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| Study on Fluorescence Spectrometric Detection of Potassium Sorbate Based on CS-SVM |
| WANG Shu-tao,ZHU Cai-yun,LIU Ming-xin, PENG Tao,CHENG Qi,KONG De-ming,WANG Yu-tian |
| Key Laboratory of Measurement Technology and Instrumentation, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract By analyzing the fluorescence spectra of potassium sorbate in orange juice solution, it is found that fluorescence spectrum of potassium sorbate has glares where emission wavelength is in the range of 450～510nm when excitation wavelength is at 375nm, which indicates that the fluorescence characteristics of orange juice could interfere with the fluorescence spectrum of potassium sorbate. The model of cuckoo search algorithm (CS) optimizing support vector machine (SVM) is built to train 15 samples and predict the concentration of 7 potassium sorbate samples. The average recovery rate of CS-SVM is 99.07% and the mean square error is 1.21×10-5g/L. The results show that the training process,the average recovery and error of CS-SVM are all better than PSO-SVM and GA-SVM, and that CS-SVM can accurately determine the concentration of potassium sorbate in orange juice solution.
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Received: 05 July 2017
Published: 05 September 2018
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| Fund:the National Natural Science Foundation of China;the National Natural Science Foundation of China |
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Corresponding Authors:
Cai-Yun ZHU
E-mail: tydf15138004018@163.com
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2018, Vol. 39 
