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Study on Pretreatment Algorithm of Near Infrared Spectroscopy

机译:近红外光谱法的预处理算法研究

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Pretreatment of near-infrared spectral data is the basis of feature extraction, quantitative and qualitative analysis and establishment of models, it plays a significant role in obtaining the data and get reliable results. The purpose of the paper is compared the advantages and disadvantages of the S-G, derivative and multiple algorithm methods of spectral preprocessing through the example of apple leaves. S-G algorithm can smooth the data relatively better, but we must according to the specific circumstances of the case while chose the width of the window and the order of polynomial; Kernel smoothing is better than S-G in two-ends data processing, but its processing speed is slower than S-G. Derivative algorithm can get more stable reflectance, but it is sensitive to noise, so it need to be used with the smoothing algorithm. Multiple scatter correction can be used effectively to eliminate the translation and offset of baseline. All of above algorithms have been applied in the system of near infrared spectroscopy processing system of leaves and satisfactory result was obtained.
机译:预处理近红外光谱数据是特征提取,定量和定性分析和模型的建立的基础,它在获取数据并获得可靠的结果时起着重要作用。本文的目的是通过苹果叶的示例比较S-G,衍生物和多种算法方法的优点和缺点。 S-G算法可以平滑数据相对较好,但我们必须根据案例的具体情况,同时选择窗口的宽度和多项式的顺序;内核平滑在两端数据处理中优于S-G,但其处理速度比S-G慢。衍生算法可以获得更稳定的反射率,但对噪声敏感,因此需要与平滑算法一起使用。可以有效地使用多个散射校正以消除基线的平移和偏移。所有上述算法都应用于叶片近红外光谱处理系统的系统中,得到了令人满意的结果。

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