After iterative testing and validating various models (Figures and), the final model can be conveniently stored by employing the class property generate_report, which exports a toml file with the suffix `*.lock` containing all information describing the fitting process of Figure . When it comes to testing and validating, the first step is to start with an initial guess model and optimize it until the fit metrics converge, as shown in Figure . By default, the fit metric includes AIC and BIC coefficients to show the causality of the model and MSE to highlight its accuracy. However, SpectraFit allows for individual configuration based on the implemented methods of goodness of fit statistics and regression metrics. For example, one can replace AIC and BIC with R 2 and EVS for the bar plot and 𝝌 2 for MSE as a line plot. An effective way to improve this optimization process for spectrum fitting is to incorporate Machine Learning concepts , starting hyperoptimization and ultimately including unsupervised learning. This can help mitigate the impact of user bias and automate the process.