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Predictive model for growth of Salmonella Newport on Romaine lettuce
Author(s) -
Oscar Thomas P.
Publication year - 2020
Publication title -
journal of food safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.427
H-Index - 43
eISSN - 1745-4565
pISSN - 0149-6085
DOI - 10.1111/jfs.12786
Subject(s) - salmonella , interpolation (computer graphics) , meal , food science , statistics , mathematics , biology , computer science , artificial intelligence , bacteria , motion (physics) , genetics
Cross‐contamination of ready‐to‐eat (RTE) salad vegetables with Salmonella from raw chicken followed by growth during meal preparation are important risk factors for human salmonellosis. To better predict and manage this risk, a model (general regression neural network) for growth of a chicken isolate of Salmonella Newport (0.91 log) on Romaine lettuce (0.18 g) at times (0–8 hr) and temperatures (16–40°C) observed during meal preparation was developed with Excel, NeuralTools, and @Risk. Model performance was evaluated using the acceptable prediction zones (APZ) method. The proportion of residuals in the APZ (pAPZ) was 0.93 for dependent data ( n = 210) and 0.93 for independent data ( n = 72) for interpolation. A pAPZ ≥0.70 indicates acceptable model performance. Thus, the model was successfully validated for interpolation and can be used with confidence to predict and manage this important risk to public health.