weight assignment using scent classifier output probabilities and an integrated uncertainty analysis framework. The former enhances the model's adaptability in diverse olfactory landscapes, while the latter fortifies its robustness and generalizability, especially in real-world settings with data imperfections. Uncertainties are quantified via probability statements and density functions, which are derived from prediction residuals for least squares estimated parameters.