6533b839fe1ef96bd12a65a1
RESEARCH PRODUCT
Modeling of Sensory Characteristics Based on the Growth of Food Spoilage Bacteria
G. DenaroSalvatore MazzolaBernardo SpagnoloBernardo SpagnoloAngelo BonannoGualtiero BasiloneSalvatore AronicaFilippo GiarratanaAlessandro GiuffridaDavide Valentisubject
Stochastic ordinary differential equationmedia_common.quotation_subjectFood spoilageOrganolepticFOS: Physical sciencesSensory systemContext (language use)BiologyPopulation dynamic01 natural sciencesSensory analysisPopulation dynamics; Predictive microbiology; Stochastic ordinary differential equations; Modeling and Simulation010305 fluids & plasmas0103 physical sciencesStatisticsQuality (business)010306 general physicsQuantitative Biology - Populations and EvolutionCondensed Matter - Statistical Mechanicsmedia_commonPredictive microbiologyStatistical Mechanics (cond-mat.stat-mech)EcologyApplied MathematicsPopulations and Evolution (q-bio.PE)Experimental dataSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Modeling and SimulationFOS: Biological sciencesPredictive microbiologydescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-09-23 |