Multivariate curve resolution: a method of evaluating the kinetics of biotechnological reactions

Abstract

In biotechnology, strong emphasis is placed on the development of wet chemical analysis and chromatography to separate target components from a complex matrix. In bioprocessing, the development of single compound biosensors is an important activity. The advantages of these techniques are their high sensitivity and specificity. Inline or online monitoring by means of spectroscopy has the potential to be used as an "all-in-one" analysis technique for biotechnological studies, but it lacks specificity. Multivariate curve resolution (MCR) can be used to overcome this limitation. MCR is able to extract the number of components involved in a complex spectral feature, to attribute the resulting spectra to chemical compounds, to quantify the individual spectral contributions, and to use this quantification to develop kinetic models for the process with or without a priori knowledge. After a short introduction to MCR, two applications are presented. In the first example, the spectral features of hemp are monitored and analysed during growth. MCR provides unperturbed spectra on the activity of, for example, lignin and cellulose during plant development. In a second example, the kinetics of a laccase enzyme-catalysed degradation of aromatic hydrocarbons are calculated from UV/VIS spectra.

Effect of hydroxypropyl cellulose and hydroxypropyl methylcellulose on carbamazepine polymorphic transformation; attenuated total reflectance-infrared spectroscopy and chemoinformatics analysis

“…It can derive the contribution of each component through a bilinear model of pure component. The algorithm for MCR was reported in detail before [16][17][18][19][20]. MCR decomposes spectra of mixtures into the n pure contributions of components involved in the spectra.…”

Formaldehyde in Alcoholic Beverages: Large Chemical Survey Using Purpald Screening Followed by Chromotropic Acid Spectrophotometry with Multivariate Curve Resolution

“…Nowadays, the MCR-ALS (Multivariate Curve Resolution-Alternating Least Squares) method developed by Tauler [46, 49, 50] is the most well-known and frequently used self-modelling algorithm for spectral deconvolution [44, 51]. Results of this method can be considerably improved by applying contraints based on the previous knowledge about the system studied (e.g., nonnegativity, closure, unimodality) [46].…”

Study of photodegradation kinetics of melatonin by multivariate curve resolution (MCR) with estimation of feasible band boundaries

“…MCR‐ALS is based on a bilinear modelling which decomposes the experimental data into the concentration and spectral contributions of the pure components present in the analyzed mixtures or evolving during a particular chemical process 28, 29. This bilinear decomposition on pure component contributions can be described according to the following equation: …”

“…Analysis 2013, 5, 96-wileyonlinelibrary.com/journal/dta mixtures or evolving during a particular chemical process. [28,29] This bilinear decomposition on pure component contributions can be described according to the following equation:…”