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 = 500 )

Uses an implementation of Bro et. al's Fast Non-Negative Least Squares on the matrix A and vector b. Returns regression coefficients in the form of a vector.

Bro, R., de Jong, S. (1997) A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics, 11, 393-401.

ITTFA(X; Factors = 1, maxiters = 500, threshold = 1e-8,
                nonnegativity = true)

This function performs a Iterative Target Transform Factor Analysis (ITTFA). It's untested. Use at your own risk.

Chemometric characterization of batch reactions. ISA Transactions 38(3):211-216. July 1999.  DOI: 10.1016/S0019-0578(99)00022-1

MCRALS(X, C, S = nothing; norm = (false, false), Factors = 1, maxiters = 20, constraintiters = 500, nonnegative = (false, false), unimodalS = false, fixedunimodal = false  )

Performs Multivariate Curve Resolution using Alternating Least Squares on X taking initial estimates for either S or C. The number of maximum iterations for the ALS updates can be set maxiters. S or C can be constrained by their norm(true/false,true/false), or by nonnegativity by using nonnegative arguments (true/false,true/false). S can also be constrained by unimodality(unimodalS). Two unimodal algorithms exist the fixedunimodal(true), and the quadratic (false). The number of resolved Factors can also be set. The number of maximum iterations for constraints can be set by constraintiters.

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, alpha = 0.05, includedvars = 1:size(X)[2], SecondDeriv = true)

Performs SIMPLISMA on Array X using either the raw spectra or the Second Derivative spectra. alpha can be set to reduce contributions of baseline, and a list of included variables in the determination of pure variables may also be provided. Returns a tuple of the following form: (Concentraion Profile, Pure Spectral Estimates, Pure Variables)

W. Windig, Spectral Data Files for Self-Modeling Curve Resolution with Examples Using the SIMPLISMA Approach, Chemometrics and Intelligent Laboratory Systems, 36, 1997, 3-16.

UnimodalFixedUpdate(x)

This function performs a unimodal least squares update at a fixed maximum for a vector x. This is faster then UnimodalUpdate() but, is less accurate.

Bro R., Sidiropoulos N. D.. Least Squares Algorithms Under Unimodality and Non-Negativity Constraints

UnimodalLeastSquares(x)

This function performs a unimodal least squares regression for a matrix A and b (X and Y).

Bro R., Sidiropoulos N. D.. Least Squares Algorithms Under Unimodality and Non-Negativity Constraints.Journal of Chemometrics, June 3, 1997

UnimodalUpdate(x)

This function performs a unimodal least squares update for a vector x. This is slower then UnimodalUpdate() but, is more accurate.

Bro R., Sidiropoulos N. D.. Least Squares Algorithms Under Unimodality and Non-Negativity Constraints. Journal of Chemometrics, June 3, 1997