基于小波变换的木材近红外光谱去噪研究

木材近红外光谱常常被一系列噪声所污染,影响光谱分析结果。为了提高近红外光谱分析精度,需要对光谱数据进行预处理。光谱导数可以消除光谱背景干扰和基线漂移等因素影响,提高光谱分辨率,但导数光谱在增强信号的同时,也使信号噪声得到增强。应用小波变换对杉木木材近红外一阶导数光谱进行去噪研究,分别采用9点平滑法、25点平滑法、非线性小波硬阈值和软阈值法、9点平滑+小波变换法和25点平滑+小波变换法对光谱数据进行去噪研究。结果显示, 小波变换能够有效去除导数光谱中的噪声信号,保留光谱中的有效信息,提高光谱信噪比,提高光谱的分析能力,在木材近红外光谱分析中具有很好的应用前景。     

基于经验模态分解的近红外光谱预处理方法

针对微分法在有效消除光谱背景和基线漂移的同时会增加光谱噪声的问题,把最新发展的经验模态分解方法(EMD)引入到近红外光谱处理中来,以烟草的一阶导数近红外(NIR)光谱为研究对象,探讨经验模态分解在近红外光谱预处理中的应用,并与小波变换消噪效果进行了对比分析。结果表明,用基于经验模态分解去噪后的光谱进行分析,预测集的决定系数r2由去噪前的0.9705提高到0.9832,均方根误差(RMSEP)由去噪前的0.5606降为0.3310,比基于小波变换的分析结果略高。因此,经验模态分解方法对消除光谱的噪声是有效的,有效地提高了光谱的分析精度和模型的稳定,为近红外光谱预处理提供了一种新方法。     

基于小波变换的体内外酒精含量近红外光谱检测与分析

应用小波分析对体外和体内的酒精近红外光谱信号进行去噪分析,通过体外光谱分析确定酒精吸收峰特征范围,为体内近红外光谱分析确定有效区间。软阈值和硬阈值下,分别采用缺省阈值、Birge-Massart阈值和最大最小值阈值,比较酒精光谱去噪,信噪比(signal noise ratio,SNR)和均方根误差(root mean square error,RMSE)去噪效果。结果表明:缺省硬阈值方法对酒精近红外光谱去噪的效果较好;小波变换可以有效去除酒精近红外光谱的噪声,提高信噪比,保留有用真实信号。在不同的酒精浓度下,去噪后的近红外光谱能够较好的显示浓度变化规律。小波分析在近红外光谱法对人体酒精无创检测及定量分析方面有较好的应用前景。     

小波系数压缩用于柴油十六烷值近红外光谱分析建模

近红外光谱分析建模中存在多变量高维数据处理问题,导致计算量大,不利于过程控制中应用.为此提出利用小波变换压缩近红外光谱数据的算法与准则,并结合柴油十六烷值定量分析研究压缩数据的建模效果.研究表明,经小波方法处理后,变量维数压缩30倍左右,光谱主要信息基本保留,而模型的预测精度和常规预处理方法分析相比有明显提高.光谱数据压缩的同时包含了噪声滤除和基线校正,简化数据处理步骤,有利于NIRS实际应用时提高建模效率.     

小波变换在近红外光谱分析中的应用进展

小波变换(WT)具有很好的时频分离特征,信息处理能力强,已广泛用于分析化学领域;本文就小波变换在近红外光谱领域的应用进行简述。小波变换用于近红外预处理,提取有用信息,消除背景干扰,可以提高近红外的分析精度和模型稳健性;用于数据压缩可以减少数据库存储空间,提高建模速度;小波系数用于模型传递,具有传递速度快,稳健性强,所需标样少等特点;小波变换可以与神经网络、遗传算法等结合,在近红外分析领域呈现出良好的发展前景。     

小波包熵的复杂体系近红外光谱信息提取

有用信息提取是复杂体系近红外检测的重点和难点之一。由于复杂体系光谱中存在各种噪声、基线漂移、谱带重叠及复杂背景的干扰,常规方法不能准确地从光谱中获得有用信息。为此,将小波包变换(DWPT)和信息熵理论相结合--小波包熵(EWPIE)提取复杂体系光谱中的有用信息。思路是采用小波包变换对光谱信号进行多频带分解,根据有用信号与噪声的频带分布特点,基于信息熵理论滤除干扰的频率分量,采用正交校正法(OSC)剔除与被测组分无关的信息,然后对处理后的频率分量进行重构,从而实现复杂体系有用信息的准确提取。通过对复杂体系光谱数据建立多元校正模型来验证该方法的效果。采用牛奶的近红外光谱数据,以牛奶中脂肪和蛋白质浓度为研究对象,建立了偏最小二乘法(PLS)模型。结果显示,牛奶中脂肪和蛋白质的预测均方根误差(RMSEP)分别为0.132%和0.121%,与单纯的DWPT和OSC相比,EWPIE能够有效地提取有用信息,避免了无用信息的干扰,明显提高了模型的预测精度,对复杂体系的准确检测具有一定的理论意义和实际应用价值。     

近红外光谱的数据预处理研究

进行了小麦样品近红外光谱数据的预处理研究,一般仪器记录的样品近红外光谱数据中包含有一系列噪声和干扰信号,因此适当的预处理是进行后续光谱定标、建模及模型传递的基础,对可靠地获得准确分析结果具有很重要的作用。结合小麦样品蛋白质含量近红外光谱分析工作,对由近红外光栅光谱仪和傅里叶变换近红外光谱分别记录的66种小麦样品光谱数据,采用高斯一阶、二阶导数小波变换方法进行了预处理。对比常用的一阶差分预处理,证明高斯函数导数小波变换方法是十分有效、实用的,预处理后光谱曲线非常光滑、噪声消除效果明显,富含有用光谱分析信息的区域更加清晰显示,因而非常有助于后续的光谱定标、建模和模型传递工作。     

应用近红外可见光谱快速测量柴油十六烷值

快速测量十六烷值对检测柴油品质及控制炼制工艺具有重大意义。首先对采集到的381份柴油样品进行近红外可见光谱波段全光谱扫描,利用小波分析(WT)对原始数据进行去噪声处理,应用竞争性自适应重加权算法(CARS)进行特征波长选择,将CARS提取的22个特征波长输入至LS-SVM预测模型,决定系数r2为0.723,预测均方根误差RMSEP为1.878%。结果表明,使用WT-CARS变量选择算法获取光谱特征波长,结合LS-SVM建模,可以快速、准确的测量柴油中的十六烷值,为进一步实现柴油十六烷值的在线检测以及其他性能参数的快速测定奠定了基础。     

基于光谱反射率数据的水面油种鉴别研究

为探讨一种快速、及时对水上油膜种类进行鉴别的方法,采用水上油膜反射率光谱数据结合聚类分析方法、主成分分析方法和小波变换分别对厚度为300,500和1 000 μm的煤油,300,1 000和1 500 μm的润滑油,50,300和500 μm的轻柴油和500,2 000 μm的180#柴油等四种常见溢油油种进行判别研究。聚类分析结果表明：采用欧氏距离计算样本间的聚类距离,在距离L=8.976以上能够将样本正确分类,准确率100%;对同一油种油膜而言,油膜厚度接近的更易聚类;主成分分析结果表明：对原始数据、小波概要系数和小波细节系数分别进行主成分分析,其中小波细节系数对油种区分效果最佳,四种油膜样品在主成分得分空间中独立分布。利用反射率光谱数据结合聚类分析和基于小波细节系数的主成分分析对水上油膜种类的鉴别是可行的。     

拉曼及近红外吸收光谱的燃油混合物量化分析

生物柴油是典型的"绿色能源",具备良好的环保性和燃料特性,通常与柴油混合使用在柴油发动机上。但是目前世界各国柴油与生物柴油混合的比例标准参次不齐,没有一个统一的标准,并且不同比例的柴油/生物柴油混合物具有不同的燃烧性能,也会对柴油发动机产生一定程度的影响。为了能够快速、准确的测量柴油/生物柴油混合物中的生物柴油浓度,近红外光谱和拉曼光谱在燃油检测方面已经得到广泛的应用。利用拉曼及近红外光谱对柴油/生物柴油混合物中的生物柴油浓度进行了量化分析研究。首先采集了柴油/生物柴油混合燃油的拉曼光谱及近红外吸收光谱,然后利用平滑、基线校正、归一化等方法对采集到的光谱进行预处理。从光谱图中观察到,在柴油/生物柴油混合物的拉曼光谱和近红外光谱中都有C=O特征光谱区域,且该光谱区域的光谱峰都随生物柴油的浓度增加而越来越明显。拉曼光谱中,随生物柴油浓度变化的主要C=O特征光谱区域是在1 743 cm~(-1)位置处的特征峰,在近红外光谱中,随生物柴油浓度变化的主要C=O特征光谱区域是在4 659 cm~(-1)处的特征峰。然后分别根据强度比方法和偏最小二乘(PLS)回归方法建立了相应的混合燃油中生物柴油浓度预测模型。结合强度比方法建立特征峰强度比的生物柴油浓度预测模型,由混合燃油的拉曼光谱和近红外光谱建立的C=O特征峰线性预测模型相关系数分别为0.947 2和0.996 2;结合偏最小二乘(PLS)回归法建立特征光谱区域的生物柴油浓度预测模型,由混合燃油的拉曼光谱和近红外光谱特征区域建立的相应预测集相关系数(R~2)分别为0.981 5和0.991 2,相应的预测均方根误差(RMSE)分别为0.093 7和0.012 9。结果表明,在混合燃油中,使用近红外光谱中的C=O光谱区域建立的生物柴油浓度预测模型会得到更准确的预测结果。     

小波多尺度正交校正在近红外牛奶成分测量中的应用

光谱分析中,干扰信号的存在直接影响所建分析模型的质量。基于信号和干扰的不同特性,提出了一种扣除背景和噪声干扰的新方法——小波多尺度正交校正(WMOSC)法。首先将原始光谱进行小波变换(DWT),消除噪声及背景信息,然后采用正交信号校正(OSC)滤除与待测组分浓度无关的全部信息。与单纯的小波变换及正交信号校正相比,WMOSC能有效地扣除背景和噪声干扰,使模型具有更强的抗干扰能力,提高了模型的预测精度。利用该方法对牛奶样品的近红外光谱进行处理,采用偏最小二乘法建立校正模型,其牛奶中脂肪、蛋白质和乳糖的预测均方根误差(RMSEP)分别为0.101 6%,0.087 1%和0.110 7%。实验结果表明该方法能有效地去除干扰,保留有用信息。     

A nonlinear model for calibration of blood glucose noninvasive measurement using near infrared spectroscopy

In order to improve prediction accuracy of calibration in human blood glucose noninvasive measurement using near infrared (NIR) spectroscopy, a modified uninformative variable elimination (mUVE) method combined with kernel partial least squares (KPLS), named as mUVE–KPLS, is proposed as an alternative nonlinear modeling strategy. Under the mUVE method, high-frequency noise and matrix background can be eliminated simultaneously, which provide a optimized data for calibration in sequence; under the kernel trick, a nonlinear relationship of response variable and predictor variables is constructed, which is different with PLS that is a complex model and inappropriate to describe the underlying data structure with significant nonlinear characteristics. Two NIR spectra data of basic research experiments (simulated physiological solution samples experiment in vitro and human noninvasive measurement experiment in vivo) are introduced to evaluate the performance of the proposed method. The results indicate that, after elimination high-frequency noise and matrix background from optical absorption of water in NIR region, a high-quality spectra data is employed in calibration; and under the selection of kernel function and kernel parameter, the best prediction accuracy can be got by KPLS with Gaussian kernel compared with Spline-PLS and PLS. It is encouraging that mUVE–KPLS is a promising nonlinear calibration strategy with higher prediction accuracy for blood glucose noninvasive measurement using NIR spectroscopy.     

Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding

The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.     

Background removal from polarized Raman spectra of tooth enamel using the wavelet transform

A wavelet transformation method is introduced to remove the large fluorescence background from polarized Raman spectra of stained tooth enamel. This method exploits the wavelet multiresolution decomposition where the experimental Raman spectrum is decomposed into signals with different frequency components, and where the lowest frequency background and highest frequency noise are removed. This method is optimized using a simulated collection of parallel‐polarized and cross‐polarized Raman spectra of the enamel and then applied to a set of experimental data. The results show that the wavelet transform technique can extract the pure spectra from background and noise, with the depolarization ratio used to discriminate between early dental caries and sound enamel preserved. Copyright © 2010 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.     

A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy

Raman spectroscopy exploits the Raman scattering effect to analyze chemical compounds with the use of laser light. Raman spectra are most commonly analyzed using the ordinary least squares (LS) method. However, LS is known to be sensitive to variability in the spectra of the analyte and background materials. In a previous paper, we addressed this problem by proposing a novel algorithm that models expected variations in the analyte as well as background signals. The method was called the hybrid LS and principal component analysis (HLP) algorithm and used an unweighted Gaussian distribution to model the noise in the measured spectra. In this paper, we show that the noise in fact follows a Poisson distribution and improve the noise model of our hybrid algorithm accordingly. We also approximate the Poisson noise model by a weighted Gaussian noise model, which enables the use of a more efficient solver algorithm. To reflect the generalization of the noise model, we from hereon call the method the hybrid reference spectrum and principal components analysis (HRP) algorithm. We compare the performance of LS and HRP with the unweighted Gaussian (HRP‐G), Poisson (HRP‐P), and weighted Gaussian (HRP‐WG) noise models. Our experiments use both simulated data and experimental data acquired from a serial dilution of Raman‐enhanced gold‐silica nanoparticles placed on an excised pig colon. When the only signal variability was zero‐mean random noise (as examined using simulated data), HRP‐P consistently outperformed HRP‐G and HRP‐WG, with the latter coming in as a close second. Note that in this scenario, LS and HRP‐G were equivalent. In the presence of random noise as well as variations in the mean component spectra, the three HRP algorithms significantly outperformed LS, but performed similarly among themselves. This indicates that, in the presence of significant variations in the mean component spectra, modeling such variations is more important than optimizing the noise model. It also suggests that for real data, HRP‐WG provides a desirable trade‐off between noise model accuracy and computational speed. Copyright © 2013 John Wiley & Sons, Ltd.     

一种新的恒星大气物理参数自动估计方案SVR(Haar)

提出了一种新的恒星大气物理参数自动估计的新方案,并称之为SVR(Haar)。由于观测光谱受到大量宇宙辐射、大气和观测设备等引起的噪声干扰,且这种噪声干扰往往是其中的频率较高成分。所以该方案的基本思想是首先使用Haar小波剔除高频噪声成份,以提高恒星大气物理参数估计的准确性;然后使用支持向量机回归方法(SVR)对恒星参数做出估计,该方法能通过ε不敏感域进一步提高对光谱微小畸变和干扰的容许能力,增强解决方案的鲁棒性。为了验证SVR(Haar)方案的有效性,针对相关研究中的权威模拟恒星光谱和SLOAN发布的实测光谱,以及文献中的典型处理方法,做了大量比较实验。实验结果表明,所提出的SVR(Haar)恒星参数估计方案比文献中常用的主成分分析和非参数回归模型均要好。     

双光谱区间遗传算法及其在模型转移中的应用

在近红外光谱分析中，将近红外光谱和浓度信息建立统计模型，通过光谱代入模型即可预测未知样本浓度。但是，检测条件的变化会导致光谱的改变，进而导致原有的模型不能准确预测光谱改变后的样本。对此，模型转移可以通过校正新测量的光谱（从光谱），使得从光谱能够被原有光谱（主光谱）建立的模型准确预测。模型转移可以使用全光谱进行校正，但是全光谱中往往包括噪声、背景等干扰信息，这些干扰会增加预测误差。故可以使用变量选择方法找出光谱中有化学意义的信息来模型转移。但是一般的变量选择算法只选择主光谱的区间，从光谱使用主光谱相同的波长区间模型转移。但是在实际工作中，主光谱和从光谱有化学意义的区间往往不一致，主从光谱使用同一区间模型转移会增加误差；此外，有时二者原光谱的波长范围并不一致，从主光谱选出的区间不能用于从光谱的校正。对此，提出了基于双光谱区间遗传算法（GA-IDS），同时选择主光谱和从光谱有化学意义的区间，进而实现模型转移。GA-IDS算法步骤包括，①随机产生种群；②分析种群中每条染色体，删去错误染色体；③根据每条染色体，找出其相应的主光谱和从光谱波段组合，并计算其模型转移后的验证均方根误差(RMSEV)；④按照概率，执行选择、交叉、变异操作。在一次迭代结束之后，返回到步骤②，重新执行纠错、计算RMSEV、选择、交叉、变异。达到停止迭代的要求后，将最低的RMSEV值所对应的染色体保存下来作为最优染色体，其所对应的主从光谱区间作为最优区间。用玉米、小麦两套数据测试了该算法，结果显示，与全光谱相比，GA-IDS选择的主从光谱区间可以显著地降低误差；与向后迭代区间选择法（IIBS）相比，在小样本情况下，GA-IDS的误差显著地小于IIBS方法。     

A method for fringe normalization by Zernike polynomial

A new approach for fringe normalization by Zernike polynomial fitting to cancel background illumination in an interferogram is proposed. With this method, background illumination can be suppressed, high frequency noise is immunized and the contrast is improved by normalization. The main idea for this paper is to use the Zernike polynomial fitting interferogram to cancel background illumination; the high frequency noise is then filtered by a Wiener filter. Finally, the paper uses the method of local region contrast modulation to enhance fringe contrast. This method can overcome the problem of the non-uniformity illumination of fringe patterns resulting from the marginal reflection of optical components and a non-uniformity light source