In-Line Pharmaceutical Powder Blending Dataset for Calibration Transfer Algorithm Development and Validation

Calibration transfer between different process conditions is essential to ensure that models developed under one set of operating parameters can be reliably applied to others without loss of predictive performance. This dataset provides a complete, fully traceable in-line near-infrared (NIR) spectroscopic record of controlled pharmaceutical blending experiments designed for chemometric modeling and calibration transfer research.

We designed a 2×2 factorial experiment on two key blending process factors:

1. BLENDER ROTATION SPEED: 27 rpm vs 13.5 rpm
2. BATCH SIZE: 1.0 kg vs 0.5 kg

Each process condition was tested at three target acetaminophen (API) concentrations — 25%, 37.5%, and 50% w/w — yielding 12 distinct trials.

PROCESS CONFIGURATIONS (X1–X4):

X1: High speed (27 rpm), small batch (0.5 kg)
X2: High speed (27 rpm), large batch (1.0 kg)
X3: Low speed (13.5 rpm), small batch (0.5 kg)
X4: Low speed (13.5 rpm), large batch (1.0 kg)

Materials included acetaminophen as the active pharmaceutical ingredient and microcrystalline cellulose, lactose monohydrate, and magnesium stearate as excipients, all meeting pharmacopeial specifications.

Blending was performed in a Patterson Kelley V-Blender with an Intensifier Bar, following a consistent loading sequence and a pharmacopeial stratified sampling plan to ensure representative sampling over time and location.

Reference acetaminophen concentrations were quantified using a validated HPLC method per the United States Pharmacopeia monograph, with duplicate injections for each sampling location.

NIR spectra were acquired in reflectance mode using a MicroNIR PAT spectrometer, aligned to material residence times with integration and scan settings adjusted to drum rotation speed. Each trial yielded approximately 20–40 spectra during the steady-state blending phase (10–15 min).

THE DATASET ENABLES EXPLORATION OF:

- Calibration transfer across operational conditions (X1–X4)
- Strategies to reduce calibration burden in drug development phase
- Robustness of chemometric models to process variability

This open dataset provides a benchmark platform for developing, testing, and validating calibration transfer algorithms under realistic pharmaceutical process conditions.