Food and Fermentation Industries >

2019 , Vol. 45 >Issue 15: 251 - 255

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.020005

Identifying royal jelly under different storage conditions based on mid-infrared spectroscopy

  • CHEN Fan ,
  • LIU Cuiling ,
  • CHEN Lanzhen ,
  • SUN Xiaorong ,
  • LI Yi ,
  • JIN Yue
Expand
  • 1School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    2Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology and Business University, Beijing 100048, China
    3Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture Institute of Apicultural Research,Chinese Academy of Agricultural Sciences Beijing 100093, China

Online published: 2019-09-03

Fold

Abstract

In order to efficiently, quickly and non-destructively detect royal jelly under different storage conditions, a qualitative analysis method was studied based on support vector machine (SVM), orthogonal partial least squares discriminant analysis (OPLS-DA) and mid-infrared spectroscopy. Royal jelly stored at 4 ℃ and at room temperature (25 ℃) for 7, 14, 21 d were tested. The spectra of the samples were collected by mid-infrared spectroscopy, followed by establishing qualitative analysis models for two-class (freezing and room temperature) and three-class (stored at room temperature for 7, 14, 21 d). The results showed that the predictive accuracy of the two-class and three-class models based on SVM were 92.31% and 100%, respectively. Moreover, the predictive accuracy of the two-class and three class models based on OPLS-DA were 95.52% and 96.97%, respectively. Therefore, mid-infrared spectroscopy combined with SVM and OPLS-DA algorithm can effectively identify frozen and room temperature stored royal jelly, which provides a possibility for rapid and non-destructive identification of royal jelly quality.

Key words： mid infrared spectroscopy; support vector machine; orthogonal partial least squares discriminant analysis; qualitative analysis

Cite this article

CHEN Fan , LIU Cuiling , CHEN Lanzhen , SUN Xiaorong , LI Yi , JIN Yue . Identifying royal jelly under different storage conditions based on mid-infrared spectroscopy[J]. Food and Fermentation Industries, 2019 , 45(15) : 251 -255 . DOI: 10.13995/j.cnki.11-1802/ts.020005

References

[1] 陈亚玲, 张晶. 蜂王浆的研究进展[J]. 食品研究与开发, 2015 (2):148-152.
[2] ZHANG Lan, HAN Bin, LI Rongli, et al. Comprehensive identification of novel proteins and N-glycosylation sites in royal jelly[J]. Bmc Genomics, 2014, 15:135-149.
[3] HAN Bin, FANG Yu, FENG Mao, et al. In-depth phosphoproteomic analysis of royal jelly derived from western and eastern honeybee species[J]. Journal of Proteome Research, 2014, 13(12):5 928-5 943.
[4] 刘娟, 高铁俊,董捷,等. 蜂王浆室温储存过程中的褐变产物[J]. 食品科学, 2012, 33(6):238-241.
[5] 孙晓军, 杨晓慧,李桂明,等. 蜂王浆贮藏过程中品质变化的研究[J]. 山东农业科技, 2015, 47(5):102-104.
[6] 张红城, 孙丽萍,董捷,等. 蜂王浆在常温储存条件下品质变化的研究[J]. 食品科学, 2007(11):159-161.
[7] SANO O, KUNIKATA T, KOHNO K, et al. Characterization of royal jelly proteins in both Africanized and European honeybees(Apis mellifera) by two-dimensional gel electrophoresis[J]. Journal of Agricultural and Food Chemistry, 2004, 52:15-20.
[8] 周才琼, 罗雪雅. 蜂王浆新鲜度指标筛选及新鲜度的评判[J]. 食品与发酵工业, 2010, 36(3):131-132.
[9] BERTELLI D, PLESSI M, SABATINI A G, et al. Classification of Italian honeys by mid-infrared diffuse reflectance spectroscopy(DRIFTS)[J]. Food Chemistry, 2007, 101(4):1 565-1 570.
[10] 吴黎明, 周群,周骁,等. 蜂王浆不同贮存条件下蛋白质二级结构的Fourier变换红外光谱研究[J]. 光谱学与光谱分析, 2009, 29(1):82-97.
[11] ROHMAN A, MAN Y B C. Fourier transform infrared (FTIR) spectroscopy for analysis of extravirgin olive oil adulterated with palm oil[J]. Food Research International, 2010, 43(3):886-892.
[12] 李晓丽, 张裕莹,何勇. 基于中红外光谱技术检测茶叶中非法添加滑石粉的研究[J]. 光谱学与光谱分析, 2017, 37(4):1 081-1 085.
[13] 吴黎明, 周群,赵静,等. FTIR光谱法整体评价蜂王浆新鲜度的研究[J]. 光谱学与光谱分析, 2009, 29(12):3 236-3 240.
[14] 徐荣, 孙素琴,辛士光,等. 中红外光谱法对肉苁蓉的定性和半定量分析[J]. 红外与毫米波学报, 2010, 29(5):325-328.
[15] GRACA G, MOREIRA A S, CORREIA A J V, et al. Mid-infrared (MIR) metabolic fingerprinting of amniotic fluid: A possible avenue for early diagnosis of prenatal disorders?[J]. Analytica Chimica Acta, 2013,764:24-31.
[16] WAN Junhui, TIAN Peiling, YIN Hao, et al. A preliminary evaluation of attenuated total reflection fourier transform infrared spectroscopy for the hematological analysis of thalassemias[J]. Clinical Biochemistry, 2013, 46(1):128-132.
[17] SANTOS P M, PEREIRA-FILHO E R, RODRIGUEZ-SAONA L E. Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis[J]. Food Chemistry, 2013, 138(1):19-24.
[18] ABDUL R, YAAKOB B, CHE Man. The use of fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil[J]. Food Chemistry,2011, 129(2):583-588.
[19] KIZIL R, IRUDAYARAJ J, SEETHARAMAN K. Characterization of irradiated starches by using FT-raman and FTIR spectroscopy[J]. Journal of Agricultural & Food Chemistry, 2002, 50(14):3 912-3 918.
[20] 卢静静, 孙炜炜. FI-IR分析乳清分离蛋白一葡聚糖接枝物的结构变化[J]. 现代食品科技, 2014, 30(8):89-94.
[21] ZHU D, DAMODARAN S, LUCEY J A. Physicochemical and emulsifying properties of whey protein isolate (WPI-dextran conjugates produced in aqueous solution [J]. Journal of Agricultural and Food Chemistry, 2010, 58(5): 2 988-2 994.
[22] TANG C H, MA C Y. Effect of high pressure treatment on aggregation and structural properties of soy protein isolate [J]. LWT-Food Science and Technology, 2009, 42(2): 606-611.
[23] 褚小立. 化学计量学方法与分子光谱分析技术[M]. 北京:化学工业出版社, 2011:311-367.
[24] DAVID V S A. Advanced support vector machines and kernel methods [J]. Neurocomputing, 2003, 55(1-2):5-20.
[25] 胡晓华, 刘伟,刘长虹,等. 基于太赫兹光谱和支持向量机快速鉴别咖啡豆产地[J]. 农业工程学报, 2017, 33(9):302-307.
[26] 李永迪, 张贻杨,彭忠,等. 基于正交偏最小二乘判别分析法分析茯砖茶和千两茶差异性品质成分[J]. 食品安全质量检测学报, 2017, 8(11):4 382-4 387.
[27] 王俊, 许多宽,肖勇,等. 基十化学指标的烟叶产区正交偏最小二乘判别分析[J]. 中国烟草科学, 2017, (1): 91-96.
[28] 郭威, 孙蓉,王亮,等. 基于指纹图谱和OPLS-DA的越南和国产土茯苓差异性化合物探索[J]. 中国实验方剂学杂志, 2017 (14): 62-67.
Options
Outlines

/

〈 〉

Powered by Beijing Magtech Co. Ltd,.