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One of the most significant improvements in meat industry during the next few years will be the development of intelligent processing systems incorporating automated and rapid chemical analysis. It is necessary to have suitable methods to monitor meat’s freshness and safety in order to be able to ensure quality. Hence, the identification and quantification of the most influential metabolic compounds contributing to safety and spoilage and the development and application of practical means, such as “diagnostic” methods and robust model systems for spoilage prediction, will be of great importance. In order to be widely used by the meat food industry, the tests to be carried out on the meat must be rapid and simple to perform on a number of samples at relatively low cost. iMeatSense highlights the potential of a multidisciplinary approach to explore the complexity of the meat/spoilage prediction problem.

Objectives

The overall aim is for the first time, to identify and easy to fuse simultaneously chemical, biochemical, imaging and spectra indices and establish their applicability as quality monitors for inspection of meat safety and quality. The first target of this project is to develop an innovative toolbox of computational intelligent decision support schemes for the reliable and inexpensive detection of bacteria associated to meat muscle freshness, spoilage. The second target of this project will be to build an intelligent predictive model for meat spoilage and its validation under constant and dynamic temperature storage conditions. Predictive models are a useful tool for the meat industry to optimize shelf life. An interesting applicability of the proposed developed methodologies will be the investigation/implementation of a prototype web user interface platform through AUA’s server. Consumers will have the opportunity to upload their sample images acquired by any mobile device and get an online assisted decision related to diagnosis/spoilage prediction.

For the benefit of industry:

Development of protocols for the simple, effective and inexpensive evaluation of the meat quality.

Development of advanced “diagnostic” methodologies based mainly on intelligent leaning-based schemes to identify robust Multiple Compound Quality Indices (MCQI).

Development of predictive models to forecast shelf life as well as formation of spoilage compounds or other hazards (e.g., mycotoxins, etc).

For the benefit of society:

The use of an intelligent software tool associated with a multi-sensorial platform able to check the quality of meat would ensure improved quality and thus would fulfil consumers’ increasing demand for safe and hygienic food production.

The introduction of an intelligent sensor system as proposed here to test meat quality by analysing various types of features/markers as it is proposed in this proposal is expected to strengthen the potential of existing quality control systems used currently by food industries. The availability of highly effective sensors should broaden the market opportunities for companies involved in manufacture of electronic systems and components, thus having a positive impact on employment within this sector. In addition new vacancies for qualified engineers to use such automated systems will be available in the food industrial sector.

Through the development of an experimental Graphical User Interface (GUI) prototype, the consumers will have the capability by uploading meat images to get online a decision regarding the diagnosis/prediction status of their sample.

For the benefit of science:

Provide basic knowledge tools and resources for the interpretation and management of the generated data relevant to microbial behaviour in relation to safety and spoilage of food.

Implementation of new technologies, e.g. multispectral image analysis, in Food Science.

"This work has been supported by the iMeatSense project 550 of the ARISTEIA-I call co -funded by EC European Social Fund and the Greek General Secretariat of Research and Technology".