First, the C and H shifts are normalized by 180 ppm and 9 ppm, respectively, and then centered around -0.5 and +0.5 by subtracting 0.5 from the normalized values. The key differences among these peak matching algorithms are that MinSum sorts peaks based on the sum of normalized H and C shifts, EucDist employs a greedy nearest neighbor search, and HungDist utilizes the Hungarian algorithm (Kuhn-Munkres algorithm ). When the number of peaks in the two sets being compared is unequal, the "Zero" padding strategy compensates by adding zerovalued peaks to one of the sets, the "Trunc" method truncates the longer sequence after an initial Euclidean distance alignme nt and disregards the excess peaks, and the "NN" approach performs an initial alignment using Euclidean distance (except for MinSum) before matching the leftover peaks in one set to their nearest neighbors in the other set through double assignment. After matching the simulated and GT spectral peaks, the average ("AVG") Euclidean distance error between the matched points can be calculated to characterize the quality of the match.