最小二乘支持向量机在对羟基苯甲酸甲酯钠荧光检测中的应用

对羟基苯甲酸甲酯钠是一种常见的食品添加剂，如果长时间食用或者超量食用会对人体造成一定的危害。采用FS920荧光光谱仪对对羟基苯甲酸甲酯钠橙汁溶液和水溶液进行检测，实验结果表明两者的特征峰发生了明显的变化。经分析得出，对羟基苯甲酸甲酯钠橙汁溶液的荧光光谱受到橙汁荧光特性干扰，一定浓度范围的溶液光谱图存在较大差距，对羟基苯甲酸甲酯钠浓度与荧光强度之间的关系复杂。为了精确地检测橙汁中对羟基苯甲酸甲酯钠的浓度，结合荧光光谱法与最小二乘支持向量机，建立了橙汁溶液中对羟基苯甲酸甲酯钠的检测模型，使用改进的粒子群优化算法得到影响模型性能的正则化参数和核函数。实验得到了较为理想的结果，与普通反向传播（BP）神经网络、基本粒子群寻参的最小二乘支持向量机等方法相比，该方法性能最优，得到的平均回收率为97.05%，平均相对误差为2.71%，均方根误差为3.04%，模型输出与真实值之间的相关系数是0.9999。该方案可以做为橙汁中对羟基苯甲酸甲酯钠浓度的精确检测方法。

Abstract

Sodium methylparaben is a common food additive, and long-period or excessive ingestion will do harm to the human body. An FS920 fluorescence spectrometer is used to detect sodium methylparaben in orange juice and aqueous solutions. The experimental results show that the characteristic peaks of the orange juice and the aqueous solutions have obvious difference. It is inferred that the interference is mainly from the fluorescence characteristics of orange juice. There exists notable difference between the two solutions with certain concentration range of sodium methylparaben, and the relationship between the relative fluorescence intensity and the sodium methylparaben concentration is complex. Therefore, in order to accurately detect the content of sodium methylparaben in orange juice, fluorescence spectroscopy and least squares support vector machine are combined to establish a model to detect sodium methylparaben in orange juice, and the regularization parameter and the kernel function are obtained with the improved particle swarm optimization algorithm. Compared with ordinary back-propagating (BP) neural network and least squares support vector machine based on particle swarm optimization, the model proposed has optimal performance, the average recovery rate is 97.05%, the average relative error is 2.71%, the root mean square error is about 3.04%, and the correlation coefficient between the model output and the real value is about 0.9999. This method can be used for accurate determination of sodium methylparaben in orange juice.

参考文献

[1] 王丽. 食品中五种常见防腐剂同时检测技术研究［D］. 杨凌: 西北农林科技大学, 2012.

Wang Li. Study on the simultaneous detection technique of five preservatives in foods［D］. Yangling: Northwest A & F University, 2012.

[2] 覃青云. 对羟基苯甲酸酯钠盐在牙膏中防腐的应用研究［J］. 香料香精化妆品, 2009(4): 14-16.

Qin Qingyun. Study of antiseptic application in toothpaste by sodium p-hydroxybenzoate［J］. Flavour Fragrance Cosmetics, 2009(4): 14-16.

[3] 岳强. 对羟基苯甲酸酯在人体中的暴露研究进展［J］. 环境与发展, 2011, 23(3): 161-165.

Yue Qiang. Advances in human exposure of parabens［J］. Environment and Development, 2011, 23(3): 161-165.

[4] 牟冠文, 李光浩. 食品防腐剂的概况及其检测方法［J］. 食品与发酵工业, 2006, 32(10): 103-107.

Mu Guanwen, Li Guanghao. The introduction of food preservatives and their determination technology［J］. Food and Fermentation Industries, 2006, 32(10): 103-107.

[5] 张全美, 黄锦燕, 王守卿. 超声提取-毛细管气相色谱法检测酱腌菜中对羟基苯甲酸酯［J］. 化学分析计量, 2012, 21(6): 85-87.

Zhang Quanmei, Huang Jinyan, Wang Shouqing. Determination of p-hydroxy benzoic acid ethyl ester in pickles by ultrasonic extracting-gas chromatography［J］. Chemical Analysis and Meterage, 2012, 21(6): 85-87.

[6] 郑金琪, 韩加怡, 李会林. HPLC测定对羟基苯甲酸甲酯钠的含量及有关物质［J］. 中国现代应用药学, 2012, 29(1): 72-76.

Zheng Jinqi, Han Jiayi, Li Huilin. Determination of content and related substances of methylparaben sodium by HPLC［J］. Chinese Journal of Modern Applied Pharmacy, 2012, 29(1): 72-76.

[7] 陈国庆. 荧光光谱技术在食品安全监控中的应用研究［D］. 无锡: 江南大学, 2010.

Chen Guoqing. Studies on application of fluoresence spectroscopy in food safety supervision［D］. Wuxi: Jiangnan University, 2010.

[8] 陶春先, 阮俊, 舒顺朋, 等. 三维荧光光谱对食用植物油中煎炸油的检测［J］. 中国激光, 2016, 43(1): 0115001.

Tao Chunxian, Ruan Jun, Shu Shunpeng, et al. Detection of fried oil in edible oil based on three-dimensional fluorescence spectroscopy［J］. Chinese J Lasers, 2016, 43(1): 0115001.

[9] 奚留华, 陈国庆, 朱焯炜, 等. 基于三维荧光光谱测定芝麻香型白酒的年份［J］. 激光与光电子学进展, 2016, 53(12): 123002.

Xi Liuhua, Chen Guoqing, Zhu Zhuowei, et al. Determination of vintages of sesame flavor liquors by three-dimensional fluoresence spectroscopy［J］. Laser & Optoelectronics Progress, 2016, 53(12): 123002.

[10] 向昌盛. 基于支持向量机的时间序列组合预测模型［D］. 长沙: 湖南农业大学, 2011.

Xiang Changsheng. Time series combination prediction model based on support vector machine［D］. Changsha: Hunan Agricultural University, 2011.

[11] 李欢欢, 卢伟, 杜昌文, 等. 基于光声光谱结合LS-SVR的稻种活力快速无损检测方法研究［J］. 中国激光, 2015, 42(11): 1115003.

Li Huanhuan, Lu Wei, Du Changwen, et al. Study on rapid and non-destructive detection of rice seed vigor based on photoacoustic spectroscopy combined with LS-SVR［J］. Chinese J Lasers, 2015, 42(11): 1115003.

[12] 王贺, 胡志坚, 张翌晖, 等. 基于IPSO-LSSVM的风电功率短期预测研究［J］. 电力系统保护与控制, 2012, 40(24): 107-112.

Wang He, Hu Zhijian, Zhang Yihui, et al. Short-term prediction of wind power based on IPSO-LSSVM［J］. Power System Protection and Control, 2012, 40(24): 107-112.

[13] 刘金颂, 原思聪, 江祥奎, 等. 基于PSO和LSSVM回归的摄像机标定［J］. 光电工程, 2010, 37(5): 47-51.

Liu Jinsong, Yuan Sicong, Jiang Xiangkui, et al. Camera calibration based on PSO and LSSVM regression［J］. Opto-Electronic Engineering, 2010, 37(5): 47-51.

[14] 龙文, 焦建军, 龙祖强. 基于PSO优化LSSVM的未知模型混沌系统控制［J］. 物理学报, 2011, 60(11): 110506.

Long Wen, Jiao Jianjun, Long Zuqiang. Control of chaos solely based on PSO-LSSVM without using an analytical model［J］. Acta Physica Sinica, 2011, 60(11): 110560.

[15] 张元志, 刘勇, 候华毅, 等. 基于粒子群优化算法的生物组织固有荧光光谱复原方法［J］. 中国激光, 2016, 43(5): 0504001.

Zhang Yuanzhi, Liu Yong, Hou Huayi, et al. Intrinsic tissue fluorescence spectrum recovery based on particle swarm optimization algorithm［J］. Chinese J Lasers, 2016, 43(5): 0504001.

[16] 郑含博, 王伟, 李晓纲, 等. 基于多分类最小二乘支持向量机和改进粒子群优化算法的电力变压器故障诊断方法［J］. 高电压技术, 2014, 40(11): 3424-3429.

Zhen Hanbo, Wang Wei, Li Xiaogang, et al. Fault diagnosis method of power transformers using multi-class LS-SVM and improved PSO［J］. High Voltage Engineering, 2014, 40(11): 3424-3429.

[17] 王书涛, 陈东营, 侯培国, 等. 基于荧光光谱技术和GA-BP神经网络的对羟基苯甲酸甲酯钠含量的测定［J］. 光谱学与光谱分析, 2015, 35(6): 1606-1610.

Wang Shutao, Chen Dongying, Hou Peiguo, et al. Determination of the sodium methylparaben content based on spectrum fluorescence spectral technology and GA-BP neural network［J］. Spectroscopy and Spectral Analysis, 2015, 35(6): 1606-1610.

王书涛, 张彩霞, 王志芳, 张强, 马晓晴, 郑亚南. 最小二乘支持向量机在对羟基苯甲酸甲酯钠荧光检测中的应用[J]. 激光与光电子学进展, 2017, 54(7): 073001. Wang Shutao, Zhang Caixia, Wang Zhifang, Zhang Qiang, Ma Xiaoqing, Zheng Ya′nan. Application of Least Squares Support Vector Machine in Fluorescence Detection of Sodium Methylparaben[J]. Laser & Optoelectronics Progress, 2017, 54(7): 073001.

最小二乘支持向量机在对羟基苯甲酸甲酯钠荧光检测中的应用

关于本站 Cookie 的使用提示

全站搜索

最小二乘支持向量机在对羟基苯甲酸甲酯钠荧光检测中的应用

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索