出 处:《农业工程学报》2016年第1期312-317,共6页Transactions of the Chinese Society of Agricultural Engineering
基 金:国家自然科学基金项目(41304098);湖南省教育厅青年项目(13B076);湖南省重点建设学科-光学基金;湖南文理学院博士启动项目
摘 要: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。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 Luminar5030, the wavelength range was 2 300 ~1 300 nm, the wavelength increment was 2 nm and the scanning time was600. Randomly, 28 fish oil samples were selected and marked as calibration set, and 20 fish oil samples were
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