The purpose of Multivariate Curve Resolution (MCR) is to recover the concentration profile and the source spectra without any prior knowledge. We hypothesis that each source is characterized by a linear superposition of Gaussian peaks of fixed spread. Multivariate curve resolution-alternating least squares (MCR-ALS) is a Conventional MCR method. MCR-ALS has some disadvantages. We proposed a solver with L1 regularizer and L2 regularizer to obtain a sparse solution within MCR-ALS. L1-norm involves a sparse but non-smooth solution, L2-norm will keep all the information and bring the smoothness, but it will lead non-sparse solutions. So we combined the L1-norm and the squared L2-norm to seek the optimal solutions. This is accomplished via Elastic Net Regularization algorithem which is LARS (least-angle regression). We named this method MCR-LARS. This paper applies MCR-LARS to resolve the hard overlapped spectroscopic signals belonging to the three aromatic amino acids (phenylalanine, tyrosine and tryptophan) in their mixtures. MCR-LARS was compared with MCR-ALS. The results show the effectiveness and efficiency of MCR-LARS and the results show that MCR-LARS provides more nicely resolved concentration profiles and spectra than pure MCR-ALS solution. © 2012 Springer Science+Business Media B.V.
CITATION STYLE
Xin-Feng, Z., Jian-Dong, W., & Bin, L. (2012). Multivariate curve resolution with elastic net regularization. In Lecture Notes in Electrical Engineering (Vol. 107 LNEE, pp. 1489–1499). https://doi.org/10.1007/978-94-007-1839-5_160
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