研究目的
To develop a noninvasive method for sexing chicken eggs at an early stage of incubation using fluorescence and Raman spectroscopy, minimizing invasiveness and maintaining high hatching rates.
研究成果
The study successfully demonstrated that near infrared Raman and fluorescence spectroscopy can be used for in ovo sexing of chicken eggs with a correct sexing rate above 90%, without negatively affecting hatching rates. This minimally invasive approach offers significant potential for practical deployment in hatcheries, improving animal welfare and economic efficiency.
研究不足
The study acknowledges the variability in membrane thickness and its effect on signal intensity, proposing a correction method to compensate for these effects. However, the method's accuracy and applicability across different chicken strains and incubation conditions were not fully explored.
1：Experimental Design and Method Selection:
The study employed near infrared Raman and fluorescence spectroscopy to perform in ovo sex determination of domestic chicken eggs by analyzing the blood in extraembryonic vessels. The method was designed to be minimally invasive by leaving the inner egg shell membrane intact.
2：Sample Selection and Data Sources:
Fertilized eggs of a commercial white layer strain (Gallus gallus f. dom.) were used. Eggs were incubated under standard conditions, and measurements were performed on day 3.5 of incubation.
3：5 of incubation.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: A spectrometer RamanRxn with a diode laser emitting at 785 nm, a fiber optic probe, and a self-built microscopy system were used for optical spectroscopy. A spectral domain optical coherence tomography (OCT) system was used to study the morphology of egg structures.
4：Experimental Procedures and Operational Workflow:
Egg shells were windowed at the blunt pole using a CO2 laser, leaving the inner shell membrane intact. Spectroscopic measurements were performed on perfused blood vessels, and the data were analyzed using principal component analysis (PCA) and supervised classification.
5：Data Analysis Methods:
Spectroscopic data were analyzed using Matlab, including PCA and a classification algorithm based on spectral feature selection and nonlinear discriminant analysis.
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