Single-pollen analysis by laser-induced breakdown spectroscopy and Raman microscopy

The application of laser-induced breakdown spectroscopy to the analysis of single biological microparticles (bioaerosols) is described, exemplified here for a range of pollens. Spectra were recorded by exposure of the pollen to a single laser pulse from a Nd:YAG laser (lambda = 1064 nm, Ep approximately 30 mJ). The intensities of the single-pulse laser-induced breakdown spectra fluctuated dramatically, but an internal signal calibration procedure was applied that referenced elemental line intensities to the carbon matrix of the sample (represented by molecular bands of CN and C2). This procedure allowed us to determine relative element concentration distributions for the different types of pollen. These pollens exhibited some distinct concentration variations, for both major and minor (trace) elements in the biomatrix, through which ultimately individual pollens might be identified and classified. The same pollen samples were also analyzed by Raman microscopy, which provided molecular compositional data (even with spatial resolution). These data allowed us to distinguish between biological and nonbiological specimens and to obtain additional classification information for the various pollen families, complementing the laser-induced breakdown spectroscopy measurement data.     

Influence of Er:YAG and Nd:YAG wavelengths on laser-induced breakdown spectroscopy measurements under air or helium atmosphere

Laser-induced breakdown spectroscopy (LIBS) is widely dependent on the conditions of its implementation in terms of laser characteristics (wavelength, energy, and pulse duration), focusing conditions, and surrounding gas. In this study two wavelengths, 1.06 and 2.94 microm, obtained with Nd:YAG and Er:YAG lasers, respectively, were used for LIBS analysis of aluminum alloy samples in two conditions of surrounding gas. The influence of the laser wavelength on the laser-produced plasma was studied for the same irradiance by use of air or helium as a buffer gas at atmospheric pressure. We used measurements of light emission to determine the temporally resolved space-averaged electron density and plasma temperature in the laser-induced plasma. We also examined the effect of laser wavelength in two different ambient conditions in terms of spectrochemical analysis by LIBS. The results indicate that the effect of the surrounding gas depends on the laser wavelength and the use of an Er:YAG laser could increase linearity by limiting the leveling in the calibration curve for some elements in aluminum alloys. There is also a significant difference between the plasma induced by the two lasers in terms of electron density and plasma temperature.     

Determination of elemental impurities in plastic calibration standards using laser-induced breakdown spectroscopy

Dual-energy computed tomography (CT) scanning is a rapidly emerging imaging technique employed in nondestructive evaluation of various materials. CT has been used for characterizing rocks and visualizing multiphase flow through rocks for over 25 years. The most common technique for dual-energy CT scanning relies on homogeneous calibration standards to produce the most accurate decoupled data. However, the use of calibration standards with impurities increases the probability of error in the reconstructed data and results in poor rock characterization. Laser-induced breakdown spectroscopy was used to determine impurity concentration in a set of commercially purchased calibration standards used in dual-energy scanning for material identification with coal samples. Two calibration models were developed by using univariate calibration with the internal ratio method and multiple linear regression. Seven elements (Al, Fe, Mg, Na, Ni, Sr, and Ti) were examined in five different samples containing varying amounts of each ion to compare calibration from univariate data analysis and from multivariate data analysis. The contaminant concentrations were also measured by a commercially available inductively coupled plasma optical emission spectroscopy instrument, and the data were used as a reference in developing calibration curves for a modified version of the single linear regression model and the multiple linear regression model.     

Influence of the laser pulse duration on spectrochemical analysis of solids by laser-induced plasma spectroscopy

Quantitative analysis of aluminum and copper alloys by means of laser-induced plasma spectroscopy (LIPS) has been investigated for three representative laser pulse durations (80 fs, 2 ps, and 270 ps). The experiments were carried out in air at atmospheric pressure with a constant energy density of 20 J/cm2. Because the decay rate of the spectral emission depends on the laser pulse duration, the optimum detection requires an optimization of the temporal gating acquisition parameters. LIPS calibration (sensitivity and nonlinearity) and the limit of detection (LOD) are discussed in detail. While the LOD of minor elements embedded in alloy samples obtained by sub-picosecond or sub-nanosecond laser pulses are both time and element dependent, provided an appropriate temporal window is chosen, the optimum LODs (several parts per million (ppm)) prove to be independent of the laser pulse duration. Finally, it is found that for elements such as those detected here, gated LIPS spectra using picosecond or sub-picosecond laser pulses provide much better LOD values than non-gated spectra.     

Laser-induced breakdown spectroscopy of composite samples: comparison of advanced chemometrics methods

Laser-induced breakdown spectroscopy is used to measure chromium concentration in soil samples. A comparison is carried out between the calibration curve method and two chemometrics techniques: partial least-squares regression and neural networks. The three quantitative techniques are evaluated in terms of prediction accuracy, prediction precision, and limit of detection. The influence of several parameters specific to each method is studied in detail, as well as the effect of different pretreatments of the spectra. Neural networks are shown to correctly model nonlinear effects due to self-absorption in the plasma and to provide the best results. Subsequently, principal components analysis is used for classifying spectra from two different soils. Then simultaneous prediction of chromium concentration in the two matrixes is successfully performed through partial least-squares regression and neural networks.     

Spectrochemical microanalysis of aluminum alloys by laser-induced breakdown spectroscopy: identification of precipitates

Multielemental microanalysis of commercially available aluminum alloys has been performed in air by laser-induced breakdown spectroscopy (LIBS) by use of UV laser pulses with energies below 10 microJ. It is shown that the LIBS technique is capable of detecting the elemental composition of particles less than 10 microm in size, such as precipitates in an aluminum alloy matrix, by using single laser shots. Chemical mapping with a lateral resolution of approximately 10 microm of the distribution of precipitates in the surface plane of a sample was also carried out. Two main types of precipitate, namely, Mn-Fe-Cu (type I) and Mg-Cu (type II), were unambiguously distinguished in our LIBS experiments, in good agreement with x-ray microanalysis measurements. The relative standard deviations of emission of the main minor constituent elements (Cu, Mg, Mn) of the aluminum 2024 alloy range from 33% to 39% when laser shots on the precipitates are included in the analysis but decrease to a range from 5.3% to 7.4% when laser shots are taken only on the matrix material, excluding the precipitates.     

Chemometrics approach to the determination of polymorphism of a drug compound by infrared spectroscopy

A chemometrics approach, multivariate calibration in particular, was used to determine the polymorphism of a drug compound based on Fourier transform infrared (FTIR) spectroscopy. The partial least-squares projection to latent structure makes use of all of the data, and the latent variables created by the method make use of hidden or partially separated peaks for quantitation. This paper illustrates the usefulness of the partial least-squares multivariate calibration method as an efficient tool to determine the polymorphism of a drug. Also, the analysis suggests the use of information from the modeling as diagnostic tools to gain more insight from the data. In particular, the diagnostic tools allow an analyst to assess design characteristics and any shortcomings of a calibration experiment for the polymorphism of a drug compound.     

Chemometrics Approach to the Determination of Polymorphism of a Drug Compound by Infrared Spectroscopy

A chemometrics approach, multivariate calibration in particular, was used to determine the polymorphism of a drug compound based on Fourier transform infrared (FTIR) spectroscopy. The partial least-squares projection to latent structure makes use of all of the data, and the latent variables created by the method make use of hidden or partially separated peaks for quantitation. This paper illustrates the usefulness of the partial least-squares multivariate calibration method as an efficient tool to determine the polymorphism of a drug. Also, the analysis suggests the use of information from the modeling as diagnostic tools to gain more insight from the data. In particular, the diagnostic tools allow an analyst to assess design characteristics and any shortcomings of a calibration experiment for the polymorphism of a drug compound.     

A simple method for linearizing nonlinear spectra for calibration

The development and acceptance of spectral calibration methods has been an important success story for the field of chemometrics. This paper contains a new study of a very old calibration method (K-matrix calibration, parallel calibration, or generalized inverse prediction) and partial least squares (PLS), the mainstay of modern chemometrics. We show that with some modest amount of modification, the old method of calibration is comparable, in terms of prediction, to PLS for spectroscopy involving nonlinear spectral responses.     

Calibration effects for laser-induced breakdown spectroscopy of gaseous sample streams: analyte response of gas-phase species versus solid-phase species

The effects of analyte phase on the calibration response for laser-induced breakdown spectroscopy is investigated for a range of carbon species. Significant differences in the atomic emission signal from carbon were observed when comparing calibration streams of gas-phase and submicrometer-sized solid-phase carbon species. The resulting calibration curve slopes varied by a factor of 8 over a comparable range of atomic carbon concentrations for five different analyte sources, while the plasma electron density and temperature remained essentially constant. The current findings challenge a widely held assumption that complete dissociation of constituent species within a highly energetic laser-induced plasma results in independence of the analyte atomic emission signal on the analyte source. A physical model of the plasma-analyte interaction is proposed that provides a framework to account for the observed dependence on the physical state of the analyte.     

Extracting coal ash content from laser-induced breakdown spectroscopy (LIBS) spectra by multivariate analysis

Laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) analysis has been applied for the quantitative analysis of the ash content of coal in this paper. The multivariate analysis method was employed to extract coal ash content information from LIBS spectra rather than from the concentrations of the main ash-forming elements. In order to construct a rigorous partial least squares regression model and reduce the calculation time, different spectral range data were used to construct partial least squares regression models, and then the performances of these models were compared in terms of the correlation coefficients of calibration and validation and the root mean square errors of calibration and cross-validation. Afterwards, the prediction accuracy, reproducibility, and the limit of detection of the partial least squares regression model were validated with independent laser-induced breakdown spectroscopy measurements of four unknown samples. The results show that a good agreement is observed between the ash content provided by thermo-gravimetric analyzer and the LIBS measurements coupled to the PLS regression model for the unknown samples. The feasibility of extracting coal ash content from LIBS spectra is approved. It is also confirmed that this technique has good potential for quantitative analysis of the ash content of coal.     

Influence of alloying elements on hot corrosion of superalloys and coatings: necessity of smart coatings for gas turbine engines

Abstract

Modern gas turbine engines require high performance materials and coatings to ensure high efficiency. The selection of high performance materials and coatings depends on the nature and concentration of alloying elements. The composition of materials and coatings, in particular, plays a major role in enhancing the life of gas turbine engines by exhibiting good resistance to oxidation and hot corrosion, which are major problems in gas turbine engines. The performances of several superalloys containing different alloying elements and MCrAlY type coatings containing a variety of major and minor alloying elements are described in detail. The effect of major and trace elements on the life of superalloys and coatings in the presence of pure Na₂SO₄, NaCl and vanadium containing environments is detailed. The relevant reaction mechanisms leading to the failure of superalloys and coatings are discussed. The major factors involved when selecting alloying elements for the preparation of superalloys to manufacture components intended for use under hot corrosion conditions and the selection of appropriate coatings are suggested. Finally, the necessity of innovation of 'smart coatings' to combat both oxidation and hot corrosion is discussed.     

Process analysis of recycled thermoplasts from consumer electronics by laser-induced plasma spectroscopy

An experimental setup for direct elemental analysis of recycled thermoplasts from consumer electronics by laser-induced plasma spectroscopy (LIPS, or laser-induced breakdown spectroscopy, LIBS) was realized. The combination of a echelle spectrograph, featuring a high resolution with a broad spectral coverage, with multivariate methods, such as PLS, PCR, and variable subset selection via a genetic algorithm, resulted in considerable improvements in selectivity and sensitivity for this complex matrix. With a normalization to carbon as internal standard, the limits of detection were in the ppm range. A preliminary pattern recognition study points to the possibility of polymer recognition via the line-rich echelle spectra. Several experiments at an extruder within a recycling plant demonstrated successfully the capability of LIPS for different kinds of routine on-line process analysis.     

Elemental analyses and determination of lead content in kohl (stone) by laser-induced breakdown spectroscopy

Elemental analyses of kohl (stone) samples collected from three different parts of the world were performed using laser-induced breakdown spectroscopy (LIBS). The analyses indicated that lead (Pb), copper (Cu), silver (Ag), iron (Fe), calcium (Ca), aluminum (Al), silicon (Si), and sodium (Na) were present in all the kohl samples. In addition to these elements, the sample from Madina, Kingdom of Saudi Arabia (KSA), contained the elements tin (Sn), zirconium (Zr), and antimony (Sb). The sample from Mount Toor, Egypt, also contained Sn. Also, quantitative analysis for lead was carried out by the standard addition method using the LIBS technique. The result showed the presence of 14.12 ± 0.28% by weight of Pb in the sample from Madina, which compares well with the measurement done using atomic absorption spectroscopy (AAS) (13.31 ± 0.46%). The standard addition method used three calibration curves drawn for three emission lines of the LIBS spectra of Pb. The limits of detection (LoD) for these calibration curves varied from 0.27% to 1.16% by weight. The lead contents of the samples from Mount Toor and the local market of Bangladesh were also measured by the AAS technique, and the results were 14.61 ± 0.48% and 8.98 ± 0.35% by weight, respectively. The reason for determining only the lead content in kohl, which may be used as an eye cosmetic, is the adverse effect that lead has on health.     

Laser-induced plasma chemistry of the explosive RDX with various metallic nanoparticles

The feasibility of exploiting plasma chemistry to study the chemical reactions between metallic nanoparticles and molecular explosives such as cyclotrimethylenetrinitramine (RDX) has been demonstrated. This method, based on laser-induced breakdown spectroscopy, involves the production of nanoparticles in a laser-induced plasma and the simultaneous observation of time-resolved atomic and molecular emission characteristic of the species involved in the intermediate chemical reactions of the nanoenergetic material in the plasma. Using this method, it has been confirmed that the presence of aluminum promotes the ejection process of carbon from the intermediate products of RDX. The time evolution of species formation, the effects of laser pulse energy, and the effects of trace metal content on the chemical reactions were also studied.     

Detection of trace elements in liquids by laser-induced breakdown spectroscopy with a Meinhard nebulizer

The laser-induced breakdown spectroscopy of magnesium, manganese, and chromium atoms by use of a commercial Meinhard nebulizer originally designed for inductively coupled plasma measurements is described. This is the first time, to our knowledge, that this nebulizer has been used for laser-induced breakdown spectroscopy measurements. The limit of detection is slightly lower when the nebulizer rather than a liquid jet is used in single-pulse laser excitation. In addition we present the response characteristics of the nebulizer, such as effects of variations in purge gas and liquid flow rate, that are different from normal operating specifications. The effects of gate delay, gate width, and laser power variations were also studied. The objective of the present research has been to consider a new operating mode and conditions in which a better limit of detection of trace elements in water can be obtained.     

Quantitative analysis of cotton-polyester textile blends from near-infrared spectra

Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton-polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton-polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton-polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.     

Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose

Sample-to-sample variability has proven to be a major challenge in achieving calibration transfer in quantitative biological Raman spectroscopy. Multiple morphological and optical parameters, such as tissue absorption and scattering, physiological glucose dynamics and skin heterogeneity, vary significantly in a human population introducing nonanalyte specific features into the calibration model. In this paper, we show that fluctuations of such parameters in human subjects introduce curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture Raman spectra. To account for these curved effects, we propose the use of support vector machines (SVM) as a nonlinear regression method over conventional linear regression techniques such as partial least-squares (PLS). Using transcutaneous blood glucose detection as an example, we demonstrate that application of SVM enables a significant improvement (at least 30%) in cross-validation accuracy over PLS when measurements from multiple human volunteers are employed in the calibration set. Furthermore, using physical tissue models with randomized analyte concentrations and varying turbidities, we show that the fluctuations in turbidity alone causes curved effects which can only be adequately modeled using nonlinear regression techniques. The enhanced levels of accuracy obtained with the SVM based calibration models opens up avenues for prospective prediction in humans and thus for clinical translation of the technology.     

Atomic-scale roles of Zn element in age-hardened AlMgSiZn alloys

Adding alloying elements to improve the performances or the manufacturing processes of Al-Mg-Si alloys has long been a serious issue in developing advanced automotive aluminum materials.The Zn element,among those promising ones,has demonstrated positive alloying effects on Al-Mg-Si alloys.However,the atomic-scale roles of Zn in an age-hardened Al-Mg-Si-Zn alloy have not been adequately understood.Using atomic-resolution electron microscopy,here we report the precise locations of Zn elements in all hardening precipitates involved and their alloying mechanism at the atomic scale when alloying the alloy.Our results show that Zn atoms enter all the major hardening phases to occupy specific featured atomic sites of the original elements,e.g.the Si1 and Mg2 sites in the β'-2 phase,and modify their crystal structures,interfacial structures and morphologies in characteristic manners.It is revealed that for theβ'-phase,Zn atoms occupy unique atomic sites,whereas for other phases,they demonstrate similar behaviors as other additive alloying elements such as Ag and Cu do.     

The state of multivariate thinking for scientists in industry: 1980–2000

Chemometrics has enjoyed tremendous success in the areas related to calibration of spectrometers and spectroscopy-based measurements. These chemometric-based spectrometers have been widely applied for process monitoring and quality assurance. However, chemometrics has the potential to revolutionize the very intellectual roots of problem solving. Are there barriers to a more rapid proliferation of chemometric-based thinking, particularly in industry? What are the potential effects of chemometrics technology and the New Network Economy (NNE) working in concert? Who will be the winners in the race for faster, better, cheaper systems and products? These questions are reviewed in terms of the principles of the NNE and in the promise of chemometrics for industry. What then is the state of multivariate thinking in industry? Several powerful principles are derived from an evaluation of the NNE and chemometrics which could allow chemometrics to proliferate much more rapidly as a key general problem-solving tool.