process cheese
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2020 ◽  
Vol 226 ◽  
pp. 107616 ◽  
Author(s):  
Takao Matsumoto ◽  
Yijun Chen ◽  
Akihiro Nakatsuka ◽  
Qunzhi Wang
Keyword(s):  

2019 ◽  
Vol 12 (4) ◽  
pp. 575-586 ◽  
Author(s):  
Sonja Lenze ◽  
Alan Wolfschoon-Pombo ◽  
Katrin Schrader ◽  
Ulrich Kulozik

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1240
Author(s):  
Marina Temerbayeva ◽  
Maksim Maksim ◽  
Olga Gorelik ◽  
Svetlana Harlap ◽  
Nikolai Maksimiuk ◽  
...  

The research identified and proposed technological parameters of production of creamy bioadditive, which is the source of probiotic microorganisms in the activated form, and intended to be used in the process cheese recipe, together with other components of milk or vegetable origin. The composition of the leaven microflora of Bifilakt-U contributes to combination of cultures of lactic acid bacteria and bifidobacteria; the concentration of protein and carbohydrates contributes to active life and growth of bifidobacteria; combination of probiotics and probiotic cultures has a positive effect on synbiotic properties of experimental products.    


Author(s):  
Hongyan Wang ◽  
Yujing Wang ◽  
Aixiang Huang

Dregea sinensis Hemsl. protease, a new enzyme source, is described with the characteristics of milk curd. However, cheese processing with this protease has yet to be described. In this study, a protease called chymosin was extracted and purified from D. sinensis Hemsl. stalk. Calf rennet and microbial chymosin were used as the control group in the production of mozzarella cheese to investigate the effect of this plant species on the quality of cheese. Results: SDS-PAGE revealed that D. sinensis Hemsl. protease can be used to process cheese because this enzyme elicits a degradation effect on α-casein in mozzarella cheese. Fresh and sweet glutamic acid and histidine are the dominant free amino acids in mozzarella cheese (P<0.5). Fifty-two flavor substances were detected through GC-MS. Volatile acids and carbonyl compounds are the main sources of the flavor of mozzarella cheese. Texture profile analysis indicated that the produced cheese was more restorative and flexible. Scanning electron microscopy demonstrated that the produced cheese was smooth, as indicated by the small pore cross-section diameter of mozzarella cheese and its close net structure. Therefore, D. sinensis Hemsl. can be applied to process cheese.


2017 ◽  
Vol 80 (9) ◽  
pp. 1478-1488 ◽  
Author(s):  
Kathleen A. Glass ◽  
Ming Mu ◽  
Brian LeVine ◽  
Frank Rossi

ABSTRACT The 1986 Food Research Institute–Tanaka et al. model predicts the safety of shelf-stable process cheese spread formulations using the parameters of moisture, pH, NaCl, and disodium phosphate (DSP) to inhibit toxin production by Clostridium botulinum. Although this model is very reliable for predicting safety for standard-of-identity spreads, the effects of additional factors have not been considered. The objective of this study was to create a predictive model to include the interactive effect of moisture, pH, fat, sorbic acid, and potassium-based replacements for NaCl and DSP to reflect modern reduced-sodium recipes. Eighty formulations were identified using a central composite design targeting seven factors: 50 to 60% moisture, pH 5.4 to 6.2, 0 to 0.2% sorbic acid, 10 to 30% fat, 1.7 to 2.4% NaCl, 0.8 to 1.6% DSP, and 0 to 50% potassium replacement for sodium salts. Samples were inoculated with proteolytic C. botulinum spores at 3 log spores per g, hot filled into sterile vials, and stored anaerobically at 27°C. Samples were assayed at 0, 1, 2, 3, 4, 8.5, 17.5, 26, and 40 weeks for the presence of botulinum toxin using the mouse bioassay. A parametric survival model was fit to the censored time-to-toxin data. All linear, quadratic, and pairwise effects were considered for model fit. As hypothesized, the effects of pH, sorbate, moisture, DSP, and NaCl were highly significant (P &lt; 0.001). Fat concentration and potassium replacement effects were significant at P &lt; 0.021 and P &lt; 0.057, respectively. The model consistently predicted the safety failure of the toxic samples, but it also predicted failure for some samples that were not toxic. This model is an adjunct to existing models by adding the factors of potassium salts, fat, and sorbic acid to predict the botulinal safety of prepared process cheese products but is not intended to be a substitute for formulation evaluation by a competent process authority.


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