Technological innovation and valorisation of traditional food: a sustainable combination?
AbstractValorization of traditional foods is nowadays a key element for market developments where national industries are strongly involved in saving product peculiarity against imitative food coming from foreign countries or even different continents. Other than the lack in well defined and garanteed sensorial quality, the production conditions, the quality of raw material and the different cultural background lead to produce foods that, despite to the name indicating some italian origin or recallin in some ways Italy and italian food and traditions, are only imitation without safety and quality proper of the original traditional food. Thus it is necessary to individuate appropriated technologies and strategies to increase le level of garantee offered to the consumer in order to promote the consumption of traditional foods with the promised quality and safety. In this paper the role that the modern food technology and the food science can assume to improve the processing conditions and yields, introducing some innovations into the old processes will be pointed out. Furthermore, the characterization of the complexity of the chemical, chemico-physical and rheological properties that influence the whole sensorial aspect of traditional foods, both from animal and vegetal (and fruit) origin, is a growing challenge of the food science since the new analytical methodologies now available. In the paper some example of objective characterization and introduction of innovation steps are reported as well as genuinity marker individuation in order to give sustainability to the production of traditional foods in particular in SME.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2009 Marco Dalla Rosa
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.