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

Abstract

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.

Laboratory Quality Optical Analysis in Harsh Environments

“…Schemes including holographic elements, liquid crystal elements, absorption elements, and interference elements have been proposed and demonstrated as platforms of integrating an optical computation into an optical element. The ICE has been referred to in the literature by several names, including the multivariate optical element, molecular factor computer, encoded photometric infrared process, using integration over the spatial, time, and wavelength domains (Nelson et al 1998;Vogt et al 2004;Coates 2005;Boysworth et al 2008;Dai et al 2007). …”

Hydrogen Sulfide Gas Detection via Multivariate Optical Computing

“…MOC has been shown to provide similar analysis accuracy as the laboratory spectrometer from which an MOE is designed [ 24 , 25 , 26 , 27 ]. Proof-of-concept systems have been developed for visible, near-infrared, and mid-infrared systems [ 28 , 29 ]. The technique has been shown to work for Raman applications, florescent applications [ 23 , 30 , 31 , 32 , 33 , 34 , 35 ], absorbance applications [ 24 , 29 , 36 , 37 ], reflectance applications [ 28 ], and hyperspectral imaging [ 30 , 38 ].…”