# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
## 4 Discussion  
To meet the requirements of hematological reference samples for EQAs in hematology, the storage time of samples should be up to 3 months at 2-8 \({}^{\circ}\)C and homogeneity and stability both within and between samples were guaranteed. It is necessary to find out the medium containing suitable base liquid, simultaneously optimizing factors that affected the storage of blood cells in this study. The research has found out that serum would be the best base liquid as well as optimized formula with a storage time at least 3 months at 2-8 \({}^{\circ}\)C of hematology EQA samples. Goose and goat RBCs after stabilization with the liquid containing aldehyde would increase the durability of the cell membrane. These RBCs demonstrated durability in a high osmotic rate from 89 to 205 mOsmol/kg. The osmotic rate of the combined sample was 200 \(\pm\) 3 mOsmol/kg and this concentration was also suitable with three kinds of RBCs in which human RBCs were stable as well as goose and goat RBCs without affecting HGB and MCV value. Serum with nutritional components, such as minerals, glucose, protein, and enzymes, is the best substrate for preservation and to conduct surveys on stability and consistency. However, to prolong the life of the sample, a combination of chemicals such as neomycin sulfate, chloramphenicol, sodium azide, and glycerol will be added to the serum. Using Plackett-Burman design refinements to the influencing factors, dilution ratios between serum, blood cells, neomycin sulfate antibiotic concentrations, and the rate of glycerol are most influential factors for the stability of blood cells (confidence \(\alpha\) = 0.1). Response surface methodology (RSM) is a statistical method using quantitative data from experiments to determine and interpret multivariate equations. RSM explores the interactive relationship between the variables or multiple response variables. An R\({}^{2}\) value of greater than 0.75 indicated that the model is compatible with experimental data, suggesting that there was a strong correlation between the factors and the number of blood cells. Results showed that the experimental data were compatible with the data predicted from the model on all three indexes, with R\({}^{2}\) values of 0.9329, 0.9129, and 0.9091 for Y\({}_{1}\), Y\({}_{2}\), Y\({}_{3}\), respectively. The predictive R\({}^{2}\) values for the human RBCs, pseudo-leucocytes, and pseudo-platelets were 0.8439, 0.7432, 0.6968, consistent with the adjusted R\({}^{2}\) values of 0.9019 (deviation at 0.058 < 0.2), 0.8620 (deviation at 0.1188 < 0.2), and 0.8430 (deviation at 0.1462 < 0.2), respectively. The signal-to-noise ratios for all three indexes were greater than 4 (21.190 for the human RBCs, 12.940 for pseudo-leucocytes, and 15.577 for pseudo-platelets), indicating that the signals were adequate. The response surface showed the interaction of each pair of factors providing the maximum response function. To verify the prediction results of the model, an experiment was conducted to compare between the experimental value and the predicted value. The compatibility results of three kinds of RBCs are greater than 99%, which proves that the model provides good predictive ability about the number of putative human RBCs, pseudo-leucocytes, and pseudo-platelets.