Olusola O. Soyemi

A single-element all-optical approach to chemometric prediction

A single-element approach to multivariate optical computing is described. Data for mixtures of Crystal Violet and Bismarck Brown dyes are analyzed as an example application. Radiometric information is combined with transmission spectra of the samples to obtain representative system responses for the samples. Direct synthesis of a multivariate optical element (MOE) is compared to a novel new approach for synthesizing simpler MOEs. The results show that less complex spectral vectors can be designed that perform adequately in this example. Experimental results for fabrication of an MOE are shown.

Application of multivariate optical computing to simple near-infrared point measurements

Quantitative multivariate spectroscopic methods seek spectral patterns that correspond to analyte concentrations even in the presence of interferents.By embedding a spectral pattern that corresponds to a target analyte in an interference filter in a beamsplitter arrangement;bulky and complex instrumentation can be eliminated with the goal of producing a field-portable instrument.A candidate filter design for an rganic analyte,of military interest,and an interferent is evaluated.

On-line reoptimization of filter designs for multivariate optical elements

An automated method for producing multivariate optical element (MOE) interference filters that are robust to errors in the reactive magnetron sputtering process is described. Reactive magnetron sputtering produces films of excellent thickness and uniformity. However, small changes in the thickness of individual layers can have severe adverse effects on the predictive ability of the MOE. Adaptive reoptimization of the filter design during the deposition process can maintain the predictive ability of the final filter by changing the thickness of the undeposited layers to compensate for the errors in deposition. The merit function used, the standard error of calibration, is fundamentally different from the standard spectrum matching. This new merit function allows large changes in the transmission spectrum of the filter to maintain performance.

Nonlinear Optimization Algorithm for Multivariate Optical Element Design

A new algorithm for the design of optical computing filters for chemical analysis, otherwise known as multivariate optical elements (MOEs), is described. The approach is based on the nonlinear optimization of the MOE layer thicknesses to minimize the standard error in sample prediction for the chemical species of interest using a modified version of the Gauss–Newton nonlinear optimization algorithm. The design algorithm can either be initialized with random layer thicknesses or with layer thicknesses derived from spectral matching of a multivariate principal component regression (PCR) vector for the constituent of interest. The algorithm has been successfully tested by using it to design various MOEs for the determination of Bismarck Brown dye in a binary mixture of Crystal Violet and Bismarck Brown.

Design of angle-tolerant multivariate optical elements for chemical imaging

Multivariate optical elements (MOEs) are multilayer optical interference coatings with arbitrary spectral profiles that are used in multivariate pattern recognition to perform the task of projecting magnitudes of special basis functions (regression vectors) out of optical spectra. Because MOEs depend on optical interference effects, their performance is sensitive to the angle of incidence of incident light. This angle dependence complicates their use in imaging applications. We report a method for the design of angle-insensitive MOEs based on modification of a previously described nonlinear optimization algorithm. This algorithm operates when the effects of deviant angles of incidence are simulated prior to optimization, which treats the angular deviation as an interferent in the measurement. To demonstrate the algorithm, a 13-layer imaging MOE (IMOE, with alternating layers of high-index Nb2O5 and low-index SiO2) for the determination of Bismarck Brown dye in mixtures of Bismarck Brown and Crystal Violet, was designed and its performance simulated. For angles of incidence that range from 42 degrees to 48 degrees, the IMOE has an average standard error of prediction (SEP) of 0.55 microM for Bismarck Brown. This compares with a SEP of 2.8 microM for a MOE designed by a fixed-angle algorithm.

Simple optical computing device for chemical analysis

Multivariate Optical Computing (MOC) devices have the potential of greatly simplifying as well as reducing the cost of applying the mathematics of multivariate regression to problems of chemical analysis in the real world. These devices utilize special optical interference coatings known as multivariate optical elements (MOEs) that are encoded with pre-determined spectroscopic patterns to selectively quantify a chemical species of interest in the presence of other interfering species. A T-format prototype of the first optical computing device is presented utilizing a multilayer MOE consisting of alternating layers of two metal oxide films (Nb2O5 and SiO2) on a BK-7 glass substrate. The device was tested by using it to quantify copper uroporphyrin in a quaternary mixture consisting of uroporphyrin (freebase), tin uroporphyrin, nickel uroporphyrin, and copper uroporphyrin. A standard error of prediction (SEP) of 0.86(mu) M was obtained for copper uroporphyrin.

Application of multivariate optical computing to near-infrared imaging

Rapid quantitative imaging of chemical species is an important tool for investigating heterogenous mixtures, such as laminated plastics, biological samples and vapor plumes. Using traditional spectroscopic methods requires difficult computations on very large data sets. By embedding a spectral pattern that corresponds to a target analyte in an interference filter in a beamsplitter arrangement; the chemical information in an image can be obtained rapidly and with a minimal amount of computation. A candidate filter design that is tolerant to the angles present in an imaging arrangement is evaluated in near-infrared spectral region for an organic analyte and an interferent.

Field applications of stand-off sensing using visible/NIR multivariate optical computing

12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.

Novel filter design algorithm for multivariate optical computing

A new algorithm for the design of optical computing filters for chemical analysis otherwise known as Multivariate Optical Elements (MOEs), is described. The approach is based on the nonlinear correlation of the MOE layer thicknesses to the standard error in sample prediction for the chemical species of interest using a modified version ofthe Gauss-Newton nonlinear optimization algorithm. The design algorithm can either be initialized by random layer thicknesses or by a pre-existing design. The algorithm has been successfully tested by using it to design a MOE for the determination of copper uroporphynn in a quaternary mixture of uroporphyrin (freebase), nickel uroporphyrin, copper uroporphynn, and tin uroporphyrin.

Wavelength Calibration of a Dispersive Near-Infrared Spectrometer Using Trichloromethane as a Calibration Standard

Since differences in wavelength calibration between instruments are arguably a primary source of error encountered in the transfer of calibration models from one instrument to another in near-infrared (NIR) spectroscopy, a readily available, convenient, inexpensive secondary wavelength calibration standard for the NIR spectral region is needed. This paper describes the advantages of trichloromethane as a wavelength standard for the calibration of dispersive NIR spectrometers used in the transmission mode. The spectrum of trichloromethane, taken with a Fourier transform NIR spectrometer whose wavenumber scale was calibrated with the ro-vibrational lines of ethyne as determined by the National Institute of Standards and Technology (NIST), is presented. The wavelengths of four strong, sharp, well-resolved bands of trichloromethane were determined with the calibrated Fourier transform NIR spectrometer and were found to be 1152.13 ± 0.01 nm (3v1), 1410.21 ± 0.01 nm (2v1 + v4), 1691.9 ± 0.7 nm (2v1), and 1861.22 ± 0.01 nm (v1 + 2v4). These bands were then used to calibrate the wavelength scale of a commercial 0.25 m monochromator equipped with a 300 line mm−1 grating. The calibration revealed that, while the wavelength scale of the monochromator was linear, there was a systematic error of about + 12 nm in the NIR region.