| 光谱学与光谱分析 |
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| Research on Prediction Method of Fatty Acid Content in Edible Oil Based on Raman Spectroscopy and Multi-Output Least Squares Support Vector Regression Machine |
| DENG Zhi-yin, ZHANG Bing, DONG Wei, WANG Xiao-ping* |
| Department of Optical Engineering，State Key Laboratory for Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China |
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Abstract For the purpose of the rapid prediction of saturated fatty acid, oleic acid, linoleic acid content in edible vegetable oil, the Raman spectra of a batch of edible vegetable oils and their one-one mixtures with different ratios were measured in the range of 800~2 000 cm-1, 91 samples were measured totally in this research, the obtained Raman spectra data were preprocessed by a new method proposed in this paper called auto-set fulcrums baseline fitting method based on peak-seeking algorithm, and 8 characteristic peak values (872 cm-1[ν(C—C)]，972 cm-1[δ(CC)trans]，1 082 cm-1[ν(C—C)]，1 267 cm-1[δ(C—H)cis]，1 303 cm-1[δ(CH2)twisting]，1 442 cm-1[δ(CH2) scissoring]，1 658 cm-1[ν(CC)cis]，1 748 cm-1[ν(CO)]) were extracted to be the eigenvalues for the whole spectra, among the 8 peaks there are three peaks(972, 1 267, 1 658 cm-1) that play an important role in the establishment of mathematical model, they are closely concerned with CC band which distinguishes the three fatty acid types. By using these eigenvalues as inputs, and actual saturated fatty acid, oleic acid, linoleic acid contents of sample oils as outputs, a prediction mathematical model that predicts simultaneously the three fatty acid contents was established using multiple regression analysis: multi-output least squares support vector regression machine(MLS-SVR) and partial least squares(PLS). Results show that the MLS-SVR has better effects. The predicting results are compared with results of gas chromatography(GC), and the obtained root mean square error of prediction(RMSEP) for saturated fatty acid, oleic acid, linoleic acid are 0.496 7%, 0.840 0% and 1.019 9%, and the correlation coefficients (r) are 0.813 3, 0.999 2 and 0.998 1, respectively. When this model is applied in the detection of new unknown oil samples, the prediction error does not exceed 5%. Results show that the Raman spectra analysis technology based on MLS-SVR can be a convenient, fast, non-destructive, and precise new method for oil detection.
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Received: 2013-03-05
Accepted: 2013-06-02
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Corresponding Authors:
WANG Xiao-ping
E-mail: xpwang@zju.edu.cn
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