This article looks at recent developments in inductively coupled plasma–mass spectrometry (ICP–MS), microwave-induced plasma–optical
emission spectrometry (MIP-OES), X-ray spectroscopy, and laser-induced breakdown spectroscopy (LIBS) by exemplifying the diverse
range of sample types they are analyzing and the unique application problems they are being asked to solve.
Atomic spectroscopic techniques are commonly used to carry out elemental determinations in a wide range of sample matrices
from sub-parts-per-trillion concentrations up to high percentage levels. Inductively coupled plasma–mass spectrometry (ICP-MS)
is universally recognized as the most sensitive ultratrace multielement technique with detection limits in the low parts-per-trillion
range for the majority of elements, whereas X-ray fluorescence (XRF) spectrometry has traditionally been the technique of
choice for carrying out high precision analysis of samples with high parts-per-million or low percentage concentrations. However,
there are many application areas that are less demanding and do not require such stringent analytical requirements as these
two techniques. For example, inductively coupled plasma–optical emission spectrometry (ICP-OES) has similar sample throughput
characteristics as ICP-MS, but depending on the analyte, its detection limits are approximately three orders of magnitude
higher (worse). More recently, a microwave-induced plasma (MIP)-OES system has become commercially available that offers the
throughput of ICP-OES, but is more aligned to flame atomic absorption (FAA) in its detection capability. Additionally, laser-induced
breakdown spectroscopy (LIBS), a relatively new commercial technique, is showing a great deal of promise for the analysis
of materials with challenging sampling requirements. However, until recently LIBS was struggling to demonstrate that it could
differentiate itself from the other more mature solid-sampling techniques like XRF or arc/spark emission.
The strength and weakness of any analytical technique are based on its ability to successfully address real-world application
segments. With that in mind, let's take a more detailed look at some of these techniques by exemplifying the diverse range
of sample types they are analyzing, with particular emphasis on the unique application problems they are being asked to solve.