Reference-versus-sample allocation in repeated isotope-ratio measurements
ID: reference-versus-sample-allocation-in-repeated-isotope-ratio-measurements
Reference-versus-sample allocation in repeated isotope-ratio measurements distributes a finite measurement budget between sample readings and the reference used to calibrate them. Suppose independent sample and reference readings have first-order gain-error variances and . Averaging sample readings and reference readings gives log stiffness-ratio variancewhere is the variance of a differential calibration error shared by all ratio estimates. The shared-reference floor in averaged isotope stiffness ratios explains why that last component does not average down. Covariance propagation must reflect which observations and calibration influences are actually shared.[ref]
Let one sample reading cost budget units and one reference reading cost , with . Treating counts continuously for the initial optimization, a constraint multiplier givesThe counts are proportional to standard uncertainty divided by the square root of cost, rather than simply to each variance. A budget-independent common component changes the attainable floor but not this optimum ratio under the stated model.
For an original example, take equal costs, , and 40 total readings. The optimum is 30 sample and ten reference readings. Without a common component, the predicted log-ratio standard uncertainty is about , compared with for twenty of each. A common standard uncertainty raises those values to about and , respectively.
An integer allocation near a continuous isotope-reference optimum must compare feasible neighboring count choices and any acquisition-order constraints. More reference readings help only to the extent that their errors are independent; repeatedly measuring an unchanged unknown calibration bias does not create independent references. If reference drift depends on elapsed time, a single constant cost and common-variance term may be inadequate.
In practice, record which reference observations calibrate which sample spectra and account for any correlated peak fitting, isotope-composition uncertainty and spectral offsets. The variance and cost model above is a chosen allocation problem, not an experimentally established precision specification for an isotope measurement system.
New to topics? Read the docs here!