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Abstract: Thermal Inactivation of African Swine Fever Virus in Feed Ingredients

Abstract: Thermal inactivation of African swine fever virus in feed ingredients

African swine fever virus (ASFV) causes a fatal infectious disease affecting domestic pigs and wild boars. ASFV is highly stable and easily transmitted by consumption of contaminated swine feed and pork products. Heat treatment of feed ingredients is a means to minimize the risk of contamination through swine feed consumption. The objectives of this study were to determine the thermal inactivation of ASFV in non-animal and animal origin feed ingredients. The rate of thermal inactivation is represented by decimal reduction time (DT) or time required to reduce ASFV per 1 log at temperature T. The mean D60, D70, D80 and D85 of meat and bone meal (MBM), soybean meal (SBM), and maize grain (MZ) are in the ranges 5.11-6.78, 2.19-3.01, 0.99-2.02, and 0.16-0.99 min, respectively. DT is used to compare the heat resistance of ASFV in the feed ingredient matrices. The mean DT of ASFV in MBM, SBM and MZ was not statistically significant, and the heat resistance of ASFV in MBM, SBM, and MZ was not different at 60, 70, 80, or 85 °C. The multiple DT was used to develop a DT model to predict DT at various inactivation temperatures. The DT models for MBM, SBM, and MZ are log DT = - [Formula: see text] + 2.69, log DT = - [Formula: see text] + 2.55, and log DT = - [Formula: see text] + 4.01. To expand and ease the field applications, a spreadsheet predicting the DT and the inactivation time (with 95% confidence interval) from these DT models is available to download.

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How Data Predicts Swine Outbreaks - Swaminathan Jayaraman

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In this episode of The Swine it Podcast Show Canada, Swaminathan Jayaraman, Research Assistant and PhD Candidate at Iowa State University College of Veterinary Medicine, explains how integrated data systems can improve disease surveillance in swine production. He discusses combining diagnostics, animal movement data, production records, and spatial analytics to identify risks earlier and support proactive decision-making for PRRS, PED, and swine influenza. Listen now on all major platforms!

"Disease surveillance remains largely reactive because outbreaks are often confirmed only after transmission has already occurred across multiple connected production sites."

Meet the guest: Swaminathan Jayaraman / swamjay is a Graduate Research Assistant at Iowa State University College of Veterinary Medicine. With academic training in management information systems and engineering, he focuses on integrating diagnostic, production, and movement data to improve disease surveillance and decision support in swine production systems. Listen to Swaminathan Jayaraman on The Swine it Podcast Show Canada, available on all major platforms.