基于变分模态分解和小波变换特征的橄榄油红外光谱分类

Omer Faruk Karaaslan, G. Bilgin
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引用次数: 0

摘要

目前,在不破坏样品的情况下确定样品的化学结构变得非常重要。由于使用红外技术,可以快速获得光谱,无需任何特殊的样品制备过程,并且它们具有特定的特征。本研究利用小波变换(WT)和变分模态分解(VMD)对橄榄油样品的傅里叶变换红外光谱进行特征提取,不需要主函数。然后,使用强大的分类器,支持向量机(SVM)和随机森林(RF)对这些属性进行比较分类。实验研究表明,两种方法得到的特征都提高了分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of FTIR Spectra of Olive Oil with Features of Variational Mode Decomposition and Wavelet Transform
Nowadays, it becomes important to determine the chemical structure without damaging the samples. As a result of the use of infrared, the spectras are obtained both quickly and without any special sample preparation process, and they contain specific characteristics. In this study, features of Fourier Transform Infrared spectra acquired from olive oil samples are extracted by Wavelet Transform (WT) and Variational Mode Decomposition (VMD) that does not require a main function.Afterwards, these attributes are classified in comparison by using the powerful classifiers, support vector machines (SVM) and random forests (RF). Experimental studies have shown that the features obtained by two proposed methods increase the classification performance.
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