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(

c

) The order of analysis should be calibration standards, followed by rinse, blank check (PB run

as a sample), check standard, control sample, sample, sample duplicate (up to 10 samples), and

finally a repeated check standard.

G. Calculations

Sample concentrations in ng/g are automatically calculated by the software using a

nonweighted least-squares linear regression calibration analysis to produce a best-fit line:

Y

= a

x

+ blank

Note that for the Agilent software used in this work, the sample blank is identical to the Cal Blk

and is essentially zero because high purity reagents are used.

The analyte concentration in the sample was then calculated:

where

x

= analyte concentration (ng/g);

y

= analyte to ISTD intensity ratio, which is the

measured count of each analyte’s standard solution data point in the calibration curve divided

by the counts of the ISTD at the same level; similarly, the blank = analyte to ISTD intensity ratio,

which is the measured count of the blank standard solution data point in the calibration curve

divided by the counts of the ISTD at the same level as the blank standard solution;

a

= slope of

the calibration curve (mL/ng); and DF = volume of the sample solution (mL) divided by sample

weight (g).

H. Method Validation

This method has undergone a thorough SLV using AOAC guidelines to probe its linearity, LOQ,

specificity, precision, accuracy, and ruggedness/robustness. Accuracy has also been affirmed by

comparison to ICP-atomic emission spectrometry (AES) results generated in the authors’ own

laboratory. In addition, reproducibility was estimated during a limited multilaboratory testing

(MLT) study employing six laboratories and four different ICP/MS instruments. Both the SLV and

MLT results are summarized in a concurrent publication.

References:

J. AOAC Int

. (future issue)

AOAC SMPR 2011.009

J. AOAC Int

.

95

, 297(2012)

DOI: 10.5740/jaoac.int.11-0441

AOAC SMPR 2014.004

J. AOAC Int

.

98

, 1042(2015)

Posted: October 1, 2015

Candidates for 2016 Method of the Year

72