基于QPSO-MLSSVM算法的拉曼光谱检测四组分调和油含量

doi:10.3964/j.issn.1000-0593(2018)05-1437-07

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Abstract This paper presents a new hybrid optimization algorithm based on the multi-output least squares support vector machine (MLSSVM) which is optimized by quantum-behaved particle swarm optimization (QPSO). The rapid quantitative identification for the peanut oil,sesame oil, sunflower oil and soybean oil in the four - component edible blending oil can be realized with the algorithm combined with laser Raman spectroscopy. The background fluorescence was removed by baseline correction, and Savitzky-Golay filters spectral smoothing method is used for the pretreation of original Raman spectra. The quantitative analysis model based on QPSO-MLSSVM hybrid optimization algorithm is established, and the prediction set composed of 20 components is used to verify the model. The experimental result shows that it is effective for the prediction of four-component blending oil with the quantitative analysis model based on QPSO-MLSSVM hybrid optimization algorithm, and the Mean Square Error (MSE) is 0.024 1, which is less than 0.05, the correlation coefficients of each component were above 98%. The results show that it is feasible to detect the content of each oil of four-component blending oil by laser Raman spectroscopy combined with QPSO-MLSSVM algorithm, it has strong adaptive ability and good prediction accuracy that can satisfy the multi-component mixed oil component identification.

Key words：Raman spectroscopy; Edible blend oil; Quantum particle swarm optimization (QPSO); Least squares support vector machine (SVM); Quantitative detection model

Received: 2017-05-31 Accepted: 2017-10-20

ZTFLH:

TN247

Corresponding Authors: FU Xing-hu E-mail: fuxinghu@ysu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)05-1437-07 OR https://www.gpxygpfx.com/EN/Y2018/V38/I05/1437