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The objective of this project is to develop predictive models on the Internet for optimisation of heat treatment in SMEs in the dairy industry. For the SMEs this means that their product quality and safety will be maximised whereas production costs are minimised.Heat treatment of raw milk (e.g. pasteurisation) is an essential step before the milk is processed into products such as yoghurt or cheese. The purpose of heat treatment is the inactivation of micro-organisms that cause spoilage of milk products (e.g. lactobacilli) and the inactivation of micro-organisms that may seriously affect human health (e.g. salmonellae). However, heat treatments have negative effects on product quality aspects such as taste, texture and nutrition. Heat treatments also account for a large part of the production costs, mainly due to fouling of the equipment with proteins. Fouling results in extra energy consumption, consumption of cleaning agents and waste production.Predictive models designed to optimise heat treatment processes in dairy companies are available on the market.
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Especially non-SME companies use these predictive models successfully in practice for three different purposes:
<OL> <LI> To improve product safety and quality.
<LI>To reduce energy consumption and thus decrease processing costs.
<LI>To enhance new product development.
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However, the available predictive models are not suitable for SMEs because these models are designed for use by experts in large companies. The costs of the models are too high for individual SMEs, too complicated for use in production facilities, designed as an R&D tool, and not user-friendly. Therefore, SMEs are not able to use predictive models. Hence their processing conditions are based on experience and empirical data (trial and error). The aim of this this project is to develop predictive models on the Internet, specifically designed for the needs of SMEs.