Predicting the combinatorial effects of water activity, pH and organic acids on Listeria growth in media and complex food matrices. (September 2018)

• Record Type: Journal Article
• Authors: Nyhan, L.; Begley, M.; Mutel, A.; Qu, Y.; Johnson, N.; Callanan, M.
• Is Part Of: Food microbiology. Volume 74(2018)
• Page Start: 75
• Page End: 85
• Publication Date: 2018-09
• Subjects: Predictive model -- Listeria -- Food -- ComBase -- Propionic -- Acetic; Food Microbiology -- Periodicals; Aliments -- Microbiologie -- Périodiques; Food -- Microbiology; Periodicals; Food -- Microbiology -- Periodicals; Food contamination -- Periodicals; 664.001579
• DOI: 10.1016/j.fm.2018.03.002
• Languages: English
• ISSNs: 0740-0020

Abstract

Abstract: The aim of this study was to develop a model to predict growth of Listeria in complex food matrices as a function of pH, water activity and undissociated acetic and propionic acid concentration i.e. common food hurdles. Experimental growth curves of Listeria in food products and broth media were collected from ComBase, the literature and industry sources from which a bespoke secondary gamma model was constructed. Model performance was evaluated by comparing predictions to measured growth rates in growth media (BHI broth) and two adjusted food matrices (zucchini purée and béarnaise sauce). In general, observed growth rates were higher in broth than in the food matrices which resulted in the model over-estimating growth in the adjusted food matrices. In addition, model outputs were more accurate for conditions without acids, indicating that the organic acid component of the model was a source of inaccuracy. In summary, a new predictive growth model for innovating or renovating food products that rely on multi-hurdle technology was created. This study is the first to report on modelling of propionic acid as an inhibitor of Listeria in combination with other hurdles. Our findings provide valuable insights into predictive model design and performance and highlight the importance of experimental validation of models in real food matrices rather than laboratory media alone. Highlights: A new model for predicting the growth of Listeria in complex foods was developed. Listeria growth lower for hurdle combinations tested in food matrices than in broth. Inclusion of interaction factor between hurdles did not improve prediction accuracy. First report on modelling of propionic acid as Listeria inhibitor.