Evaluation of the potential for virus dispersal during hand drying: a comparison of three methods

Aims To use a MS2 bacteriophage model to compare three hand-drying methods, paper towels (PT), a warm air dryer (WAD) and a jet air dryer (JAD), for their potential to disperse viruses and contaminate the immediate environment during use. Methods and Results Participants washed their gloved hands with a suspension of MS2 bacteriophage and hands were dried with one of the three hand-drying devices. The quantity of MS2 present in the areas around each device was determined using a plaque assay. Samples were collected from plates containing the indicator strain, placed at varying heights and distances and also from the air. Over a height range of 0.15-1.65 m, the JAD dispersed an average of >60 and >1300-fold more plaque-forming units (pfu) compared to the WAD and PT (P <0.0001), respectively. The JAD dispersed an average of >20 and >190-fold more pfu in total compared to WAD and PT at all distances tested up to 3 m (P <0.01), respectively. Air samples collected around each device 15 minutes after use indicated that the JAD dispersed an average of >50 and >100-fold more pfu compared to the WAD and PT (P <0.001), respectively. Conclusions Use of the JAD lead to significantly greater and further dispersal of MS2 bacteriophage from artificially contaminated hands when compared to the WAD and PT. Significance and Impact of Study The choice of hand drying device should be considered carefully in areas where infection prevention concerns are paramount, such as healthcare settings and the food industry.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology