Abstract：
The objective of this study was to evaluate the ability of a handheld near-infrared device (900-1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and 18 of incubation and the data was analysed using principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machines classification (SVM). The overall classification rates for the prediction of fertile and infertile egg samples ranged from 73 % to 84 % and between 93 % to 95 % using LDA and SVM classification, respectively. The highest classification rate was obtained on day 7 of incubation. The classification between male and female embryos achieved lower classification rates, between 62 % and 68 % using LDA and SVM classification, respectively. Although the classification rates for in-ovo sexing obtained in this study are higher than those obtained by chance (50 %), the classification results are currently not sufficient for industrial in-ovo sexing of chicken eggs. These results demonstrated that short wavelengths in the NIR range may be useful to distinguish between fertile and infertile egg samples at days 7 and 14 during incubation.
摘要：
这项研究的目的是评估手持式近红外设备（900-1600nm）预测生育力和性别（男性和女性）特征的能力。在孵化的第0、7、14和18天收集卵样品的近红外反射光谱，并使用主成分分析（PCA）分析数据，线性判别分析(LDA)和支持向量机分类(SVM)。使用LDA和SVM分类，预测可育和不育卵样本的总体分类率在73％至84％之间，在93％至95％之间，分别。在孵育的第7天获得最高的分类率。雄性和雌性胚胎之间的分类实现了较低的分类率，使用LDA和SVM分类在62%到68%之间，分别。尽管在这项研究中获得的卵内性别分类率高于偶然获得的分类率（50％），分类结果目前不足以进行鸡蛋的工业卵内性别鉴定。这些结果表明，NIR范围内的短波长可能有助于区分孵化过程中第7天和第14天的可育和不育卵样品。
