Multivariate optical computing

Multivariate optical computing (MOC) is a very recent and innovative strategy to build near-infrared spectrometers. It is based on the creation of 

Pharmaceutical and medical applications of near-infrared spectroscopy 

Multivariate optical elements provide a number of advantages over other types of spectrometers the cost of creating the filters is low, and the design of the spectrophotometer is simple there is no moving part, thus reducing the need for instrument maintenance, and the instrument footprint is limited, with devices that can be reduced to hold in the hand. 

While not all specific to near infrared, a number of publications have shown the applicability of multivariate optical elements [8-11]. It is anticipated that the technology will improve in the coming years, and that the number of applications will increase. A review of the current applications was performed by Priore [12]. 

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A. Swanstrom, L. S. Bruckman, M. R. Pearl, M. N. Simcock, K. A. Donaldson, T. L. Richardson, T. J. Shaw, and M. L. Myrick, Taxonomic Classification of Phytoplankton with Multivariate Optical Computing. Part I. Design and Theoretical Performance of Multivariate Optical Elements, Appl. Spectrosc., 67,620 (2013). 

As already mentioned, any multivariate analysis should include some validation, that is, formal testing, to extrapolate the model to new but similar data. This requires two separate steps in the computation of each model component calibration, which consists of finding the new components, and validation, which checks how well the computed components describe the new data. Each of these two steps needs its own set of samples calibration samples or training samples, and validation samples or test samples. Computation of spectroscopic data PCs is based solely on optic data. There is no explicit or formal relationship between PCs and the composition of the samples in the sets from which the spectra were measured. In addition, PCs are considered superior to the original spectral data produced directly by the NIR instrument. Since the first few PCs are stripped of noise, they represent the real variation of the spectra, presumably caused by physical or chemical phenomena. For these reasons PCs are considered as latent variables as opposed to the direct variables actually measured. 

Spectroscopic methods can provide fast, non-destructive analytical measurements that can replace conventional analytical methods in many cases. The non-destructive nature of optical measurements makes them very attractive for stability testing. In the future, spectroscopic methods will be increasingly used for pharmaceutical stability analysis. This chapter will focus on quantitative analysis of pharmaceutical products. The second section of the chapter will provide an overview of basic vibrational spectroscopy and modern spectroscopic technology. The third section of this chapter is an introduction to multivariate analysis (MVA) and chemometrics. MVA is essential for the quantitative analysis of NIR and in many cases Raman spectral data. Growth in MVA has been aided by the availability of high quality software and powerful personal computers. Section 11.4 is a review of the qualification of NIR and Raman spectrometers. The criteria for NIR and Raman equipment qualification are described in USP chapters <1119> and < 1120>. The relevant highlights of the new USP chapter on analytical instrument qualification <1058> are also covered. Section 11.5 is a discussion of method validation for quantitative analytical methods based on multivariate statistics. Based on the USP chapter for NIR <1119>, the discussion of method validation for chemometric-based methods is also appropriate for Raman spectroscopy. The criteria for these MVA-based methods are the same as traditional analytical methods accuracy, precision, linearity, specificity, and robustness however, the ways they are described and evaluated can be different. 

The acronym NIRA, or near-infrared analysis, is a term that implies the use of computer algorithms and multivariate data-handling techniques to provide either qualitative or quantitative analysis of a sample (or samples). NIRS includes a single spectral measurement and as such is a more generic definition. For example, an optical engineer involved in the design of a MR instrument would be involved with NIRS but not necessarily NIRA. 

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