Construction and validation of tertiary models for predicting growth of salmonella infantis in chicken liver during a processing chain deviation

Authors: Oscar, Thomas

Submitted to: Frontiers in Sustainable Food Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/26/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary: Most chicken livers purchased at the supermarket contain no or low levels of Salmonella. However, if the chicken liver is left at room temperature for too long during meal preparation in a restaurant, institution, or home kitchen, Salmonella can grow to high levels that are more difficult to eliminate during cooking and thus, can get people sick. So, how long is too long? That depends on the temperature in the kitchen. Therefore, a study was undertaken to determine how long is too long. Data (432 samples) for growth of Salmonella in chicken liver were collected for up to 8 h at 64 to 86F. They were used to develop a computer model. How does the computer model work? It's simple, just enter the Salmonella dose, time, and temperature of interest, and the computer model tells you how much growth of Salmonella is expected. For example, what if the chicken liver was held at 77F for 2 hours, is it still safe to cook and eat? The computer model predicts that it would take Salmonella about 3 hours to start to grow at 77F in chicken liver, so, yes, the chicken liver is still safe to cook and eat. Thus, the computer model can provide an objective and science based answer to the simple question, how long is too long? Where can I find the model? Once this research is published, the computer model will be incorporated into the USDA, Pathogen Modeling Program, which is free and open access to everyone at: https://portal.errc.ars.usda.gov/.

Technical Abstract: Salmonella Infantis is a top human clinical isolate that is found at low levels in chicken liver after primary processing. However, temperature abuse of chicken liver during secondary processing can lead to growth of Salmonella and higher risk of salmonellosis. Therefore, a three-phase linear, polynomial regression, tertiary model (TMPR) and a multiple layer feedforward neural network with two nodes in the hidden layer, tertiary model (TMNN) for growth of Salmonella Infantis in chicken liver as a function of dose (10^1 to 10^6), time (0 to 8 h), and temperature (18 to 30°C) were constructed, validated, and compared using the criteria of the Acceptable Prediction Zones (APZ) method. When the proportion of residuals in the APZ or pAPZ was = 0.7, predictions were considered acceptable. The pAPZ for the dependent data (n = 360) was 0.979 for the TMPR and 0.976 for the TMNN, whereas the pAPZ for the independent data for interpolation (n = 72) was 0.968 for the TMPR and 0.964 for the TMNN. Thus, both the TMPR and TMNN were validated for interpolation, had similar performance, and can be used with confidence to predict the growth of Salmonella Infantis in chicken liver during a secondary processing deviation of temperature abuse. However, construction of the TMPR involved three steps, whereas construction of the TMNN involved one step. Thus, the TMNN was easier to construct and validate. Nonetheless, the final TM included the TMPR and TMNN because the TMPR predicted lag time and growth rate, whereas the TMNN did not.