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Method for extracting Raman spectra characteristic variables of biological sample based on Hilbert–Huang transform
Journal of Raman Spectroscopy ( IF 2.5 ) Pub Date : 2020-03-09 , DOI: 10.1002/jrs.5866 Xiaoyu Zhao 1 , Yan He 1 , Zihao Liu 1 , Wei Zhang 1 , Liang Tong 2
Journal of Raman Spectroscopy ( IF 2.5 ) Pub Date : 2020-03-09 , DOI: 10.1002/jrs.5866 Xiaoyu Zhao 1 , Yan He 1 , Zihao Liu 1 , Wei Zhang 1 , Liang Tong 2
Affiliation
Because Raman peaks of the biological sample are superimposed on each other, the use of characteristic peak attribution is limited to some extent. In this study, we show that Hilbert–Huang transform (HHT) provides a Raman spectral feature extracting method, especially for biological samples. First, the empirical mode decomposition algorithm was used to decompose Raman spectra into intrinsic mode functions (IMFs). It is worth noticing that the IMF frequency is single or nearly single, so its further transformation (instantaneous amplitude, instantaneous angle, instantaneous angular frequency, Hilbert spectrum, and Hilbert marginal spectrum) from Hilbert processing is monotonous instead of the raw overlapping. Then, the Hilbert marginal spectrum was selected by one‐way analysis of variance and related with the rice type to establish a partial least squares regression (PLS) model with a 95.00% accuracy. This result is better than those based on characteristic variables screened by PLS, interval PLS, principal component analysis, independent component analysis, successive projections algorithm, haar, db, and coif (85.00%, 90.00%, 82.50%, 77.50%, 90.00%, 92.50%, 80.00%, and 85.00%). These results illustrate that HHT can accurately extract the characteristic variables from biological Raman spectra. The classification accuracy based on HHT is slightly lower than those based on bior 2.4, three‐layer decomposition (97.50%) and sym 5, five‐layer decomposition (97.50%). Significantly, no parameters need to be set such as the wavelet mother function and the decomposition layer in the HHT feature extraction process. This paper provides a HHT method for Raman spectral feature extraction, which is simple and effective.
中文翻译:
基于希尔伯特-黄变换的生物样品拉曼光谱特征变量提取方法
因为生物样品的拉曼峰彼此叠加,所以特征峰归属的使用在一定程度上受到限制。在这项研究中,我们表明希尔伯特-黄变换(HHT)提供了拉曼光谱特征提取方法,特别是对于生物样品。首先,使用经验模式分解算法将拉曼光谱分解为固有模式函数(IMF)。值得注意的是,IMF频率是单个或几乎单个,因此从希尔伯特处理中进行的进一步变换(瞬时振幅,瞬时角度,瞬时角频率,希尔伯特频谱和希尔伯特边际频谱)是单调的,而不是原始重叠。然后,通过单因素方差分析选择希尔伯特边际谱,并与水稻类型相关,以建立具有95.00%准确性的偏最小二乘回归(PLS)模型。此结果优于基于PLS,间隔PLS,主成分分析,独立成分分析,连续投影算法,haar,db和coif筛选的特征变量的结果(85.00%,90.00%,82.50%,77.50%,90.00% ,92.50%,80.00%和85.00%)。这些结果说明,HHT可以从生物学拉曼光谱中准确提取特征变量。基于HHT的分类准确性略低于基于bior 2.4,三层分解(97.50%)和sym 5,五层分解(97.50%)的分类准确性。重要的是 在HHT特征提取过程中,不需要设置诸如小波母函数和分解层之类的参数。本文提供了一种用于拉曼光谱特征提取的HHT方法,该方法简单有效。
更新日期:2020-03-09
中文翻译:
基于希尔伯特-黄变换的生物样品拉曼光谱特征变量提取方法
因为生物样品的拉曼峰彼此叠加,所以特征峰归属的使用在一定程度上受到限制。在这项研究中,我们表明希尔伯特-黄变换(HHT)提供了拉曼光谱特征提取方法,特别是对于生物样品。首先,使用经验模式分解算法将拉曼光谱分解为固有模式函数(IMF)。值得注意的是,IMF频率是单个或几乎单个,因此从希尔伯特处理中进行的进一步变换(瞬时振幅,瞬时角度,瞬时角频率,希尔伯特频谱和希尔伯特边际频谱)是单调的,而不是原始重叠。然后,通过单因素方差分析选择希尔伯特边际谱,并与水稻类型相关,以建立具有95.00%准确性的偏最小二乘回归(PLS)模型。此结果优于基于PLS,间隔PLS,主成分分析,独立成分分析,连续投影算法,haar,db和coif筛选的特征变量的结果(85.00%,90.00%,82.50%,77.50%,90.00% ,92.50%,80.00%和85.00%)。这些结果说明,HHT可以从生物学拉曼光谱中准确提取特征变量。基于HHT的分类准确性略低于基于bior 2.4,三层分解(97.50%)和sym 5,五层分解(97.50%)的分类准确性。重要的是 在HHT特征提取过程中,不需要设置诸如小波母函数和分解层之类的参数。本文提供了一种用于拉曼光谱特征提取的HHT方法,该方法简单有效。