Modeling of Sensory Characteristics Based on the Growth of Food Spoilage Bacteria

During last years theoretical works shed new light and proposed new hypothesis on the mechanisms which regulate the time behaviour of biological populations in dierent natural systems. Despite of this, a relevant physical and biological issue such as the role of environmental variables in ecological systems is still an open question. Filling this gap of knowledge is a crucial task for a deeper comprehension of the dynamics of biological populations in real ecosystems. The aim of this work is to study how dynamics of food spoilage bacteria in uences the sensory characteristics of fresh sh specimens. This topic is worth of investigation in view of a better understanding of the role played by the bacterial growth on the organoleptic properties, and becomes crucial in the context of quality evaluation and risk assessment of food products. We therefore analyze and reproduce the time behaviour, in fresh sh specimens, of sensory characteristics starting from the growth curves of two spoilage bacterial communities. The theoretical study, initially based on a deterministic model, is performed by using the tem- perature proles obtained during the experimental analysis. As a rst step, a model of predictive microbiology is used to reproduce the experimental behaviour of the two bacterial populations. Afterwards, the theoretical bacterial growths are converted, through suitable dierential equa- tions, into \sensory" scores, based on the Quality Index Method (QIM), a scoring system for freshness and quality sensory estimation of shery products. As a third step, the theoretical curves of QIM scores are compared with the experimental data obtained by sensory analysis. Finally, the dierential equations for QIM scores are modied by adding terms of multiplicative white noise, which mimics the eects of uncertainty and variability in sensory analysis. A better agreement between experimental and theoretical QIM scores is observed, in some cases, in the presence of suitable values of noise intensity respect to the deterministic analysis.