DOI: 10.11648/j.wjast.20260403.14 ISSN: 2994-7332

Exploring a Multilayer Perceptron Model on Dual-Species Biofilm Growth Data

Kyriaki Chatzikyriakidou, Christina Kamarinou, Agapi Doulgeraki
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Salmonella Typhimurium in ready-to-eat leafy salads is a well-known threat in food production. The aim of this study was to design an approximation multi-layer perceptron model (MLP) in order to predict the final population of S . Typhimurium in dual-species biofilms. Previously collected data (n=48; log CFU/cm 2 ) of various isolates from either rocket or spinach salads, which grew together with S . Typhimurium to form biofilms, were used as attributes for the development of the two models. The target (output) variable was the final population of S . Typhimurium in both models of the rocket and spinach datasets. For the rocket isolates, the highest efficiency (learning epoch=1000, learning rate=0.001, and momentum value=0.1) was achieved with a shallow MLP with one hidden layer of 3 neurons, and a correlation coefficient 75% (RMSE<0.23). For the spinach isolates, similar results were found (correlation coefficient 76%), although with higher error (RMSE <0.35), and a hidden layer with 4 neurons, which might indicate more complex microbial interactions in this leafy vegetable. Further research is needed to train a model with a large data set of dual-species biofilms along with more input variables. Such neural networks could be helpful in efforts to control Salmonella diseases linked to the consumption of contaminated fresh salads.

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