Department of Animal Sciences and Industry/Food Science Institute, Kansas State University, Manhattan 66506.
Department of Animal Sciences and Industry/Food Science Institute, Kansas State University, Manhattan 66506.
J Dairy Sci. 2019 Oct;102(10):8756-8767. doi: 10.3168/jds.2019-16810. Epub 2019 Aug 14.
Proteinaceous matter can leak into the permeate stream during ultrafiltration (UF) of milk and whey and lead to financial losses. Although manufacturers can measure protein content in the finished permeate powders, there is currently no rapid monitoring tool during UF to identify protein leak. This study applied front-face fluorescence spectroscopy (FFFS) and chemometrics to identify the fluorophore of interest associated with the protein leak, develop predictive models to quantify true protein content, and classify the types of protein leak in permeate streams. Crude protein (CP), nonprotein nitrogen (NPN), true protein (TP), tryptone-equivalent peptide (TEP), α-lactalbumin (α-LA), and β-lactoglobulin (β-LG) contents were measured for 37 lots of whey permeate and 29 lots of milk permeate from commercial manufacturers. Whey permeate contained more TEP than did milk permeate, whereas milk permeate contained more α-LA and β-LG than did whey permeate. The types of protein leak were thus identified for predictive model development. Based on excitation-emission matrix (EEM) of high- and low-TP permeates, tryptophan excitation spectra were collected for predictive model development, measuring TP content in permeate. With external validation, a useful model for quality control purposes was developed, with a root mean square error of prediction of 0.22% (dry basis) and a residual prediction deviation of 2.8. Moreover, classification models were developed using partial least square discriminant analysis. These classification methods can detect high TP level, high TEP level, and presence of α-LA or β-LG with 83.3%, 84.8%, and 98.5% cross-validated accuracy, respectively. This method showed that FFFS and chemometrics can rapidly detect protein leaks and identify the types of protein leak in UF permeate. Implementation of this method in UF processing plants can reduce financial loss from protein leaks and maintain high-quality permeate production.
蛋白质物质可能会在牛奶和乳清的超滤（UF）过程中渗漏到渗透物流中，从而导致经济损失。尽管制造商可以测量成品渗透粉末中的蛋白质含量，但目前在 UF 过程中没有快速监测工具来识别蛋白质泄漏。本研究应用前沿荧光光谱（FFFS）和化学计量学来识别与蛋白质泄漏相关的荧光团，开发定量真实蛋白质含量的预测模型，并对渗透物流中的蛋白质泄漏类型进行分类。为了开发预测模型，对来自商业制造商的 37 批乳清渗透物和 29 批牛奶渗透物进行了粗蛋白（CP）、非蛋白氮（NPN）、真实蛋白（TP）、色氨酸等效肽（TEP）、α-乳白蛋白（α-LA）和β-乳球蛋白（β-LG）含量的测定。乳清渗透物中的 TEP 含量高于牛奶渗透物，而牛奶渗透物中的 α-LA 和 β-LG 含量高于乳清渗透物。因此，确定了蛋白质泄漏的类型，以开发预测模型。基于高和低 TP 渗透物的激发-发射矩阵（EEM），收集色氨酸激发光谱以开发预测模型，测量渗透物中的 TP 含量。通过外部验证，开发了一种用于质量控制目的的有用模型，预测值的均方根误差为 0.22%（干基），剩余预测偏差为 2.8。此外，还使用偏最小二乘判别分析开发了分类模型。这些分类方法可以分别以 83.3%、84.8%和 98.5%的交叉验证准确性检测到高 TP 水平、高 TEP 水平以及存在 α-LA 或 β-LG。该方法表明，FFFS 和化学计量学可以快速检测蛋白质泄漏并识别 UF 渗透物中的蛋白质泄漏类型。在 UF 加工厂实施此方法可以减少因蛋白质泄漏造成的经济损失，并保持高质量的渗透物生产。
