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Principal component analysis scores resulting from modeling the GAI-binned 1H-13C HSQC data matrix, indicating a high degree of separation between experimental groups. Model fit (R2X) and predictive ability (Q2) were 0.68 and 0.64 for the first principal component (Q1) and 0.12 and 0.09 for the second (t2). Class separations of this magnitude are readily achievable using data matrices generated by GAI-binning, due in large part to the low variable counts it generally produces.
