This study investigated the feasibility of front-face fluorescence spectroscopy (FFFS) to predict the solubility index and relative dissolution index (RDI) of milk protein concentrate (MPC) powders during storage. Twenty MPC powders with varying protein contents from 4 different commercial manufacturers were used in this study. The MPC powders were stored at 2 temperatures (25 and 40°C) for 0, 1, 2, 4, 8, and 12 wk. The front-face fluorescence spectra of tryptophan and Maillard products were recorded and analyzed with chemometrics to predict solubility of MPC powders. The similarity maps showed clear discrimination of the MPC samples stored at 25 and 40°C. Partial least squares regression models were developed using the fluorescence spectra of tryptophan and Maillard products to predict the solubility index and RDI measurements of MPC powders, and the prediction models were validated using an independent test set. Coefficients of determination (R²) of 0.76, 0.84, and 0.68 were obtained between fluorescence spectra (tryptophan emission, Maillard emission, and Maillard excitation, respectively) and solubility index. The R² values for the RDI predictions were 0.58 and 0.60 for the data set of tryptophan emission and Maillard emission, respectively. The ratio of prediction error to standard deviation was >2 for Maillard emission fluorescence spectra and solubility index measurements, indicating good practical utility of the partial least squares regression prediction models. The results indicated that the solubility and dissolution behavior of MPC powders were related to their protein content and storage conditions that could be measured using FFFS. Hence, FFFS can be used as a rapid nondestructive analytical technique to predict the solubility and dissolution characteristics of MPC powders.

这项研究调查了正面荧光光谱法（FFFS）预测牛奶浓缩蛋白（MPC）粉在储存过程中的溶解度指数和相对溶解指数（RDI）的可行性。本研究使用了20种来自4个不同商业制造商的蛋白质含量不同的MPC粉末。MPC粉末在0、1、2、4、8和12 wk的2个温度（25和40°C）下存储。记录色氨酸和美拉德产品的正面荧光光谱，并用化学计量学进行分析，以预测MPC粉末的溶解度。相似性图显示了对在25和40°C下储存的MPC样品的清晰区分。使用色氨酸和美拉德产品的荧光光谱建立了偏最小二乘回归模型，以预测MPC粉末的溶解度指数和RDI测量值，并使用独立的测试集对预测模型进行了验证。测定系数（R在荧光光谱（分别为色氨酸发射，美拉德发射和美拉德激发）和溶解度指数之间获得0.76、0.84和0.68的²）。R ²色氨酸发射和美拉德发射的数据集的RDI预测值分别为0.58和0.60。对于美拉德发射荧光光谱和溶解度指数测量，预测误差与标准偏差的比率> 2，这表明偏最小二乘回归预测模型具有良好的实用性。结果表明，MPC粉末的溶解度和溶解行为与它们的蛋白质含量和可使用FFFS测定的储存条件有关。因此，FFFS可以用作快速无损分析技术来预测MPC粉末的溶解度和溶解特性。