Quality Assurance / Quality Control

Environmental Testing Solutions, Inc.'s ultimate objective is to produce data of prescribed quality and documentation necessary to demonstrate that quality. This involves establishing and maintaining high standards, identifying and resolving problems, and encouraging and accepting recommendations for improvement. Quality assurance at ETS is achieved through management, planning and control of work processes, establishing performance criteria, assessing achievement of quality criteria, evaluating technical capabilities and ensuring the traceability of data.

Internal Quality Control

The purpose of quality control (QC) is to reduce variability in executing procedures, in taking measurements, and in obtaining field and laboratory data.  In addition, QC assesses whether activities are performed and/or measuring and test equipment and test systems being used to collect data are meeting established acceptance criteria and the project data quality requirements.

QC samples are used at ETS to:

  • monitor sample collection and handling techniques
  • evaluate equipment and container sterilization processes
  • assess the sensitivity of test organisms and the credibility of the test system
  • measure the precision of methods
  • document equipment calibration
  • document and verify analyst proficiency, included in training program

Internal QC at ETS includes control samples, split samples, duplicate samples, replicates, certified standards, and other certified reference materials.


Project data quality requirements at ETS include:

  • comparability (adherence to approved methods and ETS standard operating procedures, performance evaluation studies)
  • representativeness (meaningful sampling plan and collection, proper sample preservation, adherence to holding times, maintaining chain-of-custody, decontamination and cleanliness control)
  • accuracy (performance evaluation samples, blanks and laboratory control samples, matrix spike recoveries, reference toxicant tests)
  • precision (sample duplicates and replication, percent minimum significant difference and coefficient of variation)
  • sensitivity (minimum detection limits, reference toxicant tests, percent minimum significant difference)
  • completeness (percent of data that meets data quality objectives)



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