2.1.6. Visible and Near-Infrared Spectroscopy

Visible and near-infrared reflectance of the sediment samples was captured using an FieldSpec II spectroradiometer by ASD (Malvern, Worcestershire, UK). Samples were measured before and after homogenisation. This allowed us to indicate an influence of material, which may be coating quartz grains and, therefore, over-represented in the unground samples (e.g., fine clay or Fe oxides, see [21]), and likewise to assess the general influence of grinding and homogenisation on sediment colour [47]. Reflectance was measured between 350 and 2500 nm in 1 nm steps. Measurements were acquired in a darkroom, where each sample was uniformly illuminated by two halogen lamps to minimise the casting of shadows. Samples were spread homogeneously on a plate, and the fibre optic cable of the

spectroradiometer was placed above the samples (Online Resource 3 in Supplementary Materials). Each spectrum was averaged from 50 single measurements to compensate for uneven sediment texture. A white reference was captured every 15 min to minimise variation between the measurements and to acquire normalised reflectance values in the range [0, 1].

The raw spectral information showed a high noise content below 450 nm and above 2300 nm, as well as several smaller aberrations. The spectra were therefore smoothed using a Savitzky–Golay filter of second polynomial order with a width of 21 values [48]. This was conducted with the hsdar package for R [49]. Due to the high amount of noise below 450 nm, the spectral data were limited to the range between 450 and 2500 nm for further processing (Figure 4).

Several studies recommend analysing spectral absorbance rather than spectral reflectance (e.g., [23,50]). Spectral reflectance R was therefore transformed into absorbance *A* = *log*(1/*R*), which allows for a better correlation of the signal with sedimentological data.

As an increase of overall reflectance was observed after homogenisation of the samples, several processing steps were carried out to compensate for this (Figure 4). Building upon Stenberg et al. [50], baseline correction was conducted by calculating the first derivative, which eliminated the existing baseline shift [51]. Furthermore, this step enhanced weak spectral signals. Since this step also introduced additional noise, the baseline corrected spectra were then smoothed using a Savitzky–Golay filter of second polynomial order with a width of 21 values. Subsequently, scatter correction was performed by a standard normal variate (SNV) transformation and de-trending [52]. These pre-processing steps allow for the direct comparison of the spectra of homogenised and non-homogenised samples as well as an enhancement of relevant spectral peaks and a reduction of, e.g., overall curvature [50]. A more detailed documentation of the workflow is delivered in Online Resource 3 (Supplementary Materials).

**Figure 4.** Exemplary spectral signal (450–2500 nm, sample no. 10) before (**a**) and after (**b**) pre-processing. Standard normal variate (SNV) transformation with detrending was applied to the first derivative of the *log*(1/*R*) transformed spectrum.
