Prediction of the growth behavior of aeromonas Hydrophila using a novel modeling approach: Support vector machine

Jing Liu, Xiao Guan, Donald W. Schaffner

Research output: Contribution to journalArticlepeer-review

Abstract

A new technique called support vector machine (SVM), which is used to predict the microbial growth, is presented in this paper. Experimental data on temperature, pH and NaCl from a previously published paper were modeled as inputs, and the kinetic growth parameters, including generation time (GT) and lag phase duration (LPD), were used as outputs of the SVM model. The results derived from SVM model, published artificial neural network (ANN) model and a traditional statistical model were compared using several evaluation criteria: accuracy factor (Af), bias factor (Bf), mean relative percentage residual, mean absolute relative residual, root mean square residual, internal validation (Q2) and external validation (Q2ext). Graphical plots were also used for model comparison. The results show that SVM outperforms ANN and statistical model on predicting microbial GT and LPD, especially when predicting data not used for model development. Sensitivity analyses of the three environmental factors show that the most influential on LPD of Aeromonas hydrophila is temperature, followed by pH and NaCl, and the most influential on GT is pH, followed by temperature and then NaCl.

Original languageEnglish (US)
Pages (from-to)292-299
Number of pages8
JournalJournal of Food Safety
Volume34
Issue number4
DOIs
StatePublished - Nov 1 2014

All Science Journal Classification (ASJC) codes

  • Parasitology
  • Food Science
  • Microbiology

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