基于组合滤波的鱼油二十碳五烯酸含量近红外光谱检测

为了提高鱼油二十碳五烯酸(eicosapentaenoic acid,EPA)含量的测定精度,该研究将经验模态分解(empirical mode decomposition,EMD)和数学形态学滤波相结合的近红外光谱去噪方法应用于鱼油的一阶导数光谱预处理中,给出了方法的原理和步骤,评估了该方法的去噪效果。运用偏最小二乘回归(partial least squares regression,PLSR)建立了鱼油EPA近红外光谱的预测模型,用处理后的光谱计算了鱼油中EPA的含量,并与九点平滑和小波变换方法的处理结果进行了对比分析。结果表明:与传统的九点平滑处理结果相比,信噪比(signal to noise ratio,SNR)从14 d B左右提高到35 d B左右,原始信号与消噪信号之间的标准差由0.005 71降到0.002 26;预测集的决定系数由0.959 3提高到0.987 9,预测均方根误差(root mean square error,RMSE)由0.060 1降为0.031 2。证明了组合的EMD和数学形态学滤波方法在光谱处理过程中的可靠性,提高了鱼油EPA含量近红外光谱的定量分析精度。

Near-infrared spectrum detection of fish oil eicosapentaenoic acid content based on combinational filtering

The near-infrared(NIR) spectral analysis technology has become an important method in the qualitative and quantitative analysis of the composition of fish oil.Yet the absorption spectrum signal of fish oil is generally weak.Especially, when the NIR spectrum is applied to the component analysis, part of the spectrum peaks are often submerged in the noise and difficult to be identified.In order to improve the accuracy of non-destructive detection of eicosapentaenoic acid(EPA) content of fish oil, a combined method was proposed to conduct the pretreatment of fish oil NIR spectrum based on the empirical mode decomposition(EMD) and the morphological filtering.The principle and steps of the method were given.Firstly, derivative spectra were decomposed into a series of modal functions based on the EMD, including high-order and low-order modal function.Then the high-order part and low-order part were separated to deal with respectively.For low-order modal function, the mathematical morphology filtering method and the adaptive threshold de-noising method were used to de-noise to retain useful spectral data as much as possible.For high-order modal function, smoothing filter was used to eliminate baseline drift.Then the sum of 2 parts was determined as the de-noised spectrum.Finally, after de-noising, the correlation analysis was conducted between spectral data and the EPA chemical composition data in fish oil.The partial least squares regression was adopted to establish the prediction model, and the EPA content of fish oil was calculated from the de-noised spectrum.The spectra of 48 fish oil samples were collected using a portable NIR spectrometer(Mini-AOTF/(NIR)), which was produced by Brimrose company in the United States of America.The model of the NIR spectrometer was Luminar 5030, the wavelength range was 2 300~1 300 nm, the wavelength increment was 2 nm and the scanning time was 600.Randomly, 28 fish oil samples were selected and marked as calibration set, and 20 fish oil samples were selected as validation set.The nine-point smoothing method, the wavelet soft-threshold, the morphological wavelet and the proposed method were respectively used as pretreatment method to deal with the spectrum.Then the EPA content of fish oil was calculated based on the de-noised spectrum and a comparative analysis of their results was conducted.The filtering method and the statistical analysis were implemented in Matlab 7.0.1.The result of the presented method was compared with that of the nine-point smoothing method which was the most traditional method.It could be seen that the signal-noise ratio(SNR) was improved from 14 to 35 dB, and the root mean square error(RMSE) between raw signal and de-noised signal was reduced from 0.005 71 to 0.002 26.These embodied the proposed method had a good performance in the retention and resistance to noise.The determination coefficient of the prediction set was improved from 0.959 3 to 0.987 9, and the RMSE was reduced from 0.060 1 to 0.031 2.The model prediction accuracy was improved.And the treatment effect was also better than the wavelet soft-threshold method or the morphological wavelet method which were widely used in the preprocessing of the spectrum.The experimental results showed that the proposed method combined the advantages of EMD and mathematical morphology filter.Under the premise that real details of fish oil spectrum signal were kept, the noise was attenuated at the maximum degree.After de-noising, the spectrum peak which was submerged in noise became clear and easy to be identified, and the quality of spectrum data was improved effectively.These improve that the proposed combined method is effective to conduct the pretreatment of NIR spectrum of fish oil and improves the accuracy of NIR spectrum detection of fish oil EPA content.The combination of EMD and morphological filtering also provides a new way for NIR spectra de-noising.