鸡蛋是一种重要的食品，蛋白质是鸡蛋的主要营养成分。本研究利用可见近红外反射光谱技术无损检测新鲜鸡蛋的蛋白质含量。使用光谱仪获取新鲜鸡蛋在400~1100 nm波段范围内的漫反射光谱; 分别使用多元散射校正(MSC)法和一阶导数法(1-D)对反射光谱进行预处理；对反射光谱、MSC处理光谱和1-D光谱，使用逐步回归法判别法选择最优波长组合，建立多元线性回归模型，使用全交叉验证法验证模型。结果表明，可见/近红外反射光谱经过多元散射校正后，确定的10个最优波长（400、403.16、407.9、714.6、715、715.58、970.4、970.75、973和974.45 nm）组合建立模型的校正和验证结果最好：选定模型的校正结果为R=0.92，SEC=0.42%；验证结果为Rcv=0.89，SECV=0.47%。研究表明可见/近红外反射光谱技术可以较好的预测新鲜鸡蛋的蛋白质含量，本研究可为可见近红外光谱技术在鸡蛋营养成分的快速检测提供一定的理论基础。

Eggs are considered an important food item, containing protein as the main nutrient. The aim of this study was to non-destructively detect protein content in eggs by visible (VIS)/near-infrared (NIR) reflectance spectroscopy. VIS/NIR raw reflectance spectra of fresh egg samples were acquired in the wavelength range of 400 to 1000 nm. The raw spectrum was pretreated with multiplicative scatter correction (MSC) and first-derivative (1-D) methods and step-wise regression discrimination method was used to select the optimal wavelength combination to establish multi-linear regression (MLR) models. Full cross-validation was used to validate the model. The results showed that after MSC treatment of VIS/NIR reflectance spectra, the MLR model, based on ten optimal wavelengths (400, 403.16, 407.9, 714.6, 715, 715.58, 970.4, 970.75, 973, and 974.45 nm) produced optimum calibration and validation results. For the calibration result, the correlation coefficient (R) was 0.92 and the standard error of calibration (SEC) was 0.47%. The model provided good prediction values for egg protein content with the correlation coefficient of cross validation (Rcv) at 0.89 and standard error of cross validation (SECV) at 0.47%. This study demonstrated that the VIS/NIR reflectance spectral technique provides a good prediction of the protein content in fresh eggs, and the VIS/NIR technique has potential applications in rapid detection of egg nutrients.

山西省高等学校科技创新项目资助；国家自然科学基金资助项目（31101359）