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

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. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  L. Eldén.  Numerical analysis , 1990 .

[2]  Karl S. Booksh,et al.  Calibration of Surface Plasmon Resonance Refractometers Using Locally Weighted Parametric Regression , 1997 .

[3]  Anjan Kumar Ghosh,et al.  Design of fiber optic adaline neural networks , 1997 .

[4]  Michael L Myrick,et al.  On-line reoptimization of filter designs for multivariate optical elements. , 2003, Applied optics.

[5]  J. Harris,et al.  Thermal lens measurements by optical computation of the laser beam spot size , 1985 .

[6]  Robert A. Lodder,et al.  Applications of integrated sensing and processing in spectroscopic imaging and sensing , 2005 .

[7]  B S Wherrett,et al.  All-optical computation: a design for tackling a specific physical problem. , 1985, Applied optics.

[8]  Karl S. Booksh,et al.  Tuning Dynamic Range and Sensitivity of White-Light, Multimode, Fiber-Optic Surface Plasmon Resonance Sensors , 1999 .

[9]  Randall D. Tobias,et al.  Chemometrics: A Practical Guide , 1998, Technometrics.

[10]  Brian S. Wherrett,et al.  All-optical computation — A parallel integrator based upon a single gate full adder , 1985 .

[11]  D. Pantelic.  Optical computation of determinants , 1987 .

[12]  D. Massart.  Chemometrics: A Textbook , 1988 .

[13]  Yehuda Albeck,et al.  Optical Computation with Negative Light Intensity with a Plastic Bacteriorhodopsin Film , 1997, Science.

[14]  Emil W. Ciurczak,et al.  Handbook of Near-Infrared Analysis , 1992 .

[15]  Stanley M. Angel,et al.  Remote Raman Microimaging Using an AOTF and a Spatially Coherent Microfiber Optical Probe , 1996 .

[17]  Michael L Myrick,et al.  Precision in multivariate optical computing. , 2004, Applied optics.

[18]  Anjan K. Ghosh,et al.  Design of least-mean-square based adaptive optical equalizers , 1992 .

[19]  J A Dobrowolski,et al.  Multivariate optical computation for predictive spectroscopy. , 1998, Analytical chemistry.

[20]  A Ghosh,et al.  Design and performance optimization of fiber optic adaptive filters. , 1991, Applied optics.

[21]  Karl S. Booksh,et al.  OPTICAL REGRESSION : A METHOD FOR IMPROVING QUANTITATIVE PRECISION OF MULTIVARIATE PREDICTION WITH SINGLE CHANNEL SPECTROMETERS , 1999 .