Near‐infrared (NIR) spectroscopy is a popular technique for the measurement of chemical and physical properties in‐line using predictive models. The success of these models in industrial settings, in terms of accuracy and precision, often relies on the removal or avoidance of non‐linear spectral changes associated with fluctuating process parameters like temperature. In this work, a NIR calibration model developed to predict the viscosity of micellar liquids in‐line is used to evaluate various methods designed to account for temperature fluctuations. The viscosity of these liquids can vary on average by ±0.5 Pa s with a 1° change in temperature. The methods trialled include global linear techniques, a multivariate filter (generalised least squares weighting [GLSW]) and direct standardisation. The performances of these techniques were compared against one another based on root mean square error of prediction (RMSEP), prediction bias and rank. The best method was found to be GLSW, which was the least complex (five latent variables) and showed the lowest RMSEP (0.429 Pa s). This study provides insight into the use of recognised methods to remove temperature‐induced spectral variation in a PLS model developed to predict viscosity, where both NIR spectra and the property of viscosity itself are sensitive to temperature.