Modelling headspace dynamics in modified atmosphere packaged meat

Information geometry for multiparameter models: new perspectives on the origin of simplicity

Complex models in physics, biology, economics, and engineering are oftensloppy, meaning that the model parameters are not well determined by the model predictions for collective behavior. Many parameter combinations can vary over decades without significant changes in the predictions. This review uses information geometry to explore sloppiness and its deep relation to emergent theories. We introduce themodel manifoldof predictions, whose coordinates are the model parameters. Itshyperribbonstructure explains why only a few parameter combinations matter for the behavior. We review recent rigorous results that connect the hierarchy of hyperribbon widths to approximation theory, and to the smoothness of model predictions under changes of the control variables. We discuss recent geodesic methods to find simpler models on nearby boundaries of the model manifold-emergent theories with fewer parameters that explain the behavior equally well. We discuss a Bayesian prior which optimizes the mutual information between model parameters and experimental data, naturally favoring points on the emergent boundary theories and thus simpler models. We introduce a 'projected maximum likelihood' prior that efficiently approximates this optimal prior, and contrast both to the poor behavior of the traditional Jeffreys prior. We discuss the way the renormalization group coarse-graining in statistical mechanics introduces a flow of the model manifold, and connect stiff and sloppy directions along the model manifold with relevant and irrelevant eigendirections of the renormalization group. Finally, we discuss recently developed 'intensive' embedding methods, allowing one to visualize the predictions of arbitrary probabilistic models as low-dimensional projections of an isometric embedding, and illustrate our method by generating the model manifold of the Ising model.

Assessment of quality characteristics and bacterial community of modified atmosphere packaged chilled pork loins using 16S rRNA amplicon sequencing analysis

Modified atmosphere packaging (MAP) is widely applied in packaging meat and meat products. While most studies had employed culture-dependent microbiological analyses or polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), the recent application of high-throughput sequencing (HTS) has been effective and reliable in detecting the microbial consortium associated with food spoilage. Since MAP application is limited in China, applying HTS in assessing the microbial consortium of meat and meat products in the country becomes imperative. In this study, quality indexes and bacterial enumeration often used as spoilage indicators were employed to assess MAP fresh pork under chilled (4 °C) storage for 21 d. The results indicated that 70%O₂/30%CO₂ (Group A) retained more redness (a*) content, while 70%N₂/30%CO₂ (Group B) markedly reduced spoilage indicators compared to the control group. Notably, high-throughput sequencing indicated that Group B and 20%O₂/60%N₂/20%CO₂ (Group C) inhibited the growth of abundant spoilers, Pseudomonas spp. and Brochothrix spp. Thus, MAP (Group B and C) has promising potential in inhibiting predominant meat spoilers during chilled storage. This study provides valuable information to food industries on the potential application of MAP to control meat spoilage in Chinese markets.