Abstract
Simultaneous multicomponent analysis is usually carried out by multivariate calibration models (such as principal component regression) that utilize the full spectrum. We demonstrate, by both experimental and theoretical considerations, that better results can be obtained by a proper selection of the spectral range to be included in calculations. We develop the theory that models the analytical uncertainty in multicomponent analysis and show the conditions where wavelength selection is essential (for example, when considerable spectral overlapping exists). An error indicator function is developed to predict the analytical performance under given experimental conditions, using a certain spectral range. This function is applied for allocation of the most informative spectral ranges to be utilized in multicomponent analysis. Selection of spectral ranges by this method is shown to ensure optimal results that considerably improve analytical performance in some cases. The similarity between the results obtained...
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
