PREDICTIVE MICROBIOLOGY
cod. 1008524

Academic year 2019/20
1° year of course - Second semester
Professor
- Valentina BERNINI
Academic discipline
Microbiologia agraria (AGR/16)
Field
Discipline delle tecnologie alimentari
Type of training activity
Characterising
58 hours
of face-to-face activities
6 credits
hub:
course unit
in ITALIAN

Learning objectives

Knowledge and ability to understand: through the lectures held during the course, the student will acquire the methods and knowledge necessary to understand the meaning of the main tools available in the field of predictive microbiology and the fields of application for the quality and microbiological safety of food.Ability to apply knowledge and understanding:Using examples (case studies, scientific articles), related to food products and processes, students learn how to apply the knowledge acquired in a real context.Autonomy of judgmentThe student must demonstrate a critical view of the potentials of predictive microbiology in the food sector, of the fields of application (new products formulation, shelf life forecast, safety forecasting, HACCP, risk analysis, etc.), of the use of the main tools available. It must also be able to interpret, process and discuss issues associated with food microbiology. Using the acquired knowledge will have to be able to evaluate the meaning of the different predictive models and experimental designs for their correct application in the context of food production and conservation.Communication skillsThrough lectures, the comparison with the teacher and any group work, the student acquires the specific vocabulary inherent to predictive microbiology. It is expected that, at the end of the course, the student will be able to transmit, in oral and written form, the main contents of the course, such as definitions of the models, meaning of the variables described, microbiological risk analysis, drawings experimental.Learning abilityThe student who has attended the course will be able to deepen their knowledge on predictive microbiology through the independent consultation of specialized texts, scientific journals, even outside the topics dealt with strictly in class, in order to effectively address the insertion in the world of work or undertake further training courses.1182/5000In particular, the objectives are as follows:a) Know the meaning of process and environmental parameters on the behavior of pathogenic microorganisms.b) Know the main predictive models available for the description and prediction of microbial behavior.c) Know the meaning of microbiological challenge tests and the phases of their planning and planning.d) Know the drawings experimental drawings available for the simplification of complex experimental planse) Know the stages of a microbiological risk analysis.The knowledge of these aspects will provide the student with the essential skills to evaluate the effects of the presence of microorganisms in raw materials, during the transformation process and during the shelf life, allowing the prediction of behavior in other production contexts. The ultimate goal is to achieve autonomy of judgment, communication and learning skills in accordance with the specific objectives of the Master's Degree Program in Food Science and Technology and the area of food microbiology learning.

Prerequisites

Basic knowledges of general microbiology and food microbiology, mathematics and statistics.

Course unit content

The first part of the course concerns about the general concepts of predictive microbiology, the applications in food microbilogy area, models characteristics. in the second part knowledges about primary and secondary models are exhamined. In particolar, about primary models are descrive the kinetics of growth and inactivations, Gompertz equativo, baranyi model, Weibull model. In secondary models, ratkowsky model, experimetal design, polinomial equations. The microbiologicla risk analysis step are also addressed. In the last part of the course product and process challenge test are studied, towards practical examples. Moreover, the students will examine cases of applicative researches in predictive microbiology and challenge test field.

Full programme

Concepts of microbiology: the structure of microbial cell; microbial growth, microbial metabolism.
The origins of predictive microbiology, the first models and application fields.
Definitions of primary, secondary and tertiary models. Probabilistic, cinetic, empirical and mechanistic models. Structural and non structural models.
Primary models. Growth modelling (Gompertz equativo, Baranyi model). Survival models (linear, biphasic, Weibull model).
Secondary models. Ratkowsky model. Experimental design. Polynomial equations.
Tertiary model. Use of Combase.
Challenge test: applications to foods and processes.

Bibliography

Fausto Gardini, Eugenio Parente. Manuale di microbiologia predittiva. Concetti e strumenti per l'ecologia microbica quantitativa. Springer Italia
Educational material used during frontal lessons.

Teaching methods

The theoretical topics of the course are explained by means of lectures.
Basis and applications of Predictive microbiology will be dicussed during lessons. The study, elaboration and discussion of specific "study cases (exercises and business cases)" are proposed on the practical parts of the course and could permit to verify the student comprehension and elaboration capacity

Assessment methods and criteria

Specific lessons aimed to assess the state of learning will be carried out at the end of each part of the course. During the course, some lessons will be devoted to student presentation and discussion of "study cases (business cases are discussed as examples of the main theoretical arguments of the course)" useful to understand and verify the state of comprehension and the elaboration capacity reached concerning predictive microbiology application fields.
Final examination will be carried out by a written test, either in form of multiple choices or open questions. The final evaluation will depend on the percentage of correct answers.

Other information

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