Use of high-pressure processing and low-temperature storage to extend the performance shelf-life of two types of String cheese.

The manufacturing method of String cheese is similar to Mozzarella, but the hot curd is extruded through narrow tubes or pipes, which align the protein fibers that provides the characteristic ability for consumers to pull strings from this cheese. Firmness is another important performance attribute for consumers who just bite into the String cheese without peeling off strings. There have only been a few studies on String cheese, but it is known that stringiness and firmness decrease during prolonged storage, which is a particular challenge for exporting String cheese. We explored 2 treatments to try to retain the stringiness and firmness of String cheese for longer storage periods. The techniques used were high pressure processing (HPP; 600 MPa for 3 min) and reduced storage temperature (0°C). In other cheese varieties, these techniques have helped extend the performance shelf-life. We tested these techniques using the 2 main types of commercial String cheese: direct acid (DASC) and cultured String cheese (CSC), that were obtained from 2 different manufacturing facilities. The DASC had higher fat (∼2.2%) and higher pH values (∼0.2 units) compared with the CSC. The CSC had higher protein content (∼3.4%), higher insoluble calcium content (∼8 mg insoluble Ca/g protein) and higher hardness values (∼4 N) compared with the DASC. Due to the compositional differences, the 2 varieties were statistically analyzed separately for all other attributes. In both cheese types, HPP caused an immediate reduction in stringiness, some solubilization of insoluble calcium, and a slight increase in the cheese pH values. HPP also caused a slight increase in the TPA hardness of the CSC samples until 14 d (possibly due to a slight increase in cheese pH). The use of the 0°C storage temperature reduced proteolysis and helped retain firmness during storage. Low temperature storage could help extend the performance shelf-life of String cheese by a couple of months, but HPP was not suitable as the process caused an immediate reduction in stringiness due to the disruption of the matrix induced by the HPP treatment.

BEESCOUT: A model of bee scouting behaviour and a software tool for characterizing nectar/pollen landscapes for BEEHAVE.

Social bees are central place foragers collecting floral resources from the surrounding landscape, but little is known about the probability of a scouting bee finding a particular flower patch. We therefore developed a software tool, BEESCOUT, to theoretically examine how bees might explore a landscape and distribute their scouting activities over time and space. An image file can be imported, which is interpreted by the model as a "forage map" with certain colours representing certain crops or habitat types as specified by the user. BEESCOUT calculates the size and location of these potential food sources in that landscape relative to a bee colony. An individual-based model then determines the detection probabilities of the food patches by bees, based on parameter values gathered from the flight patterns of radar-tracked honeybees and bumblebees. Various "search modes" describe hypothetical search strategies for the long-range exploration of scouting bees. The resulting detection probabilities of forage patches can be used as input for the recently developed honeybee model BEEHAVE, to explore realistic scenarios of colony growth and death in response to different stressors. In example simulations, we find that detection probabilities for food sources close to the colony fit empirical data reasonably well. However, for food sources further away no empirical data are available to validate model output. The simulated detection probabilities depend largely on the bees' search mode, and whether they exchange information about food source locations. Nevertheless, we show that landscape structure and connectivity of food sources can have a strong impact on the results. We believe that BEESCOUT is a valuable tool to better understand how landscape configurations and searching behaviour of bees affect detection probabilities of food sources. It can also guide the collection of relevant data and the design of experiments to close knowledge gaps, and provides a useful extension to the BEEHAVE honeybee model, enabling future users to explore how landscape structure and food availability affect the foraging decisions and patch visitation rates of the bees and, in consequence, to predict colony development and survival.