Review articleOn the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review
Graphical abstract
Introduction
Sample characterization using Laser-Induced Breakdown Spectroscopy (LIBS) technique has been dynamically advancing in recent years. The parameters of conventionally utilized analytical instrumentation (lasers, spectrometers, and detectors) are being constantly improved. Moreover, the complicated or basic lab-built systems have been transformed to the sophisticated and commercially available systems, which enable an effortless and fast spectroscopic analysis. Contemporary state-of-the-art LIBS systems are capable of a high-end performance analysis (repetition rate, resolution, sensitivity). The high-end performance of LIBS is in certain cases superior to the performance of its analytical counterparts or reference techniques, such as Laser-Ablation Inductively Coupled Plasma (LA-ICP) based techniques, X-ray Fluorescence (XRF), etc.
LIBS is a well-established technique in many different applications, such as biology [[1], [2], [3], [4]], geology [5], and industry [6]. The reason is the simplicity and robustness of the LIBS instrumentation together with its capability of a fast-throughput multielemental analysis. Its potential has been repeatedly demonstrated by its high-end lab-based [7], in-situ and stand-off [8,9], and even extraterrestrial [10,11] utilization.
LIBS is one of the atomic emission spectroscopic techniques [6,12,13] based on the laser ablation sampling. Thorough articles were published with the aim to review the basic theory of the Laser-Induced Plasma (LIP) formation [[14], [15], [16]] and LIBS in general [[17], [18], [19], [20]].
The introduction covering the basic theory about LIBS technique was brief because this review article targets namely the aspects of data processing. The reader should follow referenced books and review articles for more detailed background of LIBS theory prior to any further data processing through MVDA algorithms. As it was emphasized by Hahn and Omenetto [17]: “advanced chemometric algorithms must be used with knowledge of what emission features (e.g. atomic or molecular emission peaks) are providing the associated discrimination.”
A typical LIBS system is able to provide a high number of measurements (given by its repetition rate) when each measurement is described by a high number of variables (especially in the case of echelle spectrometers). Note that the high repetition rate systems are mentioned for their leading edge in the LIBS applications, however, obtaining large number of measurements is not strictly related to LIBS systems with high repetition rate. The collected LIP spectrum is rich in information and represents the sample from which it originated, i.e. the chemical/spectral fingerprint of the sample [21,22]. The processing of large scale datasets is a demanding task which can be accomplished by using the so-called multivariate data analysis (MVDA; often related to as chemometric, exploratory data analysis or pattern recognition). It is noteworthy that unique LIP spectra are strongly affected by the matrix effect [19] which requires special attention when it comes to the conventional univariate calibration and quantitative analysis. On the contrary, the relation to the sample matrix enables a classification of samples according to their spectral fingerprints using simple MVDA algorithms. When processing large datasets, there are two more requirements to be met, namely, to process the data in the least possible time and in the most efficient manner. Efficiency can be measured by the conservation of variance during the dimensionality reduction, the sensitivity to outliers and the specificity to discriminate between individual matrices of analytes.
MVDA algorithms are massively spread throughout the LIBS community and are used in a number of applications. It may be stated that the future of the LIBS data analysis lies in the implementation of MVDA algorithms. The use of multivariate algorithms for processing of spectroscopic data has already been well documented [[23], [24], [25], [26]]. Moreover, several review articles [5,17,27,28] dealt solely with the multivariate processing of LIBS data. A full chapter in the LIBS book by Cremers and Radziemski [12] was also dedicated to this topic. Based on the literature survey, the most popular MVDA algorithm in the LIBS community is the Principal Component Analysis (PCA). This simple linear algorithm provides powerful means of data visualization and pattern recognition on a lower-dimensional scale.
Based on our thorough literature research, the methodological approaches in the processing of LIBS data through MVDA algorithms significantly differ. This is given i) by the needs of a particular application, ii) by the uniqueness of the data acquisition and data size, iii) by the data topology, iv) by the variety of MVDA algorithms and also v) by the internal methodology of each research group. Consequently, there is not a unified approach and it might not exist in the future. Moreover, a wide range of MVDA algorithms together with the available software for the processing of data creates an option to perform a reachable and easy-to-use analysis. This might lead to the misguided implementation of these algorithms and software, i.e. when their use leads to aesthetic improvement of low-quality data (high fluctuation, low sensitivity, etc.) [29]. Nevertheless, it has to be stressed that a stable and optimized analytical system providing a reproducible high-performance analysis (high-quality data) should be the cornerstone of any experimental work. The same is valid for the understanding of the theory of i) LIBS (e.g. laser-ablation and plasma dynamics and its properties) and ii) MVDA algorithms and their considerate and judicious implementation in the data analysis process [17].
In this work we bring a summary of the most common approaches in the implementation of PCA in LIBS data analysis for: low-dimensional visualization, clustering, outliers filtering, variable selection, quantification, classification, and non-conventional multivariate mapping. Additionally, general suggestions for the data preprocessing and the model building, as well as a comparison with the performance of other MVDA counterparts, are given.
Section snippets
Data preprocessing
Prior to an implementation of any MVDA algorithm such as PCA and its variations, it is strongly advised to preprocess the obtained data [28,30]. Detected multivariate signal in its raw state is burdened with unwanted background signal, fluctuation in the experimental parameters, etc. It has to be kept in mind that the data structure is changing during the data handling. This leads to consecutive changes in the performance of MVDA algorithm applied to the final data [31]. In general, there is a
PCA in LIBS
Advances in instrumentation development enable measurements with higher repetition rates, broader spectral ranges and better resolutions. Nowadays, an analysis results in datasets with thousands of variables [78] and millions of spectra [7]. Thus, the state-of-the-art LIBS system routinely provides big datasets (high number of spectra and variables) and so it is crucial to manage an effective and fast-response data processing. The MVDA algorithms must be applied into the analytical data
Summary of publications (Table 1)
Conclusion and future prospects
Based on the literature survey, LIBS combined with MVDA algorithms proved the capability to classify unknown samples and quantify analytes in many applications. However, the majority of reviewed articles represented only feasibility and preliminary studies. The impact of presented alterations in data pre-processing and MVDA algorithms on the resulting figures of merit was demonstrated on a limited number of samples, with a low number of spectra per sample, etc.
Generally, LIBS is on its rise and
Acknowledgement
Authors affiliated with CEITEC would like to acknowledge the financial support obtained from the National Sustainability program - CEITEC NPU II (LQ1061) and supported also from the ERDFund-Project CEITEC Nano+ (CZ.02.1.01/0.0/0.0/16_013/0001728). PP is grateful to the Fulbright commission for supporting his research at the University of Florida (E0583833).
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