An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data

Anti-germ performance test is critical in the production of detergents. However, actual biochemical tests are often costly and time-consuming. In this paper, we present an Elman neural network-based model to predict detergents’ anti-germ performance and ingredient levels, respectively. The model made it much faster and cost effective than doing actual biochemical tests. We also present preprocessing methods that can reduce data conflicts while keeping the monotonicity on limited experimental data. The model can find out the relationship between ingredient levels and the corresponding anti-germ performance, which is not widely used in solving biochemical problems. The results of experiments are generated on the base of two detergent products for two types of bacteria, and appear reasonable according to natural rules. The prediction results show a high accuracy and fitting with the monotonicity rule mostly.

Research highlights
► A model to predict both detergents’ anti-germ performance and ingredient levels.
► Preprocessing methods to reduce data conflicts on data with high experimental errors.
► High accuracy and fitting with the monotonicity rule with limited training data.
► Finding the relationship between ingredient levels and anti-germ performance.