Weighted multivariate curve resolution – alternating least squares based on sample relevance

Alternating least squares, within the multivariate curve resolution framework has seen a lot of practical applications and shows its distinction with its relatively simple and flexible implementation. However, the limitations of least squares should be considered carefully when deviating from the standard assumed data structure. Within this work we highlight the effects of noise in the presence of minor components, and we propose a novel weighting scheme within the weighted multivariate curve-resolution-alternating least squares framework, to resolve it. Two simulated and one Raman imaging case is investigated, by comparing the novel methodology against standard multivariate curve resolution-alternating least squares and essential spectral pixel selection. A trade-off is observed between current methods, while the novel weighting scheme demonstrates a balance, where the benefits of the previous two methods are retained.