Quantitative measurement of two-component pH-sensitive colorimetric spectra using multilayer neural networks |
| |
Authors: | Chii-Wann Lin Joseph C LaManna Yashiyasu Takefuji |
| |
Institution: | (1) Department of Biomedical Engineering, Center of Automation and Intelligent System Research, Case Western Reserve University, 44106 Cleveland, OH, USA;(2) Department of Neurology, Center of Automation and Intelligent System Research, Case Western Reserve University, 44106 Cleveland, OH, USA;(3) Department of Electrical Engineering and Applied Physics, Center of Automation and Intelligent System Research, Case Western Reserve University, 44106 Cleveland, OH, USA |
| |
Abstract: | The purpose of this research was to develop a noise tolerant and faster processing approach for in vivo and in vitro spectrophotometric
applications where distorted spectra are difficult to interpret quantitatively. A PC based multilayer neural network with
a sigmoid activation function and a generalized delta learning rule was trained with a two component (protonated and unprotonated
form) pH-dependent spectrum generated from microspectrophotometry of the vital dye neutral red (NR). The network makes use
of the digitized absorption spectrum between 375 and 675 nm. The number of nodes in the input layer was determined by the
required resolution. The number of output nodes determined the step size of the quantization value used to distinguish the
input spectra (i.e. defined the number of distinct output steps). Mathematic analysis provided the conditions for which this
network is guaranteed to converge. Simulation results showed that features of the input spectrum were successfully identified
and stored in the weight matrix of the input and hidden layers. After convergent training with typical spectra, a calibration
curve was constructed to interpret the output layer activity and therefore, predict interpolated pH values of unknown spectra.
With its built-in redundant presentation, this approach needed no preprocessing procedures (baseline correction or intensive
signal averaging) normally used in multicomponent analyses. The identification of unknown spectra with the activities of the
output layer is a one step process using the convergent weight matrix. After learning from examples, real time applications
can be accomplished without solving multiple linear equations as in the multiple linear regression method. This method can
be generalized to pattern oriented sensory information processing and multi-sensor data fusion for quantitative measurement
purposes. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|