# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## 5 Discussion  
### Comparative Performance of the Calibration Methods  
The goodness-of-fit statistics demonstrate the predictive power of the four calibration methods (Figure 8, Tables A1 and A2). The comparative evaluation highlights the lack of fit of the NMS method (Figure 8), which scaled the XRF-scan intensities by the mean value of conventional-fractions and converted the intensities to fractions using linear regression equations [9]. However, the scaling process used in the NMS can only reduce the scale differences between XRF-scan and conventional-fractions, but fails to adequately reduce specimen effects [7]. Notably, linear regression equations can only be applied to elements which exhibit linear relations (such as K, Ca, Ti and Fe of core FXH-B2), but this does not hold true for most elements [10].  
The LRCE calibration is only slightly more effective than the NMS calibration (Figure 8b,c), because there are linear relations between the log-ratios of element fractions and log-ratios of the XRF-scan data [10]. However, the quality of the LRCE calibrations depends on which element is selected as the common log-ratio denominator and the data range and variances of the denominator may influence the true variances of the calibrated-XRF. Both NMS and LRCE constrain the calibrated-XRF to unity, which may cause a significant error if the sediment element components do not match those of the XRF-scan or if a major element found in the XRF-scan is not included in the conventional-fractions [9]. The performance of the NPS method was shown to be markedly better compared with the NMS and  
Figure 8: Goodness-of-fit statistics of calibrated-XRF for the remaining samples from core FXH-B2. **(a)** The correlation coefficient R\({}^{2}\) between the XRF-scan/calibrated-XRF and conventional-fractions of all fourteen elements in the remaining samples. **(b,c)** The mean squared relative error (MSRE) and the squared relative error (SRE) of the XRF-scan/calibrated-XRF (NMS, NPS, LRCE and MLC) for all fourteen elements in the remaining samples, respectively.  
LREC methods (Figure 8b,c). This is because the influence of long-term changes in water content on the XRF-scan has been eliminated using a quadratic polynomial fit prior to the XRF-scan being converted to calibrated-XRF for the NPS method. However, where the variances of downcore water content are not taken into account, especially at the top of the core where the highest water content occurs, this results in the inaccurate calibration of Al and Si which are easily influenced by pore water fluctuations [11] (Figure 5). The MLC calibration exhibited the best performance with the highest correlation coefficient values (R\({}^{2}\)) for all elements (Figure 8a), and the lowest MSRE (Figure 8b) and SRE (Figure 8c). [PERSON] et al. [21] highlight that the MLC approach benefits from clr-transformation and partial least squares regression in order to better eliminate the influences of matrix effects, specifically attenuation or enhancement of specific XRF-scan intensities due to the presence of other elements or water content [11].