&lt;title&gt;Calibration systems for surface plasmon resonance spectroscopy&lt;/title&gt;

Boysworth, Marc K.; Obando, Louis A.; Booksh, Karl S.

doi:10.1117/12.371305

Cited by 4 publications

(2 citation statements)

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“…The ability to calibrate broad SPR dips from tapered fibers has been further demonstrated where five different multivariate calibration methods are compared on spectra collected with tapered fibers [135]. True prediction errors for weight-percent sucrose in solution were determined to be equivalent to the precision of the constructed samples [136].…”

Section: Optical Sensors: Surface Plasmon Resonance (Spr)mentioning

confidence: 99%

Chemical sensors for portable, handheld field instruments

et al. 2001

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A review of three commonly used classes of chemical sensor technologies as applicable to implementation in portable, handheld field instruments is presented. Solid-state gas and chemical sensors have long been heralded as the solution to a wide variety of portable chemical sensing system applications. However, advances in optical sensing technology have reduced the size of supporting infrastructure to be competitive with their solid-state counterparts. Optical, solid-state, and hybrid arrays of sensors have application for portable instruments, but issues of insufficient selectivity and sensitivity continue to hamper the widespread introduction of these miniaturized sensors for solving chemical sensing problems in environments outside the laboratory. In this article, we evaluate three of the major classes of compact chemical sensors for portable applications: (solid-state) chemiresistors, (solidstate) CHEMFETs, and (optical) surface plasmon resonance sensors (SPR). These sensors are evaluated and reviewed, according to the current state of research, in terms of their ability to operate at low-power, small-size, and relatively low-cost in environments, with numerous interferents and variable ambient conditions.

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Section: Optical Sensors: Surface Plasmon Resonance (Spr)mentioning

confidence: 99%

Chemical sensors for portable, handheld field instruments

et al. 2001

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“…A simple, modular, fiber optic spectrometer was constructed [19,20]. Wavelength separation was achieved via a birefringent AOTF (Brimrose, Baltimore, MD) that operated over the wavelength range of 500-1000 nm.…”

Section: Instrumentalmentioning

confidence: 99%

Generalization of multivariate optical computations as a method for improving the speed and precision of spectroscopic analyses

Boysworth

Banerji

Wilson

et al. 2008

Journal of Chemometrics

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Multivariate optical computations (MOCs) offer improved analytical precision and increased speed of analysis via synchronous data collection and numerical computation with scanning spectroscopic systems. The improved precision originates in the redistribution of integration time from spurious channels to informative channels in an optimal manner for increasing the signal-to-noise ratio with multivariate analysis under the constraint of constant total analysis time. In this work, MOCs perform the multiplication and addition steps of spectral processing by adjusting the integration parameters of the optical detector or adjusting the scanning profile of the tunable optical filter. Improvement in the precision of analysis is achieved via the implicit optimization of the analytically useful signal-to-noise ratio. The speed improvements are realized through simpler data post-processing, which reduces the computation time required after data collection. Alternatively, the analysis time may be significantly truncated while still seeing an improvement in the precision of analysis, relative to competing methods. Surface plasmon resonance (SPR) spectroscopic sensors and visible reflectance spectroscopic imaging were used as test beds for assessing the performance of MOCs. MOCs were shown to reduce the standard deviation of prediction by 15% compared to digital data collection and analysis with the SPR and up to 45% for the imaging applications. Similarly, a 30% decrease in the total analysis time was realized while still seeing precision improvements.

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Array Optimization and Preprocessing Techniques for Chemical Sensing Microsystems

et al. 2002

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Physical sensors, defined by their direct chemical interaction with the sensing environment, are a valuable and often essential contribution to the solution of stringent chemical sensing problems. Methods for conditioning signals from these sensors to optimize their presentation to subsequent decision-making models in the signal processing flow are presented. The assembly of sensors into an array and the preprocessing of these signals are the two primary techniques for conditioning a chemical image for concentration detection, chemical discrimination, and, in some cases, odor localization. Array optimization involves locating the point at which adding additional sensors to an array generates more noise than information and is highly dependent on the application and number of analytes to be sensed or differentiated. Signal preprocessing techniques include noise reduction, feature extraction, the reduction of array inputs, and the scaling of individual sensor signals and provide a means by which to reconstruct an array of chemical sensor signals into a subset of information that enables a decisionmaking model to do its job with greater efficiency and accuracy. In this chapter, surveys of methods are accompanied by representative examples employing a wide variety of sensors including ChemFETs, chemiresistors, acoustic wave devices, and surface plasmon resonance sensors.

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<title>Calibration systems for surface plasmon resonance spectroscopy</title>

Cited by 4 publications

References 0 publications

Chemical sensors for portable, handheld field instruments

Chemical sensors for portable, handheld field instruments

Generalization of multivariate optical computations as a method for improving the speed and precision of spectroscopic analyses

Array Optimization and Preprocessing Techniques for Chemical Sensing Microsystems

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