Application of LogitBoost Classifier for Traceability Using SNP Chip Data

PLoS One. 2015 Oct 5;10(10):e0139685. doi: 10.1371/journal.pone.0139685. eCollection 2015.

Abstract

Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4,122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animal Husbandry / methods
  • Animal Identification Systems*
  • Animals
  • Area Under Curve
  • Bias
  • Breeding
  • Computer Simulation*
  • Food Safety / methods*
  • Genetics, Population
  • Genotype
  • Meat*
  • Models, Theoretical*
  • Oligonucleotide Array Sequence Analysis
  • Polymorphism, Single Nucleotide*
  • ROC Curve
  • Sample Size
  • Sampling Studies
  • Sus scrofa / classification
  • Sus scrofa / genetics*

Grants and funding

This work was carried out with the support of "Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ009274)" Rural Development Administration, Republic of Korea. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.