Margolies Brenda, Adams Michael C, Pranata Joice, Gondoutomo Kathleen, Barbano David M
Northeast Dairy Foods Research Center, Department of Food Science, Cornell University, Ithaca, NY 14853.
Northeast Dairy Foods Research Center, Department of Food Science, Cornell University, Ithaca, NY 14853.
J Dairy Sci. 2017 Aug;100(8):6822-6852. doi: 10.3168/jds.2016-12295. Epub 2017 Jun 7.
Our objective was to develop a computer-based cheese yield, fat recovery, and composition control performance measurement system to provide quantitative performance records for a Cheddar and mozzarella cheese factory. The system can be used to track trends in performance of starter cultures and vats, as well as systematically calculate theoretical yield. Yield equations were built into the spreadsheet to evaluate cheese yield performance and fat losses in a cheese factory. Based on observations in commercial cheese factories, sensitivity analysis was done to demonstrate the sensitivity of cheese factory performance to analytical uncertainty of data used in the evaluation. Analytical uncertainty in the accuracy of milk weight and milk and cheese composition were identified as important factors that influence the ability to manage consistency of cheese quality and profitability. It was demonstrated that an uncertainty of ±0.1% milk fat or milk protein in the vat causes a range of theoretical Cheddar cheese yield from 10.05 to 10.37% and an uncertainty of yield efficiency of ±1.5%. This equates to ±1,451 kg (3,199 lb) of cheese per day in a factory processing 907,185 kg (2 million pounds) of milk per day. The same is true for uncertainty in cheese composition, where the effect of being 0.5% low on moisture or fat is about 484 kg (1,067 lb) of missed revenue opportunity from cheese for the day. Missing the moisture target causes other targets such as fat on a dry basis and salt in moisture to be missed. Similar impacts were demonstrated for mozzarella cheese. In analytical performance evaluations of commercial cheese quality assurance laboratories, we found that analytical uncertainty was typically a bias that was as large as 0.5% on fat and moisture. The effect of having a high bias of 0.5% moisture or fat will produce a missed opportunity of 484 kg of cheese per day for each component. More accurate rapid methods for determination of moisture, fat, and salt contents of cheese in large cheese factories will improve the accuracy of yield performance evaluation and control of consistency of cheese composition and quality.
我们的目标是开发一个基于计算机的奶酪产量、脂肪回收率和成分控制性能测量系统，为一家切达干酪和马苏里拉奶酪工厂提供定量的性能记录。该系统可用于跟踪发酵剂培养物和发酵罐的性能趋势，并系统地计算理论产量。产量方程被编入电子表格，以评估奶酪工厂的奶酪产量性能和脂肪损失。基于在商业奶酪工厂的观察结果，进行了敏感性分析，以证明奶酪工厂性能对评估中所用数据的分析不确定性的敏感性。牛奶重量以及牛奶和奶酪成分准确性方面的分析不确定性被确定为影响管理奶酪质量一致性和盈利能力的重要因素。结果表明，发酵罐中牛奶脂肪或牛奶蛋白的不确定性为±0.1%时，切达干酪的理论产量范围为10.05%至10.37%，产量效率的不确定性为±1.5%。这相当于在一家每天加工907,185千克（200万磅）牛奶的工厂中，每天奶酪产量的不确定性为±1,451千克（3,199磅）。奶酪成分的不确定性情况也是如此，水分或脂肪含量低0.5%的影响是，当天奶酪会错过约484千克（1,067磅）的收入机会。错过水分目标会导致其他目标，如干基脂肪和水分中的盐分也错过。马苏里拉奶酪也表现出类似的影响。在商业奶酪质量保证实验室的分析性能评估中，我们发现分析不确定性通常是一种偏差，在脂肪和水分方面高达0.5%。水分或脂肪偏差高0.5%的影响是，每种成分每天会产生484千克奶酪的错过机会。在大型奶酪工厂中，采用更准确的快速方法测定奶酪的水分、脂肪和盐分含量，将提高产量性能评估的准确性，并控制奶酪成分和质量的一致性。
