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proximetricsR Spectral preprocessing and chemometric calibration of near-infrared (NIR) sensors

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Last update: 2026-06-25

Version: 0.6.4 – Saentis

About

The proximetricsR package provides tools for developing, validating, and deploying quantitative chemometric models for near-infrared (NIR) spectroscopy, with dedicated support for BUCHI NIR sensors and workflows.

The package implements partial least squares (PLS) regression and related methods together with spectral preprocessing, model validation, visualisation, and native support for BUCHI calibration and application formats.

Key features

Installation

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("l-ramirez-lopez/proximetricsr")

Requires R ≥ 4.2.0 and compilation tools (C++ via Rcpp and RcppArmadillo).

A couple of examples

library(proximetricsR)

data("NIRcannabis")
# the list of formulas for the models to be built
app_formulas <- list(THC ~ spc, THCA ~ spc, CBD ~ spc, CBDA ~ spc)

# the list of pre-processing recipes to be tested
precipes <- list(
  recipe_1 = preprocess_recipe(
    prep_resample(grid = c(1001, 1700, 2)),
    prep_snv(),
    prep_derivative(m = 1, w = 9, p = 7, algorithm = "nwp"),
    device = "proximate"
  ),
  recipe_2 = preprocess_recipe(
    prep_resample(grid = c(1001, 1700, 2)),
    prep_snv(),
    prep_derivative(m = 2, w = 11, p = 9, algorithm = "nwp"),
    device = "proximate"
  )
)

optimized_app <- calibrate_models(
  formulas = app_formulas,
  data = NIRcannabis,
  preprocess_recipes = precipes,
  methods = list(fit_plsr(15, type = "nwp")),
  return_inputs = TRUE,
  save_all = FALSE
)
optimized_app
Grid search results: 
       formula recipe min property max property ncomp   rsq  rmse
1   THC ~ spc       1       0.0136         5.44     2 0.532 0.679
2 * THC ~ spc       2       0.0136         5.44     3 0.690 0.578
3   THCA ~ spc      1       0.0200        12.13     7 0.780 1.539
4 * THCA ~ spc      2       0.0200        12.13     6 0.820 1.407
5   CBD ~ spc       1       0.0262         5.89     1 0.148 0.675
6 * CBD ~ spc       2       0.0262         5.89     1 0.175 0.668
7   CBDA ~ spc      1       0.0100        24.64     4 0.684 3.454
8 * CBDA ~ spc      2       0.0100        24.64     2 0.682 3.431
  largest_residual rsq_sd rmse_sd largest_residual_sd outliers    method
1             1.87 0.1112   0.171               0.978        0 PLS (nwp)
2             1.67 0.1025   0.191               1.047        0 PLS (nwp)
3             4.27 0.1033   0.366               1.323        0 PLS (nwp)
4             3.63 0.0726   0.265               0.944        0 PLS (nwp)
5             2.17 0.0849   0.301               1.599        0 PLS (nwp)
6             2.18 0.0734   0.300               1.573        0 PLS (nwp)
7            11.37 0.2049   1.312               6.147        0 PLS (nwp)
8            11.66 0.2310   1.477               6.749        0 PLS (nwp)
 
*best model 
---
Suggested models: 
Model:  THC ~ spc 
Spectral preprocessing recipe (device: "proximate"): 
 - Step 1: prep_resample
    min_wav: 1001; max_wav: 1700; resolution: 2
 - Step 2: prep_snv
 - Step 3: prep_derivative
    m: 2; w: 11; p: 9; algorithm: 'nwp'
Method:  PLS (nwp) 

Model:  THCA ~ spc 
Spectral preprocessing recipe (device: "proximate"): 
 - Step 1: prep_resample
    min_wav: 1001; max_wav: 1700; resolution: 2
 - Step 2: prep_snv
 - Step 3: prep_derivative
    m: 2; w: 11; p: 9; algorithm: 'nwp'
Method:  PLS (nwp) 

Model:  CBD ~ spc 
Spectral preprocessing recipe (device: "proximate"): 
 - Step 1: prep_resample
    min_wav: 1001; max_wav: 1700; resolution: 2
 - Step 2: prep_snv
 - Step 3: prep_derivative
    m: 2; w: 11; p: 9; algorithm: 'nwp'
Method:  PLS (nwp) 

Model:  CBDA ~ spc 
Spectral preprocessing recipe (device: "proximate"): 
 - Step 1: prep_resample
    min_wav: 1001; max_wav: 1700; resolution: 2
 - Step 2: prep_snv
 - Step 3: prep_derivative
    m: 2; w: 11; p: 9; algorithm: 'nwp'
Method:  PLS (nwp)

Core functionality

Spectral preprocessing

A collection of preprocessing methods is available through functions with the prep_* prefix. The preprocess_recipe() framework enables the construction of reproducible preprocessing pipelines that can be applied consistently during calibration and prediction.

Supported operations include:

Model calibration

The package supports multiple regression approaches, including:

The calibrate_models() function enables automated calibration, parameter optimisation, and model comparison using a range of cross-validation strategies.

Model validation and visualisation

Built-in tools support:

Publication-ready diagnostic plots can be generated using plot.spectral_model().

BUCHI ProxiMate & ProxiScout integration

proximetricsR implements algorithms that are numerically consistent with BUCHI NIRWise PLUS software, enabling reproducible workflows between R and production BUCHI devices.

Key capabilities include:

Documentation

For a complete overview of available vignettes:

browseVignettes("proximetricsR")

Additional documentation can be accessed through:

help(package = "proximetricsR")

References

Support

For bug reports, feature requests, and discussions, please create an issue on GitHub:

https://github.com/l-ramirez-lopez/proximetricsr/issues

For questions, contact:

ramirez-lopez.l@buchi.com

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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