Feedstock optimization of in-vessel food waste composting systems for inactivation of pathogenic microorganisms

Deniz Cekmecelioglu, Ali Demirci, Robert E. Graves

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

An optimum composting recipe was investigated to reduce pathogenic microorganisms in a forced-aerated in-vessel system (55 liters). The feedstocks used for in-vessel composting were food waste, cow manure, and bulking materials (wood shavings and mulch hay). A statistical extreme vertices mixture design method was used to design the composting experiments and analyze the collected data. Each mixture (nine total) was replicated randomly three times. Temperature was monitored as an indicator of the efficiency of the composting experiments. The maximum temperature values of the mixtures were used as a response for both extreme vertices mixture design and statistical analyses. Chemical changes (moisture content, carbon/nitrogen ratio, volatile solids, and pH) and reductions of indicator (fecal coliforms and fecal streptococci) and pathogenic microorganisms (Salmonella and Escherichia coli O157:H7) were measured by the most-probable-number method before and after a 12-day composting period. Maximum temperatures for the tested compost mixtures were in the range of 37.0 to 54.7°C. Extreme vertices mixture design analysis of the surface plot suggested an optimum mixture containing 50% food waste, 40% manure, and 10% bulking agents. This optimum mixture achieved maximum temperatures of 54.7 to 56.6°C for about 3.3 days. The total reduction of Salmonella and E. coli O157:H7 were 92.3%, whereas fecal coliforms and fecal streptococci reductions were lower (59.3 and 27.1%, respectively). Future study is needed to evaluate the extreme vertices mixture design method for optimization of large-scale composting.

Original languageEnglish (US)
Pages (from-to)589-596
Number of pages8
JournalJournal of food protection
Volume68
Issue number3
DOIs
StatePublished - Mar 2005

All Science Journal Classification (ASJC) codes

  • Food Science
  • Microbiology

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