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

Ashwin Kumar Myakalwar,Ishan Barman,Manoj Kumar Gundawar,Narahara Chari Dingari,Ramachandra Rao Dasari,Mark G Kuzyk

doi:10.1371/journal.pone.0103546

Ashwin Kumar Myakalwar, Ishan Barman + Show 4 more

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https://doi.org/10.1371/journal.pone.0103546

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Abstract

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.

Highlights

One of the major objectives of Process Analytical Technologies, conceptualized by the U.S Food and Drug Administration in the last decade, is the development of novel sensor devices that can be incorporated in the manufacturing process loop to enable inprocess material characterization [1]
We report the application of Laser-induced breakdown spectroscopy (LIBS) measurements to classify pharmaceutical formulations in solid dosage forms, without employing gated detectors or echelle spectrographs
By correlating the spectra with the corresponding class of samples, we have developed predictive models based on soft independent modeling of class analogy (SIMCA), artificial neural networks (ANN) and partial least squares discriminant analysis (PLSDA)

Summary

Introduction

One of the major objectives of Process Analytical Technologies, conceptualized by the U.S Food and Drug Administration in the last decade, is the development of novel sensor devices that can be incorporated in the manufacturing process loop to enable inprocess material characterization [1]. Because of its real-time diagnostic capability, LIBS can be potentially used for testing a larger number of samples in comparison to existing analytical tools (e.g. high performance liquid chromatography (HPLC)), with the additional possibility of high-resolution surface mapping and depth profiling. Despite these intrinsic advantages, LIBS systems have hitherto not been employed for online process monitoring [4,6]. This is further compounded by the large spatial footprint and weight of these systems as well as the considerable maintenance and technical expertise required for its routine use

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