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

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

Spectral preprocessing and preprocessing pipelines
Quantitative calibration using PLS-based methods
Model validation, diagnostics, and uncertainty assessment
Native support for BUCHI file formats and applications
Publication-ready visualisation tools

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:

Spectral resampling
Savitzky-Golay smoothing and derivatives
Standard normal variate (SNV)
Detrending
Gap-segment derivatives
Transformations and scaling

Model calibration

The package supports multiple regression approaches, including:

Partial least squares (PLS)
Modified PLS (MPLS)
NIRWise PLUS-compatible workflows
XLS variants

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:

Model performance assessment
Outlier detection
Uncertainty estimation
Model interpretation

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:

Native file I/O: Read and write ProxiMate and ProxiScout formats (.cal, .prj, .nax, .rtf, .nad, .tsv)
Application bundles: Create deployable .nax applications containing calibration models, preprocessing metadata, and device-specific parameters
Sensor-aware workflows: Support preprocessing and calibration workflows tailored to BUCHI sensor characteristics
Cross-software reproducibility: Develop and validate models in R and deploy them directly to BUCHI instruments without re-parameterisation

Documentation

For a complete overview of available vignettes:

browseVignettes("proximetricsR")

Additional documentation can be accessed through:

help(package = "proximetricsR")

References

Wold, S. (1975). Pattern recognition by means of disjoint principal components models. Pattern Recognition, 8(3), 127–139. doi:10.1016/B978-0-12-103950-9.50017-4
Shenk, J. S., & Westerhaus, M. O. (1991). The application of near infrared reflectance spectroscopy (NIRS) to compositional analysis of agricultural products. Crop Science, 31(2), 409–413. doi:10.2135/cropsci1991.0011183X003100020049x
Westerhaus, M. O. (2014). Eastern Analytical Symposium Award for outstanding innovations in near infrared spectroscopy. NIR News, 25(7), 7–10. doi:10.1255/nirn.1492

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

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