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

Last update: 2026-07-17

Version: 0.6.5 – Matterhorn

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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Version

Install

install.packages('proximetricsR')

Version

0.6.5

License

MIT + file LICENSE

Issues

0

Pull Requests

0

Stars

0

Forks

0

Maintainer

Leonardo Ramirez-Lopez

Last Published

July 17th, 2026

Functions in proximetricsR (0.6.5)

get_samples_from_strata

A function for stratified calibration/validation sampling
is_numeric_like

Test if a string can be coerced to a numeric
locate_serialnumber_index

Function to locate the serial number index
pkg_info

Get the package version info
plot.spectral_model

Plot results of a given model
get_info_tsvs

get the tsv info
get_fname

get file name
matrix_prj_string

Converts a matrix into a string in style of .prj files
optim_sample_strata

A function to construct an optimal strata for the samples, based on the distribution of the given y.
get_proxiscout_wavenumbers

ProxiScout standard wavenumbers
prep_detrend

Detrending constructor for spectral preprocessing
prep_derivative

Derivative constructor for spectral preprocessing
preprocess_recipe

Build and execute spectral preprocessing recipes
print.spectral_fit

Print method for an object of class spectral_fit
get_sample_strata

A function to assign values to sample distribution strata
print.nax

Print method for an object of class nax
matrix_cal_string

Converts a matrix into a string in style of .cal files
print.spectral_model

Print method for an object of class spectral_model
prep_wav_trim

Wavelength trimming constructor for spectral preprocessing
prep_snv

Standard Normal Variate constructor for spectral preprocessing
prep_resample

Resampling constructor for spectral preprocessing
prep_smooth

Smoothing constructor for spectral preprocessing
print.spectral_multimodel

print method for spectral_multimodel
proximate_merge

Merge datasets of class proximate_data
proximate_data

Create a data frame for NIRWise PLUS applications
prep_transform

Reflectance/absorbance conversion constructor for spectral preprocessing
proximate_write_model

Write calibration (.cal), project (.prj) and report (.rtf) files to a specified directory
proximate_write_data

Write NIRWise PLUS readable tab-separated files
print.spectral_prediction

Print method for an object of class spectral_prediction
proximate_recalibrate_nax

Recalibrate a nax file
proximate_read_nax

Reads and summarizes ProxiMate spectroscopic applications (nax files)
print.spectral_validation

Print method for an object of class spectral_validation
proximate_add2nax

Prepare data for augmenting a nax application
proxiscout_read_data

Read and parse ProxiScout data from CSV or XLSX files
proximate_read_cal

Read model parameters from ProxiMate .cal files
proximate_read_data

Read ProxiMate (.tsv) files
read_spc

Read and format spectral data from a file
template

Generates a template for .prj files
proxiscout_repetition_pattern

ProxiScout repetition pattern
prepro_to_string

Create a string from a single preprocess step
read_rtfs

extract info from rtf calibration reports
spectral_fit

The spectral_fit class
string_diff

Calculate the ASCII Value Difference Between Two Strings
proximetricsR-package

Overview of the proximetricsR package
proximate_write_nax

Create an application file for the given list of models
sgf

Calculate filter for Savitzky-Golay
validate_prediction

Validate predictions of class 'spectral_prediction'
sample_loo

A function to create calibration and validation sample sets for leave-one-out cross-validation
sample_kfold

A function to create calibration and validation sample sets for k-fold cross-validation
proxiscout_write_data

Write data files for ProxiScout devices
write_nad

Writes metadata required for a Proximate application file
simple_kfold_sampling

Simple k-fold sampling
sample_loo_basic

Simple leave-one-out sampling
proxiscout_write_model

Write a calibration model to ProxiScout JSON format
sample_stratified

A function to create calibration and validation sample sets for leave-group-out cross-validation
.calibration_statistics

Computes the NIRWise QVAL statistic
add_application_metadata

A function for adding application metadata to a list of spectral_model objects
calibrate_models

Calibrate models for multiple response variables
add_model_metadata

A function for adding model metadata to a spectral_model object
calibrate

Calibrate a spectral model
calibration_control

A function that controls the calibration of models
.estimate_model

A method for estimating the model
NIRcannabis

NIRcannabis
.calibrate_basic

NIRWise PLUS modeling methods (basic)
add_zero_cols

Adds a chosen number of columns with entries equal to zero to a matrix object
get_model_summary

Summary of spectral_model
extract_property_names

Extract the property names from a given data.frame
fit_constructors

Fitting method constructors
get_nad_info

get info from nad files