Awards Nomination 20+ Million Readerbase
Indexed In
  • Online Access to Research in the Environment (OARE)
  • Open J Gate
  • Genamics JournalSeek
  • JournalTOCs
  • Scimago
  • Ulrich's Periodicals Directory
  • Access to Global Online Research in Agriculture (AGORA)
  • Electronic Journals Library
  • Centre for Agriculture and Biosciences International (CABI)
  • RefSeek
  • Directory of Research Journal Indexing (DRJI)
  • Hamdard University
  • EBSCO A-Z
  • OCLC- WorldCat
  • Scholarsteer
  • SWB online catalog
  • Virtual Library of Biology (vifabio)
  • Publons
  • MIAR
  • University Grants Commission
  • Euro Pub
  • Google Scholar
Share This Page
Journal Flyer
Flyer image

Abstract

Statistical Approaches to Optimize Detection of MIB Off-Flavor in Aquaculture Raised Channel Catfish

Paul V Zimba and Casey C Grimm

The catfish industry prides itself on preventing inadvertent sale of off-flavor fish. Typically, several fish are taste tested over several weeks before pond harvest to confirm good fish flavor quality. We collected several data sets of analytically measured off-flavor concentrations in catfish to assess the type of distribution (parametric/non-normal). Coincident measures of fat content were made on three subsections of each fillet. These data were then used to model the number of fish required to detect off-flavor in mixed populations containing on and off flavor fish. In fish collected from the same pond, off-flavor concentrations typically were not normally distributed, thereby requiring specialized statistical procedures. Even with log transformation, data still violated assumptions of normality. We used a non-parametric approach, using fish samples that were ordered, and then randomly sampled 1000 times, to determine the number of fish necessary to detect off-flavor. A sample of 40 fish was required to detect off-flavor when the population was nearly all on-flavor (97%) and <11 fish when populations contain >20% off-flavor fish. A sample size of six fish in a mixed population was effective in identifying off-flavor occurrence in 60% of ponds having off-flavor present. Sampling more fish fewer times can more accurately identify ponds containing mixed flavor fish populations than the current sampling procedure.