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首页> 外文期刊>Journal of dairy science >Front-face fluorescence spectroscopy combined with chemometrics to detect high proteinaceous matter in milk and whey ultrafiltration permeate
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Front-face fluorescence spectroscopy combined with chemometrics to detect high proteinaceous matter in milk and whey ultrafiltration permeate

机译：前面荧光光谱与化学计量学结合，以检测牛奶中的高蛋白质物质，乳清超滤渗透物

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Proteinaceous matter can leak into the permeatestream during ultrafiltration (UF) of milk and wheyand lead to financial losses. Although manufacturerscan measure protein content in the finished permeatepowders, there is currently no rapid monitoring toolduring UF to identify protein leak. This study appliedfront-face fluorescence spectroscopy (FFFS) and chemometricsto identify the fluorophore of interest associatedwith the protein leak, develop predictive models toquantify true protein content, and classify the types ofprotein 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 for37 lots of whey permeate and 29 lots of milk permeatefrom commercial manufacturers. Whey permeatecontained more TEP than did milk permeate, whereasmilk permeate contained more α-LA and β-LG thandid whey permeate. The types of protein leak were thusidentified for predictive model development. Based onexcitation-emission matrix (EEM) of high- and low-TPpermeates, tryptophan excitation spectra were collectedfor predictive model development, measuring TPcontent in permeate. With external validation, a usefulmodel 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 usingpartial least square discriminant analysis. These classificationmethods can detect high TP level, high TEPlevel, and presence of α-LA or β-LG with 83.3%, 84.8%,and 98.5% cross-validated accuracy, respectively. Thismethod showed that FFFS and chemometrics canrapidly detect protein leaks and identify the types ofprotein leak in UF permeate. Implementation of thismethod in UF processing plants can reduce financialloss from protein leaks and maintain high-quality permeateproduction.
机译：蛋白质物质可以泄漏到渗透物中在超滤（UF）的牛奶和乳清期间并导致财务损失。虽然制造商虽然可以测量成品渗透物中的蛋白质含量粉末，目前没有快速监控工具在UF期间识别蛋白质泄漏。这项研究适用于前面荧光光谱（FFFS）和化学计量学识别相关的兴趣荧光团随着蛋白质泄漏，开发预测模型量化真正的蛋白质含量，并对类型进行分类蛋白质泄漏在渗透物流中。粗蛋白（CP），非蛋白氮（NPN），真蛋白（TP），胰蛋白酶 - 等效肽（TEP），α-乳白蛋白（α-LA），测量β-乳糖苷蛋白（β-LG）含量37许多乳清渗透物和29大牛奶渗透物来自商业制造商。乳清渗透含有比牛奶渗透更多的Tep，而牛奶渗透物含有比α-la和β-lg更多乳清渗透了。因此蛋白质泄漏的类型确定预测模型开发。基于高和低TP的激发 - 发射矩阵（EEM）收集渗透物，收集色氨酸励磁光谱用于预测模型开发，测量TP渗透中的内容。外部验证，一个有用的质量控制目的模型开发，具有0.22％的根均线误差（干基）和2.8的残余预测偏差。此外，使用分类模型使用部分最小二乘判别分析。这些分类方法可以检测高TP水平，高TEP水平，α-LA或β-LG的存在，83.3％，84.8％，和98.5％的交叉验证准确性。这方法表明，FFFS和化学仪器可以快速检测蛋白质泄漏并识别类型UF渗透物中的蛋白质泄漏。实施这一点UF处理工厂中的方法可以减少金融蛋白质泄漏的损失并保持高质量的渗透物生产。

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