Characterization And Sensory Preference Of Fermented Dairy Beverages Prepared With Different Concentrations Of Whey And Araticum Pulp

The objective of this study was to develop a fermented dairy beverage flavored with araticum pulp, assess its physicochemical characteristics, microbiological quality, and sensory preference by the consumer. Araticum pulp was prepared using two different methods: with or without bleaching (50 °C/5 minutes). Formulations of fermented dairy beverages consisting of whey (50%), standardized pasteurized milk (50%), and seven different concentrations of bleached araticum pulp (5.0, 7.5, 10.0, 12.5, 15.0, 17.5, and 20.0% w/v) were prepared. In addition, seven formulations of fermented dairy beverage, without adding araticum pulp, and consisting of varying proportions of whey (40, 50, 60, 70, 80, 90, and 100%) were developed. In all formulations, thickeners/stabilizers were added. All araticum pulp samples (with and without bleach) and fermented dairy beverages (with and without araticum pulp) were analyzed for the relevant physicochemical properties: pH, titratable acidity, acidity of pulp, acidity of fermented beverage, moisture, ash, fat, protein, crude fiber, ascorbic acid, carbohydrates, total solids, and caloric values. Microbiological counts of coliforms at 35 °C and 45 °C in the pulp and beverage, and molds and yeasts and Salmonella sp. in the pulp were obtained. Additionally, sensory analysis regarding preferences of the different fermented dairy beverage formulations was also performed. The araticum pulp samples without bleach, showed higher values of pH, moisture, protein, total fiber, and ascorbic acid, as compared to bleached pulp samples, while bleached araticum pulp showed higher values for other physicochemical parameters. Microbiological results showed that all pulps and fruitdairy beverages were suitable for consumption. It was found that there was no significant consumer preference between different fermented beverage formulations, according to the different percentages of pulp. However, the formulations consisting of 40, 50, 60, and 70% whey were preferred over the one consisting of 100% whey.3764011402

Pileup mitigation at CMS in 13 TeV data

With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional "pileup" collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle "δβ" correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb$^{-1}$ collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at √s = 13TeV, corresponding to an integrated luminosity of 35.9 fb−1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency