README

Vivienne Maxwell 11/30/2021

Predicting Biogenic Silica percentages in lake sediment cores from High Arctic settings

Contributors

• Vivienne Maxwell

• Dr. Stoudt

• Dr. de Wet

I. Background

Biogenic Silica (BSi) is used as a proxy for past temperatures in High Arctic settings. Typically, greater amounts of BSi in sediment cores indicate warmer temperatures. Recently paleoclimatologists have begun to use Fourier Transform Infrared (FTIR) spectroscopy to collect information on BSi. However, the offloaded relative absorbance data make comparison with other proxies difficult. The goal of this project is to develop a universal calibration model using PLS to convert absorbance spectra into percentages of BSi and TOC so as to provide paleoclimatologists with a more universal way of analyzing and understanding their results.

II. Objectives

The first step in this project was to create a PLS model using 28 samples from Greenland.

Currently, the goal is to input 100 Alaskan samples into the Greenland model and predict BSi percentages. However, there is a resolution error between the Greenland samples and the Alaskan samples, which require interpolation.

Once the Alaskan sample resolution issue has been resolved, the next step to is begin answering some of our questions.

Questions we’d like to answer

1. Specific Spectrum vs. All Spectrum: Is it beneficial to run the calibration model over a specific portion of the spectrum versus the entire spectrum? If so, which portion of the spectrum should we use? -> We’ve determined that it is more beneficial to run the model on a specific portion of the spectrum (3750 - 368 cm^-1)

2. Recommended number of samples: Can we pinpoint the recommended number of samples required to run the calibration model?

3. Universal Preprocessing: Can all of the data be preprocessed in the same way, or do we need to provide our user with various options for preprocessing the data?

4. Transferable Results: How transferable are these results from one lake site to another? Do we observe a difference between cold and warm climates? What about at different localities within a cold climate?

5. Marine cores: How is this all transferable to the marine environment?

III. End Goal

The end goal is to create a Shiny App that would allow paleoclimatologists and other users to upload their list of lake sediment core files, run them through the calibration model and download the predicted percentages of BSi.

IV. Repo Organization

The repository is organized into the following four folders:

• Code

• csvFiles

• dataViz

• Samples

R Set-Up

In order to run the necessary code, you will need the following R packages:

• tidyverse

• pls

• scales

• readr

• Metrics

• ggplot2

• moderndive

Use the install.packages(" ") function to install the packages. Use the library() function to load the packages.

Code

The code is separated into three different scripts.

• 01_createDataFrameScript.R

The code loads the OPUS files into R, creates two separate dataframes (one consists of wavenumbers and the other consists of absorbance values), and adds the actual BSi percentages to the absorbance dataframe.

• 02_createModelScript.R

This code loads the absorbance data that contains the actual BSi percentages. That data is run through the partial least squares regression model. After the model is run, you create a root mean squared error plot (RMSEP) to determine the number of components. Then you load in the wavenumber data and combine it with the loadings from the first three components of the pls model. Once the dataframe is created, you can generate the loading plot to determine the parts of the spectrum that are most heavily weighted in the model.

• 03_modelAccuracy.R

The final script assess the model’s prediction accuracy in two ways. You will calculate the regression error, which is the predicted BSi percentages minus the actual BSi percentages. Then there is code for two visualizations. The first is a comparison of the regression error; it is a side-by-side of the actual BSi percentage versus the predicted BSi percentage for each sample. The second visualization shows you where the model is overpredicting (green) and underpredicting (red).

csvFiles

This folder contains all of the generated csv files.

• absorbance.csv : Absorbance values

• wavenumber.csv : Wavenumber values

• wet-chem-data.csv : BSi percentages that were calculated using the wet chemical digestion method. These are considered the “actual” BSi values.

• wetChemAbsorbance.csv : This contains the “actual” BSi values and the absorbance values. This dataset is what is inputted into the PLS model.

dataViz

This folder is divided into four sub-folders:

• crossValidation

• LoadingPlots

• modelAccuracy

• residuals

Within each of these folders, there are visualizations for:

• full spectrum: 7500 - 368cm^{-1}

• truncated spectrum: 3750 - 368cm^{-1}

• specific spectrum: 435 - 480cm^{-1}

• specific spectrum : 790 - 830cm^{-1}

• specific spectrum : 1050 - 1280cm^{-1}

Samples

For the samples folder there are two subfolders. The alaskaSamples folder contains the 100 alaskan samples. The greenlandSamples contains the 28 samples from Greenland.