Precision in multivariate optical computing

Haibach, Frederick G.; Myrick, Michael L.

doi:10.1364/ao.43.002130

Cited by 21 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(See Table II for the results of all the filters in comparison to the four-component digital regression model.) MOEs demonstrated an improvement of two orders of magnitude in both SEP and SEC over digital PCR for the prediction of analyte concentration [6]. The authors speculated that this is partly due to the inclusion of more model error in the PCR model, whereas the MOE avoids model error because it passes a smooth transmission waveform to the detector.…”

Section: Select Results From Multivariate Optical Elementsmentioning

confidence: 99%

“…The overall purpose of these optical interference filters, termed multivariate optical elements (MOE) [4][5][6][24][25][26][27], is to minimize the standard error of prediction (SEP), or the rootmean-squared error of calibration (RMSEC) of chemical species. MOE methods begin with the collection of spectra of target analytes.…”

Section: Theorymentioning

confidence: 99%

“…Acoustic and optical spectroscopy coupled with multivariate mathematics is routinely used for analyte quantification and identification, such as in NIR imaging for the quantification of organics in the presence of interferents [4,5], scanning monochromator instruments and multichannel array spectrometers [6], and ARS for the classification of tablets and quantification of active pharmaceutical ingredient in semi-solids [7,8]. As the complexity and dimensionality of data sets increases with each emerging generation of hyperspectral imaging, the amount of computing power necessary for processing increases significantly.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applications of integrated sensing and processing in spectroscopic imaging and sensing

Medendorp¹,

Lodder²

2005

Journal of Chemometrics

View full text Add to dashboard Cite

Integrated sensing and processing (ISP) encompasses the use of optical computing and adapted excitation signals to physically implement chemometric calculations in spectroscopic sensors for imaging. As data sets become larger and more complex with each emerging generation of hyperspectral imagers, the 'pixel-to-pupil' ratio increases at a rate faster than computing power can accommodate. In response to the need for faster and more efficient methods of processing, many analog solutions to the problem of high data dimensionality have emerged. The successful development of ISP has strong implications for military imaging, biosensing, spectroscopic imaging, and pharmaceutical process analytical technology (PAT). ISP developments in spectroscopy and PAT have emerged as alternatives to conventional Fourier transform infrared (FT-IR), near-infrared (NIR), IR, UV-visible, fluorescence, Raman, and acoustic-resonance spectrometry (ARS). Flourishing applications of ISP have demonstrated predictive ability equivalent to conventional approaches for sample differentiation and analyte quantification, in only a fraction of the time required for traditional spectrometric measurements.

show abstract

Section: Select Results From Multivariate Optical Elementsmentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applications of integrated sensing and processing in spectroscopic imaging and sensing

Medendorp¹,

Lodder²

2005

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…More recently Lewis et al employed optical computations for edge finding in chemical images [11] and Prakash et al demonstrated the advantages of optical computations for multivariate calibration of rhodomine dye mixtures [1]. Myrick et al investigated glass filters for quantitative optical computation [12] and have recently demonstrated excellent correlation between optical transmittance and analyte concentration [13,14]. The application of integrating optics and electronics for sensors and imaging has been reviewed by Lodder [15].…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…As shown previously, 45°MOC devices have the potential to achieve this increase in speed without surrendering either precision or model accuracy. 6 …”

Section: Introductionmentioning

confidence: 99%

Precision in imaging multivariate optical computing

Simcock

Myrick

2007

Appl. Opt.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Precision in multivariate optical computing

Cited by 21 publications

References 14 publications

Applications of integrated sensing and processing in spectroscopic imaging and sensing

Applications of integrated sensing and processing in spectroscopic imaging and sensing

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

Precision in imaging multivariate optical computing

Contact Info

Product

Resources

About