FARKLI SICAKLIKLARDA DEPOLANAN MAYDANOZDA SALMONELLA’NIN GELİŞME DAVRANIŞI

Abstract

Bu çalışma, maydanoz üzerinde Salmonella spp.’nın üç farklı depolama sıcaklığında (15°C, 25°C ve 35°C) gelişme dinamiklerini, Baranyi birincil modeli ve ilgili ikincil modeller kullanarak incelemiştir. Çalışmada, maksimum özgül büyüme hızı (μmax), gecikme süresi (tlag) ve maksimum popülasyon yoğunluğu (ymax) gibi büyüme parametrelerinin sıcaklıktan önemli ölçüde etkilendiği belirlenmiştir. 35°C’de, Salmonella’nın hızlı çoğalma gösterdiği (μmax =1.40 sa⁻¹), en kısa gecikme süresine sahip olduğu (tlag =1.79 sa) ve yüksek popülasyon yoğunluğuna ulaştığı (ymax =7.05 log kob/g) saptanmıştır; bu koşullar altında bakterinin 24 saatten kısa sürede tehlikeli seviyelere eriştiği görülmüştür. Elde edilen bulgular, maydanoz üzerinde Salmonella gelişimini sınırlamak için soğuk zincirin bütünlüğünün korunmasının kritik önemini vurgulamaktadır. Ayrıca, tahmine dayalı modellerin kullanımı, mikrobiyal risk değerlendirmesi için değerli bir araç sağlayarak, özellikle yetersiz soğutma koşullarına sahip ortamlarda gıda güvenliği yönetimi stratejilerinin geliştirilmesine katkıda bulunabilir.

Keywords

• Patojenler
• gıda güvenliği
• prediktif mikrobiyoloji
• taze meyve ve sebzeler

THE GROWTH BEHAVIOR OF SALMONELLA IN PARSLEY STORED AT DIFFERENT TEMPERATURES

Abstract

This study investigated the growth dynamics of Salmonella spp. on parsley under three storage temperatures (15°C, 25°C, and 35°C) using the Baranyi primary model and relevant secondary models. Growth parameters, including maximum specific growth rate (μmax), lag time (tlag), and maximum population density (ymax), were significantly influenced by temperature. At 35°C, Salmonella exhibited rapid proliferation (μmax =1.40 h⁻¹), minimal lag (tlag =1.79 h), and high population density (ymax =7.05 log CFU/g), reaching hazardous levels in less than 24 hours. These findings highlight the critical importance of maintaining cold chain integrity to limit Salmonella growth on parsley. The application of predictive models provides a valuable tool for microbial risk assessment and can inform food safety management strategies, particularly in settings with inadequate refrigeration.

Keywords

• Pathogens
• food safety
• predictive microbiology
• fresh produce

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