PDF(Pubmed)
Abstract：
Duck eggs are widely-consumed food and cooking ingredient. The heavier yolk weight (YW) corresponds to a larger size and greater value. However, there is no nondestructive method available to estimate the weight of the yolk. Accurate weight prediction of duck egg yolks must combine both phenotypic and internal information. In this research, we used Visible-Near Infrared (VIS-NIR) spectroscopy to obtain internal information of duck eggs, and a high-definition camera to capture their phenotypic features. YW was predicted by combining the reduced spectral and RGB image information with the whole egg weight. We also investigated the impact of color and thickness of the duck egg on spectral transmittance (ST), as these factors can influence the extent of ST. The results showed that the spectral curves of duck eggs produced 2 peaks and 1 valley, which may be caused by the dual-frequency absorption of the C-H group and O-H group, and can be used to symbolize the internal information of duck eggs. The ST was somewhat affected by the color and thickness of the duck eggshell. Before modelling, Principal component analysis (PCA) was used to significantly reduce the dimensionality of the RGB image with spectral data. A partial least squares regression (PLSR) model was utilized to fit all the features. The test set yielded a coefficient of determination (R2) of 0.82 and a Root Mean Squared Error (RMSE) of 1.05 g. After removing the eggshell\'s color and thickness features, the model showed an R2 of 0.79 and an RMSE of 1.11 g. This study demonstrated that the yolk weight of duck eggs can be estimated using VIS-NIR spectroscopy, RGB images and whole egg weight. Furthermore, the effects of shell color and thickness can be neglected.
摘要：
鸭蛋是广泛食用的食物和烹饪原料。较重的蛋黄重量(YW)对应于较大的尺寸和较大的值。然而,没有非破坏性的方法来估计蛋黄的重量。准确预测鸭蛋蛋黄的体重必须结合表型和内部信息。在这项研究中,我们使用可见近红外（VIS-NIR）光谱来获取鸭蛋的内部信息，和高清摄像机来捕捉它们的表型特征。通过将减少的光谱和RGB图像信息与整个蛋重相结合来预测YW。我们还研究了鸭蛋的颜色和厚度对光谱透射率（ST）的影响，因为这些因素会影响ST的程度。结果表明,鸭蛋的光谱曲线产生2个峰和1个谷,这可能是由C-H基团和O-H基团的双频吸收引起的，可以用来象征鸭蛋的内部信息。ST在一定程度上受到鸭蛋壳颜色和厚度的影响。在建模之前，主成分分析（PCA）用于显着降低具有光谱数据的RGB图像的维数。利用偏最小二乘回归(PLSR)模型来拟合所有特征。测试集的决定系数（R2）为0.82，均方根误差（RMSE）为1.05g。去除蛋壳的颜色和厚度特征后，该模型显示的R2为0.79，RMSE为1.11g。这项研究表明，可以使用VIS-NIR光谱估算鸭蛋的蛋黄重量，RGB图像和整个鸡蛋的重量。此外，壳颜色和厚度的影响可以忽略。
