激光诱导击穿光谱在煤质检测中的应用现状

我国当前主要能源仍是煤炭资源,煤质快速检测有利于其清洁高效利用。激光诱导击穿光谱（Laser-Induced Breakdown Spectroscopy,LIBS）作为一种快速光谱检测技术,具有样品需求量小、制样简便、可多元素同时测量等优点,其在煤质快速检测中的应用潜力已得到广泛认可。本文从激光诱导击穿光谱仪器（实验室台式、在线式和便携式）的研发现状、激光诱导击穿光谱对煤质（金属元素、非金属元素和工业指标）的检测现状、煤质分析性能提升方法,以及激光诱导击穿光谱定量分析模型研究等方面介绍了近几年来LIBS技术在煤质检测中的应用现状及未来展望。     

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。     

近红外光谱分析中的化学计量学算法研究新进展

近红外光谱是一种绿色、快捷的分析技术,在科学研究、工业生产以及日常检测中得到广泛应用。化学计量学算法的应用在近红外光谱技术的发展过程中发挥了重要作用。化学计量学方法通过寻找测量变量之间的相关性,构建数学模型,量化样本间的差异性,并发现事物变化的内在规律,实现较合理准确的未知预测。这也是"大数据"战略的重要环节和主旨所在。该文针对近红外光谱吸收信号较弱、谱峰重叠严重,以及光谱测量过程中易受背景、噪声、无信息变量和外界环境因素干扰等,导致借助化学计量学方法建立的光谱与研究目标的定性定量分析模型变差问题,总结了近年来在近红外光谱领域所提出的一些化学计量学新方法,包括光谱预处理、变量选择、多元校正和模型转移,从不同角度阐述了这些方法在消除近红外光谱模型的干扰因素,提高模型的可靠性、预测准确性和适用性等方面的作用。     

支持向量机结台主成分分析辅助激光诱导击穿光谱技术识别鲜肉品种

为提高激光诱导击穿光谱技术(Laser-induced breakdown spectroscopy,LIBS)对鲜肉品种的识别率,采用支持向量机结合主成分分析算法辅助LIBS技术对鲜肉品种进行识别.对鲜肉切片用载玻片压平,采用LIBS技术对鲜肉组织(猪肉、牛肉和鸡肉)表面进行光谱数据的采集,每种鲜肉采集150幅光谱并进行随机排列,取前75幅光谱作为训练集建立模型,后75幅作为测试集测试建模结果.研究选取K、Ca、Na、Mg、Al、H、O等元素的49条归一化谱线数据进行主成分分析,并用所得数据建立支持向量机分类模型.结果表明,通过主成分分析降维,输入变量从49个优化减少到18个,模型建模速度从88.91 s降至55.52 s,提高了支持向量机的建模效率;并使预测集的平均识别率提高到89.11%.本研究为激光诱导击穿光谱技术在鲜肉品种快速分类领域提供了方法和数据参考.     

激光诱导击穿光谱技术结合偏最小二乘回归快速预测废钢中8种元素的含量北大核心CSCD

基于激光诱导击穿光谱(LIBS)技术,结合偏最小二乘回归(PLSR),建立了废钢中铬、镍、铜、硅、锰、钒、碳、钛等8种元素的定量分析模型。采用便携激光诱导击穿光谱废钢成分检测仪对12个钢铁标准样品进行扫描,对光谱数据进行剔除、平均、基线校正、归一化等预处理,以美国国家标准与技术研究院发射谱线数据库为参考依据,以筛选出的各元素建模波段的光谱数据作为模型输入,采用十折交叉验证方法,确定铬、镍、铜、硅、锰、钒、碳、钛的潜变量数分别为16,24,18,22,25,14,9,22,在优化的参数下分别对8种元素进行建模。结果显示:8种元素模型的相关决定系数为0.9571~0.9996,均方根误差为0.0034~0.0706,平均百分比误差为6.6764~75.645,残差平方和为0.0020~0.6532;除碳元素外,其余7种元素测定值的相对标准偏差(n=5)均不大于7.0%;模型用于预测实际废钢样品中锰、硅、铜元素的含量,所得结果与火花直读光谱法的基本一致。     

复杂过程光谱数据分析的化学计量学方法研究进展

使用光谱仪器对复杂化学体系进行原位实时光谱定量分析时,测试条件(如温度)通常无法控制,而且对被测样本不能进行任何预处理.从复杂的原位实时光谱数据中提取出有用的化学信息对分析化学家来说是一项极具挑战性的任务.本文综述了近些年针对原位实时光谱数据的定量分析而发展的一些化学计量学模型与方法,并给出了一些方法的具体算法.     

激光诱导击穿光谱:从实验平台到现场仪器

激光诱导击穿光谱(Laser-induced breakdown spectroscopy, LIBS)技术利用激光实现对分析样品的快速原位剥蚀和光谱激发,是一种具有广阔应用前景的分析手段,尤其是在现场、原位分析中优势明显,快速原位的分析特点符合未来分析仪器的发展方向.近年来基于该技术开展各类仪器研发的相关工作,引起广大研究者的极大关注.本文综述了激光诱导击穿光谱仪器中关键部件的组成及发展,从便携式、手持式及远程系统三个方面综述了各类现场应用仪器的研发进展,并对未来发展方向进行了展望.     

化学计量学在锕系元素分析中的应用

锕系元素的化学性质相似,各元素的分离和分析都很困难,用传统的数据解析手段,难以实现各元素的同时、快速分析。化学计量学是一种高效、功能强大的数据解析方法,对于样品复杂,基体干扰严重以及多组分样品的分析具有独特优势。将化学计量学应用于锕系元素的分析中,利用数学分离代替化学分离,可直接对样品进行测定。化学计量学方法也可用来指导试样的科学采集,进行实验设计、仪器分析操作条件选择等。从吸收光谱、ICP–AES及放射性测量3个方面综述了化学计量学在锕系元素分析中的应用,阐明了化学计量学在锕系元素分析中的应用难点及发展前景。     

激光诱导荧光辅助激光诱导击穿光谱检测有源发光玻璃中的微量元素

在有源发光玻璃的制备过程中,通常需要掺杂微量元素,用于改善玻璃的发光性能,因此在生产过程中进行快速检测非常重要.本实验针对激光诱导击穿光谱技术(LIBS)分析玻璃中微量元素灵敏度不足的问题,利用激光诱导荧光辅助激光诱导击穿光谱技术(LIBS-LIF)检测了玻璃中3种微量元素Yb,Al和P.使用波长可调谐激光激发等离子体中的Yb+离子、Al原子和P原子,并对这3种粒子在激光诱导荧光中的跃迁过程进行了分析.结果表明,通过激光诱导荧光辅助激光诱导击穿光谱技术,Yb+离子、Al原子和P原子的光谱强度分别增强了23,50和8倍,大幅度提高了LIBS分析的灵敏度.     

飞秒-纳秒双脉冲激光诱导击穿光谱(LIBS)技术对合金的定量分析

激光诱导击穿光谱(Laser-induced breakdown spectroscopy, LIBS)技术几乎不受聚变环境中的强磁场影响,是一种最有希望实现托卡马克装置中面向等离子体材料(Plasma facing materials, PFMs)原位在线诊断的技术,已被用于多个托卡马克PFMs壁诊断。然而,LIBS技术对PFMs表面元素的探测限、定量分析以及PFMs的服役状态判定依旧面临很大挑战。采用同轴飞秒-纳秒激光协同技术,建立了飞秒-纳秒双脉冲激光诱导击穿光谱(fs-ns-DP-LIBS)技术,通过高峰值功率、低激光能量的飞秒激光诱导等离子体,再用纳秒激光增强常规单脉冲LIBS技术信号发射强度,进而提升常规单脉冲LIBS的探测灵敏度,同时结合6种合金标准样品,采用fs-ns-DP-LIBS技术对样品中的主要元素进行了定量分析,并进一步结合机器学习方法对6种合金进行种类判别。结果显示：在纳秒单脉冲和飞秒单脉冲LIBS检测中,Ni、Fe和Mo在400～800 nm波段没有观察到明显特征峰,仅观察到Cr的特征峰;在飞秒-纳秒脉冲间2μs延时,NiⅠ498.02 nm、FeⅠ517....     

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.     

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.     

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。     

Automatic window factor analysis—A more efficient method for determining concentration profiles from evolutionary spectra

An algorithm called automatic window factor analysis (AUTOWFA) is developed for the purpose of determining, efficiently and automatically, the concentration profiles of the spectroscopically active components present in evolutionary processes, such as chemical titration, chromatography and kinetics. The method not only yields windows and profiles in agreement with those reported in the literature, but also reveals components not detected by precursor techniques. The method, however, has not been optimized and may require user interaction to fine-tune the windows.     

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).