Characterization and quantification of individual whey proteins is crucial in many industrial dairy processes. With labor-intensive wet-chemical methods still being used as reference, a rapid quantification method for individual whey proteins is yet to be established. In this work Fourier Transform Mid-Infrared (FT-MIR) spectroscopy and Fourier Transform Near-Infrared (FT-NIR) spectroscopy were investigated as rapid methods for the analysis of the two main whey proteins (β-lactoglobulin and α-lactalbumin) in complex aqueous whey solutions simulating production process streams. MIR and NIR spectra were obtained on whey samples with known and varying amounts of the proteins of interest and regressed onto the spectra using Partial Least Squares (PLS). Selecting wavelengths allowed for prediction of β-lactoglobulin and α-lactalbumin concentrations with very high precision and accuracy (RMSECV of 0.6% and R2 of 0.99), suggesting the possibility of implementing systematic rapid in-line monitoring of protein composition in industrial whey streams. While the protein secondary structure fingerprint in the MIR region is well established in literature, this paper goes a step further by identifying the most informative parts of the NIR spectra in relation to protein secondary structure analysis through application of two-dimensional MIR-NIR correlation spectroscopy. Multivariate Curve Resolution (MCR) was applied to the MIR data to resolve mixture spectra and to elucidate the spectral ranges that were most useful in distinguishing between the two main whey proteins. The study concludes that NIR spectroscopy is a potential technology for accurate protein structural quantification, and that the method proposed here opens the possibilities for novel in-line industrial applications.

中文翻译：

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EXPRESS：使用傅立叶变换中红外和近红外光谱预测乳清水溶液的α-乳清蛋白和β-乳球蛋白成分

单个乳清蛋白的表征和量化在许多工业乳制品过程中至关重要。由于劳动密集型湿化学方法仍被用作参考，尚未建立单个乳清蛋白的快速定量方法。在这项工作中，傅里叶变换中红外 (FT-MIR) 光谱和傅里叶变换近红外 (FT-NIR) 光谱作为两种主要乳清蛋白（β-乳球蛋白和 α-乳清蛋白）的快速分析方法进行了研究在模拟生产过程流的复杂乳清水溶液中。MIR 和 NIR 光谱是在具有已知和不同数量的目标蛋白质的乳清样品上获得的，并使用偏最小二乘法 (PLS) 回归到光谱上。选择波长可以以非常高的精度和准确度（RMSECV 为 0.6% 和 R2 为 0.99）预测 β-乳球蛋白和 α-乳清蛋白浓度，这表明在工业乳清流中实施系统的快速蛋白质成分在线监测的可能性. 虽然 MIR 区域中的蛋白质二级结构指纹在文献中已经很好地建立，但本文更进一步，通过应用二维 MIR-NIR 相关光谱确定与蛋白质二级结构分析相关的 NIR 光谱中信息最丰富的部分. 将多元曲线分辨率 (MCR) 应用于 MIR 数据以解析混合物光谱并阐明在区分两种主要乳清蛋白方面最有用的光谱范围。