Application of Mechanistic Models and the Gaussian Process Model to Predict Bacterial Growth on Baby Spinach During Refrigerated Storage

Models that predict bacterial growth in food products can help industry with decision-making with regard to microbial food spoilage. Such models have recently been developed using machine learning (ML) rather than a mechanistic understanding of bacterial growth. Thus, our aim was to compare the performance of mechanistic (M) models and the Gaussian process (GP) model (i.e., an ML approach) for predicting bacterial growth on spinach from a US-based supply chain as well as a China-based supply chain; models were developed using previously published data, as well as new data collected in this study from the China-based supply chain. For the packaged spinach collected in this study from the China-based supply chain, the mean net growth of aerobic, mesophilic bacteria over 10 days of shelf life was 1.16 log₁₀ (n = 11, local distribution) and 1.29 log₁₀ (n = 8, eCommerce distribution); bacterial growth on spinach did not differ significantly by distribution channel. The data obtained in this study, as well as previously published data on the growth of (i) individual bacterial strains (i.e., strain-level growth) and (ii) the overall bacterial population on baby spinach (i.e., population-level growth), were used to fit models. Specifically, GP models were fit to population-level growth data only, while M models were fit to strain-level and population-level growth data. The RMSE values for the M models (i.e., 0.72, 0.77 and 1.09 log₁₀ CFU/g, for three M models assessed here) and GP models (i.e., 0.68 and 0.81 log₁₀ CFU/g, for the two GP models assessed here) are similar, which suggests that both M and GP models show comparable accuracy at predicting bacterial growth on spinach.