Hierarchical neural network modeling for infrared spectra interpretation of modified starches

The goal of this research was to demonstrate a new approach to multilevel hierarchical modeling using neural networks. in a situation where the data set is small and error-prone, it is necessary to build multiple models. of input-output relationships, combining these models into a hierarchical structure. This allows utilization of the best aspects of every model, eliminating the need to choose "the best one", when the general sample itself is not known exactly. At the first stage of modeling we used feed-forward artificial neural networks for spectra interpretation. Then at the second stage a simultaneous recurrent network was used to correct the predictions made at the first stage. This architecture enables enhancement of the generalization ability according to the similarity of the input patterns. The suggested method was applied to the interpretation of spectra of modified Starches. This has definite practical value, since authentication of food is very important for the consumers and the food industry at each level of the food chain from raw materials to finished products. It is demonstrated that multilevel modeling offers accuracy advantages compared to choosing the best model or model averaging.