Multivariate analysis. MVA

Multivariate analysis (MVA) is the collection of statistical techniques which we use to relate product performance (taste panel, processing conditions) data to product composition data (e.g., ppm of extracted volatiles as measured by GC). 

To determine the oil, water, and solids contents simultaneously, sophisticated statistical techniques must usually be applied, such as partial least-squares analysis (PLS) and multivariate analysis (MVA). This approach requires a great deal of preparation and analysis of standards for calibration. Near-infrared peaks can generally be quantified by using Beer s law consequently, NIRA is an excellent analytical tool. In addition, NIRA has a fast spectral acquisition time and can be adapted to fiber optics this adaptability allows the instrument to be placed in a control room somewhat isolated from the plant environment. 

For a huge dataset to make sense to us humans, it is mandatory that the information in the original dataset must be distilled to a humane size so that we can recognize patterns. This is the essence of data mining. A collection of multivariate analysis (MVA) methods have been developed to this end (see, for example, Ref. 1). A typical and the most classic example is the linear regression analysis that tries to find a linear relationship or linear pattern... 

Various tools and techniques continue to emerge to assist this goal. Not only can understanding of individual properties, such as solubility, be of use to teams, but tactics that combine data from more than one technique, are emerging. One tool, multivariate analysis (MVA), seeks to understand the relationships of properties and identify which properties are most critical for a desired result. 

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. 

Multivariate analysis (MVA) is the statistical analysis of many variables at once. Many problems in the pharmaceutical industry are multivariate in nature. The importance of MVA has been recognized by the US FDA in the recent guidance on process analytical technology [3]. MVA has been made much easier with the development of inexpensive, fast computers, and powerful analytical software. Chemometrics is the statistical analysis of chemical data. Spectral data from modern instruments is fundamentally multivariate in character. Furthermore, the powerful statistical methods of chemometrics are essential for the analysis and application of spectral data including NIR and Raman. In this section, we will briefly review the subject of chemometrics and MVA. 

Analysis of Off-Flavor Compounds in Food 2,4,6-Trichloroanisole (TCA) in Wine Analysis of flavor compounds in food comprises different approaches (1) target compound analysis focused on the detection and quantification of known compounds responsible for specific flavor features, (2) profiling volatile compounds done either to get a knowledge of food flavor/volatile compounds composition or, aided with multivariate analysis (MVA), for the identification of the origin of specific foods or their adulteration, and (3) sensory-oriented identification and quantification of key odorants (also off-odorants) of particular foods. 

This data explosion has created a daunting mass of information, one for which the automated pattern-recognition techniques of multivariate analysis (MVA) are perfectly suited. The underlying principle of MVA is that useful patterns and relationships not intuitively obvious lie hidden inside enormous, unwieldy databases. Mill personnel have tried to establish relationships between the process variables by considering only a few at a time, an impossible task, hence their interest in co-operating with ole Polytechnique on a new approach. 

One approach recently reported is referred to as SPME-MS-MVA (13,14). This technique uses solid-phase microextraction (SPME), mass spectrometry (MS), and multivariate analysis (MVA) as an e-nose system. A conventional GC/ MS analytical capillary column can be used at an elevated temperature in place of the short uncoated deactivated retention gap. Coelution of volatile components occurs, but this is of no concern when using the GC/MS as an e-nose. With this configuration, one can switch from using the GC/MS as an e-nose to a conventional GC/MS simply by changing the temperature program of the column. 

Ambrozic, C., Hutson, L., Multivariate Analysis (MVA) for Quality Detection in Injection Molding Systems in the Medical Device Community. Annual Technical Conference - ANTEC, Conference Proceedings. 2006. 

Perhaps the most interesting aspect of this set of studies is the question posed in the recent paper by Schmidt et al. (2004) and deals with the reality of the patterns they observed. Is the polymorphism observed a result of the calculation methods used in the study, neural network (NN), and multivariate statistical analysis (MVA) Would increased sampling result in a greater number of chemo-types It is entirely possible, of course, that the numbers obtained in this study are a true reflection of the biosynthetic capacities of the plants studied. The authors concluded—and this is a point made elsewhere in this review—that ... for a correct interpretation a good knowledge of the biosynthetic background of the components is needed. ... 

Multivariate analysis is not a panacea for all flavor problems. It is a valuable tool which should be used in conjunction with other sensory and analytical skills to solve flavor problems. The availability of a programmable chromatographic data system makes implementation of MVA straightforward. 

MALDI MCM-41 MCR MD ME MEM MI MPM MRI MS MVA Matrix-assisted Laser Desorption/Ionization Mobile Crystalline Material-41 Multivariate Curve Resolution Molecular Dynamics Matrix-enhanced Magnetic Force Micrscopy Multivariate Image Multiphoton Microscopy Magnetic Resonance Imaging Mass Spectroscopy Multivariate Analysis... 

ASD amorphous solid dispersions, API active pharmaceutical ingredient, MVA multivariate analysis, EFG electric field gradient, DSC differential scanning calorimetry... 

If the initial screening indicates a potential shelf life problem (for example, if SPME-MS-MVA predicted a shelf life of 10 days or less instead of the typical 15-17 days), then the chromatographic file (total ion chromatogram) could be subjected to further scmtiny in order to uncover the cause of the off-flavor. The .ms file, which contains all the information necessary to perform conventional MS identification of chromatographic peaks, could be e-mailed to a corporate research lab. The corporate lab could then perform more sophisticated analysis of the data. It could, for example, do further multivariate analysis investigations... 

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