Rapid detection of Salmonella in foods is often limited by the high demand for the sensitivity of detection, poor physiological conditions of the target cells, and high concentration of background flora. In this study, the conditions of nonselective enrichment cultivation were modified in order to improve the quantitative detection of heat-injured Salmonella in minced meat. The effect of the modifications on the recovery was observed by means of RNA-based sandwich hybridization, which was adjusted for the quantification of Salmonella enterica 23S rRNA in crude cell extracts. The supplementation of buffered peptone water with the enzyme-controlled substrate delivery system EnBase-Flo and ferrioxamine E was shown to improve the recovery of cells in both single strain cultures and in the presence of minced meat. The presented results can be used for the development of more efficient enrichment cultivation media for faster detection of food borne Salmonella.

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Beer‐spoiling lactic acid bacteria (LAB) belong to the most harmful contaminants in the brewing industry and various rapid molecular detection methods have been introduced for the detection of these organisms. However, the enrichment cultivation steps needed prior to detection by molecular methods can extend the duration of the analytical procedure by up to several days. The use of brewery‐specific enrichment cultivation media has been recommended due to the large variety within the group of LAB, as well as differences in the microbes encountered at the breweries. In contrast to using a general medium that may support only the growth of some LAB, the combination of several media could allow detection of all relevant LAB. The aim of the present study was to show the effects of shifts in the cultivation conditions and media on the growth of beer‐spoiling LAB using one of the most frequently encountered species, Pediococcus damnosus, as an example. Based on the variable analysis, significant factors could be recognized and their effects on lag‐times and growth rates were compared by means of response surface modelling.

Maintenance operations have significant influence on the economy and performance of mining companies. Unpredicted repairs cause interruptions and breakdowns in production. This means economic losses but, in some cases, also increasing environmental emissions in off-gases and wastewater. Condition based maintenance (CBM) can significantly reduce maintenance costs. Sensors and measurement devices offer a lot of data and assist workers to identify upcoming maintenance needs in advance. Typical measurement variables are for example vibration, temperature, different speeds and pressures. DEVICO project aims to develop a framework for solutions and combine condition monitoring and process data to integrate CBM to control and timing of the maintenance actions. On-line and periodic CM measurements can be combined with process measurements by using signal processing and feature extraction. Case study is conducted in Pyhäsalmi mine with Sandvik load haul dump (LHD) machinery. The condition monitoring system is installed on LHD front axle. The choice for the installation position was made based on the feedback and maintenance data gathered from mining companies. This information indicates that the axles are among the most critical parts in LHD machines.

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