激光诱导击穿光谱结合CARS变量选择方法定量检测倍硫磷含量

利用双脉冲激光诱导击穿光谱(LIBS)技术对溶液中的倍硫磷含量进行定量检测。采用二通道高精度光谱仪采集不同浓度倍硫磷样品在206.28~481.77 nm波段的LIBS光谱,并对光谱进行多元散射校正(MSC)、标准正态变量变换(SNV)及3点平滑预处理,根据偏最小二乘(PLS)建模确定最优的预处理方法。在此基础上,利用竞争性自适应重加权算法(CARS)筛选与倍硫磷相关的重要变量,然后应用PLS回归建立溶液中倍硫磷含量的定量分析模型,并与单变量定量分析模型及未变量选择的PLS定量分析模型进行比较。结果表明,相比单变量定量分析模型及原始光谱PLS定量分析模型,CARS-PLS定量分析模型的性能更优,其模型的校正集和预测集的决定系数及平均相对误差分别为0.969 4、15.537%和0.995 9、5.016%。此外,与原始光谱PLS模型相比,CARS-PLS模型仅使用其中1.9%的波长变量,但预测集平均误差却由9.829%下降为5.016%。由此可见,LIBS技术检测溶液中的倍硫磷含量具有一定的可行性,且CARS方法能简化定量分析模型,提高模型的预测精度。     

基于自加权变量组合集群分析法的近红外光谱变量选择方法研究

变量选择技术是光谱建模的重要环节。本研究提出了一种新的变量选择方法——自加权变量组合集群分析法(AWVCPA),首先通过二进制矩阵采样法(BMS)对变量空间进行采样;其次通过对变量出现频率(Fre)和偏最小二乘回归系数(Reg)两种信息向量(IVs)做加权处理,得到了每个光谱变量的贡献值,进而考虑到了Fre和Reg两类IVs对于光谱建模的影响;最后通过指数衰减函数(EDF)删除贡献小的波长点,进而实现特征变量选取。以啤酒和玉米两组近红外光谱数据为例,基于偏最小二乘法(PLS)建立啤酒中酵母浓度预测模型和玉米中油浓度预测模型,对比其它变量选择方法。研究表明,在相同条件下,基于AWVCPA变量选择方法建立的预测模型都取得了最优的预测精度,对啤酒中酵母浓度的预测,相比全光谱PLS模型,RMSEP由0.5348下降到0.1457,预测精度提高了72.7%;对玉米含油量的预测,相比全光谱PLS模型,预测均方根误差(RMSEP)由0.0702下降到了0.0248,预测精度提高了64.7%。     

激光诱导击穿光谱结合CARS变量选择方法定量检测奶粉中的镁

为监测奶粉中的镁(Mg)元素含量,本研究利用激光诱导击穿光谱(LIBS)技术对奶粉中Mg元素进行定量检测。对于每个样品,采用压片机在20 MPa压力下进行压片处理,然后利用高精度光谱仪在200~750 nm波段范围内获取压片样品的LIBS光谱。根据LIBS光谱特征,将光谱划分为4个波段,并进行初步的波段优选和光谱预处理分析。在此基础上,采用竞争性自适应重加权算法(CARS)对波长变量进行优选,再应用偏最小二乘法(PLS)建立奶粉中Mg元素含量的预测模型,并对预测集样本进行预测。研究结果表明,LIBS技术结合CARS变量选择方法可以用于奶粉中Mg元素含量的定量检测,最优CARS-PLS预测模型的校正集和预测集的决定系数及平均相对误差分别为0.9999,0.20%和0.9742,3.29%,优于原始光谱所建立的PLS模型,且所用波长变量仅为PLS模型的7.7%。由此表明,CARS方法能有效选择有用的波长变量,可简化预测模型及提高预测模型的稳定性。本研究为奶粉中镁元素含量的快速定量分析提供参考。     

基于LASSO算法的光谱变量选择方法研究

光谱分析技术由于具有简单、快速、无损等优势,在复杂体系的定性和定量分析中得到了广泛应用。然而光谱中往往包含成百上千的波长点,有些波长点与研究的目标性质并不相关,加大了计算量并降低了模型的预测准确度。因此,在建立模型前需要进行变量选择。最小绝对收缩与选择算子（LASSO）可将回归系数收缩为0,进而达到变量选择的目的。该研究将LASSO用于三元调和油样品近红外光谱和生物样品拉曼光谱的变量选择,基于偏最小二乘（PLS）和多元线性回归（MLR）模型,分别对香油和肌氨酸的含量进行定量分析,并与无信息变量消除－PLS（UVE－PLS）、蒙特卡罗结合UVE－PLS（MCUVE－PLS）和随机检验－PLS（RT－PLS）3种变量选择方法进行比较。结果表明,基于LASSO的变量选择方法保留的变量数最少,运算速度最快。对三元调和油样品,LASSO－PLS预测的准确度最高;对生物样品,LASSO－MLR预测的准确度最高。因此,基于LASSO的变量选择算法有望在光谱分析领域中得到良好应用。     

基于灰狼算法的近红外光谱变量选择方法研究

基于群体智能的灰狼优化(GWO)算法具有参数少、结构简单、易于实现的优点,但在光谱领域的应用较少。该研究将GWO算法引入近红外光谱的变量筛选中,以玉米数据为例,考察了GWO算法中狼群性能、迭代次数、狼群数量及运算效率,并建立了偏最小二乘(PLS)模型对玉米样品中蛋白质、脂肪、水分以及淀粉含量的测定。结果显示,GWO算法运算效率很高,经过参数调优后建立PLS模型,其蛋白质、脂肪、水分及淀粉的保留变量数分别为19、19、14、34,预测均方根误差(RMSEP)从全波长PLS建模的0.245 8、0.122 4、0.339 8、1.105 8分别下降到0.147 7、0.080 1、0.176 2、0.739 8,分别下降了40%、35%、48%、33%,相关系数也相应地提高。因此,GWO算法不仅优化速度快,选择变量数少,还可以显著提高PLS模型的预测精度,是一种近红外光谱变量选择的有效方法。     

基于交互式自模型混合物分析的近红外光谱波长变量优选方法

为了提高近红外光谱定量分析的预测精度和建模效率,提出了一种基于交互式自模型的混合物分析的波长优选方法,根据光谱各波长变量的纯度值和标准差值,选择含有用信息的波长变量,并引入相关权函数解决变量间共线性问题.通过依次迭代选择的变量建立定量校正模型,由交互验证均方根预测误差(RMSECV)确定最佳波长变量个数.应用该波长变量优选方法对具有不同葡萄糖含量的两组(四成分葡萄糖水溶液实验和人体血浆实验)近红外光谱数据进行分析,两组数据中分别只选择了全部变量的0.3%建立定量校正模型,其验证集葡萄糖浓度的均方根预测误差(RMSEP)分别减少为669和15 mg/L.与全谱范围及优选波段建立的定量校正模型比较,本方法能够通过波长变量优选最小化冗余信息、提高预测精度及建模效率.     

不同比色皿对近红外光谱法检测水质酸度的影响及校正分析北大核心CSCD

为减小不同比色皿对近红外光谱测量结果的影响,提高水质酸度定量分析模型的预测精度,探讨了正交信号校正(OSC)用于不同比色皿的光谱背景干扰的去除效果。用两个同一批次的石英比色皿对32个不同pH的水样装样,采集近红外光谱数据,采用OSC对原始光谱进行预处理。比较OSC预处理前后两组光谱间的差异,并建立了水质酸度的偏最小二乘(PLS)定量分析模型,分析了光谱差异对模型预测精度的影响。结果表明:经OSC预处理后,两组光谱的平均差异值由0.0042降低至0.0013,光谱校正率达90%;与原始光谱建立的PLS模型相比,基于OSC预处理后的光谱建立的PLS模型的预测精度显著提高,预测均方根误差差值由0.912降低至0.205,相关系数差值由0.364降低至7.00×10^(-3),二者分别减小了78%和98%。     

基于多光谱特征融合技术的面粉掺杂定量分析方法

提出了一种基于拉曼光谱技术(Raman)和激光诱导击穿光谱技术(LIBS)的多光谱特征融合技术(MFFT),利用拉曼光谱中分子组分信息和激光诱导击穿光谱中原子组分信息之间的互补特性,采用自适应小波变换(AWT)-竞争性自适应加权(CARS)-偏最小二乘回归(PLS)建模技术,获取了面粉体系更为全面的特征信息。在多光谱特征融合技术中,首先采用AWT-CARS方法分别提取拉曼光谱和激光诱导击穿光谱中的特征变量,然后将两者的特征变量融合为一个向量,采用PLS方法构建MFFT模型,实现了面粉掺杂物的定量分析。通过对二氧化钛、硫酸铝钾等面粉掺杂体系建模分析,考察MFFT模型的有效性。结果表明,与单一拉曼光谱技术或激光诱导击穿光谱技术建立的预测模型相比,MFFT模型显著提升了模型的预测性能,二氧化钛和硫酸铝钾预测模型的线性相关系数分别从相对较差的Raman模型的0.884、0.877提升到0.981、0.980,其预测均方根误差分别从相对较差的Raman模型的0.151、0.154降低到0.069、0.068。表明多光谱特征融合技术可以准确提取Raman光谱中的分子信息和LIBS光谱中的元素信息,使其互为补充、互为校正,进而有效克服面粉基质对掺杂组分定量分析的干扰,显著提高模型的预测精度。     

基于支持向量机的近红外特征变量选择算法用于树种快速识别

将稳定度自适应重加权采样特征变量选择算法用于支持向量机定性分析(Support vector machine-stability competitive adaptive reweighted sampling,SVM-SCARS)。该算法通过对数据多次采样建模计算各变量的稳定度值,稳定度值能更加客观准确地评估变量在建模中的作用,因此可作为变量重要性的评价依据。通过循环迭代方式,采用自适应重加权采样技术逐步筛选变量,然后以每次循环所得变量子集建立SVM模型,并以模型交叉验证分类正确率(Correct classification rate of cross validation,CCRCV)评估子集优劣,确定最优特征变量子集。将该算法结合漫反射近红外光谱技术建立了制浆造纸常用木材的树种识别模型,实现了对4种桉木和2种相思木的快速识别分类。最终共筛选出15个特征变量建立分类模型,模型对各树种分类的正确率达97.9%,具有较好的分类效果。与全光谱模型和递归特征消除支持向量机模型相比,SVM-SCARS能够筛选出更少的特征变量,且模型具有更好的预测性能和稳定性。研究结果表明,SVM-SCARS算法能够有效优化光谱特征变量,提高近红外在线分析模型在木材材性分析中的稳健性和适用性。     

激光诱导击穿光谱(LIBS)结合机器学习算法快速测定石油焦中微量元素

石油焦中微量元素对其作为预焙阳极的性能起着决定性的作用。首先，通过基于LIBS光谱构建用于石油焦中铁(Fe)和铜(Cu)定量分析的PLS校正模型。然后，考察了不同光谱预处理(归一化、多元散射校正、标准正态变换、一阶导数和二阶导数)以及变量选择算法(粒子群优化算法和变量重要性投影)对PLS校正模型预测性能的影响。建立了一种基于激光诱导击穿光谱(Laser-induced breakdown spectroscopy, LIBS)结合偏最小二乘(Partial least squares, PLS)的石油焦中微量元素定量分析方法。结果显示，与其他PLS校正模型相比，基于二阶导数和变量重要性投影的PLS模型对Fe的预测性能最优，最优的交叉验证相关系数(R-squared cross validation,R²cv)为0.966 7,均方根误差(Root mean squared error cross validation, RMSEcv)为10.282 1 mg/kg,预测集的相关系数(R-squared prediction,R²p)为0.86...     

On-line fermentation gas analysis: Error analysis and application of mass spectrometry

On-line fermentation gas analysis is of general interest because it permits the determination of metabolic rates in almost any biological process using living organisms. The consumption and production of gases (O₂, CO₂, CH₄, etc.) and volatile compounds may be determined without causing any risk of infection. Elemental balancing permits the determination of other metabolic rates if the stoichiometry is known. This was studied with the production of poly-β-hydroxybutyrate (PHB) by Alcaligenes latus. Estimations were based on the measurement of gas partial pressure and flow-rates, pH and alkali consumption rate. Experiments with a small quadrupole mass spectrometer showed unacceptable error propagation. Therefore, dynamic error propagation for all rates was studied using simulation. It was found that, for example, a 1% relative offset-calibration error for oxygen can result in an error in PHB estimation of > 50%. It is suggested that this culture is used in combination with elemental balancing for thorough tests of the accuracy of on-line gas analysis equipment. An on-line process gas analyser based on a quadrupole mass spectrometer (Balzers PGM 407) gave the following precision values (abs. vol.?%) during cultivation of Bacillus subtilis: nitrogen (m/z 14), 0.024; oxygen (m/z 32), 0.020; argon (m/z 40), 0.0011; and carbon dioxide (m/z 44), 0.0034. These values, combined with automatic recalibration, would be sufficient for reasonable estimation of PHB, biomass and substrates.     

Impact of alkali treatment on physico-chemical,thermal, structural and tensile properties of Carica papaya bark fibers

This study aims to examine the effect of sodium hydroxide (NaOH) treatment on the physico-chemical properties, structure, thermal, tensile and surface topography of Carica papaya fibers (CPFs). The surface of raw CPFs was modified by soaking with 5% NaOH solution for 15, 30, 45, 60, 75 and 90?min. The results of thermo-gravimetric analysis revealed that the optimum treatment time for alkali treatment was 60?min. It was found that the alkali treatment improved the properties of the CPFs. The results of TGA, FT-IR, XRD and AFM suggest that the treated CPF is a suitable alternative as reinforcement in polymer composites.     

Chemometric and trend analysis of water quality of the South Chennai lakes: an integrated environmental study

In order to bring out the nature of the factors influencing lake water composition, multivariate statistical analysis and trend analysis were performed based on the hydrochemical data of the study area, namely, South Chennai. Change in land use pattern and settlements along the banks of the lakes alters the quality and quantity of the surface water. In the present study, the R‐mode factor analysis and cluster analysis were applied to the geochemical parameters of the water to identify the factors affecting the chemical composition of the lake water. Dendograms of both the seasons give three major clusters, reflecting the groups of unpolluted to moderately polluted, polluted, and heavily polluted stations. The movement of stations from one cluster to another clearly brings out the seasonal variation in the chemical composition of the lake water. The complex hydrochemical data of the surface water were interpreted by condensing them into three major factors. Factor score analysis was used successfully to delineate the stations under study and the role of the contributing factors, and the nature of factors responsible for the variation in chemical composition of the water has been clearly brought out. Results of trend analysis using ArcGIS clearly indicate that the trend in water quality is deteriorating at a faster rate in the eastern part of the study area. It is understood that although natural shifts probably can account for some of the variation, it is most likely that human activities play a major role in affecting the water quality on a regional scale. Copyright © 2014 John Wiley & Sons, Ltd.     

Time-series analysis in analytical process control

Analysis of time series tries to extract tendencies from measured values dependent on time. For this purpose the cusum technique has proved to be a very sensitive tool for the evaluation of both current and completed time series. Even very weak tendencies can be detected at a high level of noise. Time-series analysis further tries to predict values to come from hitherto performed measurements. As a very flexible model exponential smoothing could be successfully used. Even for processes with a high extent of non-stationarity this model allowed a good prediction owing to the dynamics of the process. Three types of time-series analysis, i.e., evaluation of current measurements, retrospective evaluation and prediction of data (also known as “in vivo”, “post mortem” and “in futurum” time-series analysis) are demonstrated for problems stemming from analytical process control.     

新材料的发展与分析化学

文章介绍了新材料的重要性及发展方向,分析化学在新材料研制中起着耳目的作用,另一方面新材料也为分析化学的进展提供了课题与条件。微量分析、微区分析、表面分析是此领域中的重点。在未来的发展中,分析化学在材料的发展中的地位不会改变,并期待着分析灵敏度与空间分辩率的进一步提高。     

Determination of rank by augmentation (DRAUG)

Determining the rank of a chemical matrix is the first step in many multivariate, chemometric studies. Rank is defined as the minimum number of linearly independent factors after deletion of factors that contribute to random, nonlinear, uncorrelated errors. Adding a matrix of rank 1 to a data matrix not only increases the rank by one unit but also perturbs the primary factor axes, having little effect on the secondary axes associated with the random errors in the measurements. The primary rank of a data matrix can be determined by comparing the residual variances obtained from principal component analysis (PCA) of the original data matrix to those obtained from an augmented matrix. The ratio of the residual variances between adjacent factor levels represents a Fisher ratio that can be used to distinguish the primary factors (chemical as well as instrumental factors) from the secondary factors (experimental errors). The results gleaned from model studies as well as those from experimental studies are used to illustrate the efficacy of the proposed methodology. The method is independent of the nature of the error distribution. Limitations and precautions are discussed. An algorithm, written in MATLAB format, is included. Copyright © 2011 John Wiley & Sons, Ltd.     

Determination of Mercury in Geological Materials by Continuous-Flow,Cold-Vapor,Atomic Absorption Spectrophotometry

Abstract

To determine mercury in geological materials, samples are digested with nitric acid and sodium dichromate in a closed teflon vessel. After bringing to a constant weight, the digest is mixed with air and a sodium chloride-hydroxylamine hydrochloride-sulfuric acid solution and then Hg(II) is reduced to Hg with stannous chloride in a continuous flow manifold. The mercury vapor is then separated and measured using cold vapor atomic absorption spectrophotometry (CV-AAS). For a 100 mg sample the limit of detection is 20 parts per billion (ppb) Hg in sample. To obtain a 1% absorption signal, the described method requires 0.21 ppb Hg solution (equal to 16 ppb in sample). Precision is acceptable at less than 1.2% RSD for a 10 ppb Hg aqueous standard. Accuracy is demonstrated by the results of the analysis of standard reference materials. Several elements do interfere but the effect is minimal because either the digestion procedure does not dissolve them (e.g., Au or Pt) or the; are normally of low abundance (e.g., Se or Te).     

钴-5-Br-PADAP极谱催化波研究

许多作者研究了某些有机试剂存在时钴的极谱行为.本文报道在氢氧化钠和盐酸羟胺底液中,钴-5-Br-PADAP配合物的极谱行为,证明峰电流为配合物吸附波.将铁分离后测定标钢中微量钴,与光度法结果一致,直接测定茶叶中钴,结果满意.