阶跃伏安法中离散数据处理方法Ⅲ.样条最小二乘法

本文对采用样条最小二乘法处理低信噪比的阶法离散数据的噪声滤除,导数卷积的应用原理作了详细讨论,从理论模拟数据和实验的测试结果表明,样条最小二乘法在处理波形复杂.含强白噪声的信号时,呈现了目前文献报道的分析信号处理方法所没有的优点,具有广阔的应用前景.     

基于小波变换平滑主成分分析

小波变换具有很强的信号分离能力,很容易把随机噪音从信号中分离出来,从而提高信号的信噪比。本文把小波变换引入到因子分析中,提出了基于小波变换平滑主成分分析,该算法既保留普通主成分分析的正交分解,又具备了小波变换的信号分离能力。模拟数据和实验数据的结果表明,该算法具有从低信噪比的数据中提取出有用信息,并提高信号的信噪比。迭代目标变换因子分析处理实验数据的结果表明,基于小波变换平滑主成分分析的处理结果优     

化学信号的近似高阶导数计算

提出了化学信号近似四阶导数计算的新方法——小波卷积法。该法通过信号与二阶样条小波函数的卷积运算对信号求导,能用于高噪音信号的直接求导,避免了普通导数运算将噪音放大的缺陷,即使对信噪比低至0．5的信号也能得到光滑的导数信号。详细讨论了尺度值、噪音、信号类型对求导的影响并建立了参数确定规则。将该法用于含噪音重叠分析化学信号的求导,能同时提高信号的分辨率和信噪比,结果满意。     

低频外信号选择性优化的钙信号与IP3信号

用数值模拟的方法研究了钙信号系统中钙振荡与IP3振荡共存时,噪音和低频外信号对它们的选择性优化和调控.研究结果显示,低频外信号能够协同噪音作用,在一定强度范围内优化钙信号和IP3信号,但能够协同优化钙信号的条件范围明显较宽,说明协同作用对钙信号具有优先选择性.此外,在两种化学信号均可以被优化的外信号参数作用下,信号被优化的噪音强度范围也具有选择性.     

计算机模拟研究目标转换因子分析分辨重迭溶出伏安峰

本文用计算机模拟研究目标转换因子分析分辨重迭溶出伏安峰．设计了两个计算机程序（ＳＰＧＲＥＬＥＰ）和（ＥＬＥＣＴＴＦＡ）用于伏安图模拟和目标转换因子分析，采用了伏安图全部信息和矩阵处理技术．ＥＬＥＣＴＴＦＡ程序用于确定组分数，验证组分种类和进行未知重迭溶出伏安图的定量分析．实验结果表明本方法是成功的，甚至对严重重迭溶出伏安图亦可奏效．     

分析化学窄带锐峰信号滤波新技术

窄带锐峰信号是指峰形狭窄、峰周围数据点极少的信号，采用常规滤波方法难以处理。本文提出了适合于这类信号的滤波新技术——Mexican Hat小波最小二乘法(MWLS)。它将原信号中的尖锐峰分离出来，以Mexican Hat小波构造拟合函数，根据最小二乘原理对各数据段进行拟合，再重构信号，以去除噪声。大量处理实践证明：该方法除噪能力强，滤波结果可靠，尖锐信号峰中的有用信息不会损失。即使对信噪比为1．5的高噪声锐峰信号仍能获得满意结果。     

一种新型的信号拟合方法─—免疫算法

通过对免疫系统免疫机制的模拟,提出了一种新型的信号拟合算法。该算法根据所提供的标样信息对重叠分析化学信号进行拟合,从重叠信号中提取单一组分的信息,从而实现了多组分混合信号的解析。对模拟信号和实验信号的处理结果表明,该方法可方便地用于多组分重叠色谱信号的解析。     

随机扰动和周期信号对基因调控系统的影响

采用数值模拟方法,研究了生物信息学中人类遗传因子——基因的转录调控系统.研究表明:噪音对该系统的影响作用,体系可以出现内随机共振现象和噪音诱导的"开-关"过程;当周期信号加到系统上时,信号振幅和频率的改变能够使细胞信号产生更为丰富的动力学行为,如周期共振现象.     

一种新型的信号拟合方法--免疫算法

通过对免疫系统免疫机制的模拟,提出了一种新型的信号拟合算法。该算法根据所提供的标样信息对重叠分析化学信号进行拟合,从重叠信号中提取单一组分的信息,从而实现了多组分混合信号的解析。对模拟信号的实验信号的处理结果表明,该方法可方便地用于多组分重叠色谱信号的解析。     

二阶样条小波卷积法分辨重叠化学信号

首次提出了二阶样条小波卷积分峰法, 通过二阶样条小波与重叠化学信号的卷积运算, 确定出各组分的峰位置和峰宽之比, 再利用样条函数构造峰分辨器, 分辨重叠信号, 同时给出了方法的理论依据, 并对参数的选择进行了讨论. 对大量仿真信号和实验信号的处理实践表明, 分辨后信号可以达到基线分离, 峰位置相对误差小于0.2%, 峰面积相对误差小于4.0%, 即使对于重叠严重的信号, 也能获得满意结果. 该方法可以直接用于含噪音的及多个峰的重叠体系, 是一种分辨重叠信号的有效新方法.     

偏最小二乘吸光光度法同时测定钡和锶的研究

二溴对甲基偶氮甲磺（ＤＢＭ－ＭＳＡ）在磷酸介质中能与Ｂａ（Ⅱ）、Ｓｒ（Ⅱ）形成稳定的络合物。研究了该显色体系的反应条件以及应用偏最小二乘法（ＰＬＳ）处理两者叠吸收峰,建立了光度法同时测定Ｂａ（Ⅱ）和Ｓｒ（Ⅱ）的方法。方法用于合成试样和除氧剂试样中两元素含量的测定,结果满意。     

基于导数光谱融合建模的红外光谱定量分析方法

设计了基于奇摄动技术的导数光谱估计器并提出基于不同阶次导数光谱空间的融合建模定量分析方法。方法充分利用导数光谱信息空间、区间最小二乘法和融合建模的优点,挖掘光谱深层次信息进行融合建模。分别利用麦汁浓度范围4.23~18.76° P (柏拉图度)的啤酒红外光谱公共数据集和配制的浓度为0.04%~5%范围的葡萄糖溶液实测光谱数据集进行定量分析方法的对比实验。实验结果表明,融合建模定量分析方法能获得最小的预测均方根误差(RMSEP),其值分别为0.121和0.087,能够准确地进行定量分析。与其它建模方法相比较,基于导数光谱的融合建模方法所建立的预测模型具有明显优越的性能。     

一种变压器油色谱峰识别算法的设计

在变压器油色谱峰识别领域,传统的一阶导数法需要斜率阈值来实现色谱峰识别,因此自动化程度低及容易失真。针对这些缺点,本文在一阶导数法的基础上进行了改进,将迭代移动平均及归一化分析技术应用到色谱峰识别中,通过对信号曲线及方波曲线多次迭代移动平均确定最优的归一化识峰参数,结合色谱峰的绝对保留时间及识峰窗口实现对色谱峰的准确识别。实验结果表明：该算法可以准确识别色谱峰,对噪声、色谱峰的峰宽及峰形变化不敏感,具有很强的自适应性,满足变压器油中气体在线监测装置的现场使用要求。     

Simultaneous Determination of Eight Bioactive Components of Radix Dipsaci by Near-infrared Spectroscopy

A new method was developed using Fourier transform near-infrared spectroscopy and high-performance liquid chromatography with diode array detection for the identification and determination of eight major compounds in crude and sweated Radix Dipsaci. Partial least square regression was selected for the analysis. Multiplicative scatter correction, first derivative, and a Savitzky–Golay filter were used for the spectral pretreatment of the crude material, while standard normal variation, first derivative, and the Savitzky–Golay filter were used for the sweated samples. The correlation coefficients of the calibration models were above 0.99 and the root mean square error of calibration, the root mean square error of prediction, and root mean square error of cross-validation were under 0.63. The developed models were used to analyze unknown crude and sweated Radix Dipsaci with satisfactory results. The established methods were rapid, simple, nondestructive, and useful for quality control of Radix Dipsaci.     

Rapid and Nondestructive Analysis of Bacillus Calmette–Guerin Polysaccharide Nucleic Acid Injection by near-Infrared Spectroscopy with Chemometrics

A novel quantitative analytical method using near-infrared (NIR) spectroscopy combined with chemometrics has been developed to determine the polysaccharides and nucleic acids for routine quality analysis of Bacillus Calmette–Guerin polysaccharide and nucleic acid injections. A Monte-Carlo method was used to detect and discard outliers and to improve the predictive ability of the model. Various other spectral preprocessing methods such as smoothing, derivative, multiplicative scattering correction, standard normal variables, and orthogonal signal correction methods were used to remove noise and other irrelevant information from the spectra. Sample-set partitioning based on joint x–y distance method was utilized to divide the sample measurements into calibration and validation datasets. The optimal wavelength variables were determined by competitive adaptive weighted sampling. The model was established and cross-validated using partial least square regression. The root mean square errors of cross-validation for polysaccharides and nucleic acids were determined to be 0.0382 and 5.218, and the root mean square errors of prediction were 0.0229 and 6.282. The overall results show that NIR spectroscopy combined with chemometry is effective for the quantitative analysis of Bacillus Calmette–Guerin polysaccharide and nucleic acid injections.     

Evaluation of alignment methods and data pretreatments on the determination of the most important peaks for the discrimination of coffee varieties Arabica and Robusta using gas chromatography-mass spectroscopy

Coffee samples were analyzed by GC/MS in order to determine the most important peaks for the discrimination of the varieties Arabica and Robusta. The resulting peak tables from chromatographic analysis were aligned and pretreated before being submitted to multivariate analysis. A rapid and easy-to-perform peak alignment procedure, which does not require advanced programming skills to use, was compared with the tedious manual alignment procedure. The influence of three types of data pretreatment, normalization, logarithmic and square root transformations and their combinations, on the variables selected as most important by the regression coefficients of partial least squares-discriminant analysis (PLS-DA), are shown. Test samples different from those used in the calibration and comparison with the substances already known as being responsible for Arabica and Robusta coffees discrimination were used to determine the best pretreatments for both datasets. The data pretreatment consisting of square root transformation followed by normalization (RN) was chosen as being the most appropriate. The results obtained showed that the much quicker automated aligned method could be used as a substitute for the manually aligned method, allowing all the peaks in the chromatogram to be used for multivariate analysis.     

激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究

利用激光诱导击穿光谱(LIBS)技术与常规化学分析方法获取28个浓度梯度含Cd元素的青菜样品的LIBS谱线信息以及Cd含量信息.对获取的光谱信息结合标准归一化处理(SNV)、一阶导数(FD)、二阶导数(SD)、中心化处理(Center)作为偏最小二乘法(PLS)模型的优选方法;再根据4种预处理方法的预测结果选取最佳方法,同时将该方法作为间隔偏最小二乘法(iPLS)与联合区间间隔偏最小二乘法(SiPLS)优选青菜LIBS谱线的最佳波长区间.结果表明:通过SiPLS优选的特征波长区间分别为214.72 ～ 215.82 nm,215.88～ 216.97 nm,225.08 ～ 226.35 nm,并且经过中心化预处理后建立的验证模型效果最好,结果显示交叉验证均方根误差(RMSECV)为1.487,验证均方根误差(RMSEP)为1.094,相关系数(R)为0.9942,平均相对误差(ARE)为11.60％.研究结果表明,所选优化方法适合青菜中重金属Cd元素的LIBS校正模型的建立,且具有较好的预测效果.     

Gamma ray spectrum smoothing by one-pass method

One-pass method with a piecewise cubic polynomial was used as a smoothing technique in gamma ray spectrum analysis. By this method, smoothed results of spectrum region is represented to several divided intervals each of which are fitted with a cubic polynomial calculated by least square technique, respectively. From the smoothing procedure of simulated photoelectric peaks and actual gamma ray spectra, following results were obtained. Photoelectric peaks which had more than 10 channels of FWHM (full width at half maximum) were fitted correctly and low count spectra of about 100 counts per channel could also be fitted smoothly. These smooth results can not easily be obtained by Savitzky's convolution technique. In conclusion, this one-pass method was found to be effective for gamma ray spectra, especially for photoelectric peaks of large FWHM and Compton region.     

Determination of Basic Nitrogen in Residues of Crude Oil Distillation Using ATR-FTIR and Chemometric Methods

A method for basic nitrogen determination in residues of crude oil distillation using infrared spectroscopy and chemometrics algorithms was developed. Interval partial least squares, synergy interval partial least squares, and backward interval partial least squares were evaluated for calibration model construction. The samples were divided into a calibration and prediction set containing 40 and 15 samples, respectively. The first derivative with a Savitzky-Golay filter and the mean centered data showed the best results and were used in all calibration models. The backward interval partial least squares algorithm with spectra divided in 60 intervals and combinations of 4 intervals (1407 to 1372; 1117 to 1082; 971 to 936; 914 to 879 cm^?1) showed the best root mean square error of prediction of 0.016 wt%. This calibration model displayed a suitable correlation coefficient between reference and predicted values.