AUTHOR=Oscar Thomas P. 

TITLE=Construction and validation of tertiary models for predicting growth of Salmonella Infantis in chicken liver during a processing chain deviation

JOURNAL=Frontiers in Sustainable Food Systems

VOLUME=Volume 9 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1565247

DOI=10.3389/fsufs.2025.1565247

ISSN=2571-581X

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 (101–106), time (0–8 h), and temperature (18–30°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.