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。     

正交信号校正-插值-RBF法同时测定原料油中的铁、镍、钒

提出了一种解析分光光度同时测定数据的正交信号校正-插值-RBF(OSC-Interp-RBF)网络方法。该法将光谱阵用浓度阵正交,滤除光谱与浓度阵无关的信号,再用一维线性插值处理使训练集样本对待辩识空间形成较好的覆盖,使RBF网络能够更有效地提取信息,从而提高预测的准确性。将该算法用于模拟原料油中铁、镍、钒的测定数据解析及实际样的组分浓度预测的结果表明,对于合成样预测结果与组分实际浓度的相对误差,及对于实际样预测结果与按常规测定方法获得的结果的相对误差,绝对值均小于10%,结果令人满意。     

小波潜变量回归和广义回归神经网络同时测定三组分混合物

本文采用小波潜变量回归（WLVR）方法，同时测定重叠的光谱信号。结合小波阈值法和主组分分析（PCA）改进除噪质量。八个误差判据用于推断因子数目。潜变量由小波处理过的信号投影到正交基矢量而获得。广义回归神经网络（GRNN）被应用于多组分同时测定。依据算法原理编制了三个程序（PWMRA、PWLVR和PGRNN）执行有关计算。三个方法（WLVR、LVR（潜变量回归）和GRNN）同时测定三组分混合物，获得满意的结果。     

红外光谱定量分析中的一种变量聚类偏最小二乘算法

偏最小二乘算法( Partial least squares, PLS)可以很好地解决分析数据中的变量共线性问题,在光谱分析,尤其是近/中红外及拉曼光谱的定量分析中应用广泛。针对PLS存在的有效信息提取和噪声抑制问题,提出一种变量聚类重加权的PLS算法。通过对光谱的各波数变量进行聚类并分别建模,然后集成为全谱模型。通过对计算并赋予各子类不同的权重,根据对模型的贡献对变量进行重加权,从而提高算法的预测精度。汽油中的辛烷值预测和烟草中的烟碱含量预测两组近红外数据验证表明,所提出算法优于经典的PLS算法,其RMSEP在两组数据中分别降低32%和22%,在光谱数据的定量分析中具有潜在的应用优势。     

模糊聚类-偏最小二乘算法应用于分光光度法同时测定铁、镍、钒、铜、钴

对铁(Ⅲ)、镍(Ⅱ)、钒(Ⅴ)、铜(Ⅱ)及钴(Ⅱ)五种金属离子分别与5-Br-PADAP在OP存在下且在pH 3.5的乙酸盐缓冲溶液中的显色反应进行了研究。结果表明,每一离子与5-Br-PADAP螯合物的吸收光谱严重重叠,使各元素的光度测定受到相互严重干扰。将模糊聚类-偏最小二乘算法应用于此反应体系的计算,使光谱重叠及光度分析计算中的校正模型的优化问题得到有效的解决。在最佳反应条件下并应用模糊聚类-PLS算法,可实现上述5种元素的同时光度测定。用5个模拟样进行回收率试验,5个元素的回收率结果在93.3%~105.5%之间。将此方法应用于原油样品中5种元素的测定,所得结果与AAS法所测得结果相符。     

小波包变换潜变量回归分辨重叠的紫外光谱

采用小波包变换潜变量回归(WPLVR)方法,同时测定联苯、苯酚和邻苯二酚。该法结合小波包变换和潜变量回归改进除噪质量。通过最佳化,选择了小波函数及小波包分解水平(L)。编制了两个程序PWPLVR和PFTLVR进行WPLVR和付立叶变换潜变量回归(FTLVR)法计算。试验结果表明WPLVR法是成功的且优于FTLVR法。     

红外、碳硫分析联用判别聚砜类材料

在对某聚砜类材料的定性分析中,首先通过红外光谱分析,确定该材料的主要成分为聚砜,利用聚砜材料含有较高的硫这一特性,同时聚芳砜(PAS)、聚醚砜(PES)在碳硫含量上存在量的差别,运用碳硫分析仪测定出未知物中的近似碳硫含量,与已知的两种聚砜材料聚芳砜、聚醚砜进行对照,从而确认出该未知材料为聚醚砜。通过实验可以确定,利用红外、碳硫分析联用来判别飓风类材料是一种有效的方法。     

基于灰色模型的乙酸乙烯酯顶空进样质量不确定度评定

鉴于静态顶空进样器进样质量的测定数据有限且分布规律未知,建立了一种基于灰色模型的不确定度评定方法。采用静态顶空-气相色谱和两次相平衡分析法,测定乙酸乙烯酯的顶空进样质量,将整个分析检测视为灰色过程,用灰色系统理论的数学模型评定乙酸乙烯酯的顶空进样质量标准差和不确定度,并与统计学方法评定结果进行比较。灰色模型的不确定度与统计学方法计算值相对偏差为3.65%。该方法可快速评定不确定度,结果可靠。     

小波包变换潜变量回归同时测定三组分混合物

采用小波包变换潜变量回归 (WPLVR)方法 ,同时测定水杨酸甲酯(MSA)、邻苯二甲酸二丁酯 (DBP)和邻苯二甲酸氢钾 (PHP)。该法结合小波包变换和潜变量回归改进除噪质量。通过最佳化 ,选择了小波函数及小波包分解水平 (L)。编制了两个程序 (PWPLVR)和 (PFTLVR)进行WPLVR和付立叶变换潜变量回归 (FTLVR)法计算。实验结果表明WPLVR法是成功的且优于FTLVR法。     

基于单体烃分析的辛烷值测定方法中模糊聚类技术的应用研究

介绍了一种应用模糊聚类技术构建辛烷值预测模型的新方法。该模型用来由汽油单体烃数据预测汽油辛烷值,通过提取单体烃分析谱图中的140个工艺特征组分以及对辛烷值贡献大的组分为特征值进行模糊聚类。实际应用时,在待测汽油样品共同参与聚类的条件下,用与待测汽油样品为同一类、并有最小欧氏距离(<1.5)的3~10个样本作为构建辛烷值预测模型的样本。这种按新的建模样本选择方式得到预测模型的方法具有更好的辛烷值预测精度、更广的适用范围和更高的数据资源的利用率。     

小波包分析用于重叠分析化学信号的处理

对小波包分析的算法进行了改进,并将此算法成功地应用于多组分重叠色谱信号的解析.结果表明,本文提出的算法解决了MRSD算法的不足,更适合处理分析化学信号,用于重叠信号的解析时不需重构(逆变换),简化了数据处理步骤,加快了数据解析速度,具有较强的解析能力.对于重叠色谱信号的解析,小波包分析比小波分析具有更强的解析能力.     

A novel method to calculate the approximate derivative photoacoustic spectrum using continuous wavelet transform

A novel method based on continuous wavelet transform (CWT) using Haar wavelet function for approximate derivative calculation of analytical signals is proposed and successfully used in processing the photoacoustic signal. An approximate nth derivative of an analytical signal can be obtained by applying n times of the wavelet transform to the signal. The results obtained from four other different methods--the conventional numerical differentiation, the Fourier transform method, the Savitzky-Golay method, and the discrete wavelet transform (DWT) method--were compared with the proposed CWT method; it was demonstrated that all the results are almost the same for signals without noise, but the proposed CWT method is superior to the former four methods for noisy signals. The approximate first and second derivative of the photoacoustic spectrum of Pr(Gly)3Cl3.3H2O and PrCl3.6H2O were obtained using the proposed CWT method; the results are satisfactory.     

连续小波变换-独立成分回归算法及其在多组分分析中的应用

采用连续小波变换（CWT）对光谱数据进行处理，用独立成分分析（ICA）进行特征提取，再用回归分析方法对被测组分进行测定，建立了连续小波变换一独立成分回归（CWT-ICR）方法。方法用于肉样品中水分、脂肪和蛋白质多组分的同时测定，所得结果与化学法测得结果相符。     

连续样条小波变换用于分解重叠峰的研究

以B-样条小波为分析小波，提出了用于分析化学重叠信号解析的新方法——连续样条小波变换，结果表明：连续样条小波变换应用于分析化学信号的处理，能使峰变窄同时还能提高信号的信噪比，是一种新型有效的重叠信号解析方法，能从含噪声重叠信号中直接得到重叠峰的峰数目及其相应的峰位置。     

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.     

小波包去噪信号的分形特征

将分形理论与小波包滤波相结合，用盒维数的大小来评判信号曲线的滤波情况。随着小波包分解尺度的增大，滤波后信号曲线的盒维数逐渐减小，并最后趋于稳定值。因此可以根据盒维数-分解尺度曲线来选择最佳分解尺度。对仿真含噪信号进行了滤波实验，结果表明：即使在信噪比低至0．5时仍能得到较好的结果，并且该法用于毛细管电泳实验数据的处理结果同样令人满意。     

基于迭代小波变换的光谱信号本底扣除方法研究

本底会对光谱分析结果产生很大的干扰作用,为获取特征峰的有效信息,必须首先去除本底。该文提出了一种基于小波变换的本底扣除算法,通过对光谱及后续光谱迭代进行小波变换,利用逼近系数估计本底,直到本底收敛。提出了判断多次估计的本底最大误差是否足够小的收敛准则。利用该算法去除本底后,即可进行特征峰信息的提取。分别利用仿真光谱和实验能量色散X射线荧光光谱对算法进行了验证,并与传统小波变换和多项式拟合法进行了对比。结果表明,该算法能够更准确扣除光谱本底,对其他光谱的本底扣除也具有借鉴意义。     

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

Elimination of interference information by a new hybrid algorithm for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the interference information for multivariate calibration of near-infrared (NIR) spectra that includes noise, background and systemic spectral variation irrelevant to concentration. The method consists of two parts: approximate derivative based on continuous wavelet transform (CWT) and orthogonal signal correction (OSC). After the approximate derivative calculated by CWT, OSC was performed. It was successfully applied to real complex NIR spectral data to eliminate the interference information. Correction for the interference of NIR spectra resulted in a substantial improvement in the predicted precision, and a more concise calibration model was obtained. The proposed procedure also compared favourably with several pretreatment methods, and the new method appears to provide a high-performance pretreatment tool for multivariate calibration of NIR spectra. In addition, the strategy proposed here can be applied to various other spectral data for quantitative purposes as well.