Quantitative measurement of two-component absorption spectra using multilayer semilinear neural networks

A PC-based multilayer neural network with sigmoid activation function, generalized delta learning rule and error back-propagation was trained with two individual components (protonated and unprotonated form) of pH dependent spectra between 400 and 700 nm generated from microspectrophotometry of Neutral Red (NR). The NR spectrum changes from one resembling the acid to one resembling the base as the solution's pH changes from acid to base. The number of nodes in the input layer was based on the degree of resolution required. The number of hidden layer units was related to the storage capacity and could be a function of maximum connection weight between input and the hidden layer. The number of output nodes determined the step size used to distinguish the input spectrum. Teaching patterns are binary encoded to compare to the activity in the output layer. Simulation results show that after successful convergence with the training spectra features of the input spectrum are separated and stored in the weight matrix of the input and hidden layers. A calibration curve can be constructed to interpret the output layer activity and therefore allow prediction of the pH. With its intrinsically redundant presentation, this novel approach to spectrophotometry needs no preprocessing procedures (baseline correction and extensive signal averaging) for spectral identification. Spectral distortion, e.g. due to light scattering effects, such as between phosphate buffer solutions and brain homogenates do not affect the outcome. This method was applied to the in vitro hippocampal slice preparation to measure anoxic pHi changes. The method can be generalized to adapt to any pattern oriented sensory information processing and multi-sensor fusion for quantitative measurement.