基于ICSO-SVM和三维荧光光谱的山梨酸钾浓度检测

doi:10.3964/j.issn.1000-0593(2020)05-1614-06

Abstract
References
Related Citation (15)

Download: PDF (3444 KB)
Export: BibTeX | EndNote (RIS)

Abstract Potassium sorbate is a typical food preservative in daily life. Excessive consumption of the preservative potassium sorbate shall do harm to people’s health seriously. Using orange juice as background solution, 22 sets of samples of potassium sorbate orange juice solution with potassium sorbate content ranging from 0.007 0～0.100 0 g·L^-1 were prepared. In this paper, the fluorescence characteristics of potassium sorbate in aqueous solution and in orange juice solution are studied by using FS920 fluorescence spectrometer. Due to the interference of orange juice, the concentration of potassium sorbate no longer satisfies the linear relationship with fluorescence intensity, and the prediction of the concentration of the substance is complicated. In this paper, an improved chicken swarm optimization support vector machine (ICSO-SVM) model is constructed to process the fluorescence spectrum data. Eighteen samples are selected as training set and four samples as prediction set. Under the optimum excitation wavelength λ_ex=375 nm, the fluorescence intensity of each samples in the range of 450～520 nm are taken as input, and the concentrations of potassium sorbate orange juice are taken as output. Firstly, the parameters of the improved chicken swarm algorithm (ICSO) are initialized, then the optimal values of penalty factor C and kernel parameter g of the support vector machine (SVM) are found by training, and the optimal values are input into the ICSO-SVM model. The predicted concentration values of four groups are 0.011 5，0.026 0，0.077 0 and 0.092 0 g·L^-1, respectively. The mean square error of ICSO-SVM model is 1.01×10^-5 g·L^-1, and the average recovery is 101.73%. Compared with chicken swarm optimization support vector machine (CSO-SVM), genetic algorithm optimization support vector machine (GA-SVM) and particle swarm optimization support vector machine (PSO-SVM) under the same conditions. The results show that the prediction accuracy of ICSO-SVM model is higher than that of CSO-SVM, GA-SVM and PSO-SVM. Moreover, the improved chicken swarm algorithm is easier to find the global optimal value in the training process and has faster iteration speed. This paper provides a new method for predicting the concentration of substances.

Key words：Fluorescence spectra; Improved chicken swarm algorithm; Support vector machine; Potassium sorbate

Received: 2019-03-27 Accepted: 2019-07-22

ZTFLH:

O433.4

Corresponding Authors: LIU Shi-yu E-mail: 2576954766@qq.com

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)05-1614-06 OR https://www.gpxygpfx.com/EN/Y2020/V40/I05/1614

[1] XIAN Zhi-feng(冼志锋). Enterprise Science and Technology and Development(企业科技与发展), 2014, 14(18): 21.
[2] Cao Yu, Li Jia-yu, Liu Jun, et al. Journal of Applied Polymer Science, 2018, 135(48): 46885.
[3] Xie Shengming, Yuan Liming. Journal of Separation Science, 2017, 40(1): 124.
[4] JI Jie, LI Xiu-qin, MAO Ting, et al(纪洁, 李秀琴, 毛婷, 等). Acta Metrologica Sinica(计量学报), 2017, 38(4): 507.
[5] Tighrine Abderrahmane, Amir Youcef, Alfaro Pilar, et al. Food Chemistry, 2019, 277: 586.
[6] WANG Shu-tao, CHEN Dong-ying, WEI Meng, et al(王书涛, 陈东营, 魏蒙, 等). Chinese Journal of Laser(中国激光), 2015, 42(5): 0515004.
[7] SHEN Hai-dong, BAI Yu-hong, ZHENG Hua, et al(沈海东, 白玉洪, 郑华, 等). Offshore Oil(海洋石油), 2017, 37(2): 61.
[8] MA Yan, ZHAO Han-dong, HUANG Xin(马焱, 赵捍东, 黄鑫). Journal of Detection and Control(探测与控制学报), 2017, 39(2): 124.
[9] YANG Jing, LI Jun-fu, ZHANG Gao-qing(杨旌, 李俊付, 张高青). Journal of Guangxi University·Natural Science Edition(广西大学学报·自然科学版), 2017, 42(4): 1623.
[10] ZHANG Ying-jie, ZHANG Shu-qun(张莹杰, 张树群). Computer Engineering and Science(计算机工程与科学), 2018, 40(12): 2252.
[11] WANG Dong-feng, MENG Li(王东风, 孟丽). Acta Automatica Sinica(自动化学报), 2016, 42(10): 1552.
[12] Baqheri Majid, Rezaei Hadi. Carbonates and Evaporites, 2019, 34：699.
[13] Torabi Shadi, Safi-Esfahani Faramarz. Soft Computing, 2019，23(20): 10129.
[14] KONG Fei, WU Ding-hui(孔飞, 吴定会). Journal of Jiangnan University·Natural Science Edition(江南大学学报·自然科学版), 2015, 14(6): 681.
[15] LI Zhen-bi, WANG Kang, JIANG Yuan-yuan(李振璧, 王康, 姜媛媛). Microellectronics and Computer(微电子学与计算机), 2017, 34(2): 30.
[16] XU Yi-xun, LI Wang, LI Dong-dong, et al(许仪勋, 李旺, 李东东, 等). Power System Protection and Control(电力系统保护与控制), 2016, 44(13): 27.
[17] MA Wang-qiong, CHEN Hua-cai, CHEN Xiao-zhen, et al(麻望琼, 陈华才, 陈小珍). Journal of China University of Metrology(中国计量学院学报), 2015, (2): 182.