Curve Resolution Models API Reference

BTEM(X, bands = nothing; Factors = 3, particles = 50, maxiters = 1000)

Returns a single recovered spectra from a 2-Array X, the selected bands, number of Factors, using a Particle Swarm Optimizer.

Note: This is not the function used in the original paper. This will be updated... it was written from memory. Also the original method uses Simulated Annealing not PSO. Band-Target Entropy Minimization (BTEM):  An Advanced Method for Recovering Unknown Pure Component Spectra. Application to the FTIR Spectra of Unstable Organometallic Mixtures. Wee Chew,Effendi Widjaja, and, and Marc Garland. Organometallics 2002 21 (9), 1982-1990. DOI: 10.1021/om0108752

BTEMobjective( a, X )

Returns the scalar BTEM objective function obtained from the linear combination vector a and loadings X.

Note: This is not the function used in the original paper. This will be updated... it was written from memory.

FNNLS(A, b; LHS = false, maxiters = 520)

Uses an implementation of Bro et. al's Fast Non-Negative Least Squares on the matrix A and vector b. We can state whether to pose the problem has a left-hand side problem (LHS = true) or a right hand side problem (default). Returns regression coefficients in the form of a vector.

Note: this function does not have guarantees. Use at your own risk for now. Fast Non-Negative Least Squares algorithm based on Bro, R., & de Jong, S. (1997) A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics, 11, 393-401.

MCRALS(X, C, S = nothing; norm = (false, false), Factors = 1, maxiters = 20, nonnegative = (false, false) )

Performs Multivariate Curve Resolution using Alternating Least Squares on X taking initial estimates for S or C. S or C can be constrained by their norm, or by nonnegativity using nonnegative arguments. The number of resolved Factors can also be set.

Tauler, R. Izquierdo-Ridorsa, A. Casassas, E. Simultaneous analysis of several spectroscopic titrations with self-modelling curve resolution.Chemometrics and Intelligent Laboratory Systems. 18, 3, (1993), 293-300.

NMF(X; Factors = 1, tolerance = 1e-7, maxiters = 200)

Performs a variation of non-negative matrix factorization on Array X and returns the a 2-Tuple of (Concentration Profile, Spectra)

Note: This is not a coordinate descent based NMF. This is a simple fast version which works well enough for chemical signals Algorithms for non-negative matrix factorization. Daniel D. Lee. H. Sebastian Seung. NIPS'00 Proceedings of the 13th International Conference on Neural Information Processing Systems. 535-54

SIMPLISMA(X; Factors = 1)

Performs SIMPLISMA on Array X. Returns a tuple of the following form: (Concentraion Profile, Pure Spectral Estimates, Pure Variables)

Note: This is not the traditional SIMPLISMA algorithm presented by Willem Windig. REAL-TIME WAVELET COMPRESSION AND SELF-MODELING CURVE RESOLUTION FOR ION MOBILITY SPECTROMETRY. PhD. Dissertation. 2003. Guoxiang Chen.