高光谱成像无损检测咸鸭蛋腌制过程中蛋清、蛋黄形态变化及含盐量

摘要：盐含量是咸鸭蛋（（ SDEs）在腌制过程中成熟度和内部品质的重要指标。然而，目前尚无有效且快速的方法能够准 确检测盐含量。在本研究中，我们利用高光谱成像技术无损测定了腌制期间蛋黄、蛋清以及整个咸鸭蛋的盐含量。首先， 通过主成分分析解释了不同成熟度咸鸭蛋在腌制过程中蛋黄和蛋清形态变化的特征。其次，通过三种光谱变换方法 （（Savitzky-Golay 平滑、连续统去除和一阶导数）以及三种特征波长选择方法（连续投影算法、无信息变量消除和竞争性 自适应重加权采样算法），提取了能够代表咸鸭蛋盐含量变化的敏感光谱因子。偏最小二乘回归（PLSR）模型的结果表 明，预测蛋黄、蛋清和整个咸鸭蛋盐含量的最佳模型分别为SG-UVE-PLSR（（ 预测决定系数R² =0.912，预测标准偏差SE p p = 0.151，残差预测偏差RPD=3.371）、CR-CARS-PLSR（R2 = 0.873, SE = 0.862，RPD=2.806）和CR-UVE-PLSR（R2 = p p p 0.877, SE = 0.680，RPD=2.851）。最终，将整个咸鸭蛋盐含量的最优预测模型应用于像素光谱矩阵，计算了咸鸭蛋高光谱 p 图像上各像素点的盐含量值，并采用伪彩色技术可视化预测盐含量的空间分布。该研究为快速检测咸鸭蛋成熟度及实现 高通量品质分选提供了理论基础。

关键词：咸鸭蛋；高光谱成像；无损检测；可视化

期刊：International Journal of Biological Macromolecules

DOI：https://doi.org/10.1016/j.ijbiomac.2024.130002

设备：GaiaFluo

Non-destructive detection of egg white and yolk morphology transformation and salt content of salted duck eggs in salting by hyperspectral imaging

Abstract：Salt content is a crucial indicator of the maturity and internal quality of salted duck eggs (SDEs) during the pickling process. However, there is currently no valid and rapid method available for accurately detecting salt content. In the present study, we utilized hyperspectral imaging to no-destructively determine the salt content in egg yolks, egg whites, and whole eggs during the curing period. Firstly, principal component analysis was applied to explain the characteristics of egg yolk and white morphology transformation of SDEs with different maturities during curing. Secondly, sensitive spectral factors representative of changes in the salt content of SDEs were extracted by three spectral transformations (Savitzky-Golay SG, continuum removal CR, and ffrst-order derivation FD) and three approaches of selecting characteristic wavelengths (successive projection algorithm SPA, uninformative variables elimination UVE and competitive adaptive reweighting sampling algorithm CARS). The results of the PLSR model suggested that the optimal models for predicting salt content in egg yolks, whites, and whole eggs were SG-UVE-PLSR (predicted coefffcient of determination R2 = 0.912, predicted standard deviation SEp=0.151, residual prediction deviation RPD = 3.371), CR- p CARS-PLSR (R2 = 0.873, SE = 0.862, RPD = 2.806), and CR-UVE-PLSR (R2 = 0.877, SE = 0.680, RPD = 2.851), respectively. p p p p Eventually, the optimal prediction model for the salt content of the whole egg was employed to a pixel spectral matrix to calculate the salt content values of pixel points on the hyperspectral image of SDEs. Additionally, pseudo-color techniques were employed to visualize the spatial distribution of predicted salt content. This work will provide a theoretical foundation for rapidly detecting maturity and enabling high-throughput quality sorting of SDEs.

Keywords：Salted duck eggs; Hyperspectral imaging; Non-destructive detection; Visualization