International journal of food microbiology · 2026
Quantitative assessment of high-pressure processing inactivation of foodborne bacteria and viruses in foods.
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Yeak Kah Yen Claire, Pampoukis George, Serra-Castelló Cristina, Bover-Cid Sara, den Besten Heidy M W, Zwietering Marcel H
Аннотация
High-pressure processing (HPP) is a non-thermal food processing technology that can inactivate microbiological hazards while preserving product quality. This study compiled 2692 D-values across 15 microbial hazards pressurized at different pressures at a temperature ≤ 45 °C. The Bigelow model was used to estimate Dref values at Pref of 500 MPa. Based on these values, microbial hazards were grouped into three HPP resistance categories across all food matrices: 1) Sensitive (D500MPa ≤ 1 min): Vibrio spp., Aeromonas caviae, Campylobacter spp., Rotavirus, Cronobacter spp., Hepatitis A virus, Norovirus; 2) Moderate (1 < D500MPa ≤ 3 min): Yersinia enterocolitica, Listeria monocytogenes, Mycobacterium tuberculosis var. bovis, non-typhoidal Salmonella enterica, Hepatitis E virus, Escherichia coli; and 3) Resistant (D500MPa > 3 min): Staphylococcus aureus, Bacillus cereus (vegetative cells). However, food matrix composition strongly influenced microbial resistance to HPP: low water activity (aw ≤ 0.95) conferred protection, while acidic (pH ≤ 4.5) or high aw matrices (aw > 0.95) enhanced inactivation. Multiple linear regression identified microbial species, aw, and pH as significant predictors of the inactivation; other product characteristics, such as protein and carbohydrate content, showed limited effects. A decision-support framework was developed to evaluate microbial hazard resistance to HPP processing. The framework integrates three complementary approaches: 1) microbial hazard resistance ranking at 500 MPa, 2) matrix-stratified inactivation estimates by food category, and 3) multiple linear regression modeling using aw and pH as independent variables. These approaches complement one another and support microbial hazard resistance evaluation under different data availability scenarios.