小波变换方法的比较──红外光谱数据压缩

介绍了小波变换和多分辨分析的基本理论以及常用小波变换压缩数据的3种方法:(1)只保留模糊信号;(2)全部保留模糊信号及锐化信号中的较大值;(3)保留模糊信号及锐化信号中的较大值.将紧支集小波和正交三次B-样条小波压缩4-苯乙炔基-邻苯二甲酸酐的红外光谱数据进行了对比,计算表明正交三次B-样条小波变换方法效果较好,而在全部保留模糊信号及只保留锐化信号中数值较大的系数时,压缩比大而重建光谱数据与原始光谱数据间的均方差较小.     

小波神经网络用于色谱数据的滤噪与压缩

介绍了小波神经网络（Ｗａｖｅｌｅｔ Ｎｅｕｒａｌ Ｎｅｔｗｏｒｋ）的结构和算法，并将其应用于色谱数据的滤噪与压缩。在小波神经网络中，采用了Ｂｕｂｂｌｅ母小波和一维搜索变步长共轭梯度优化方法。结果表明，小波神经网络将原始数据压缩至１：２０时，仍能得到很好的重建谱图。重建的色谱图不仅能保留原始的谱图特征而且有光滑滤噪的效果。对于色谱数据的存储与平衡滤噪具有重要意义。     

小波变换用于近红外光谱数据压缩

近红外光潜数据量大,需要较大数据存储空间和较长的建模时间、本文以成品柴油性质分析为例．将小波变换用于近红外光谱数据压缩处理,详细考察了小波压缩参数,比较了压缩前后潜图差异以及性质分析偏差的变化。研究结果表明．采用Daubechies小波函数(N=2)为母函数．进行3次分解,直接采用其逼近系数(Ca3)作为谱图压缩数据,其重构光谱与原始光谱基本一致直接利用逼近系数进行性质分析,其分析精度与原始光谱数据基本相当,存储空间减少至原来的1／8,且能够明显缩短其建模时间和分析时间。     

自适应小波算法用于近红外光谱的多元校正

实现了一种构建自适应小波滤波器的方法,并将其用于近红外光谱数据的多元校正。该方法根据一定的目标函数,针对信号的特性自适应地构造小波滤波器。用该法构建的滤波器对烟草样品的近红外光谱进行压缩,并将压缩后的数据采用偏最小二乘法建模,实现了烟草样品常规组分的定量分析。     

基于料酒的三维同步荧光光谱模式识别研究

提出了一种应用同步荧光光谱技术无损快速鉴别料酒品牌的新方法.利用主成分分解法和小波变换法对料酒样品的同步荧光光谱信号进行了压缩处理,分别采用同步荧光光谱数据的第一主成分和小波细节系数为特征变量进行主成分分析和聚类分析,分类结果表明小波系数作为料酒的特征变量对料酒品牌分类正确率更高.利用偏最小二乘和径向基人工神经网络方法...     

小波变换用于近红外光谱性质分析

以汽油研究法辛烷值分析为例,研究了小波变换在近红外光谱分析中的应用。对近红外光谱的小波特性、小波变换参数以及变量提取方法进行了详细研究。研究结果表明：光谱噪音、有用信息和背景分别分布在小波高、中和低频区域;母小波函数对性质分析结果影响很大;小波变换可以同时扣除光谱背景、去除噪音和压缩变量,具有运算速度快、分析精度高以及无需去噪后处理等优点,在近红外光谱分析中具有很好的应用前景。     

小波变换-可见-近红外光谱技术用于鉴别品牌料酒的研究

模式识别技术广泛应用于食品种类、品牌和原产地的分类鉴别.本文测定了三个品牌114个料酒样品的可见-近红外光谱,利用小波变换技术对光谱信号进行了去噪和压缩处理,并采用Fisher权重法计算了16个小波细节系数的Fisher权重.以16个小波细节系数为特征变量采用向量相似度法对三种不同品牌料酒进行了相似度分析,主成分分析法...     

联用色谱数据的多尺度分析

引入基于正交小波基的多尺度分析，并将其用于色谱／光谱二维数据压缩和消除背景影响的研究，结果表明，该法不但成功地消除了复杂的色谱／光谱背景有同时大计压缩数据量，对模拟和实际体系的均获得满意的结果。     

离散小波变换-遗传算法-交互检验法用于近红外光谱数据的高倍压缩与变量筛选

用遗传算法(GA)与交互检验(CV)相结合建立了一种用于对近红外光谱(NIR)数据及其离散小波变换(DWT)系数进行变量筛选的方法，并应用于烟草样品中总挥发碱和总氮的同时测定。结果表明：NIR数据经DWT压缩为原始大小的3．3％时基本没有光谱信息的丢失；有效的变量筛选可以极大地减少模型中的变量个数，降低模型的复杂程度，改善预测的准确度。     

小波包压缩-RBF网络同时测定润滑油中铁、铜、锌

将小波包压缩-RBF网络方法用于润滑油中铁铜锌三组分分光光度同时测定该方法采用小波包函数对光谱数据进行压缩处理,用较大的小波色系数构成新的校正集和预测集代替原始的校正集和预测集,然后用RBF网络进行数据解析。研究表明,用bior2．4小波包处理原始测定数据,最佳小波基用logenerge熵标准,选择适当阈值将变量数由46个压缩成25个（压缩比为0．54）,整体预测效果最好。将此方法用于合成样预测．预测结果与实际浓度的相对误差绝对值在2．50％～9．40％之间;用于实际润滑油样品中铁、铜和锌的同时测定,解析值与原子吸收法（AAS）的测定值的相对误差绝对值作3．55％～7．86％之间。应用结果令人满意。     

红外光谱小波压缩性能的评价

提出了用于红外光谱小波压缩中压缩性能的评价方法.所建立的控制恢复根均方差算法能在给定恢复根均方差的情况下得到压缩比,控制的精度较高,相对标准偏差约为8%,可以满足对恢复谱图的误差估计和最佳压缩参数评价的需要.对于不同的信号,同一小波函数所表现出来的压缩性能也不一样,所以在预控根均方差算法下评价压缩性能时,还应考虑选择有代表性的红外谱图来进行.     

Detecting the quality of glycerol monolaurate: A method for using Fourier transform infrared spectroscopy with wavelet transform and modified uninformative variable elimination

Glycerol monolaurate (GML) products contain many impurities, such as lauric acid and glucerol. The GML content is an important quality indicator for GML production. A hybrid variable selection algorithm, which is a combination of wavelet transform (WT) technology and modified uninformative variable eliminate (MUVE) method, was proposed to extract useful information from Fourier transform infrared (FT-IR) transmission spectroscopy for the determination of GML content. FT-IR spectra data were compressed by WT first; the irrelevant variables in the compressed wavelet coefficients were eliminated by MUVE. In the MUVE process, simulated annealing (SA) algorithm was employed to search the optimal cutoff threshold. After the WT-MUVE process, variables for the calibration model were reduced from 7366 to 163. Finally, the retained variables were employed as inputs of partial least squares (PLS) model to build the calibration model. For the prediction set, the correlation coefficient (r) of 0.9910 and root mean square error of prediction (RMSEP) of 4.8617 were obtained. The prediction result was better than the PLS model with full-spectra data. It was indicated that proposed WT-MUVE method could not only make the prediction more accurate, but also make the calibration model more parsimonious. Furthermore, the reconstructed spectra represented the projection of the selected wavelet coefficients into the original domain, affording the chemical interpretation of the predicted results. It is concluded that the FT-IR transmission spectroscopy technique with the proposed method is promising for the fast detection of GML content.     

Resolution of multicomponent NMR signals using wavelet compression and immune algorithm

A novel algorithm for fast resolution of multicomponent overlapping nuclear magnetic resonance (NMR) spectrum using the wavelet compression and the immune algorithm (IA) is proposed. Taking advantage of the linear property of the wavelet transform (WT), the overlapping NMR spectrum (antigen) can be compressed by WT before it is input into the immune network, the antibodies are also compressed to the same scheme. After extraction of the compressed information of each component, the resolved NMR spectra can be restored by the WT reconstruction. It was proven that the result is almost the same with the result from an IA, but the calculation speed is much faster.     

Application of Embedded Zerotree Wavelet to the Compression of Infrared Spectra

In this paper the embedded zerotree wavelet (EZW) method and Huffinan coding are proposed to compress infrared (IR) spectra. We found that this technique is much better than others in terms of efficiently coding wavelet coefficients because the zerotree quantization is an effective way of exploiting the self-similarities of wavelet coefficients at various resolutions.     

Determination of Total Sugar in Tobacco by Near-Infrared Spectroscopy and Wavelet Transformation-Based Calibration

Based on wavelet transformation (WT) and mutual information (MI), a simple and effective procedure is proposed for multivariate calibration of near-infrared spectroscopy. In such a procedure, the original spectra of the training set are first transformed into a set of wavelet representations by wavelet prism transform. Then, the MI value between each wavelet coefficient variable and the dependent variable is calculated, resulting in a MI spectrum; by retaining a subset set of coefficients with higher MI, an update training set consisting of wavelet coefficients is obtained and reconstructed/converted back to the original domain. Based on this, a partial least square (PLS) model can be constructed and optimized. The optimal wavelet and decomposition level are determined by experiment. A NIR quantitative problem involving the determination of total sugar in tobacco is used to demonstrate the overall performance of the proposed procedure, named RPLS, meaning PLS in reconstructed original domain coupled with MI-induced variable selection in wavelet domain (RPLS). Three kinds of procedures, that is, conventional full-spectrum PLS in original domain (FPLS), PLS in original domain coupled with MI-induced variable selection (OPLS), and direct PLS in MI-based wavelet coefficients (WPLS), are used as reference. The result confirms that it can build more accurate and robust calibration models without increasing the complexity.     

共聚物红外光谱的小波变换

用傅里叶变换红外光谱研究了辐射合成的丙烯酰胺、丙烯酸钠均聚物水凝胶和几种丙烯酰胺／丙烯酸钠配比不同的共的水凝胶的组成变化，发现共聚物的特征吸收带重叠得比较严重，尤其当丙烯酸单元含量较代时，其特征吸收带大部分则不能分辨，将其红外光谱经波波变换后，这类共聚物的特征吸收带较好地得到分离，成功地提高了红外光谱图的分辨率，有助于更好地定性或定量表征共聚物的组成。     

Radial basis function network calibration model for near-infrared spectra in wavelet domain using a genetic algorithm

Near-infrared (NIR) spectrometry is now widely used in various fields and great attention is paid to the application of it to addressing complex problems, which brings about the need for the calibration of systems that fail to exhibit satisfactional linear relationship between input-output data. In this work we present a novel method to build a multivariate calibration model for NIR spectra, i.e. genetic algorithm-radial basis function network in wavelet domain (WT-GA-RBFN), which combines the advantages of wavelet transform and genetic algorithm. The variable selection is accomplished in two stages in wavelet domain: at the first stage, the variables are pre-selected (compressed) by variance and at the second stage the variables are further reduced by a special designed GA. The proposed method is illustrated through presenting its application to three NIR data sets in different fields and the comparison to PLS model.     

Passive Airborne Fourier Transform Infrared Remote Detection of Methanol by Use of Wavelet Analysis as A Feature Extraction Method

Wavelet analysis was evaluated as a data preprocessing tool in the construction of automated classifiers for the detection of volatile organic compounds from passive Fourier transform infrared remote sensing data collected in a downward-looking mode from an aircraft platform. The discrete wavelet transform was applied to single-beam spectra and patterns were formed with either the wavelet coefficients directly or with spectra reconstructed with selected resolution levels of the wavelet decomposition. Automated classifiers were constructed with support vector machines (SVM) and used to detect releases of methanol from an industrial site. A key issue in this work was the desire to use data collected during controlled experiments on the ground to train the SVM classifiers. Spectral backgrounds in these ground-collected data are different than those encountered as the aircraft flies, however, and the development of successful classification models requires spectral preprocessing to suppress background signatures. Biorthogonal wavelets were used to generate patterns and resulted in SVM models that produced no missed methanol detections and false detection rates of less than 0.1% when applied to prediction data not used in the development of the model. The SVM classifiers constructed with wavelet processing were compared to one based on unprocessed spectra and also to one computed with spectra preprocessed with Butterworth high-pass digital filters.     

In-adamantane, a small inside-out molecule

The hydrocarbon in-adamantane (1), a high-energy adamantane isomer in which one methine hydrogen atom is inside the cage, is predicted by ab initio calculations to be isolable at dry ice temperature. It has 440 kJ/mol of hydrogenic strain but appears to be stable against dimerization, moisture, and air. The inverted CH bond is compressed, and the IR and NMR spectra are unusual. The symmetrical pentadecafluoro derivative (2) has an estimated half-life of 100 years at room temperature.