Where the error vector 𝑬 = [ 𝜀 1 ⋯ 𝜀 𝑖 ⋯ 𝜀 𝑛 ] contains all the 𝜀 𝑖 values, defined as the counts that come from noise and/or contamination at the i th m/z bin. This error vector 𝑬 prevents the resolution of the linear equation 𝒀 = 𝑿𝑨, so that an approximate solution 𝑿 ̃ must be sought. This approximation involves minimizing the residual sum of squares between the observed data and the model. Mathematically, the objective function to be minimized is: