Citation: FU Dan-Dan, WANG Qiao-Hua, GAO Sheng, MA Mei-Hu. Analysis of S-Ovalbumin Content of Different Varieties of Eggs during Storage and Its Nondestructive Testing Model by Visible-Near Infrared Spectroscopy[J]. Chinese Journal of Analytical Chemistry, ;2020, 48(2): 289-297. doi: 10.19756/j.issn.0253-3820.191329 shu

Analysis of S-Ovalbumin Content of Different Varieties of Eggs during Storage and Its Nondestructive Testing Model by Visible-Near Infrared Spectroscopy

FU Dan-Dan ^1, ,
WANG Qiao-Hua ^1,2,3 ,
GAO Sheng ¹ ,
MA Mei-Hu ^2,4

1.
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China;
2.
National Research and Development Center for Egg Processing, Huazhong Agricultural University, Wuhan 430070, China;
3.
Ministry of Agriculture Key Laboratory of Agricultural Equipment in the Middle and Lower Reaches of the Yangtze River, Wuhan 430070, China;
4.
College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China

Received Date: 11 June 2019
Revised Date: 5 December 2019

Fund Project: This work was supported by the National Natural Science Foundation of China (No.31871863), the National Science and Technology Major Project of the Ministry of Science and Technology of China during the 12th Five-year Plan Period (No.2015BAD19B05) and the Special Scientific Research Fund of Agricultural Public Welfare Profession Project of China (No.201303084).

The visible-near-infrared (Vis-NIR) transmission spectroscopy technique was used to analyze the content of S-ovalbumin (S-ova), which had high correlation with egg freshness, and to establish a nondestructive prediction model. The visible/near-infrared fiber spectroscopy were used to collect the transmission spectrum of two varieties of eggs at 349-1000 nm, and the S-ovalbumin content of 270 eggs was measured by wet chemistry method. By comparing the average spectra of eggs of different varieties during storage, it was found that the spectral absorption peaks of different varieties of eggs had the same position, and only the spectral energy values in the visible range differed. The original spectrum was preprocessed by standard normal variate (SNV), and 67 characteristic wavelengths were extracted from the full spectrum of 500-950 nm using uninformative variables elimination (UVE). It was concluded that partial least squares (PLS) regression model based on 67 characteristic wavelengths could predict the S-ovalbumin content. To further eliminate the multi-collinearity between the characteristic wavelengths, the stepwise regression algorithm was used to perform secondary screening on the characteristic wavelengths, and finally 16 characteristic wavelengths were selected. By using the 16 characteristic wavelengths to establish a multivariate regression model, the coefficient of determination (R²) of the training set was 0.9511, the root mean square error (RMSE) was 0.0478, and the R² of the prediction set was 0.8380. Besides, the RMSE was 0.1116, and the residual predictive deviation (RPD) was 2.2620. The general predictive model was used to predict the S-ovalbumin content of 50 eggs with Roman pink shell and 40 eggs with sea blue brown shell in the prediction set. The R² of the predicted and measured values were 0.8119 and 0.9116, respectively, and the RMSEs were 0.1298 and 0.0834, respectively. Therefore, the general model could perform nondestructive testing on the S-ovalbumin content of these two different varieties of eggs better, and the model was more applicable. The results showed that the visible/near-infrared spectroscopy could accurately detect the S-ovalbumin content of eggs in different varieties, and the established general prediction model laid a foundation for the development of portable non-destructive testing device for protein content.
- Visible-near infrared spectroscopy,
- Egg,
- S-Ovalbumin,
- Correlation,
- General model
1. [1]
2. [2]
3. [3]
4. [4]
5. [5]
6. [6]
7. [7]
8. [8]
9. [9]
10. [10]
11. [11]
12. [12]
13. [13]
14. [14]
15. [15]
16. [16]
17. [17]
18. [18]
19. [19]
20. [20]
21. [21]
22. [22]
23. [23]
1. [1]
  Yang Wang , Yunpeng Fu , Xiaoji Liu , Guotao Zhang , Guobin Li , Wanqiang Liu , Jinglun Wang . Structural Analysis of Nitrile Solutions Based on Infrared Spectroscopy Probes. University Chemistry, 2025, 40(4): 367-374. doi: 10.12461/PKU.DXHX202406113
2. [2]
  Yi Li , Zhaoxiang Cao , Peng Liu , Xia Wu , Dongju Zhang . Revealing the Coloration and Color Change Mechanisms of the Eriochrome Black T Indicator through Computational Chemistry and UV-Visible Absorption Spectroscopy. University Chemistry, 2025, 40(3): 132-139. doi: 10.12461/PKU.DXHX202405154
3. [3]
  Yanan Fan , Jingjing Huang . Interactive Electronic Courseware Facilitates the Development of Integrated Undergraduate-Graduate Instrumental Analysis Laboratory Courses: A Case Study of UV-Vis Spectroscopy Analysis Experiment. University Chemistry, 2025, 40(10): 282-287. doi: 10.12461/PKU.DXHX202411009
4. [4]
  Qin Li , Huihui Zhang , Huajun Gu , Yuanyuan Cui , Ruihua Gao , Wei-Lin Dai . In situ Growth of Cd_0.5Zn_0.5S Nanorods on Ti₃C₂ MXene Nanosheet for Efficient Visible-Light-Driven Photocatalytic Hydrogen Evolution. Acta Physico-Chimica Sinica, 2025, 41(4): 100031-0. doi: 10.3866/PKU.WHXB202402016
5. [5]
  Xue Wu , Yupeng Liu , Bingzhe Wang , Lingyun Li , Zhenjian Li , Qingcheng Wang , Quansheng Cheng , Guichuan Xing , Songnan Qu . Rationally assembling different surface functionalized carbon dots for enhanced near-infrared tumor photothermal therapy. Acta Physico-Chimica Sinica, 2025, 41(9): 100109-0. doi: 10.1016/j.actphy.2025.100109
6. [6]
  Jiahui CHEN , Tingting ZHENG , Xiuyun ZHANG , Wei LÜ . Research progress of near-infrared absorption inorganic nanomaterials in photothermal and photodynamic therapy of tumors. Chinese Journal of Inorganic Chemistry, 2024, 40(12): 2396-2414. doi: 10.11862/CJIC.20240106
7. [7]
  Han ZHANG , Jianfeng SUN , Jinsheng LIANG . Hydrothermal synthesis and luminescent properties of broadband near-infrared Na₃CrF₆ phosphor. Chinese Journal of Inorganic Chemistry, 2025, 41(2): 349-356. doi: 10.11862/CJIC.20240098
8. [8]
  Ze-Yuan Ma , Mei Xiao , Cheng-Kun Li , Adedamola Shoberu , Jian-Ping Zou . S-(1,3-Dioxoisoindolin-2-yl)O,O-diethyl phosphorothioate (SDDP): A practical electrophilic reagent for the phosphorothiolation of electron-rich compounds. Chinese Chemical Letters, 2024, 35(5): 109076-. doi: 10.1016/j.cclet.2023.109076
9. [9]
  Jie Li , Huida Qian , Deyang Pan , Wenjing Wang , Daliang Zhu , Zhongxue Fang . Efficient Synthesis of Anethaldehyde Induced by Visible Light. University Chemistry, 2024, 39(4): 343-350. doi: 10.3866/PKU.DXHX202310076
10. [10]
  Tongyan Yu , Pan Xu . Visible-Light Photocatalyzed Radical Rearrangement Reaction. University Chemistry, 2025, 40(7): 169-176. doi: 10.12461/PKU.DXHX202409070
11. [11]
  Qi Wang , Yicong Gao , Feng Lu , Quli Fan . Preparation and Performance Characterization of the Second Near-Infrared Phototheranostic Probe: A New Design and Teaching Practice of Polymer Chemistry Comprehensive Experiment. University Chemistry, 2024, 39(11): 342-349. doi: 10.12461/PKU.DXHX202404141
12. [12]
  Ruonan Li , Shijie Liang , Yunhua Xu , Cuifen Zhang , Zheng Tang , Baiqiao Liu , Weiwei Li . Chlorine-Substituted Double-Cable Conjugated Polymers with Near-Infrared Absorption for Low Energy Loss Single-Component Organic Solar Cells. Acta Physico-Chimica Sinica, 2024, 40(8): 2307037-0. doi: 10.3866/PKU.WHXB202307037
13. [13]
  Bo YANG , Gongxuan LÜ , Jiantai MA . Nickel phosphide modified phosphorus doped gallium oxide for visible light photocatalytic water splitting to hydrogen. Chinese Journal of Inorganic Chemistry, 2024, 40(4): 736-750. doi: 10.11862/CJIC.20230346
14. [14]
  Xinzhe HUANG , Lihui XU , Yue YANG , Liming WANG , Zhangyong LIU , Zhongjian WANG . Preparation and visible light responsive photocatalytic properties of BiSbO₄/BiOBr. Chinese Journal of Inorganic Chemistry, 2025, 41(2): 284-292. doi: 10.11862/CJIC.20240212
15. [15]
  Junqiao Zhuo , Xinchen Huang , Qi Wang . Symbol Representation of the Packing-Filling Model of the Crystal Structure and Its Application. University Chemistry, 2024, 39(3): 70-77. doi: 10.3866/PKU.DXHX202311100
16. [16]
  Shule Liu . Application of SPC/E Water Model in Molecular Dynamics Teaching Experiments. University Chemistry, 2024, 39(4): 338-342. doi: 10.3866/PKU.DXHX202310029
17. [17]
  Ruilin Han , Xiaoqi Yan . Comparison of Multiple Function Methods for Fitting Surface Tension and Concentration Curves. University Chemistry, 2024, 39(7): 381-385. doi: 10.3866/PKU.DXHX202311023
18. [18]
  Zixuan Jiang , Yihan Wen , Kejie Chai , Weiming Xu . Exploring Chemistry Bridging Education from Data-Driven to Symbol Establishment within the Framework of AI Models. University Chemistry, 2025, 40(9): 132-141. doi: 10.12461/PKU.DXHX202502004
19. [19]
  Yalu Ma , Yun Tian , Xiaofei Ma . DeepSeek Large Model: Implications for Inorganic Chemistry Teaching and Learning. University Chemistry, 2025, 40(9): 171-177. doi: 10.12461/PKU.DXHX202502109
20. [20]
  Xiaolong Zhang , Mingshan Jin , Shaoli Liu , Bingfei Yan , Yun Li . Constructing High-Precision Potential Energy Surfaces Based on Physical Models: A Comprehensive Computational Chemistry Experiment. University Chemistry, 2025, 40(10): 257-262. doi: 10.12461/PKU.DXHX202411049

Get Citation

PDF

XML

Metrics

PDF Downloads(9)
Abstract views(1015)
HTML views(91)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142