Core-scanning X-ray Fluorescence (XRF-CS) is a well-established technique for rapid (< 30 s/interval) analysis of sediment core geochemistry at sub-mm resolution with substantially less analytical cost compared to methods that rely on physical sub-sampling. Due to issues inherent in analyzing wet sediment of heterogeneous particle size and composition with irregular surface topography using XRF, XRF-CS results are generally considered semi-quantitative. The result of early efforts to calibrate XRFCS data with conventional geochemical results (e.g. WD- or ED-XRF, ICP-AES, ICP-MS) showed weak correlations for less abundant or poorly detectable elements, however, more recent methods have been proposed to improve accuracy. These methods include: 1) converting XRF-CS results to dry mass concentration; 2) normalizing XRF-CS data to conservative elements (Si, Ca), total counts/second, or Xray scatter (CIR); and 3) calibration of data using multivariate analysis of elemental log-ratios (MLC). These approaches are not yet widely employed, and require additional testing on a variety of sediment compositions. Recently developed equipment enables analysis of discrete sediment samples, providing >30 replicate analyses for up to 180 samples in a single XRF-CS run. These replicate measurements allow for rigorous testing of precision and accuracy of XRF-CS data. To determine the ideal method of data transformation to improve XRF-CS calibration to quantitative geochemical concentration, 100 lake sediment-surface samples collected from Harvey Lake, New Brunswick, Canada, were analyzed using Itrax-XRF-CS, and then with ICP-MS analysis after multi-acid digestion. Normalization using the CIR and correction for water content showed strong correlation coefficients (Kendall’s τ) for elements with atomic number >18 and high concentrations in the sediment. Results for lighter elements and those with lower concentrations did not perform well using these calibration methods. The MLC provided the most accurate reproduction of observed ICP-MS trends and strong correlations (R) between predicted and actual geochemical concentrations. Based on these results, CIR-normalized or wet-corrected calibrations are ideal for studies where absolute geochemical values are of lesser importance, and the MLC method is appropriate for studies with large numbers of sediment samples (n > 100), or those where absolute concentrations of elements are of greater importance.