Nonlinear Optimization Algorithm for Multivariate Optical Element Design

Soyemi, Olusola O.; Haibach, Frederick G.; Gemperline, Paul J.; Myrick, Michael L.

doi:10.1366/0003702021954935

Cited by 23 publications

(20 citation statements)

References 14 publications

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“…[8][9][10] MOEs are encoded with one of many possible spectral patterns by using the optical transmission and reflection characteristics of the interference filter to detect/measure a complex chemical signature in the presence of a strongly interfering background. [11] Simple instruments incorporating MOEs can realize the advantages of a multivariate calibration without the active use of a computer or an experienced operator for post-processing of the data. …”

Section: Multivariate Optical Computingmentioning

confidence: 99%

“…[11] Designing a specific, wideband multi-layer optical filter entails the sampling of a multidimensional surface where each dimension corresponds to a layer thickness. The target and background spectroscopic data in addition to the defined composite instrument response function are the inputs for designing the IMOEs.…”

Section: Designing the Imaging Multivariate Optical Elementsmentioning

confidence: 99%

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Spectral imaging of chemical compounds using multivariate optically enhanced filters integrated with InGaAs VGA cameras

Priore¹,

Jacksen

2016

SPIE Proceedings

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Infrared hyperspectral imagers (HSI) have been fielded for the detection of hazardous chemical and biological compounds, tag detection (friend versus foe detection) and other defense critical sensing missions over the last two decades. Low Size/Weight/Power/Cost (SWaPc) methods of identification of chemical compounds spectroscopy has been a long term goal for hand held applications. We describe a new HSI concept for low cost / high performance InGaAs SWIR camera chemical identification for military, security, industrial and commercial end user applications.Multivariate Optical Elements (MOEs) are thin-film devices that encode a broadband, spectroscopic pattern allowing a simple broadband detector to generate a highly sensitive and specific detection for a target analyte. MOEs can be matched 1:1 to a discrete analyte or class prediction. Additionally, MOE filter sets are capable of sensing an orthogonal projection of the original sparse spectroscopic space enabling a small set of MOEs to discriminate a multitude of target analytes.This paper identifies algorithms and broadband optical filter designs that have been demonstrated to identify chemical compounds using high performance InGaAs VGA detectors. It shows how some of the initial models have been reduced to simple spectral designs and tested to produce positive identification of such chemicals. We also are developing pixilated MOE compressed detection sensors for the detection of a multitude of chemical targets in challenging backgrounds/environments for both commercial and defense/security applications. This MOE based, real-time HSI sensor will exhibit superior sensitivity and specificity as compared to currently fielded HSI systems.

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Section: Multivariate Optical Computingmentioning

confidence: 99%

Section: Designing the Imaging Multivariate Optical Elementsmentioning

confidence: 99%

Spectral imaging of chemical compounds using multivariate optically enhanced filters integrated with InGaAs VGA cameras

Priore¹,

Jacksen

2016

SPIE Proceedings

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“…4 and then proceeded as previously described. 5,7 The spectral radiance of the lamp, the spectral reflectance of the diffuse reflecting paper, the reflectivity and transmission of the Inconel beam splitters (one reflection and one transmission through the beam splitters was experienced in both T-IMOE and R-IMOE modes), the transmission spectrum of the 715 nm long-pass filter, the spectral efficiency of the lens, and the spectral responsivity of the camera array were all measured separately in our laboratory and multiplied together to obtain an absolute spectral response for the system without the samples. This absolute response was then normalized to unit area to produce a relative system spectral response.…”

Section: Imaging Multivariate Optical Element Design and Fabricationmentioning

confidence: 99%

“…The MOC model, however, is produced by designing an optical interference filter whose spectrum encodes a regression vector. 5 We have referred to these specialized interference filters as multivariate optical elements (MOEs).…”

Section: Introductionmentioning

confidence: 99%

Precision in imaging multivariate optical computing

Simcock

Myrick

2007

Appl. Opt.

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Multivariate optical computing (MOC) is a method of performing chemical analysis using a multilayer thin-film structure known as a multivariate optical element (MOE). Recently we have been advancing MOC for imaging problems by using an imaging MOE (IMOE) in a normal-incidence geometry and employing normalization by the 1-norm. There are several important differences between the previously described 45°and the normal-incidence imaging, one of which is the measurement precision due to photon counting. We compare this precision to 45°MOC. We also discuss how MOE models with similar values of standard errors of calibration and prediction and similar gain values may vary in precision because of the sign or offset of the regression vector encoded in the IMOE spectrum. Experimental verification of a key result is provided by near-infrared imaging of slides coated with a dye-doped polymer film.

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“…Theoretical treatment of this methodology can be found in the literature (18,19). Myrick et al have demonstrated some practical applications of this methodology in UV-visible and NIR spectroscopy (20)(21)(22)(23)(24)(25)(26)(27)(28). Encoding applications are based on the fabrication of thin film solid-state optical filters, termed multivariate optical elements (MOEs).…”

Section: Introductionmentioning

confidence: 99%