The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (LOD) in laser-induced breakdown spectroscopy (LIBS) analytical applications. The derivation of the commonly used ‘3-sigma over slope’ rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the LOD to LIBS multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by LIBS.
1 September 2012 in Journal of Archaeological Science
A unique sample of ink from an inkwell in the Schoyen Collection allegedly found at Qumran has been subjected to analyses by several analytical techniques: GC-MS, proteomic analysis, PXRD, Raman, (ATR) FT-IR, LIBS, ICP-MS and MS. The results reveal to an unexpected level of detail how the ink was manufactured, which gives insight into, the industrial processes and craftsmanship that were practiced at the Qumran settlement during the Second Temple period (100 BCE-CE 70). The identified minerals and other organic and inorganic materials are sufficiently multiple and diverse that it is probable that this specific ink can be recognized if analyses. of inks are performed on manuscripts from Qumran and other locations in Israel and the Middle East. The present work exposes a distinct and unique possibility to shed light on early Jewish manuscript controversies, including their provenance. (C) 2012 Elsevier Ltd. All rights reserved
Although approximately half of global lithium consumption is used in the rechargeable battery industry, lithium is also in demand for other specialized applications, such as high-temperature lubricants, ceramics, glass, and pharmaceuticals. The growing need for efficient lithium recovery and recycling underscores the importance of fast and accurate analytical tools for determining lithium concentrations in non-compliant and waste materials generated by industrial processes. In this paper, we present a machine learning-based procedure utilizing Laser-Induced Breakdown Spectroscopy (LIBS) to accurately quantify lithium concentrations in lithium-rich non-compliant materials derived from the industrial production of enamels used for coating metallic surfaces. This procedure addresses challenges such as strong self-absorption and matrix effects, which limit the effectiveness of conventional univariate calibration methods. By employing a multivariate approach, we developed a single model capable of quantifying lithium content across a wide concentration range. A comparison of the LIBS results with those obtained using conventional laboratory analysis (Inductively Coupled Plasma–Optical Emission Spectrometry, ICP-OES) confirms that LIBS can deliver the speed, precision, and reliability required for potential routine applications in the lithium recovery and recycling industry.