Correction of temperature variations in kinetic-based determinations by use of pruning computational neural networks in conjunction with genetic algorithms.

The joint use of genetic algorithms and pruning computational neural networks is shown to be an effective means for selecting the number of inputs required to correct temperature variations in kinetic-based determinations. The genetic algorithm uses a pruning procedure based on Bayesian regularization and is highly efficient as a feature selector; it provides quite good results in the generalization process without the need to use a validation set. The fitness function is defined as the sum of two subfunctions: one controls the learning ability of the network and the other its complexity. The training, pruning, and generalization processes were initially tested with simulated data in order to acquire preliminary information for the ensuing work with real data. The performance of the proposed method was assessed by applying it to the determination of the amino acid L-glycine by its classical spectrophotometric reaction with ninhydrin. A straightforward network topology including temperature as input (40+T:2:1 with 19 connections after the pruning process) was used to estimate the L-glycine concentration from kinetic curves affected by temperature variations over the range 60-75 degrees C, using kinetic data acquired up to only 1.5 half-lives. The trained network estimates this concentration with a standard error of prediction for the testing set of ca. 8%, which is much smaller than those provided by a classical parametric method such as nonlinear regression (even if kinetic data acquired at longer half-lives are used). Finally, a kinetic interpretation of the pruning process is provided in order to better demonstrate its potential for kinetic analysis.