| 光谱学与光谱分析 |
|
|
|
|
|
| Identification of Fertilized Chicken Eggs Based on Visible/Near-Infrared Spectrum During Early Stage of Incubation |
| QIN Wu-chang, TANG Xiu-ying*, PENG Yan-kun, ZHAO Xing-hua |
| College of Engineering, China Agricultural University, Beijing 100083, China |
|
|
|
|
Abstract In the process of chicken egg hatching, some eggs can not be hatched successfully due to the absence of fertilization. These eggs not only cause a lot of waste, but also infect other normal eggs with bacteria. In the study, the fertilized eggs and clear eggs is identified by using the visible/near-infrared spectrum. It is of great necessity to get the best time of identifying the clear eggs in the early of hatching, so the variation of eggs’ quality in the condition of hatching over time is studied. The results show that eggs are fresh after 24 hours’ hatching and eggs can not be eaten after 72 hours’ hatching while the best time of identification is within 36 hours. Static acquisition system is developed based on visible/near-infrared transmission spectrum for acquiring spectrum. Comparing the effect of the model of the different samples of same breed and samples of different breed, the different part of spectrum among fertilized eggs and clear eggs is deleted which caused by the color of eggshell and yolk, the effective spectral band are 355～590 and 670～1 025 nm. Adopting the pretreatment of PCA and comparing the accuracy of the various mathematical models with different time and the number of principal components decide the best number of principal components. Considering the production efficiency and comparing the different pretreatment methods of spectrum, for examples, SNV, MSC, Derivative correction and PCA, and various mathematical models are combined to establish the most efficient discriminant model. The result shows that the most efficient discriminant model is established with Fisher and based on the pretreatment of PCA after 24 hours’ hatching. And the precision rate is 87.18%. The study provides a new way for nondestructive and online identification of the fertilized eggs and clear eggs.
|
|
Received: 2015-12-15
Accepted: 2016-04-16
|
|
|
|
Corresponding Authors:
TANG Xiu-ying
E-mail: txying@cau.edu.cn
|
|
[1] Elster R T, Goordurm J W. Transactions of the ASAE, 1991, 34(1): 307.
[2] Das K, Evans M D. Transaction of the ASABE, 1992, 35(4): 1335.
[3] Klein S, Rokitta M, Baulain U, et al. Worlds Poultry Science Journal, 2003, 59(1): 39.
[4] SHEN Lin-sheng, CHEN Ning, WANG Guang-liang(沈林生，陈 宁，王光亮). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 1996, 3(12): 163.
[5] Bamelis F R, Tona K, De Baerdemaeker J G, et al. British Poultry Sicece, 2002, 43(2): 204.
[6] ZOU Xiu-rong, PAN Lei-qing, TU Kang, et al(邹秀容, 潘磊庆, 屠 康，等). Science and Technology of Food Industry(食品工业科技), 2010, 2(89): 342.
[7] HUANG Wen-qian, CHEN Li-ping, LI Jiang-bo, et al(黄文倩, 陈立平, 李江波，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(1): 272.
[8] Jie Dengfei, Xie Lijuan, Rao Xiuqin, et al. Postharvest Biology and Technology, 2014, 90: 1.
[9] Xie Lijuan, Ying Yibin, Ying Tiejin. Chemometrics and Intelligent Laboratory Systems, 2009, 97: 141.
[10] Nicolas A N, Michael N, Shiv P. Food Bioprocess Technol., 2009, (1): 265.
[11] Monira K N, Salahuddin M, Miah G. International Journal of Poultry Science, 2003, 2(4): 261.
[12] LIU Mei-yu, LIAN Hai-ping, REN Fa-zheng(刘美玉，连海平，任发政). Food Science(食品科学), 2011, 32(06): 270. |
| [1] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [2] |
MU Da1, 2, WANG Qi-shu1, 2*, CUI Zong-yu1, 2, REN Jiao-jiao1, 2, ZHANG Dan-dan1, 2, LI Li-juan1, 2, XIN Yin-jie1, 2, ZHOU Tong-yu3. Study on Interference Phenomenon in Terahertz Time Domain
Spectroscopy Nondestructive Testing of Glass Fiber Composites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3031-3040. |
| [3] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [4] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
| [5] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
| [6] |
YU Yang1, ZHANG Zhao-hui1, 2*, ZHAO Xiao-yan1, ZHANG Tian-yao1, LI Ying1, LI Xing-yue1, WU Xian-hao1. Effects of Concave Surface Morphology on the Terahertz Transmission Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2843-2848. |
| [7] |
LI Yu-tang1, WANG Lin-zhu1, 2*, LI Xiang3, WANG Jun1. Characterization and Comparative Analysis of Non-Metallic Inclusions in Zirconium Deoxidized Steel[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2916-2921. |
| [8] |
TANG Ruo-han1, 2, LI Xiu-hua1, 2*, LÜ Xue-gang1, 2, ZHANG Mu-qing2, 3, YAO Wei2, 3. Transmittance Vis-NIR Spectroscopy for Detecting Fibre Content of
Living Sugarcane[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2419-2425. |
| [9] |
LI Xiong1, 2, LIU Yan-de1, 2*, WANG Guan-tian1, JIANG Xiao-gang1, 2. Grapefruit Light Energy Decay Law and Analysis of the Effect of
Transmission Depth on Model Accuracy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2574-2580. |
| [10] |
LUO Zheng-fei1, GONG Zheng-li1, 2, YANG Jian1, 2*, YANG Chong-shan2, 3, DONG Chun-wang3*. Rapid Non-Destructive Detection Method for Black Tea With Exogenous Sucrose Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2649-2656. |
| [11] |
ZHANG Yue1, 2, LI Yang1, 2, SONG Yue-peng1, 2*. Nondestructive Detection of Slight Mechanical Damage of Apple by Hyperspectral Spectroscopy Based on Stacking Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2272-2277. |
| [12] |
ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2*. High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1711-1718. |
| [13] |
ZHANG Fu1, 2, 3, CAO Wei-hua1, CUI Xia-hua1, WANG Xin-yue1, FU San-ling4*, ZHANG Ya-kun1. Non-Destructive Detection of Soluble Solids in Cherry Tomatoes by
Visible/Near Infrared Spectroscopy Based on SG-CARS-IBP[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 737-743. |
| [14] |
SHENG Qiang1, 2, ZHENG Jian-ming1*, LIU Jiang-shan2, SHI Wei-chao1, LI Hai-tao2. Advances and Prospects in Inner Surface Defect Detection Based on Cite Space[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 9-15. |
| [15] |
CUI De-jian1, LIU Yang-yang1, XIA Yuan-tian1, JIA Wei-e1, LIAN Zheng-xing2, LI Lin1*. Non-Destructive Detection of Egg Fertilization Status Based on Hyperspectral Diffuse Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3685-3691. |
|
|
|
|
