Laser induced breakdown spectroscopy for monitoring the molten phase desulfurization process of blister copper.

The development of real-time monitoring sensors for pyro-metallurgical processes is an analytical challenge, mainly due to adverse environmental conditions, high spectral interferences and multiphase (molten and gas) reactions. This work demonstrates the suitability of stand-off LIBS (ST-LIBS) for real time monitoring of the desulfurization of blister copper which is carried out in molten phase. Here sulfur is removed by the formation of SO2 by supplying oxygen in molten phase. Using ST-LIBS the relative emission intensities of Cu(I) at 351.06 nm, O at 777.34 nm and S at 921.29 nm in both molten and gaseous phase were considered simultaneously during the process. This was possible only by the use high energy laser pulse over up to 270 mJ per pulse. In the case of copper, the selection of emission lines was assessed considering non-linear behavior, which is caused by self-absorption. For the first time, real time determination of sulfur in ppm range is reported by ST-LIBS using low sensitive lines from the NIR region. These results were validated with differential optical absorption spectroscopy (DOAS) as gold standard method. The analytical information obtained by LIBS can precisely determine the critical end-point of the desulfurization where the removal of sulfur is finished, and copper started to oxidize.

Combining prior knowledge with input selection algorithms for quantitative analysis using neural networks in laser induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for the analysis of rocks and mineral samples. Artificial neural networks (ANNs) have been used to estimate the concentration of minerals in samples from LIBS spectra. These spectra are very high dimensional data, and it is known that only specific wavelengths have information on the atomic and molecular features of the sample under investigation. This work presents a systematic methodology based on the Akaike information criterion (AIC) for selecting the wavelengths of LIBS spectra as well as the ANN model complexity, by combining prior knowledge and variable selection algorithms. Several variable selection algorithms are compared within the proposed methodology, namely KBest, a least absolute shrinkage and selection operator (LASSO) regularization, principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS). As an illustrative example, the estimation of copper, iron and arsenic concentrations in pelletized mineral samples is performed. A dataset of LIBS emission spectra with 12 287 wavelengths in the range of 185-1049 nm obtained from 131 samples of copper concentrates is used for regression analysis. An ANN is then trained considering the selected reduced wavelength data. The results are satisfactory using LASSO and CARS algorithms along with prior knowledge, showing that the proposed methodology is very effective for selecting wavelengths and model complexity in quantitative analyses based on ANNs and LIBS.

Single-frequency fiber laser based on a fiber ring resonator filter tunable in a broad range from 1023 nm to 1107 nm.

We present a broadly tunable single-frequency ytterbium-doped fiber laser. Its broadband tunability is made possible by low resonator losses. The wavelength is determined by a grating filter, while the single-frequency regime is achieved by filtering the longitudinal modes in a fiber ring resonance filter. We obtained a tuning range from 1023 nm to 1107 nm. A feedback loop driving a fiber stretcher actuated by a piezo-element prevents mode hopping. Based on the coherent delayed self-heterodyne interferometry, the laser linewidth is estimated to be 600 Hz with feedback open and 11.7 kHz with feedback closed.

Real-time fingerprinting of structural isomers using laser induced breakdown spectroscopy.

Laser induced breakdown spectroscopy (LIBS) has surfaced as an attractive alternative to mass spectrometry and wet chemistry methods for chemical identification, driven by its real-time, label-free nature. Rapid analysis needs, especially in high-energy materials and pharmaceutical compounds, have further fueled an increasing number of refinements in LIBS. Yet, isomers are seldom identifiable by LIBS as they generate nearly identical spectra. Here we employ a suite of chemometric approaches to exploit the subtle, but reproducible, differences in LIBS spectra acquired from structural isomers, a set of pyrazoles, to develop a sensitive and reliable segmentation method. We also investigate the possible mechanistic principles (causation) behind such spectral variations and confirm their statistically significant nature that empowers the excellent classification performance.

Non-gated laser induced breakdown spectroscopy provides a powerful segmentation tool on concomitant treatment of characteristic and continuum emission.

We demonstrate the application of non-gated laser induced breakdown spectroscopy (LIBS) for characterization and classification of organic materials with similar chemical composition. While use of such a system introduces substantive continuum background in the spectral dataset, we show that appropriate treatment of the continuum and characteristic emission results in accurate discrimination of pharmaceutical formulations of similar stoichiometry. Specifically, our results suggest that near-perfect classification can be obtained by employing suitable multivariate analysis on the acquired spectra, without prior removal of the continuum background. Indeed, we conjecture that pre-processing in the form of background removal may introduce spurious features in the signal. Our findings in this report significantly advance the prior results in time-integrated LIBS application and suggest the possibility of a portable, non-gated LIBS system as a process analytical tool, given its simple instrumentation needs, real-time capability and lack of sample preparation requirements.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability.

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis.

We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen (∼777 nm) to nitrogen (742.36 nm, 744.23 nm and 746.83 nm) compositional values yielded an optimal value at 746.83 nm with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry.