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| Early Identification of Male and Female Embryos Based on UV/Vis Transmission Spectroscopy and Extreme Learning Machine |
| ZHU Zhi-hui1, 2, HONG Qi1, 2, WU Lin-feng1, 2, WANG Qiao-hua1, 2, MA Mei-hu3* |
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2. Key Laboratory of Agricultural Equipment, Ministry of Agriculture, Wuhan 430070, China
3. College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China |
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Abstract In order to identify male and female embryos of chicken eggs, the feasibility of using UV/Vis/NIR transmission spectrum to identify the male and female embryos is explored. The transmission spectrum detection system of chicken eggs is established with blunt end vertically placed upwards and horizontally placed separately to obtain the 0~15 d spectrum (ranging from 360 to 1 000 nm) of 210 hatched eggs. The identification model of the embryo learning male and female of the extreme learning machine (ELM) is constructed. By comparing the identification accuracy of different placement and the number of hatching days, it is found that the recognition effect of the vertical placement hatching on the 7th day is the best. The spectrum of the 7th day of vertical incubation is initially divided into ultraviolet (360～380 nm), visible light (380～780 nm), near-infrared (780～1 000 nm), ultraviolet/visible (360～780 nm) and full-band (360～1 000 nm). Five different band ranges are analyzed, and the prediction set accuracy rates are 82.86%, 77.14%, 75.71%, 84.29%, and 81.43%, respectively. The ultraviolet/visible bands of 360～780 nm are selected as effective bands; In the ultraviolet/visible (360～780 nm) band, Multiplicative scatter correction (MSC) is used to denoise, and the characteristic wavelength reduction is selected by Competitive adaptive reweighted sampling (CARS) and Successive projection algorithm (SPA). Three kinds of wavelengths without screening, CARS screening characteristic wavelength and SPA screening characteristic wavelength are established ELM model. Among them, the ELM model without screening characteristic wavelengths has the best recognition effect, but the input variables are the most. When the hidden layer neuron is 680 and the activation function is sig, the prediction set accuracy is 84.29%. The ELM model of the SPA screening characteristic wavelength has the second recognition effect, and there are 9 input variables. When the hidden layer neurons are 840 and the activation function is hardlim, the prediction set accuracy is 81.43%. The ELM model with the CARS screening characteristic wavelength has the worst recognition effect, and there are 27 input variables. When the hidden layer neurons are 100 and the activation function is sig, the prediction set accuracy is 78.57%. Using Genetic algorithm (GA) to optimize the weight variable and hidden layer threshold of ELM model, the prediction set accuracy rate is 87.14%, 87.14% and 81.43% separately under the condition of the GA-ELM model established without screening the characteristic wavelength, the GA-ELM model established by SPA screening characteristic wavelength, and the GA-ELM model established by the CARS screening characteristic wavelength. The recognition effect of GA-ELM model in the ultraviolet/visible band without screening characteristic wavelength is the same as that in the GA-ELM model with SPA screening characteristic wavelength, which indicates that the characteristic wavelength variable of SPA screening can effectively reflect the information of 360～780 nm band. The number of variables used by the SPA is only 2.14% of the ultraviolet/visible range. Therefore, the best model for male and female identification is the GA-ELM model for screening characteristic wavelengths with SPA in the ultraviolet/visible range. The accuracy of the prediction set is 87.14%, of which the female recognition rate is 88.57%, the male recognition rate is 85.71%, and the average discrimination time of a single sample is 0.080 ms. The results show that UV/Vis transmission spectroscopy and ELM model provide a feasible method for the identification of chicken embryo eggs in early hatching.
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Received: 2018-07-31
Accepted: 2018-12-02
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Corresponding Authors:
MA Mei-hu
E-mail: mameihuhn@163.com
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