Taking control of microplastics data: A comparison of control and blank data correction methods

Area of Science:

• Environmental Science
• Analytical Chemistry

Background:

• Harmonization of microplastic analysis methods is advancing.
• However, the analysis and interpretation of control data remain under-addressed.
• Lack of consensus leads to arbitrary application of correction methods.

Purpose of the Study:

• To evaluate commonly used methods for correcting microplastic contamination using control data.
• To identify reliable strategies for handling background contamination in microplastic analysis.

Main Methods:

• Six core strategies and 45 variants for control data correction were tested.
• A dummy dataset was used to assess the performance of 51 distinct methods.
• Methods evaluated included no correction, subtraction, spectral similarity, LOD/LOQ, and statistical analysis.

Main Results:

• Most tested methods (44 out of 51) were inadequate due to inflexibility with microplastic data variation.
• Only seven methods, specifically six LOD/LOQ approaches and one statistical method, showed promise.
• These effective methods removed 96.3% to 100% of contamination from the dummy dataset.

Conclusions:

• Many current methods for correcting microplastic background contamination are flawed and should be avoided.
• Limits of Detection/Quantification (LOD/LOQ) methods or statistical analysis comparing means are recommended.
• Adopting these robust methods will prevent skewed results, particularly in low-abundance samples.