Guelph Food Research Centre, Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON, Canada N1G 5C9.
J Dairy Sci. 2010 Dec;93(12):5517-37. doi: 10.3168/jds.2010-3262.
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.
预测干酪产量的公式从 1895 年只基于酪蛋白和脂肪的公式发展而来。改进包括将水分和盐分别纳入干酪和乳清固形物,用副酪蛋白替代酪蛋白,并从与干酪蛋白相关的水分中排除乳清固形物。使用 22 桶切达干酪的牛奶、乳清和干酪的产量和成分对通用、巴班诺和范斯莱克公式进行了严格测试。通用公式基于干酪成分的总和:脂肪、蛋白质、水分、盐、无脂肪和蛋白质的乳清固形物,以及与副酪蛋白相关的乳盐。测试结果出人意料。令人惊讶的是,干酪中各成分的总和<100%;平均值为 99.51%(N×6.31)。预测产量的平均值仅为实际产量的 99.17%(PY%AY);PY%AY 是比较不同干酪桶产量的有用术语。PY%AY 与干酪中成分的总和(SofC)呈正相关。SofC 的估计值较低,导致人们想到对每个干酪桶配方中的 5 个测量成分进行向上调整,调整幅度为观察到的 SofC 的分数。调整后的预测产量平均值为实际产量的 99.99%。通用、巴班诺和范斯莱克公式的调整形式给出了与实际产量相等的预测产量。显然,未经调整的产量公式不能准确预测产量;然而,未经调整的 PY%AY 可以作为奶酪和牛奶分析的控制工具,很有用。令人惊讶的是,调整后的通用公式中的总牛奶蛋白给出了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并非总是成功预测产量所必需的。在调整后的通用公式中使用常数来回收脂肪和蛋白质,得到了与实际产量相等的调整后预测产量,这表明对蛋白质和脂肪的奶酪分析可能并非总是预测产量所必需的。使用预测公式估算奶酪的成分,需要实际产量来估算成分。建议调整公式来估计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形物的常数,并减少通用公式的复杂性。脂肪回收率的归一化增加了预测产量的可变性。