Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.
The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets. The results revealed that the proposed method can be used to handle different types of baselines with cave, convex, curvelinear, triangular and sinusoidal patterns. For further evaluation of the performances of this method, it has been compared with benchmarking baseline correction methods such as corner-cutting (CC), morphological weighted penalized least squares (MPLS), adaptive iteratively-reweighted penalized least squares (airPLS) and iterative polynomial fitting (iPF). In order to compare the methods, the projected difference resolution (PDR) criterion has been calculated for the data before and after the baseline correction procedure. The calculated values of PDR after the baseline correction using iBRANN, airPLS, MPLS, iPF and CC algorithms for the GC metabolomic data were 4.18, 3.64, 3.88, 1.88 and 3.08, respectively. The obtained results in this work demonstrated that the developed iterative Bayesian regularized neural network (iBRANN) method in this work thoroughly detects the baselines and is superior over the CC, MPLS, airPLS and iPF techniques. A graphical user interface has been developed for the suggested algorithm and can be used for easy implementation of the iBRANN algorithm for the correction of different chromatography, NMR and Raman data sets.
Chemometrics and Chemoinformatics Laboratory, Department of Chemistry, Faculty of Sciences, Tarbiat Modares University, P.O. Box 14115-175, Tehran, Iran. Electronic address: a.mani@modares.ac.ir.
Ahmad
A
Mani-Varnosfaderani
Chemometrics and Chemoinformatics Laboratory, Department of Chemistry, Faculty of Sciences, Tarbiat Modares University, P.O. Box 14115-175, Tehran, Iran.
Atefeh
A
Kanginejad
Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran.
Kambiz
K
Gilany
Department of Analytical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, P. O. Box 14335-186, Tehran, Iran.
Abolfazl
A
Valadkhani
D000465:Algorithms; D001499:Bayes Theorem; D016571:Neural Networks, Computer
Baseline correction; Bayesian regularized neural networks; Penalized least squares; projected difference resolution