光谱学与光谱分析, 2020, 40 (5): 1614, 网络出版: 2020-12-10   

基于ICSO-SVM和三维荧光光谱的山梨酸钾浓度检测

The Determination of Potassium Sorbate Concentration Based on ICSO-SVM Combining Three-Dimensional Fluorescence Spectra
作者单位
燕山大学电气工程学院河北省测试计量技术及仪器重点实验室, 河北 秦皇岛 066004
摘要
山梨酸钾是日常生活中一种典型的食品防腐剂。 过量食用防腐剂山梨酸钾, 会严重危害人身体健康。 以橙汁作为背景溶液, 配制山梨酸钾含量在0.007 0~0.100 0 g·L-1之间的山梨酸钾橙汁溶液样本共22组。 应用FS920荧光光谱仪对防腐剂山梨酸钾的水溶液以及橙汁溶液的荧光特性进行了研究。 由于山梨酸钾和橙汁的荧光特性相互干扰, 山梨酸钾橙汁溶液的浓度与荧光强度不再满足线性关系, 所以物质浓度的预测比较复杂。 通过构建改进鸡群算法优化支持向量机(ICSO-SVM)的模型对荧光光谱数据进行处理。 模型选取18个样本作为训练集, 4个样本作为预测集。 提取各样本在最佳激发波长λex=375 nm下, 发射波长在450~520 nm范围内的荧光强度值作为输入, 以山梨酸钾橙汁溶液的浓度值作为输出。 首先对改进鸡群算法(ICSO)的各个参数进行初始化, 然后经过训练输出支持向量机(SVM)的惩罚因子C和核参数g的最佳值, 再将得到的最佳值输入SVM模型, 得到4组预测浓度值分别为0.011 5, 0.026 0, 0.077 0和0.092 0 g·L-1。 ICSO-SVM模型的均方误差为1.02×10-5 g·L-1, 平均回收率为101.88%。 相同条件下与鸡群算法优化支持向量机(CSO-SVM)、 遗传算法优化支持向量机(GA-SVM)和粒子群算法优化支持向量机(PSO-SVM)进行对比。 结果表明ICSO-SVM模型的预测精度高于CSO-SVM, GA-SVM和PSO-SVM, 而且改进鸡群算法在训练过程中更容易找到全局最优值, 迭代速度更快。 该研究为物质浓度预测提供了一种新方法。
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 λex=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.

王书涛, 刘诗瑜, 王志芳, 张靖昆, 孔德明, 王玉田. 基于ICSO-SVM和三维荧光光谱的山梨酸钾浓度检测[J]. 光谱学与光谱分析, 2020, 40(5): 1614. WANG Shu-tao, LIU Shi-yu, WANG Zhi-fang, ZHANG Jing-kun, KONG De-ming, WANG Yu-tian. The Determination of Potassium Sorbate Concentration Based on ICSO-SVM Combining Three-Dimensional Fluorescence Spectra[J]. Spectroscopy and Spectral Analysis, 2020, 40(5): 1614.

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