RESUMO
In situ Sr isotopes analysis of apatite by LA-(MC)-ICP-MS is challenged by the difficulty to monitor and correct isobaric interferences from atomic and polyatomic ions. We present a new routine procedure for analysing rock-forming apatites with a Thermo Scientific Neptune XT MC-ICP-MS coupled with a Teledyne Cetac Analyte Excite+ 193 nm laser ablation system. Five apatite standards that cover a large range of REE/Sr ratios were selected, and their 87Sr/86Sr ratios were measured in solution after dissolution and purification of Sr [Durango: 0.706321(5); Madagascar: 0.711814(5); Slyudyanka; 0.707705(4); Sumé: 0.707247(4); and Ipirá: 0.710487(4)]. The optimisation of both instrument setup and data reduction schemes was achieved through repeated measurements of calibration solutions and of apatite standards at four different rectangular-shaped laser ablation beam sizes (50 × 50, 25 × 25, 13 × 13 and 10 × 10 µm). Two complementary methods were developed for data reduction: Method 1, which corrects measured intensities for gas blank and instrumental mass bias only; and Method 2, which additionally corrects for isobaric interferences of 87Rb+, 166, 168 and 170Er++, 170, 172, 174 and 176Yb++, 40Ca44Ca+, 40Ca46Ca+, 44Ca43Ca+ and 40Ca48Ca+. A precision of ca. 100 ppm (2 s.e.) can be achieved on the 87Sr/86Sr ratio with a 50 µm laser ablation beam when using Method 2, and it remains better than 3000 ppm at 10 µm with Method 1. Method 1 gives precise and accurate 87Sr/86Sr ratios when 173Yb++ is below the global limit of detection (with LODglobal = 3 s.d. of the means of all gas blanks measurements). When 173Yb++ is above the LODglobal, Method 2 should be preferred as it provides more accurate 87Sr/86Sr ratios. Overall, this study offers a robust and reliable approach for LA-MC-ICP-MS analysis of Sr isotopes in rock-forming apatite at a high spatial resolution (i.e. down to 10 µm), overcoming previous limitations associated with instrumental set up and data reduction.
RESUMO
Reliable assessment of fish origin is of critical importance for exploited species, since nursery areas must be identified and protected to maintain recruitment to the adult stock. During the last two decades, otolith chemical signatures (or "fingerprints") have been increasingly used as tools to discriminate between coastal habitats. However, correct assessment of fish origin from otolith fingerprints depends on various environmental and methodological parameters, including the choice of the statistical method used to assign fish to unknown origin. Among the available methods of classification, Linear Discriminant Analysis (LDA) is the most frequently used, although it assumes data are multivariate normal with homogeneous within-group dispersions, conditions that are not always met by otolith chemical data, even after transformation. Other less constrained classification methods are available, but there is a current lack of comparative analysis in applications to otolith microchemistry. Here, we assessed stock identification accuracy for four classification methods (LDA, Quadratic Discriminant Analysis [QDA], Random Forests [RF], and Artificial Neural Networks [ANN]), through the use of three distinct data sets. In each case, all possible combinations of chemical elements were examined to identify the elements to be used for optimal accuracy in fish assignment to their actual origin. Our study shows that accuracy differs according to the model and the number of elements considered. Best combinations did not include all the elements measured, and it was not possible to define an ad hoc multielement combination for accurate site discrimination. Among all the models tested, RF and ANN performed best, especially for complex data sets (e.g., with numerous fish species and/or chemical elements involved). However, for these data, RF was less time-consuming and more interpretable than ANN, and far more efficient and less demanding in terms of assumptions than LDA or QDA. Therefore, when LDA and QDA assumptions cannot be reached, the use of machine learning methods, such as RF, should be preferred for stock assessment and nursery identification based on otolith microchemistry, especially when data set include multispecific otolith signatures and/or many chemical elements.