Traditionally, VTNA involves normalising the time axis of concentration time data with respect to a particular reaction species, whose initial concentration varies across different experiments. Several reaction orders are entered in a spreadsheet by trial-and-error, until the order giving the best visual overlay of the concentration profiles is identified. Concentration profiles linearise if the time axis is normalised with respect to every reaction component raised to its correct order. However, most examples of VTNA involve overlay of non-linear monotonically increasing or decreasing product or reactant concentration profiles (Fig. ). To develop a robust automated method, a Python script was developed to emulate the manual VTNA routine which calculates the transformed time axis for a chosen reaction species and corresponding order value(s) using a selected subset of the kinetic data, tested on selected literature examples (see ESI, Section S4). Subsequently, a robust automatic method of assessing the overlay of reaction progress profiles was developed, removing the need for manual visual inspection. This method involves fitting all profiles to a common flexible function and utilising a goodness-of-fit score (e.g. RMSE), as an 'overlay score', to quantify the degree of overlay. VTNA overlay plots from literature with the reported order values giving concentration profile overlay. In order to process non-linear fitting, a 5 th degree monotonic polynomial fitting, a statistical method with several scientific applications, was selected as the default method. Although a more computationally efficient ordinary polynomial fitting can be selected that is circa. 10 times faster, there is an increased risk of overfitting effects for reaction profiles with few datapoints. Additionally, Auto-VTNA allows linear fitting, which is particularly useful when reaction profiles linearise upon complete normalisation of the time axis, and can evaluate the 𝑘 𝑜𝑏𝑠 of the global rate equation (see ESI, Section S3.2).