> In science, one man’s noise is another man’s signal – (Ng, 1990)

# 1 Preamble

`prospectr` provides a set of tools for signal processing and chemometrics, particularly for the pre-processing and sample selection of spectral data. It is increasingly used in spectroscopic applications, as reflected by the growing number of scientific publications citing the package.

Although similar functions are available in other packages, such as `signal`, many functions in `prospectr` are designed to work consistently with `data.frame`, `matrix`, and `vector` inputs. In addition, several functions are optimised for speed and rely on C++ code through the `Rcpp` and `RcppArmadillo` packages.

# 2 Introduction

Several spectroscopic techniques such as Near-Infrared (NIR) spectroscopy are high-throughput, non-destructive, and low-cost sensing methods with applications in agricultural, medical, food, and environmental science. A number of R packages relevant to spectroscopists are already available for processing and analysis of spectroscopic data. Since the publication of the special volume on Spectroscopy and Chemometrics in R (Mullen and Stokkum, 2007), many spectroscopy-related R packages have been released. Several are listed in relevant CRAN Task Views, including:

- Machine Learning & Statistical Learning
- Chemometrics and Computational Physics

In addition, Bryan Hanson maintains a curated list of free and open-source software (FOSS) for spectroscopic applications; see https://bryanhanson.github.io/FOSS4Spectroscopy/.

# 3 Citing the package

If you use `prospectr` in your work, please cite it. The recommended citation can be obtained in R with:

    library(prospectr)

    citation(package = "prospectr")

    To cite package 'prospectr' in publications use:

      Antoine Stevens and Leornardo Ramirez-Lopez (2026). An introduction
      to the prospectr package. R package Vignette R package version 0.2.9.

    A BibTeX entry for LaTeX users is

      @Manual{,
        title = {An introduction to the prospectr package},
        author = {Antoine Stevens and Leornardo Ramirez-Lopez},
        publication = {R package Vignette},
        year = {2026},
        note = {R package version 0.2.9},
      }

# 4 Further reading

The functionality of `prospectr` is documented in two additional vignettes:

- Signal processing: pre-processing methods including smoothing, derivatives, scatter corrections, baseline removal, and resampling.
- Calibration sampling: algorithms for selecting representative calibration and validation subsets from spectral data.

Mullen, K.M., Stokkum, I.H. van, 2007. An introduction to the’special volume spectroscopy and chemometrics in r’. Journal of Statistical Software 18.
Ng, E.W., 1990. In science, one man’s noise is another man’s signal.