measure

Overview

measure extends tidymodels with preprocessing steps for analytical measurement data such as spectroscopy, chromatography, and other instrument-generated signals. It provides a recipes-style interface for common spectral preprocessing techniques.

measure helps you:

Convert measurement data from wide or long formats into an internal representation
Preprocess spectra using techniques like smoothing, derivatives, and normalization
Transform data back to wide or long format for modeling or visualization
Handle multi-dimensional data like LC-DAD, EEM fluorescence, and 2D NMR with native nD support
Decompose complex signals using PARAFAC, Tucker, and MCR-ALS methods

Installation

You can install the development version of measure from GitHub:

# install.packages("pak")
# pak::pak("JamesHWade/measure")
install.packages("measure", repos = c('https://jameshwade.r-universe.dev', 'https://cloud.r-project.org'))

Usage

The measure workflow follows the familiar recipes pattern: define a recipe, add steps, prep, and bake.

library(measure)
library(recipes)
library(ggplot2)

# NIR spectroscopy data for predicting meat composition
data(meats_long)
head(meats_long)
#> # A tibble: 6 × 6
#>      id water   fat protein channel transmittance
#>   <int> <dbl> <dbl>   <dbl>   <int>         <dbl>
#> 1     1  60.5  22.5    16.7       1          2.62
#> 2     1  60.5  22.5    16.7       2          2.62
#> 3     1  60.5  22.5    16.7       3          2.62
#> 4     1  60.5  22.5    16.7       4          2.62
#> 5     1  60.5  22.5    16.7       5          2.62
#> 6     1  60.5  22.5    16.7       6          2.62

Building a preprocessing recipe

rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  # Assign sample ID role (not used as predictor)
  update_role(id, new_role = "id") |>
  # Convert long-format measurements to internal representation
  step_measure_input_long(transmittance, location = vars(channel)) |>
  # Apply Savitzky-Golay smoothing with first derivative
  step_measure_savitzky_golay(window_side = 5, differentiation_order = 1) |>
  # Standard Normal Variate normalization
  step_measure_snv() |>
  # Convert back to wide format for modeling
  step_measure_output_wide(prefix = "nir_")

Preparing and applying the recipe

# Prep learns any parameters from training data
prepped <- prep(rec)
#> Dropping 1 list column for wide output: channel

# Bake applies the transformations
processed <- bake(prepped, new_data = NULL)

# Result is ready for modeling
processed[1:5, 1:8]
#> # A tibble: 5 × 8
#>      id water   fat protein  nir_01  nir_02  nir_03  nir_04
#>   <int> <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#> 1     1  60.5  22.5    16.7 -0.126  -0.110  -0.0928 -0.0745
#> 2     2  46    40.1    13.5  0.0184  0.0381  0.0601  0.0841
#> 3     3  71     8.4    20.5  0.105   0.114   0.125   0.136 
#> 4     4  72.8   5.9    20.7  0.0716  0.0786  0.0871  0.0974
#> 5     5  58.3  25.5    15.5 -0.132  -0.118  -0.101  -0.0817

Visualizing the preprocessing

# Get data at intermediate step (before output conversion)
rec_for_viz <- recipe(water + fat + protein ~ ., data = meats_long) |>
update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_savitzky_golay(window_side = 5, differentiation_order = 1) |>
  step_measure_snv()

processed_long <- bake(prep(rec_for_viz), new_data = NULL)

# Extract and plot a few spectra
library(tidyr)
library(dplyr)

plot_data <- processed_long |>
  slice(1:10) |>
  mutate(sample_id = row_number()) |>
  unnest(.measures)

ggplot(plot_data, aes(x = location, y = value, group = sample_id, color = factor(sample_id))) +
  geom_line(alpha = 0.7) +
  labs(
    x = "Channel",
    y = "Preprocessed Signal",
    title = "NIR Spectra After Preprocessing",
    subtitle = "Savitzky-Golay first derivative + SNV normalization",
    color = "Sample"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

Available Steps

Input/Output Steps

Step	Description
`step_measure_input_wide()`	Convert wide format (measurements in columns) to internal format
`step_measure_input_long()`	Convert long format (measurements in rows) to internal format
`step_measure_output_wide()`	Convert back to wide format for modeling
`step_measure_output_long()`	Convert back to long format

Spectral Math

Step	Description
`step_measure_absorbance()`	Convert transmittance to absorbance
`step_measure_transmittance()`	Convert absorbance to transmittance
`step_measure_log()`	Log transformation with configurable base/offset
`step_measure_kubelka_munk()`	Kubelka-Munk transformation for reflectance
`step_measure_derivative()`	Simple finite difference derivatives
`step_measure_derivative_gap()`	Gap (Norris-Williams) derivatives

Filtering & Scatter Correction

Step	Description
`step_measure_savitzky_golay()`	Smoothing and/or differentiation
`step_measure_snv()`	Standard Normal Variate normalization
`step_measure_msc()`	Multiplicative Scatter Correction
`step_measure_emsc()`	Extended MSC with wavelength-dependent correction
`step_measure_osc()`	Orthogonal Signal Correction

Smoothing & Noise Reduction

Step	Description
`step_measure_smooth_ma()`	Moving average smoothing
`step_measure_smooth_median()`	Median filter (robust to spikes)
`step_measure_smooth_gaussian()`	Gaussian kernel smoothing
`step_measure_smooth_wavelet()`	Wavelet denoising
`step_measure_filter_fourier()`	Fourier low-pass/high-pass filtering
`step_measure_despike()`	Spike/outlier detection and removal

Sample-wise Normalization

Step	Description
`step_measure_normalize_sum()`	Divide by sum (total intensity)
`step_measure_normalize_max()`	Divide by maximum value
`step_measure_normalize_range()`	Scale to 0-1 range
`step_measure_normalize_vector()`	L2/Euclidean normalization
`step_measure_normalize_auc()`	Divide by area under curve
`step_measure_normalize_peak()`	Normalize by peak region (tunable)

Variable-wise Scaling

Step	Description
`step_measure_center()`	Mean centering
`step_measure_scale_auto()`	Auto-scaling (z-score)
`step_measure_scale_pareto()`	Pareto scaling
`step_measure_scale_range()`	Range scaling
`step_measure_scale_vast()`	VAST scaling

Baseline Correction

Step	Description
`step_measure_baseline_als()`	Asymmetric least squares
`step_measure_baseline_poly()`	Polynomial baseline fitting
`step_measure_baseline_rf()`	Rolling ball/LOESS baseline
`step_measure_baseline_rolling()`	Rolling ball algorithm
`step_measure_baseline_airpls()`	Adaptive Iteratively Reweighted PLS
`step_measure_baseline_arpls()`	Asymmetrically Reweighted PLS
`step_measure_baseline_snip()`	SNIP (Statistics-sensitive Non-linear Iterative Peak-clipping)
`step_measure_baseline_tophat()`	Top-hat morphological filter
`step_measure_baseline_morph()`	Iterative morphological correction
`step_measure_baseline_minima()`	Local minima interpolation
`step_measure_baseline_auto()`	Automatic method selection
`step_measure_detrend()`	Polynomial detrending

Reference Corrections

Step	Description
`step_measure_subtract_blank()`	Blank/background subtraction
`step_measure_subtract_reference()`	Reference spectrum subtraction
`step_measure_ratio_reference()`	Reference ratio with optional blank

Region Operations

Step	Description
`step_measure_trim()`	Keep measurements within specified x-range
`step_measure_exclude()`	Remove measurements within specified range(s)
`step_measure_resample()`	Interpolate to new regular grid

Alignment & Registration

Step	Description
`step_measure_align_shift()`	Cross-correlation shift alignment
`step_measure_align_reference()`	Align to external reference spectrum
`step_measure_align_dtw()`	Dynamic Time Warping alignment
`step_measure_align_ptw()`	Parametric Time Warping
`step_measure_align_cow()`	Correlation Optimized Warping (tunable)

Quality Control

Step	Description
`step_measure_qc_snr()`	Calculate signal-to-noise ratio
`step_measure_qc_saturated()`	Detect saturated measurements
`step_measure_qc_outlier()`	Detect outlier samples
`step_measure_impute()`	Interpolate missing values

Peak Operations

Step	Description
`step_measure_peaks_detect()`	Detect peaks using prominence or derivative methods
`step_measure_peaks_integrate()`	Calculate peak areas
`step_measure_peaks_filter()`	Filter peaks by height, area, or count
`step_measure_peaks_deconvolve()`	Deconvolve overlapping peaks
`step_measure_peaks_to_table()`	Convert peaks to wide format for modeling

SEC/GPC Analysis

Step	Description
`step_measure_mw_averages()`	Calculate Mn, Mw, Mz, Mp, and dispersity
`step_measure_mw_distribution()`	Generate molecular weight distribution curve
`step_measure_mw_fractions()`	Calculate molecular weight fractions

Feature Engineering

Step	Description
`step_measure_integrals()`	Calculate integrated areas for specified regions
`step_measure_ratios()`	Calculate ratios between integrated regions
`step_measure_moments()`	Calculate statistical moments from spectra
`step_measure_bin()`	Reduce spectrum to fewer points via binning

Data Augmentation

Step	Description
`step_measure_augment_noise()`	Add random noise for training augmentation
`step_measure_augment_shift()`	Random x-axis shifts for shift invariance
`step_measure_augment_scale()`	Random intensity scaling

Drift & Batch Correction

Step/Function	Description
`step_measure_drift_qc_loess()`	QC-RLSC drift correction using LOESS
`step_measure_drift_linear()`	Linear drift correction
`step_measure_drift_spline()`	Spline-based drift correction
`step_measure_qc_bracket()`	QC bracketing interpolation
`step_measure_batch_reference()`	Reference-based batch correction
`measure_detect_drift()`	Detect significant drift in QC samples

Analytical Validation Functions

measure provides a comprehensive suite of functions for analytical method validation, designed for compatibility with ICH Q2(R2), ISO 17025, and similar regulatory frameworks.

Calibration & Quantitation

Function	Description
`measure_calibration_fit()`	Fit weighted calibration curves (linear/quadratic)
`measure_calibration_predict()`	Predict concentrations with uncertainty
`measure_calibration_verify()`	Continuing calibration verification
`measure_lod()` / `measure_loq()`	Detection and quantitation limits

Precision & Accuracy

Function	Description
`measure_repeatability()`	Within-run precision
`measure_intermediate_precision()`	Between-run precision with variance components
`measure_reproducibility()`	Between-lab precision
`measure_gage_rr()`	Gage R&R / Measurement System Analysis
`measure_accuracy()`	Bias, recovery, and accuracy assessment
`measure_linearity()`	Linearity with lack-of-fit testing
`measure_carryover()`	Carryover evaluation

Method Comparison

Function	Description
`measure_bland_altman()`	Bland-Altman analysis with limits of agreement
`measure_deming_regression()`	Deming regression for method comparison
`measure_passing_bablok()`	Passing-Bablok non-parametric regression
`measure_proficiency_score()`	z-scores, En scores, zeta scores for PT

Matrix Effects & Sample Prep QC

Function/Step	Description
`measure_matrix_effect()`	Quantify ion suppression/enhancement
`step_measure_standard_addition()`	Standard addition correction
`step_measure_dilution_correct()`	Back-calculate diluted concentrations
`step_measure_surrogate_recovery()`	Surrogate/internal standard recovery

Uncertainty & Quality Control

Function	Description
`measure_uncertainty_budget()`	ISO GUM uncertainty budgets
`measure_uncertainty()`	Combined and expanded uncertainty
`measure_control_limits()`	Shewhart, EWMA, or CUSUM limits
`measure_control_chart()`	Westgard multi-rule control charts
`measure_system_suitability()`	System suitability testing

Criteria System

Function	Description
`measure_criteria()`	Define acceptance criteria
`measure_assess()`	Evaluate data against criteria
`criteria_bioanalytical()`	FDA/EMA bioanalytical presets
`criteria_ich_q2()`	ICH Q2 validation presets

Learning more

Getting Started - A comprehensive introduction to measure
Preprocessing Techniques - Deep dive into available preprocessing methods
Analytical Validation - Calibration, uncertainty, and method validation

Datasets

Included Datasets

The package includes datasets for examples and testing:

Dataset	Technique	Samples	Description
`meats_long`	NIR	215	NIR transmittance spectra of meat samples (from modeldata)
`bioreactors_small`	Raman	210	Raman spectra from 15 small-scale bioreactors
`bioreactors_large`	Raman	42	Raman spectra from 3 large-scale bioreactors
`hplc_chromatograms`	HPLC-UV	20	Simulated HPLC chromatograms with 5 compounds
`sec_chromatograms`	SEC/GPC	10	Simulated SEC chromatograms (5 standards + 5 polymers)
`sec_calibration`	SEC/GPC	5	Calibration standards for molecular weight curves
`maldi_spectra`	MALDI-TOF	16	Simulated mass spectra (4 groups × 4 replicates)

# Load datasets
data(meats_long)
data(glucose_bioreactors)  # loads bioreactors_small and bioreactors_large
data(hplc_chromatograms)
data(sec_chromatograms)
data(sec_calibration)
data(maldi_spectra)

External Data Sources

For additional test data beyond what’s included with measure, these sources provide publicly available analytical measurement data:

R Packages with Spectral Data:

Package	Dataset	Technique	Description
modeldata	`meats`	NIR	Meat composition (wide format version)
prospectr	`NIRsoil`	NIR	Soil analysis with 825 samples
ChemoSpec	Various	IR, NMR	Multiple spectroscopy datasets
hyperSpec	Various	Raman, IR	Hyperspectral data examples

# Example: Load NIRsoil from prospectr
# install.packages("prospectr")
data(NIRsoil, package = "prospectr")

Online Repositories:

Mendeley Data - Search “spectroscopy”, “chromatography”, or “mass spectrometry”
Zenodo - Open science data repository
Kaggle Datasets - Community-contributed datasets
NIST Chemistry WebBook - Reference spectra (IR, MS, UV-Vis)
SDBS - Spectral Database for Organic Compounds (NMR, IR, MS)

Domain-Specific Databases:

Database	Data Type	URL
MassBank	Mass spectra	https://massbank.eu/MassBank/
HMDB	NMR, MS metabolomics	https://hmdb.ca/
NMRShiftDB	NMR spectra	https://nmrshiftdb.nmr.uni-koeln.de/
Crystallography Open Database	XRD patterns	https://www.crystallography.net/cod/

Related packages

measure builds on the tidymodels ecosystem:

recipes - The foundation for preprocessing pipelines
parsnip - Unified modeling interface
workflows - Bundle preprocessing and modeling
tune - Hyperparameter tuning (works with measure’s tunable steps!)

For spectral analysis in R, you might also find these packages useful:

prospectr - Spectral preprocessing functions
ChemoSpec - Exploratory chemometrics
mdatools - Multivariate data analysis

Contributing

This package is under active development. Contributions are welcome! Please see the contributing guidelines.

Code of Conduct

Please note that the measure project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.