不同品种鸡蛋贮期S-卵白蛋白含量分析及其可见/近红外光谱无损检测模型研究

引用本文: 付丹丹, 王巧华, 高升, 马美湖. 不同品种鸡蛋贮期S-卵白蛋白含量分析及其可见/近红外光谱无损检测模型研究[J]. 分析化学, 2020, 48(2): 289-297. doi: 10.19756/j.issn.0253-3820.191329 shu

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

不同品种鸡蛋贮期S-卵白蛋白含量分析及其可见/近红外光谱无损检测模型研究

1.
华中农业大学工学院, 武汉 430070;
2.
国家蛋品加工技术研发分中心, 武汉 430070;
3.
农业部长江中下游农业装备重点实验室, 武汉 430070;
4.
华中农业大学食品科学技术学院, 武汉 430070
基金项目:
本文系国家自然科学基金项目（No.31871863）、国家科技支撑计划项目（No.2015BAD19B05）、湖北省科技支撑计划项目（No.2015BBA172）、公益性行业（农业）科研专项项目（No.201303084）和国家留学基金项目资助

摘要: 利用可见/近红外光纤光谱采集罗曼粉壳和海蓝褐壳两个品种的鸡蛋在349-1000 nm的透射光谱，对270枚鸡蛋的天然卵白蛋白的S-型空间构象异构体（S-卵白蛋白，S-ovalbumin，S-ova）含量进行了定量分析，实现了不同品种鸡蛋中S-卵白蛋白含量的快速无损检测。通过比较贮期不同品种鸡蛋的平均光谱发现，两个品种鸡蛋的光谱吸收峰位置相同，仅可见光范围内的光谱吸收能量值有所不同。通过标准正态变量校正（SNV）对原始光谱进行预处理，并利用无信息变量消除算法（UVE）从500~950 nm的全光谱中提取了67个特征波长，建立的偏最小二乘（PLS）回归模型可以很好地预测不同品种的S-卵白蛋白含量。为了更进一步消除特征波长之间的多重共线性，利用逐步回归（Stepwise regression）算法对特征波长进行二次筛选，最终筛选出了16个特征波长，建立多元回归模型，其校正集的决定系数（R²）为0.9511，均方根误差（RMSE）为0.0478，预测集的R²为0.8380，RMSE为0.1116，预测集相对分析误差（RPD）为2.2620。此模型对预测集中50个罗曼粉壳鸡蛋和40个海蓝褐壳鸡蛋样本的R²分别为0.8119和0.9116，RMSE分别为0.1298和0.0834，模型适用性更佳。本研究结果表明，可见/近红外光谱能够对不同品种的S-卵白蛋白含量进行无损检测，建立的通用预测模型为开发便携式蛋白含量无损检测装置奠定了基础。

关键词:

English

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

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: 05 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).

Abstract: 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.

Key words:

1. [1]
  Hirose M. Foods Food Ingred. J. JP.,2005,210(8):770-777
2. [2]
  Huang Q, Qiu N, Ma M H. Poultry Sci.,2012,91(3):739-743
3. [3]
  FU Dan-Dan, WANG Qiao-Hua, MA Mei-Hu, XU Feng. Food Science,2018,39(19):8-13 付丹丹, 王巧华, 马美湖, 许锋.食品科学,2018,39(19):8-13
4. [4]
  MA Mei-Hu, ZENG Qi. Modern Food Science & Technology,2016,(12):329-336 马美湖, 曾齐.现代食品科技,2016,(12):329-336
5. [5]
  Fu D D, Wang Q H, Ma M H, Ma Y X. Int. J. Food Propert.,2019,22(1):1077-1086
6. [6]
  DAI Shuang-Feng, WANG Nan, ZHANG Li-Fu, HUANG Chang-Ping. Spectroscopy and Spectral Analysis,2019,39(2):548-552 代双凤, 王楠, 张立福, 黄长平.光谱学与光谱分析,2019,39(2):548-552
7. [7]
  WANG Fan, LI Yong-Yu, PENG Yan-Kun, SUN Hong-Wei, LI Long. Chinese J. Anal. Chem.,2018,46(9):1424-1431 王凡, 李永玉, 彭彦昆, 孙宏伟, 李龙.分析化学,2018,46(9):1424-1431
8. [8]
  Nawar S, Mouazen A M. Soil Tillage Res.,2019,190:120-127
9. [9]
  Zhu Z H, Li W Q, Wang Q H, Tang Y. J. Food Process Engineer.,2017, 40(3):UNSP12345
10. [10]
  DUAN Yu-Fei, WANG Qiao-Hua, MA Mei-Hu, LU Xi, WANG Cai-Yun. Spectroscopy and Spectral Analysis,2016,36(4):981-985 段宇飞, 王巧华, 马美湖, 卢茜, 王彩云.光谱学与光谱分析,2016,36(4):981-985
11. [11]
  Dong X G, Dong J, Peng Y K, Tang X Y. Spectrosc. Lett.,2017,50(9):463-469
12. [12]
  Syduzzaman M, Rahman A, Alin K, Fujitani S, Kashimori A, Suzuki T, Ogawa Y, Kondo N. Engineer. Agric. Environ. Food, 2019,12(3):289-296
13. [13]
  Dong J, Dong X G, Li Y L, Peng Y K, Chao K L, Gao C Y, Tang X Y. Comput. Electron. Agric.,2019,157:471-478[LM] 14 WU Jian-Hu, HUANG Jun. Modern Food Science & Technology,2015, (5):285-290 吴建虎, 黄钧.现代食品科技,2015,(5):285-290
14. [14]
  LI Hai-Feng, FANG Meng-Meng. Science and Technology of Food Industry,2017,38(20):286-289, 293 李海峰, 房萌萌.食品工业科技,2017,38(20):286-289, 293
15. [15]
  Suktanarak S, Teerachaichayut S. J. Food Engineer.,2017,215:97-103.
16. [16]
  WANG Zhuan-Wei, CHI Xi, GUO Wen-Chuan, ZHAO Chun-Jiang. Transactions of the Chinese Society for Agricultural Machinery,2018,49(05):355-361 王转卫, 迟茜, 郭文川, 赵春江.农业机械学报,2018,49(05):355-361
17. [17]
  LI Qian-Qian, TIAN Kuang-Da, LI Zu-Hong. Chinese J. Anal. Chem.,2013,41(6):917-921 李倩倩, 田旷达, 李祖红.分析化学,2013,41(6):917-921
18. [18]
  TU Yu-Long, ZOU Bin, JIANG Xiao-Lu, TAO Chao, TANG Yu-Qi, FENG Wei-Wei. Spectroscopy and Spectral Analysis,2018,38(2):575-581 涂宇龙, 邹滨, 姜晓璐, 陶超, 汤玉奇, 冯徽徽.光谱学与光谱分析,2018,38(2):575-581
19. [19]
  Feng C H, Makino Y, Yoshimura M, Francisco J. Rodríguez P. Food Chem.,2018,264:419-426
20. [20]
  XIE Yue, ZHOU Cheng, TU Cong, ZHANG Zu-Liang, WANG Jian-Fei. Chinese J. Anal. Chem.,2017,45(3):363-368 谢越, 周成, 涂从, 张祖亮, 汪建飞.分析化学,2017,45(3):363-368
21. [21]
  Qi H J, Tarin P K, Arnon K, Jin X, Li S W. Soil Tillage Res.,2018,175:267-275
22. [22]
  XIONG Huan, XU Hui-Rong, ZHOU Wan-Huai. Transactions of the Chinese Society of Agricultural Engineering,2013,29(Supp.1):264-269 熊欢, 徐惠荣, 周万怀.农业工程学报,2013,29(Supp.1):264-269
23. [23]
  FU Dan-Dan, WANG Qiao-Hua. Food Science,2016,37(22):173-179 付丹丹, 王巧华.食品科学,2016, 37(22):173-179

扫一扫看文章

计量

PDF下载量: 0
文章访问数: 12
HTML全文浏览量: 0

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

不同品种鸡蛋贮期S-卵白蛋白含量分析及其可见/近红外光谱无损检测模型研究

English

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

计量

文章相关

通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处