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Invited review: A commentary on predictive cheese yield formulas

机译:特邀评论:关于奶酪产量预测公式的评论

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摘要

Predictive cheese yield formulas have evolved from one based only on casein and fat in 1895. Refinements have included moisture and salt in cheese and whey solids as separate factors, paracasein instead of casein, and exclusion of whey solids from moisture associated with cheese protein. The General, Barbano, and Van Slyke formulas were tested critically using yield and composition of milk, whey, and cheese from 22 vats of Cheddar cheese. The General formula is based on the sum of cheese components: fat, protein, moisture, salt, whey solids free of fat and protein, as well as milk salts associated with paracasein. The testing yielded unexpected revelations. It was startling that the sum of components in cheese was <100%; the mean was 99.51% (N × 6.31). The mean predicted yield was only 99.17% as a percentage of actual yields (PY%AY); PY%AY is a useful term for comparisons of yields among vats. The PY%AY correlated positively with the sum of components (SofC) in cheese. The apparent low estimation of SofC led to the idea of adjusting upwards, for each vat, the 5 measured components in the formula by the observed SofC, as a fraction. The mean of the adjusted predicted yields as percentages of actual yields was 99.99%. The adjusted forms of the General, Barbano, and Van Slyke formulas gave predicted yields equal to the actual yields. It was apparent that unadjusted yield formulas did not accurately predict yield; however, unadjusted PY%AY can be useful as a control tool for analyses of cheese and milk. It was unexpected that total milk protein in the adjusted General formula gave the same predicted yields as casein and paracasein, indicating that casein or paracasein may not always be necessary for successful yield prediction. The use of constants for recovery of fat and protein in the adjusted General formula gave adjusted predicted yields equal to actual yields, indicating that analyses of cheese for protein and fat may not always be necessary for yield prediction. Composition of cheese was estimated using a predictive formula; actual yield was needed for estimation of composition. Adjusted formulas are recommended for estimating target yields and cheese yield efficiency. Constants for solute exclusion, protein-associated milk salts, and whey solids could be used and reduced the complexity of the General formula. Normalization of fat recovery increased variability of predicted yields.
机译:可预测的奶酪产量公式已从1895年仅基于酪蛋白和脂肪的公式发展而来。完善的方法包括将奶酪和乳清固体中的水分和盐作为单独的因素,用酪蛋白代替酪蛋白,并从与奶酪蛋白相关的水分中排除乳清固体。 General,Barbano和Van Slyke配方均使用22桶切达干酪的牛奶,乳清和干酪的产量和成分进行了严格测试。通用配方基于奶酪成分的总和:脂肪,蛋白质,水分,盐,不含脂肪和蛋白质的乳清固体,以及与酪蛋白有关的乳盐。测试产生了意外的启示。令人惊讶的是,奶酪中的成分之和小于100%;平均为99.51%(N×6.31)。平均预测产量仅占实际产量的99.17%(PY%AY); PY%AY是比较大桶产量的有用术语。 PY%AY与奶酪中的总和(SofC)正相关。对SofC的明显低估导致了通过观察到的SofC向上调整每个大桶中公式中的5个测量成分的想法。调整后的预测产量与实际产量的百分比平均值为99.99%。 General,Barbano和Van Slyke公式的调整形式给出的预测产量等于实际产量。显然,未经调整的收益率公式无法准确预测收益率;但是,未经调整的PY%AY可用作分析奶酪和牛奶的控制工具。出乎意料的是,调整后的通用配方奶粉中的总乳蛋白具有与酪蛋白和副酪蛋白相同的预测产量,这表明酪蛋白或副酪蛋白可能不一定总是成功进行产量预测所必需的。在调整后的通用公式中使用常数回收脂肪和蛋白质得出的调整后预测产量等于实际产量,这表明对奶酪进行蛋白质和脂肪分析可能不一定总是需要进行产量预测。奶酪的成分使用预测公式进行估算;估计组成需要实际产量。建议使用调整后的公式估算目标产量和奶酪产量效率。可以使用溶质排除,蛋白质相关乳盐和乳清固体的常数,从而降低了通式的复杂性。脂肪回收率的正常化增加了预计产量的可变性。

著录项

  • 来源
    《Journal of dairy science》 |2010年第12期|p.5517-5537|共21页
  • 作者

    D. B. Emmons; H. W. Modler;

  • 作者单位

    Guelph Food Research Centre, Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON, Canada N1G 5C9 657 Westminster Ave., Ottawa, ON, K2A 2V7, Canada;

    rnGuelph Food Research Centre, Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON, Canada N1G 5C9 1253 River Road, Kemptville, ON, KOG 1J0, Canada;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    cheese; yield; prediction; formula;

    机译:起司;让;预测;式;

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