Buying another LC-MS/MS system does not guarantee higher PFAS testing capacity. Samples can accumulate at receipt, extraction, evaporation, data review, or corrective action while the instrument sits ready.
Scaling requires laboratories to measure the complete workflow and protect the controls that make low-level results defensible.
Measure Capacity by Stage
A laboratory should map the time, staff and equipment required at each step. The review should cover sample receipt, preparation, LC-MS/MS analysis, data processing, review, and reporting.
Instrument run time provides only one measure. A method that analyzes 20 samples overnight may still require several analyst-days to prepare the batch and review its results.
Useful capacity measures include:
- Samples prepared per analyst shift
- Complete batches released per week
- Instrument utilization
- Repeat-preparation and reinjection rates
- Time spent on data review
- Turnaround time from receipt to release
- Percentage of batches delayed by QC failures
These measures reveal the constraint that limits output. Adding capacity elsewhere will have little effect until the laboratory addresses that bottleneck.
Standardize Before Automating
Automation reproduces the process given to it. If analysts follow different manual practices, automation may preserve those weaknesses at a larger scale.
The laboratory should first establish a controlled manual workflow with acceptable blanks, recoveries, and precision. It can then compare automated performance against that baseline.
Working in analytical science?
Register for a FREE Separation Science account to subscribe to the Separation Science Newsletter.
A case study of PFAS method optimization describes how one drinking-water laboratory used manual extraction to establish expectations before moving to automated SPE. The transition increased throughput while reducing analyst-to-analyst variability.
Automation should undergo the same verification as any meaningful method change. The study must show that the system meets requirements for recovery, precision, blank performance, and reporting limits.
Target High-Labor Steps
Sample transfers, SPE, evaporation, and liquid handling can consume analyst time and create opportunities for contamination or error. Automation can improve consistency when these steps follow repeatable rules.
Its value depends on the matrix. Water samples may fit a standardized SPE workflow, while tissue and biosolids still require variable handling and homogenization. An automated fish-tissue preparation workflow shows how reducing manual transfers can simplify extraction and concentration.
The laboratory should also account for setup, cleaning, maintenance, and failed runs. A system with high nominal capacity may deliver less routine output if it requires extensive intervention between batches.
Design Staffing Around Skills
PFAS testing needs different skills at different stages. Preparation analysts, instrument specialists, and data reviewers may not be interchangeable.
Cross-training can prevent work from stopping when one specialist is absent. It also allows staff to move toward the current bottleneck. However, cross-training must include documented competency for each task.
Staffing plans should reserve time for maintenance, training, investigations, and method updates. A schedule based on uninterrupted routine production will collapse when a blank fails or a complex matrix requires reanalysis.
Senior staff often become the hidden constraint because they review data and approve corrective actions. Scaling preparation without expanding review capacity can increase the backlog without improving turnaround time.
Protect Batch Design
Larger batches may appear efficient, but the governing method controls their maximum size and QC frequency. EPA Method 1633A limits a preparation batch to 20 field samples started through extraction during the same work shift.
Laboratories should calculate throughput using complete batches, including blanks, control samples, spikes, and duplicates. These QC samples use preparation positions and instrument time, but removing them does not create legitimate capacity.
Batch scheduling should also consider matrix and expected concentration. Separating high-level samples from low-level work can reduce carryover and repeat analysis.
Calculate the Full Cost per Sample
Consumables represent only part of the cost. A useful calculation includes:
- Analyst and reviewer time
- Standards and labelled compounds
- SPE cartridges, filters, and vials
- Solvents and reagents
- Instrument time
- Maintenance and replacement parts
- QC samples
- Waste handling
- Reanalysis and failed batches
- Accreditation and proficiency testing
The laboratory should divide the complete batch cost by the number of reportable field samples, not the number of instrument injections. This exposes the cost of repeats and unused capacity.
Automation may raise fixed costs while lowering labor and rework. Its financial value depends on sustained sample volume, utilization, and the failure rate of the previous process.
Scale Without Hiding Failures
High throughput should make problems easier to detect. Blank trends, recovery charts, and automated flags can identify shifts before they affect several batches.
Laboratories should monitor throughput alongside first-pass success. A higher number of prepared samples means little if repeat work, review delays, or QC failures increase.
A scalable PFAS workflow produces more reportable results from controlled batches. The goal is not maximum instrument activity. It is dependable capacity that the laboratory can sustain without weakening data quality.
This article was produced under Separation Science's AI Editorial Guidelines.
