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

TL;DR

This study models how the growth of food spoilage bacteria affects sensory qualities of fish, using deterministic and stochastic models to improve predictions of freshness scores based on bacterial dynamics.

Contribution

It introduces a novel approach by converting bacterial growth curves into sensory scores through differential equations, incorporating noise to better match experimental data.

Findings

Stochastic models improve prediction accuracy of sensory scores.

Bacterial growth dynamics significantly influence sensory quality assessments.

Noise addition enhances model alignment with experimental sensory data.

Abstract

During last years theoretical works shed new light and proposed new hypothesis on the mechanisms which regulate the time behaviour of biological populations in different natural systems. Despite of this, 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. In this work we study how the dynamics of food spoilage bacteria influences the sensory characteristics of fresh fish specimens. This topic is crucial for a better understanding of the role played by the bacterial growth on the organoleptic properties, and for the quality evaluation and risk assessment of food products. We therefore analyze the time behaviour, in fresh fish specimens, of sensory characteristics starting from the growth curves of two spoilage bacterial communities. The theoretical study, initially based on a deterministic model, exploits experimental temperature profiles. As a first 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 differential equations, into "sensory" scores, based on the Quality Index Method (QIM), a scoring system for freshness and quality sensory estimation of fishery products. As a third step, the theoretical curves of QIM scores are compared with the experimental data obtained by sensory analysis. Finally, the differential equations for QIM scores are modified by adding terms of multiplicative white noise, which mimics the effects 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.