In recent years robust control theory has met wide acceptance and made significant advances especially in terms of development of tools for analysis and design. However, the application of those tools, like the H-infinity optimal theory and mu-synthesis in MATLAB, to real-time control of chemical processes has not been very successful. Among the factors hindering widespread application is lack of systematic way to model uncertainty that occurs at different locations in the feedback structure. In this work, time series data is used to model additive plant uncertainty through power spectral methods by performing least-squares regression in frequency domain. The concept is illustrated for real-time control of axial temperature distribution in a packed-bed column, which is modelled by partial differential equations and Fourier transforms to obtain an eigen-mode plant for controller design and robust analysis. Spectral uncertainty description is obtained from error analysis of smooth signals from the nominal and the perturbed plant and robustness is evaluated using the structured singular value (SSV). It is shown that through a proper choice of parameters, the spectral method gives better uncertainty description for robustness analysis than parametric methods.