Weight loss prediction model gor pig carcass based on a genetic algorithm back-propagation neural network

C. Sun, L. Chen, Y. Li, H. Yao, N. Zhang, C. Li, G. Zhou, Y. Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production.

Original languageEnglish
Pages (from-to)1071-1077
Number of pages7
JournalTransactions of the ASABE
Volume63
Issue number4
DOIs
Publication statusPublished - 2020

Keywords

  • BP neural network
  • Genetic algorithm
  • Pig carcass
  • Predictive model
  • Weight loss

ASJC Scopus subject areas

  • Forestry
  • Food Science
  • Biomedical Engineering
  • Agronomy and Crop Science
  • Soil Science

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