Improved Quantification of 18O Labeled LC-MS Based on I-Ching Divination Evolutionary Algorithm

An innovative quantification method for 18O labeled LC-MS data is proposed based on I-Ching divination evolutionary algorithm(IDEA). Considering label efficiency for calculating the least squares regression function, traditional methods based on genetic algorithm(GA) or other optimized algorithms will bring high level of computation complexity. The proposed method applies very flexible I-Ching operators(ICOs)— intrication operator, turnover operator, and mutual operator. The objective is the function of determining coefficients, which include the 18O/16O ratio r, the label efficiency f, and the abundance a of 16O. Comparing with GA, the proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements and better performs in the evolution procedure over mathematically calculating the function. Simultaneously we run the experiment with mix peptide raw data of predefined ratio. The result shows that our proposal algorithm is superior to the conventional GA in exploring optimum solution for better quantification accuracy.