Incorporation of Support Vector Machines in the LIBS Toolbox for Sensitive and Robust Classification Amidst Unexpected Sample and System Variability

Abstract

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 non-linear 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 application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), due to 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.

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

“…Additionally, such studies in the later stages of development will also require the identification and systematic characterization of less common types of lesions not observed in the initial investigations here. Depending on the number of classes required and the complexity of the tissue biochemical composition, we will also assess the performance of other classification algorithms, such as soft independent modeling class analogy [32] and support vector machines [33]. Our long-term goal is to evaluate and translate Raman spectroscopy not only for the real-time detection of cancerous lesions but to be able to discriminate between the grades and stages of such tumors.…”

Standoff Detection of Explosives at 1 m using Laser Induced Breakdown Spectroscopy

“…The details of the experimental setup used for recording the data can be found in literature 5,8,31 , with the exception of the distance of the sample from the focussing lens. In brief, 532 nm laser pulses of 7 ns pulse duration from a Q-switched Nd: YAG were employed.…”

An Approach to Reduce the Sample Consumption for LIBS based Identification of Explosive Materials

“…It has been demonstrated that with the application of various chemometric algorithms, it is possible to distinguish the similar type of materials based on their LIBS spectra. Different methods like soft independent modelling of class analogy (SIMCA) 3 , partial least squares -discriminate analysis (PLS-DA) 28,29 , support vector machines (SVM) 30,31 and artificial neural networks (ANN) 32,33 etc. have been successfully implemented for the classification of a wide variety of materials.…”