Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification

Using a series of thirteen organic materials that includes novel high-nitrogen energetic materials, conventional organic military explosives, and benign organic materials, we have demonstrated the importance of variable selection for maximizing residue discrimination with partial least squares discriminant analysis (PLS-DA). We built several PLS-DA models using different variable sets based on laser induced breakdown spectroscopy (LIBS) spectra of the organic residues on an aluminum substrate under an argon atmosphere. The model classification results for each sample are presented and the influence of the variables on these results is discussed. We found that using the whole spectra as the data input for the PLS-DA model gave the best results. However, variables due to the surrounding atmosphere and the substrate contribute to discrimination when the whole spectra are used, indicating this may not be the most robust model. Further iterative testing with additional validation data sets is necessary to determine the most robust model.     

Time-resolved optical emission spectroscopic measurements of He plasma induced by a high-power CO2 pulsed laser

Laser-induced breakdown spectroscopy of helium plasma, initially at room temperature and pressures ranging from 12 to 101 kPa was investigated using a transverse excitation atmospheric CO₂ pulsed laser (λ = 9.621 and 10.591 μm, a full width at half maximum of 64 ns, and an intensity from 1.5 to 5.36 GW cm⁻²). The helium breakdown spectrum is mainly due to electronic relaxation of excited He, He⁺ and H. Plasma characteristics were examined in detail on the emission lines of He and He⁺ by the time-integrated and time-resolved optical emission spectroscopy technique. Optical breakdown threshold intensities, ionization degree and plasma temperatures were obtained. An auxiliary metal mesh target was used to analyze the temporal evolution of the species in the plasma. The results show a faster decay of the continuum emission and He⁺ species than in the case of neutral He atoms. The velocity and kinetic energy distributions for He and He⁺ species were obtained from time-of-flight measurements. Electron density in the laser-induced plasma was estimated from the analysis of spectral data at various times from the laser pulse incidence. Temporal evolution of electron density has been used for the estimation of the three-body electron-ion recombination rate constant.     

Diagnosis of lubricating oil by evaluating cyanide and carbon molecular emission lines in laser induced breakdown spectra

To prevent engine failure it is essential to change lubricating oil regularly before it loses its protective properties. It is also necessary to monitor the physical and chemical conditions of the oil to reliably determine the optimum oil-change intervals. The present work focuses on studying evolution of the cyanide (CN) and carbon (C₂) molecular spectral emission lines in the laser induced breakdown spectra of lubricating oil as a function of its consumption. The intensities of these molecular bands have been taken as indicator of engine oil degradation at certain mileage. Furthermore, the percentage of decay of CN and C₂ integral intensity values at the corresponding mileage was calculated in order to relate it to the degree of consumption of the motor oil. Such percentage decay of the CN and C₂ integral intensities have been found to increase gradually with increasing mileage which is accompanied with increasing depletion of engine oil. The results of using LIBS technique in the present measurements proved that it is possible to have a direct, straightforward and easy method for prediction of lubricating oil degree of consumption. This may facilitate scheduling the proper time and/or mileage intervals for changing the oil to avoid any possibility of engine failure.     

Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks

A method based on laser induced breakdown spectroscopy (LIBS) and neural networks (NNs) has been developed and applied to the identification and discrimination of specific bacteria strains (Pseudomonas aeroginosa, Escherichia coli and Salmonella typhimurium). Instant identification of the samples is achieved using a spectral library, which was obtained by analysis using a single laser pulse of representative samples and treatment by neural networks. The samples used in this study were divided into three groups, which were prepared on three different days. The results obtained allow the identification of the bacteria tested with a certainty of over 95%, and show that only a difference between the bacteria can cause identification. Single-shot measurements were sufficient for clear identification of the bacterial strains studied. The method can be developed for automatic real time, fast, reliable and robust measurements and can be packaged in portable systems for non-specialist users.     

Optimal deteriorating items production inventory models with random machine breakdown and stochastic repair time

This study develops deteriorating items production inventory models with random machine breakdown and stochastic repair time. The model assumes the machine repair time is independent of the machine breakdown rate. The classical optimization technique is used to derive an optimal solution. A numerical example and sensitivity analysis are shown to illustrate the models. The stochastic repair models with uniformly distributed repair time tends to have a larger optimal total cost than the fixed repair time model, however the production up time is less than the fixed repair time model. Production and demand rate are the most sensitive parameters for the optimal production up time, and demand rate is the most sensitive parameter to the optimal total cost for the stochastic model with exponential distribution repair time.     

LIBS对钛合金焊缝中成分的原位统计分布分析表征

钛合金凭借其强度高、耐蚀性好、耐热性高等特点已经被广泛应用于航天、海洋、生物医药等诸多领域,其中Ti-6Al-4V（TC4）合金的耐热性、强度、塑性、韧性、成形性、可焊性、耐蚀性和生物相容性均较好,已成为钛合金工业中的王牌合金。钛合金在激光焊接时,加入表面活性剂可以增加焊缝熔深、提高焊接效率、改善焊缝微观组织的不均匀性,但是可能会改变熔合区和焊缝区中元素含量及其分布状态,从而可能会对材料的性能产生一定的影响。运用LIBS分析技术对TC4钛合金焊接试样表面进行面扫描同步获得多元素成分信息,同时结合原位统计分布分析方法（OPA）,实现了对钛合金母材、熔合区、焊缝成分及其分布状态的快速表征,为活性剂的选择和焊接后钛合金的材料性能提供一种新的评价手段。选取了两个使用不同活性剂进行焊接的TC4钛合金薄板试样,选取焊缝纵切面方向作为分析面,采用320目的氧化铝砂纸进行表面处理,利用LIBSOPA系统进行成分分布统计表征。首先,对激发光斑和剥蚀条件进行条件优化,最终选择200 μm的激发光斑、10个预剥蚀脉冲10个剥蚀脉冲进行实验,并建立了钛合金中C,Al,V,Fe,Si和Ti六个元素的校准曲线（其中Si元素主要来自活性剂）;然后对钛合金焊接样品进行了区域扫描,并对元素含量和分布状态进行了统计表征。同时,在钛合金焊接样品的不同部位进行分区取样,采用高频红外法分析C元素含量,并与LIBSOPA结果进行比对,两种测试方法结果吻合。元素Al,V,Fe,Si和Ti分布结果与微束X荧光光谱法对应性较好。运用LIBSOPA 技术实现了对钛合金母材、熔合区、焊缝中多元素的成分分布表征,为快速判定钛合金焊缝中成分及分布状态提供了全新的评价表征手段。     

LIBS结合单变量定标法对土壤中铅元素含量检测

重金属污染一直影响着人们的健康生活,如镉,铅和铜等的污染,故而土壤重金属的快速检测和如何预防,一直受各国学者关注和研究。传统土壤重金属检测方法（如原子吸收光谱法、X荧光光谱法等）样品预处理复杂,分析成本较高,易形成样品的二次污染,不能满足快速分析的要求。激光诱导击穿光谱（LIBS）是一种典型的原子发射光谱,它是基于分析物质中原子和离子受激发而发射的特征谱线信息,进而研究物质成分的分析方法。LIBS技术能够快速检测任何状态（固、液和气态）物质元素的成分和含量,被看作是未来化学检测和快速绿色分析领域的新兴技术。LIBS技术具有对样本简单预处理（或不需要处理）、多元素同步分析、远距离测量、适用性广等优势,被广泛用于生活生产的各个领域,已成为近年来国内外学者广泛关注和研究的热点之一。在农业信息快速感知的大背景下,以激光诱导击穿光谱技术为技术手段,以土壤重金属铅元素为研究对象,运用理论分析和数学建模相结合,建立了多种基于单变量定标曲线的土壤重金属铅检测模型,并进行了模型验证。自制15个已知的铅元素浓度梯度的谱线土壤样本,在获取了土壤LIBS数据之后,对其进行预处理对比,建立了基于谱峰强度、谱峰积分、洛伦兹拟合强度三种定标曲线模型,对土壤中铅元素含量进行定量分析,得出基于三种定标曲线模型对土壤中铅元素含量的预测决定系数R2分别为0.918 0,0.910 1和0.914 3,三种定标曲线分析方法的预测结果都较好,说明了LIBS结合单变量定标曲线法对土壤中铅含量的检测可靠性高。最后选取部分样本数据进行验证,结果较好。研究结果为研发便携式农田土壤污染物检测技术与装备提供技术支撑,也为农田精准管理和科学施肥奠定基础。     

指纹光谱病原菌快速检测研究进展与趋势

病原菌检测对于保障饮用水和食品安全,应对突发公共卫生事件至关重要。现行检测标准或方法存在操作耗时费力,成本高等缺陷,难以满足现代社会高时效性要求,因此开发操作简单、低成本的病原菌快检技术迫在眉睫。近年来,随着激光技术和光电探测技术的高速发展,能够快速获取微生物指纹信息的激光光谱引起了研究者的广泛关注,其中表面增强拉曼光谱（SERS）和激光诱导击穿光谱（LIBS）由于具有快速、原位无损或微损检测等优点,在病原菌快速检测领域广受关注。SERS作为一种分子振动光谱技术,是在常规拉曼（Raman）光谱中引入具有光学信号放大作用的贵金属纳米结构,实现Raman信号数量级的提升同时能够猝灭荧光,因此可以快速获取目标分子的指纹光谱信息。然而受贵金属纳米粒子的材质、形貌、大小等自身属性,以及与待测物距离等多种因素的影响,重现性仍然是SERS在细菌检测中的一大瓶颈。LIBS作为一种新兴的原子发射光谱技术,具有多元素实时检测的能力,可以快速获取样品包括微量和痕量元素在内的所有元素信息。LIBS进行细菌分类和鉴别时,为了降低基底、共存基质的元素干扰,需采集大量纯培养细菌的光谱数据,不仅增加了检测周期,同时带来定性定量难两全的局面。结合SERS和LIBS技术在病原菌快检领域的研究现状,综述了两类方法各自的优势和局限性,并对其在病原菌快速检测领域的发展趋势进行了展望,为开发基于激光光谱的病原菌快检技术提供参考意见。     

Determination of Chloride Content in Different Types of Cement Using Laser-Induced Breakdown Spectroscopy

The characterization and accurate determination of the chloride content in cement/concrete is very important for the assessment of the durability and safety of a concrete structure. The available analytical techniques are relatively expensive and time consuming. In this study, a laser-induced breakdown spectroscopy (LIBS) system was used for determination of elemental composition in three different types of cement samples. The plasma was generated by focusing a pulsed Nd: YAG laser at 1064 nm on the cement samples. The concentrations of different elements of significance for structural stability in cement samples were determined. The evaluation of the potential and the capabilities of LIBS as a rapid tool for characterization of cement samples is discussed. The optimum LIBS setup and experimental conditions to detect and measure chloride in building materials are reported. The LIBS results were compared with the results obtained using a standard analytical technique such as inductively coupled plasma emission spectroscopy (ICP–ES). The limits of detection were determined, and calibration curves were measured. The results of this investigation indicate the reliability of LIBS to characterize different cement samples and to assess the chloride content in these cements.     

水体重金属激光诱导击穿光谱定量分析方法对比研究

建立了适用于激光诱导击穿光谱探测的多元线性回归、神经网络回归和支持向量机回归三种定量反演算法模型, 以水体重金属Ni为例进行了回归实验测试和对比分析. 多元线性回归、神经网络回归和支持向量机回归的平均相对标准偏差分别为7.60%, 4.86%, 2.35%; 最大相对标准偏差分别为23.35%, 15.20%, 8.29%; 平均相对误差分别为25.98%, 10.58%, 2.72%, 最大相对误差分别为116.47%, 47.38%, 9.89%. 研究为进一步实现水中痕量金属元素的快速定量分析提供了方法和数据参考. 关键词： 光谱学 激光诱导击穿光谱 支持向量机回归 重金属