Streamlining spectral data processing and modeling for spectroscopy applications
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README.md

simplerspec

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Short description

The simplerspec package aims to facilitate spectra and additional data handling and model development for spectroscopy applications such as infrared soil spectroscopy. Different helper functions are designed to create a data and modeling workflow. Data inputs and outputs are stored in common S3 R objects (lists and data frames), using in addition data.table and tibble extensions. The functions are built to work in a pipeline and cover commonly used procedures for spectral model development and application.

Installation

The newest version of the package is available on this GitHub repository. Note that the package is still under development. If you find bugs you are highly welcome to report issues (write me an email or create an issue). You can install simplerspec using the remotes package.

if (!require("remotes")) install.packages("remotes")
remotes::install_github("philipp-baumann/simplerspec")

Key features

The current version of the package features among others the following functions:

  1. read_opus_univ(): Read spectra and metadata from Bruker OPUS binary files into R list
  2. gather_spc(): Gather spectra and metadata from list into a tibble object (list-columns)
  3. resample_spc(): Resample spectra to new wavenumber intervals
  4. average_spc(): Average spectra for replicate scans
  5. preprocess_spc(): Perform pre-processing of spectra
  6. select_spc_vars(): Select every n-th spectral variable and corresponding X-unit values.
  7. join_spc_chem(): Join chemical and spectral data sets by sample_id
  8. plot_spc_ext(): Extended spectral plotting; e.g. group spectra using different panels or color spectra based on chemical reference values to explore trends.
  9. fit_pls(): Perform model tuning and evaluation based on Partial Least Squares (PLS) regression
  10. select_ref_spc(): Select a set of reference samples to measured by traditional analysis methods when no a priori sample data except spectra are available (based on Kennard-Stones sampling)
  11. predict_from_spc(): Predict multiple chemical properties from a list of calibrated models and new soil spectra
  12. assess_multimodels(): Assess model performance given multiple pairs of predicted and measured variables.

Cheatsheet

Motivation and key concepts

Many R packages are available to do tasks in spectral modeling such as pre-processing of spectral data. The motivation to create this package was:

  1. Avoid repetition of code in model development (common source of errors).
  2. Provide a reproducible data analysis workflow for FT-IR spectroscopy.
  3. R packages are an ideal way to organize and share R code.
  4. Make soil FT-IR spectroscopy modeling accessible to people that have basic R knowledge.
  5. Provide an integrated data-model framework that features tidy data structures designed for both user-friendly printing and efficient data processing.

This package builds mainly upon functions from the following R packages:

  • prospectr: Various utilities for pre-processing and sample selection based on spectroscopic data. An introduction to the package with examples can be found here.
  • plyr and dplyr: Fast data manipulation tools with an unified interface. See here for details.
  • ggplot2 : Alternative plotting system for R, based on the grammar of graphics. See here.
  • caret : Classification and regression training. A set of functions that attempt to streamline the process for creating predictive models. See here for details.

Consistent and reproducible data and metadata management is an important prerequisite for spectral model development. Therefore, simplerspec functions are based on storing spectral data and related data in R data structures which keep related data in rows. Every row representing an observation contains data related to a single spectral measurement. Simplerspec functions uses tibble data frames as principal data structures because they allow to store lists within the well-known data frame structures. Lists are flexible data structures and can e.g. contain other lists, vectors, data.frames, or matrices.

List-columns features provided within the tibble framework are an excellent base to work with functional programming tools in R, which allows to efficiently write code. Simplerspec internally uses popular functional programming extension tools provided by the purrr package for processing and transforming spectra. For learning more, I would recommend this nice purrr list-column tutorial provided by Jenny Brian. Further, simplerspec well integrates with the data processing API provided by the dplyr package, which makes spectroscopic analysis tidy and easy to understand.

Example workflow

Bruker FTIR spectrometers produce binary files in the OPUS format that can contain different types of spectra and many parameters such as instrument type and settings that were used at the time of data acquisition and internal processing (e.g. Fourier transform operations). Basically, the entire set of setup measurement parameters, selected spectra, supplementary metadata such as the time of measurement are written into OPUS binary files. In contrast to simple text files that contain only plain text with a defined character encoding, binary files can contain any type of data represented as sequences of bytes (a single byte is sequence of 8 bits and 1 bit either represents 0 or 1).

Simplerspec comes with reader function read_opus_univ() that is intended to be a universal Bruker OPUS file reader that extracts spectra and key metadata from files. Usually, one is mostly interested to extract the final absorbance spectra (shown as AB in the OPUS viewer software).

# Load simplerspec package for spectral model development wrapper functions
library(simplerspec)
# Load tidyverse packages: loads packages frequently used for data manipulation,
# data tidying, import, and plotting
library(tidyverse)

################################################################################
## Part 1: Read and pre-process spectra, read chemical data, and join
## spectral and chemical data sets
################################################################################

## Read spectra in list ========================================================

# List of OPUS binary spectra files
lf <- dir("data/spectra/soilspec_eth_bin", full.names = TRUE)

# Read spectra from files into R list
spc_list <- read_opus_univ(fnames = lf, extract = c("spc"))
# Returns messages:
#> Extracted spectra data from file: <BF_lo_01_soil_cal.0>
#> Extracted spectra data from file: <BF_lo_01_soil_cal.1>
#> Extracted spectra data from file: <BF_lo_01_soil_cal.2>
#> Extracted spectra data from file: <BF_lo_02_soil_cal.0>
#> ...

Pipes can make R code more readable and allows step-wise data processing when developing spectral models. The pipe operator (%>%, called “then”) is a new operator in R that was introduced with the magrittr package. It facilitates readability of code and avoids to type intermediate objects. The basic behavior of the pipe operator is that the object on the left hand side is passed as the first argument to the function on the right hand side. When loading the tidyverse packages, the pipe operator is attached to the current R session. More details can be found here.

The model development process can be quickly coded as the example below illustrates:

## Spectral data processing pipe ===============================================

soilspec_tbl <- spc_list %>%
  # Gather list of spectra data into tibble data frame
  gather_spc() %>% 
  # Resample spectra to new wavenumber interval
  resample_spc(wn_lower = 500, wn_upper = 3996, wn_interval = 2) %>%
  # Average replicate scans per sample_id
  average_spc() %>%
  # Preprocess spectra using Savitzky-Golay first derivative with a window size
  # of 21 points
  preprocess_spc(select = "sg_1_w21")
  
soilspec_tbl
# A tibble: 284 x 11
#>                                 unique_id             file_id         sample_id
#>                                     <chr>               <chr>             <chr>
#> 1 BF_lo_01_soil_cal.0_2015-11-06 14:34:10 BF_lo_01_soil_cal.0 BF_lo_01_soil_cal
#> 2 BF_lo_01_soil_cal.1_2015-11-06 14:38:14 BF_lo_01_soil_cal.1 BF_lo_01_soil_cal
#> 3 BF_lo_01_soil_cal.2_2015-11-06 14:40:55 BF_lo_01_soil_cal.2 BF_lo_01_soil_cal
#> 4 BF_lo_02_soil_cal.0_2015-11-06 17:27:55 BF_lo_02_soil_cal.0 BF_lo_02_soil_cal
#> 5 BF_lo_02_soil_cal.1_2015-11-06 17:30:19 BF_lo_02_soil_cal.1 BF_lo_02_soil_cal
#> 6 BF_lo_02_soil_cal.2_2015-11-06 17:32:47 BF_lo_02_soil_cal.2 BF_lo_02_soil_cal
#> 7 BF_lo_03_soil_cal.0_2015-11-09 11:32:55 BF_lo_03_soil_cal.0 BF_lo_03_soil_cal
#> 8 BF_lo_03_soil_cal.1_2015-11-09 11:35:26 BF_lo_03_soil_cal.1 BF_lo_03_soil_cal
#> 9 BF_lo_03_soil_cal.2_2015-11-09 11:38:08 BF_lo_03_soil_cal.2 BF_lo_03_soil_cal
#> 10 BF_lo_04_soil_cal.0_2015-11-06 10:36:13 BF_lo_04_soil_cal.0 BF_lo_04_soil_cal
#> # ... with 274 more rows, and 8 more variables: spc <list>, wavenumbers <list>,
#> #   metadata <list>, spc_rs <list>, wavenumbers_rs <list>, spc_mean <list>,
#> #   spc_pre <list>, xvalues_pre <list>
  

## Read chemical reference data and join with spectral data ====================

# Read chemical reference analysis data
soilchem_tbl <- read_csv(file = "data/soilchem/soilchem_yamsys.csv")
#> Parsed with column specification:
#> cols(
#>   .default = col_double(),
#>   sample_ID = col_character(),
#>   country = col_character(),
#>   site = col_character(),
#>   material = col_character(),
#>   site_comb = col_character()
#> )
#> See spec(...) for full column specifications.

# Join spectra tibble and chemical reference analysis tibble
spec_chem <- join_spc_chem(
  spc_tbl = soilspec_tbl, chem_tbl = soilchem_tbl, by = "sample_id")
#> Joining, by = "sample_id"

################################################################################
## Part 2: Run PLS regression models for different soil variables
################################################################################

# Example Partial Least Squares (PLS) Regression model for total Carbon (C)
# Use repeated k-fold cross-validation to tune the model (choose optimal 
# number of PLS components) and estimate model performance on hold-out 
# predictions of the finally chosen model (model assessment).
# This allows to use the entire set for both model building and evaluation;
# recommended for small data sets
pls_C <- fit_pls(
  # remove rows with NA in the data
  spec_chem = spec_chem[!is.na(spec_chem$C), ],
  response = C,
  evaluation_method = "resampling",
  tuning_method = "resampling",
  resampling_method = "rep_kfold_cv",
  pls_ncomp_max = 7 # maximum number of PLS components tested during tuning
) 

Projects using simplerspec

Package help

After successfully installing simplerspec, you can use the R build-in help using ?simplerspec::<fun_name>

Credits

I would like to thank the following people for the inspiration by concepts, code and packages:

  • Antoine Stevens and Leonardo Ramirez-Lopez for their contributions to the prospectr package and the Guide to Diffuse Reflectance Spectroscopy & Multivariate Calibration
  • Andrew Sila, Tomislav Hengl, and Thomas Terhoeven-Urselmans for the read.opus() function from the soil.spec package developed at ICRAF.
  • Hadley Wickham for his work and concepts on data science within R
  • Max Kuhn for the creation of the caret package and for his excellent teaching materials on applied predictive modeling