Response surface methodology for optimizing LIBS testing parameters: A case to conduct the elemental contents analysis in soil
Introduction
Laser-induced breakdown spectroscopy (LIBS), a kind of atomic emission spectroscopy (AES), is a novel analytical technique for materials and elements [[1], [2], [3]]. LIBS technology is carried out by focusing laser pulses with proper energy onto the sample surface, which can induce the high temperature laser-induced plasma. The laser-induced plasma is generated from atoms, ions, and electrons released in the course of the laser pulses interacting with the tested sample. During the plasma cooling process, the spectra with different frequencies in various wavelengths is radiated and collected by an optical fiber coupled to a spectrometer. The spectrometer separates out the collected spectra into various wavelengths and a detector coupled with the spectrometer transforms the optical signal into electronic signal. At last, a software, which is employed to control detector and analyze spectral emission lines, can display spectra in different intensities at different wavelengths of the tested sample. Those LIBS intensities and wavelengths of spectral emission lines can be employed to finish the further qualitative and quantitative analyses.
Compared with conventional spectrochemical analysis methods, like atomic absorption spectrometry (AAS), X-ray fluorescence spectroscopy (XRFS), inductively coupled plasma-atomic emission spectrometry (ICP-AES), etc., LIBS offers several unique advantages, such as rapid, real-time, nondestructive, no sample preparation for the analysis of gases, liquids, and solids, simultaneous multi-element detection, ability to detect high and low concentration elements, good sensitivity for many elements, stand-off analysis capabilities [4,5]. Due to those advantages, it has witnessed tremendous growth and exhibits a wide application in various fields of environmental monitoring [6,7], archaeological investigations [8], geological applications [9], industrial analysis [10], agriculture [11], food [12,13], and space exploration [14,15].
Despite its versatility and universality of analytical characteristics, there are some weaknesses of LIBS performance which are not competitive enough with the performance of well-established analytical atomic spectrometry methods [16]. LIBS signals are susceptible to potential factors, like variations of the testing parameters, sample state, and the mutual effect between the laser pulse and the tested sample [17]. Therefore, the main traits (limits of detection and repeatability) of LIBS need be enhanced [16]. Testing parameters include laser intrinsic properties (pure energy, excitation wavelength, shot-to-shot energy fluctuation, and pulse duration, etc.), detector characteristics (delay time, integration time, amplification detector gain, emission lines selection, etc.), optical design (lens focusing, collection optics, mono-pulse or dual-pulse, etc.), ambient atmosphere (gas composition, pressure, etc.). Sample state involves in characters of the tested samples (take soil as example: moisture content, particle size, porosity, etc.). And the mutual effect is up to the laser, matrix, surface and homogeneity of the tested sample [3]. Variations of above parameters can affect LIBS signals as well as the sensitivity and precision of detection. Therefore, it is of great significance to study and optimize the experimental conditions for the collection of LIBS signals, which lays a theoretical foundation for the subsequent qualitative and quantitative analysis of soils.
According to several reports and reviews [5,[17], [18], [19]], the terms for describing of LIBS system used most frequently are laser, focusing on the target, the choice of detector and spectrometer, etc. The factor laser energy (LE) largely determines the intensity of the emission lines to be analyzed, the signal-to-background ratio, and the accuracy of subsequent analysis. Delay time (DT) can reduce the continuum background radiation and improve the record of atomic or ionic emission lines. Lens to sample distance (LTSD) can determine the size of the spot diameter of the laser beam and indirectly ascertain the laser energy density of the tested sample’s surface. In earlier reports, Aguilera, Aragon and Penalba [18] investigated the variation of emission line intensities of laser-produced plasmas with the laser pulse energy and the lens-to-sample distance. Unnikrishnan, Choudhari, Kulkarni, Nayak, Kartha and Santhosh [20] studied various experimental conditions of the system (laser powers, gate widths, delay times.), then employed principal component analysis (PCA) for plastic classification. Multari, Foster, Cremers and Ferris [21] carried out the investigation on LIBS measurements for the effect of sampling geometry, like focal length, lens to sample distances (LTSD), angle-of-incidence. Castro and Pereira-Filho [22] optimized the LIBS experimental conditions (energy, delay time and spot size) using factorial design and compared the performance of multivariate and univariate calibration strategies for ten analytes (Al, Cr, Cu, Fe, Mn, Mo, Ni, Ti, V, and Zn) identification.
Most existing studies analyzed the plasma spectral characteristics based on univariate analysis of parameters in LIBS system or the tested sample for obtaining the optimized testing parameters. However, it is necessary to consider and explore the change in plasma spectral characteristics caused by interactive effects among multiple parameters in the process of LIBS data collection. Response surface methodology (RSM), a kind of statistical and mathematical methodology, has been widely employed to assess the effects of independent variables and their possible interactions [23,24]. It can simultaneously optimize the levels of independent variables for the best system performance and has been employed in food, chemical process, and other fields [25].
In present work, RSM was employed to study the influence of different testing parameters of LIBS on the spectral characteristics of main elements in soils, and LIBS combined partial least-squares regression (PLSR) were used to analyze the main elemental contents in soil based on the optimized testing parameters. The specific objectives were as follows: (1) to carry out the three factors (LTSD, DT, and LE) experimental design using central composite design (CCD) based RSM, (2) to structure an objective function (named as YSBR) based on signal-background-ratio (SBR) of characteristic spectral lines of main elements in soil, (3) to explore the interaction effects among three factors on soil plasma characteristics and obtain the LIBS optimized testing parameters, (4) to conduct quantitative analysis of multi-elements content in soil using LIBS technique coupled with PLSR method for verifying the optimized testing parameters.
Section snippets
Soil samples
Soil samples were randomly and manually collected from experimental field at Zhejiang University, Hangzhou (120.2°E, 30.3°N). Soil samples were taken to the lab and oven-dried at 60 °C after removing impurities (such as rocks, plant roots, etc.). Then, about 3 g soil power in a series of operations of grinding, sieving (100 mesh), weighing, was used for the research. To avoid splashing and obtain the homogeneous surface of the soil for laser ablating, the soil powder was pressed into a tablet
Three factors for experiment
As mentioned above, the SBR of characteristic spectral line is influenced by various factors. For obtaining the best analytical performance of a tested sample, the experimental testing parameters must be carefully selected [17]. In present study, three main factors (LTSD, DT and LE) were selected as influencing parameters on YSBR based on previous results. To select an adequate range for each factor, scope of factors were designed as follows: 94 mm ≤ LTSD ≤102 mm (Note: focal length of lens is
Conclusions
This work employed the RSM to investigate the influence of LIBS testing parameters LE, DT, and LTSD on the spectral characteristics of main elements in soils, and the testing parameters were verified. The effect of LE, DT, and LTSD were modeled by RSM. Central composite design (CCD) in RSM was used for experimental runs and the values of LE, DT, and LTSD were optimized. Then, combined signal-background-ratio (YSBR) of characteristic spectral lines from the Si, Fe, Mg, Ca, Al, Na, and K elements
Declaration of competing interest
The authors have declared no conflict of interest.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Program No.: 61705188), Natural Science Basic Research Plan in Shaanxi Province of China (Program No.: 2017JQ3008), China Postdoctoral Science Foundation (Program No.: 2017M613218), Shaanxi Postdoctoral Science Foundation (Program No.: 2017BSHYDZZ61), the Fundamental Research Funds for the Central Universities (Program No.: 2452017125), the Doctoral Scientific Research Foundation of Northwest A&F University (Program
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