications in food and food products. The potential and future work of hyperspectral imaging for food quality and safety control is also discussed. Full article
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)

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