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Open Specy 1.0

Analyze, Process, Identify, and Share Raman and (FT)IR Spectra

CRAN version Project Status R-CMD-check Codecov test coverage License: CC BY 4.0 DOI Website Gitter

Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2021, doi: 10.1021/acs.analchem.1c00123). With read_any(), Open Specy provides a single function for reading individual, batch, or map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies processing spectra, including smoothing, baseline correction, range restriction and flattening, intensity conversions, wavenumber alignment, and min-max normalization. Spectra can be identified in batch using an onboard reference library (Cowger et al. 2020, doi: 10.1177/0003702820929064) using match_spec(). A Shiny app is available via run_app() or online at https://openanalysis.org/openspecy/.

Installation

OpenSpecy is available from CRAN and GitHub.

Install from CRAN (stable version)

You can install the latest release of OpenSpecy from CRAN with:

install.packages("OpenSpecy")

Install from GitHub (development version)

To install the development version of this package, paste the following code into your R console (requires devtools):

if (!require(devtools)) install.packages("devtools")
devtools::install_github("wincowgerDEV/OpenSpecy-package")

Install on Web Assemply through webr (experimental), you can test here: https://webr.r-wasm.org/latest/

library(caTools)
library(data.table)
library(jsonlite)
library(yaml)
library(hyperSpec)
library(mmand)
library(plotly)
library(digest)
library(signal)
library(glmnet)
library(jpeg)
library(shiny)
webr::install("OpenSpecy", repos = "https://wincowger.com/OpenSpecy-package/")
library(OpenSpecy)

Getting started

library(OpenSpecy)
run_app()

Simple workflow for single spectral identification

See package vignette for a detailed standard operating procedure.

# Fetch current spectral library from https://osf.io/x7dpz/
get_lib("derivative")

# Load library into global environment
spec_lib <- load_lib("derivative")

# Read sample spectrum
raman_hdpe <- read_extdata("raman_hdpe.csv") |> 
  read_any()

# Look at the spectrum
plotly_spec(raman_hdpe)

# Process the spectra and conform it to the library format
raman_proc <- raman_hdpe |>
  process_spec(conform_spec_args = list(range = spec_lib$wavenumbers), 
               smooth_intens = T, make_rel = T)

# Compare raw and processed spectra
plotly_spec(raman_hdpe, raman_proc)

top_matches <- match_spec(raman_proc, library = spec_lib, na.rm = T, top_n = 5,
                          add_library_metadata = "sample_name",
                          add_object_metadata = "col_id")

# Print the top 5 results with relevant metadata
top_matches[, c("object_id", "library_id", "match_val", "SpectrumType",
                "SpectrumIdentity")]

# Get all metadata for the matches
get_metadata(spec_lib, logic = top_matches$library_id)

Citations

Cowger W, Steinmetz Z, Gray A, Munno K, Lynch J, Hapich H, Primpke S, De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification Needs an Open Source Community: Open Specy to the Rescue!” Analytical Chemistry, 93(21), 7543–7548. doi: 10.1021/acs.analchem.1c00123.

Cowger W, Steinmetz Z, Leong N, Faltynkova A, Sherrod H (2024). “OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra.” R package, 1.0.8. https://github.com/wincowgerDEV/OpenSpecy-package.