Identifying and assessing matrix effect severity in inductively coupled plasma optical emission spectrometry using non-analyte signals and unsupervised learning.

An unsupervised data-driven methodology is used to quantify matrix effects caused by carbon and easily ionizable elements (EIEs) in inductively coupled plasma optical emission spectrometry (ICP OES). Background signals from nine plasma naturally-occurring species of Ar, H and O are used with principal component analysis (PCA) and affinity propagation (AP) clustering to evaluate the effects of complex matrices on ionic emission lines of Cd, Co, Cr and Pb. Matrix effect severity is then quantified based on Euclidean distance in principal component space from an average calibration curve point. The method has been applied to spiked solutions of Mediterranean Sea and Dead Sea water samples, and a significant correlation (- 0.997) was found between Euclidean distance and analyte recoveries. For sea water analysis, accurate results are found using external standard calibration (EC) when Euclidean distance < 1 for a given sample, and/or when that sample point groups with the calibration curve after affinity propagation clustering. Thus, by applying the PCA-AP strategy, one needs to perform no addition/recovery experiment to evaluate EC applicability. In addition, it can be carried out on the fly, as the background species used to monitor plasma changes are simultaneously recorded with the analytical signals, and a specific algorithm can be added to the instrument control software to flag instances in which EC may be ineffective. This is a proof-of-concept study, and additional work is required to evaluate the method's applicability to a larger number of analytes and sample matrices. However, the PCA-AP method described here for ICP OES can be used to quantify matrix effects, allowing for informed decisions regarding calibration. It requires no additional sample preparation and can be easily implemented in routine analyses of such complex-matrix samples as sea water.

Multi-energy calibration applied to atomic spectrometry.

Multi-energy calibration (MEC) is a novel strategy that explores the capacity of several analytes of generating analytical signals at many different wavelengths (transition energies). Contrasting with traditional methods, which employ a fixed transition energy and different analyte concentrations to build a calibration plot, MEC uses a fixed analyte concentration and multiple transition energies for calibration. Only two calibration solutions are required in combination with the MEC method. Solution 1 is composed of 50% v v-1 sample and 50% v v-1 of a standard solution containing the analytes. Solution 2 has 50% v v-1 sample and 50% v v-1 blank. Calibration is performed by running each solution separately and monitoring the instrument response at several wavelengths for each analyte. Analytical signals from solutions 1 and 2 are plotted on the x-axis and y-axis, respectively, and the analyte concentration in the sample is calculated from the slope of the resulting calibration curve. The method has been applied to three different atomic spectrometric techniques (ICP OES, MIP OES and HR-CS FAAS). Six analytes were determined in complex samples (e.g. green tea, cola soft drink, cough medicine, soy sauce, and red wine), and the results were comparable with, and in several cases more accurate than, values obtained using the traditional external calibration, internal standardization, and standard additions methods. MEC is a simple, fast and efficient matrix-matching calibration method. It may be applied to any technique capable of simultaneous or fast sequential monitoring of multiple analytical signals.

Microwave-Induced Plasma Optical Emission Spectrometry (MIP OES) and Standard Dilution Analysis to Determine Trace Elements in Pharmaceutical Samples.

In this work, we evaluate the application of microwave-induced plasma optical emission spectrometry (MIP OES) to determine of Al, Cr, Co, Cu, Fe, Mn, Ni and Zn in children's cough syrup, eye drops, and oral antiseptic using standard dilution analysis (SDA). The SDA method is simple, with only two calibration solutions prepared per sample. The first solution (S1), composed of 50% sample +50% of a standard solution, is introduced into the plasma and the analytical signals are monitored in a time-resolved fashion. Then, the second solution (S2), composed of 50% sample +50% blank, is poured into the vial containing S1. As the solutions mix, the analytical signals gradually drop to a stable baseline. The calibration curve is computed by plotting the ratio of the analyte signal (SA) over the internal standard signal (which is also part of S1) (SIS) on the y-axis, versus the inverse of the IS concentration on the x-axis (i.e., SA/SIS versus 1/CIS). In this study, SDA results were compared with values obtained with the traditional methods of external calibration (EC), internal standardization (IS), and standard additions (SA) in MIP OES determinations. The precision (represented as percent RSD) for SDA showed values in the range of 2.50-8.00% for all samples, while conventional calibration methods showed RSDs in the range of 6.40-32.50% for EC, 8.30-21.80% for IS, and 5.20-17.40% for SA. The LODs calculated for SDA are below the maximum limits allowed by the major pharmaceutical regulatory agencies, and presents superior precision and accuracy compared to the traditional calibration methods. Considering its simplicity and efficiency, SDA is an important new tool for accurate analyses of pharmaceuticals.

Standard dilution analysis of beverages by microwave-induced plasma optical emission spectrometry.

In this work, standard dilution analysis (SDA) is combined with microwave-induced plasma optical emission spectrometry (MIP OES) to determine seven elements in coffee, green tea, energy drink, beer, whiskey and cachaça (Brazilian hard liquor). No sample preparation other than simple dilution in HNO3 1% v v(-1) is required. Due to relatively low plasma temperatures, matrix effects may compromise accuracies in MIP OES analyzes of complex samples. The method of standard additions (SA) offers enhanced accuracies, but is time-consuming and labor intensive. SDA offers a simpler, faster approach, with improved accuracies for complex matrices. In this work, SDA's efficiency is evaluated by spike experiments, and the results are compared to the traditional methods of external calibration (EC), internal standard (IS), and standard additions (SA). SDA is comparable to the traditional calibration methods, and it provides superior accuracies for applications involving ethanol-containing beverage samples. The SDA-MIP OES procedure is effective. Using only two calibration solutions, it may be easily automated for accurate and high sample throughput routine applications.