You can, but interpret carefully: matrices differ dramatically and can shift apparent recovery. For strong conclusions, validate each matrix separately with parallelism and recovery checks, then keep comparisons primarily within the same matrix type. If you must compare across matrices, use normalization strategies and include shared reference controls. This keeps your analysis grounded and avoids mistaking matrix effects for biological differences.
The most common causes are insufficient washing, overly long incubation with detection reagents, or sample matrix components that promote non-specific binding. To address this, increase wash rigor (without damaging the plate), ensure consistent aspiration, and avoid splashing between wells. Run matrix blanks to identify whether the background is sample-driven. If the issue is matrix-related, diluting samples or modifying sample preparation often improves specificity.
Before scaling, run a short validation: (1) dilution linearity/parallelism in your matrix, (2) spike-and-recovery in representative samples, (3) replicate precision (duplicate/triplicate), and (4) stability check for storage conditions (fresh vs frozen aliquots). Because the kit is designed for quantitative measurement via colorimetric sandwich ELISA, these steps help confirm that your matrix behaves similarly to standards and that results will be reliable across a cohort.
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