1,777 research outputs found

    Influence of peptide and amino acids on the formation of cheese flavour

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    U sirevima se tijekom zrenja odvijaju kompleksne mikrobioloĆĄke i biokemijske promjene koje imaju značajan utjecaj na formiranje organoleptičkih karakteristika gotovog proizvoda. Proteolitički enzimi razgrađuju kazein do većih i manjih peptida te slobodnih aminokiselina. Količina i omjeri pojedinih aminokiselina i topljivih peptida znatno utječu na teksturu i organoleptička svojstva sira. Razgradnjom aminokiselina u siru nastaju alkoholi, aldehidi, esteri, kiseline i sumporni spojevi koji formiraju specifične arome raznih vrsta sireva. U siru je identificirano viĆĄe od 200 različitih hlapljivih komponenti. Okus sira je koncentriran u frakciji topljivoj u vodi (peptidi, aminokiseline, organske kiseline i amini), dok je aroma uglavnom koncentrirana u hlapljivoj frakciji (organske kiseline, aldehidi, amini, esteri). Mnogi sirevi sadrĆŸe iste ili slične komponente, ali u različitim koncentracijama i odnosima. Posebnosti arome sira ne zasnivaju se samo na jednom specifičnom spoju već na kombinaciji različitih spojeva nastalih tijekom zrenja. Djelovanjem na proteolitičke procese moĆŸe se ubrzati i modificirati tehnoloĆĄki proces proizvodnje sira, te ih se stoga intenzivno proučava.Complex microbiological and biochemical changes take place during cheese ripening process, having a significant impact on the formation of organoleptic characteristics of the final product. Proteolytic enzymes degraded casein to larger and smaller peptides and free amino acids. The quantity and the ratios of particular amino acids and soluble peptides significantly influence the texture and organoleptic properties of cheese. The products of amino acid degradation in cheese are alcohols, aldehydes, esters, acids and sulphur compounds, which form specific aromas of various cheese types. More than 200 different volatile components have been identified in cheese. Cheese flavour is concentrated in the watersoluble fraction (peptides, amino acids, organic acids and amines), while aroma is mainly concentrated in the volatile fraction (organic acids, aldehydes, amines, esters). Many cheeses contain the same or similar components, but in different concentrations and ratios. Specific characteristics of cheese aroma are not based only on one specific compound, but on combination of different compounds produced in the maturing process. The technological process of cheese production can be accelerated and modified by influencing proteolytic processes, which are therefore the subject of intense research

    The CMS Drift Tube Trigger Track Finder

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    Muons are among the decay products of many new particles that may be discovered at the CERN Large Hadron Collider. At the first trigger level the identification of muons and the determination of their transverse momenta and location is performed by the Drift Tube Trigger Track Finder in the central region of the Compact Muon Solenoid experiment, using track segments detected in the Drift Tube muon chambers. Track finding is performed both in pseudorapidity and azimuth. Track candidates are ranked and sorted, and the best four are delivered to the subsequent high level trigger stage. The concept, design, control and simulation software as well as the expected performance of the system are described. Prototyping, production and tests are also summarized

    Measurement of differential cross sections for top quark pair production using the lepton plus jets final state in proton-proton collisions at 13 TeV

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    National Science Foundation (U.S.

    Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

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    Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated tt‟\mathrm{t}\overline{\mathrm{t}} events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)

    Particle-flow reconstruction and global event description with the CMS detector

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    The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic tau decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions

    Search for heavy resonances decaying to a top quark and a bottom quark in the lepton+jets final state in proton–proton collisions at 13 TeV

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    info:eu-repo/semantics/publishe

    Evidence for the Higgs boson decay to a bottom quark–antiquark pair

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    info:eu-repo/semantics/publishe

    Pseudorapidity and transverse momentum dependence of flow harmonics in pPb and PbPb collisions

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    info:eu-repo/semantics/publishe
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