(3a)) in order to obtain the slope and intercept for the best linear fit, and the other one is to obtain the weighting factor 𝒘𝒘 by maximizing the R 2 value based on Eq.(3b). Since 𝒘𝒘 is a 𝑑𝑑 × problems form one single model (Figure ) with 𝒘𝒘 and R 2 as output. SLCA than explores all possible subsets 𝑿𝑿 � of 𝑿𝑿 without requiring any filtering procedure (from prior knowledge) before data input. In the present case, six elements are considered, n = 6. This results in 𝐶𝐶 is the model in which all elements are considered in the fitting process). Each such combination represents one individual model for which the above optimization is done using the full dataset of 56 samples. The weight vector 𝒘𝒘 therefore varies in its dimensionality d, corresponding to the model dimensionality of 𝑋𝑋 � . In the following, the models will be grouped according to their dimensionality, from group I to VI.