Author(s): Korryn Narvaez, Eliza Ballantyne, Savannah West
Mentor(s): Dustin Shipp
Institution UVU
The food industry relies on accurately measuring the endpoint temperature of meat, poultry, and egg products to ensure the thermal inactivation of foodborne illness-causing pathogens such as Salmonella, Escherichia coli, Listeria monocytogenes, and Campylobacter. The internal endpoint temperature (EPT) of meat indicates its level of doneness, and the EPT of a meat product is specific to the temperature at which virtually all pathogens are deactivated. Food thermometers are the current gold standard for measuring EPTs but become ineffectual as the meat begins to cool. Furthermore, single-point measurements fail to capture the spatial variation of EPT in a sample. Raman spectroscopy offers a non-destructive, non-contact, and consistent method of measuring the EPT of meat after it has been cooled and stored. Our research presents two Raman classification models for predicting the EPT in cooked pork. A principal component analysis-random forest (PCA-RF) model classifies spectra into temperature categories based on known protein conformational changes with 87.5% accuracy. A partial least squares (PLS) model predicts the EPT on a continuous scale with a root mean square (RMS) error of 3.8*C. We apply this PLS model to create hyperspectral images that show the EPT in cooked tissue cross-sections. These images show that three cooking methods exhibit differences in the depth at which the EPT decays from the hot outer surface to the meat’s center. This technique for imaging the spatial variation in EPT may inform future decisions regarding meat safety and quality in the food industry. Furthermore, our results and methodology may assist future studies in exploring the potential differences in various cooking methods for meat, poultry, and egg products.