激光技术, 2017, 41 (4): 489, 网络出版: 2017-08-09   

同步荧光结合神经网络同时测定3种抗生素

Simultaneous determination of three antibiotics based on synchronous fluorescence combined with neural network
作者单位
1 江南大学 理学院 光电信息科学与工程系, 无锡 214122
2 江苏省轻工光电工程技术研究中心, 无锡 214122
摘要
为了更快速、简便、准确地同时测定多种抗生素混合物, 采用同步荧光光谱结合径向基神经网络的方法, 对3种氟喹诺酮类抗生素(乳酸环丙沙星、乳酸左氧氟沙星、盐酸左氧氟沙星)的同步荧光光谱进行研究。选择3组分浓度均为1.67ng/mL的混合溶液, 测量其3维同步荧光光谱; 分别测量39种不同浓度的混合溶液样本的同步荧光光谱; 选取其中35种作为训练组, 其余4种作为预测组, 将训练组样本对应的光谱数据作为输入, 建立和训练径向基神经网络; 在发射波长与激发波长的差Δλ=194nm条件下, 利用训练好的神经网络对预测组中各组分的浓度进行预测, 得到3种组分浓度预测的平均相对误差分别达到3.59%,3.47%,3.09%。结果表明, 当Δλ设定为194nm时, 3种抗生素的同步荧光峰差异最为明显、区分度高, 该方法能实现对3种抗生素混合物中各组分的同时测定。这为多种抗生素混合物同时测定提供了一种快速、简便、准确的方法。
Abstract
In order to determine the antibiotic mixture more quickly, conveniently and accurately at the same time, synchronous fluorescence spectra of 3 kinds of fluoroquinolones (ciprofloxacin, levofloxacin lactate, levofloxacin hydrochloride) were studied based on synchronous fluorescence spectroscopy combined with radial basis function neural network. The 3-D synchronous fluorescence spectrum for the 3-component mixed solution with concentration of 1.67ng/mL was measured. Then, simultaneous fluorescence spectra of 39 mixed solutions with different concentrations were measured. 35 of them were selected as the training group, and the other 4 were used as the prediction group. The spectral data corresponding to the training samples were taken as input to build and train the radial basis function neural network. The results show that, when Δλ=194nm, the concentration of each component in the prediction group is predicted by the trained neural network. The average relative errors of intensity prediction of 3 components were 3.59%, 3.47% and 3.09%, respectively. The method provides rapid, simple and accurate method for simultaneous determination of multiple antibiotic mixtures.

庄宇, 马超群, 王小晗, 陶悦, 任芮, 那明珠, 陈国庆. 同步荧光结合神经网络同时测定3种抗生素[J]. 激光技术, 2017, 41(4): 489. ZHUANG Yu, MA Chaoqun, WANG Xiaohan, TAO Yue, REN Rui, NA Mingzhu, CHEN Guoqing. Simultaneous determination of three antibiotics based on synchronous fluorescence combined with neural network[J]. Laser Technology, 2017, 41(4): 489.

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