Validated Transfer of a Working Food Method from a Dispersive Instrument to the Antaris FT-NIR Analyzer

Importance of the topic

Near-infrared (NIR) spectroscopy is widely used for rapid, non-destructive quality control in food and other industries. The practical value of NIR increases substantially if chemometric methods can be transferred between different instruments without re-collecting full calibration sets. As many laboratories migrate from dispersive NIR instruments to Fourier-transform NIR (FT-NIR) platforms, validated, low-effort transfer procedures are essential to preserve analytical performance while minimizing downtime and resource consumption.

Objectives and study overview

This application note documents a validated transfer of an existing quantitative food powder method developed on a dispersive NIR spectrometer to a Thermo Scientific Antaris FT-NIR analyzer. Key aims were to (1) reproduce the predictive performance of the original dispersive method using TQ Analyst chemometric software, (2) evaluate a minimal set of transfer (inoculation) standards run on the FT instrument, and (3) quantify prediction agreement and precision metrics (R2, RMSEC, RMSEP) between dispersive, baseline (converted), and transfer calibrations.

Methodology and experimental design

The workflow comprised three steps:

• Baseline calibration creation: Existing dispersive spectra (307 calibration standards) and concentration data were converted from wavelength (nm) to wavenumber (cm-1) with interpolation to produce evenly spaced frequency-domain spectra and imported into TQ Analyst as the baseline method. Two processing adjustments were made relative to the original dispersive method: the spectral range was truncated from 9090–4000 cm-1 to 8800–4100 cm-1 to avoid a spectral artifact, and Norris smoothing was changed from 4,4 to 5,4 (derivative smoothing).
• Transfer (inoculation) calibration: 25 standards were scanned on the Antaris FT-NIR (diffuse reflectance, Sample Cup Spinner). Collection parameters: 32 scans per sample (~25 s), resolution 2.0 cm-1, Norton-Beer medium apodization, no zero filling. Ten of these FT spectra were incorporated into the baseline set as inoculation standards to form the transfer calibration; the remaining 15 served for validation of transfer performance.
• Validation testing: The same 25 standards scanned on the FT instrument were also measured on the original dispersive instrument and used to compare predictions across the original dispersive method, the baseline TQ Analyst method, and the transfer method. Percent differences, R2, RMSEC and RMSEP were computed to quantify agreement and prediction precision.

Used instrumentation

• Original platform: FOSS dispersive NIR spectrophotometer (customer instrument and data files).
• Target platform: Thermo Scientific Antaris FT-NIR analyzer, diffuse reflectance sampling with Sample Cup Spinner.
• Chemometrics: TQ Analyst software (Thermo Scientific) used to import spectra, handle missing concentration entries, create baseline calibration, and build transfer calibration with inoculation standards.

Main results and discussion

Key quantitative outcomes:

• Baseline vs. original dispersive method: Predictions for 25 validation standards analyzed on the dispersive instrument differed on average by 2.7% across four components—indicating the TQ Analyst baseline method reproduced the original dispersive predictive ability within ~2.7%.
• Transfer method performance: After incorporating 10 FT-run inoculation standards (~3% of the total calibration count) into the baseline calibration, predicted concentrations for the 25 dispersive spectra were within 1.0% of the baseline method on average—demonstrating excellent agreement across platforms.
• Precision metrics (selected summary): R2 values across original, baseline and transfer calibrations were nearly identical for components A–D. RMSEC values were comparable; RMSEP generally showed improved precision for baseline and transfer methods versus the original dispersive method in this data set.
• Inoculation effect on RMSEP: For a subset of FT-run validation standards, percent improvements in RMSEP after inoculation were substantial: Component A 41%, B 66%, C 34%, D 10%—showing that a small number of representative FT spectra can capture platform-specific variance and materially improve predictive precision.
• Practical processing notes: Conversion from nm to cm-1, careful handling of missing concentration entries (TQ Analyst's missing-data algorithm), and minor preprocessing adjustments (range truncation and smoothing parameter change) were sufficient to harmonize data from different instrument types.

Benefits and practical applications

• Minimized rework: The protocol avoids re-scanning hundreds of calibration standards on the new FT instrument; only a small inoculation set (approximately 3% of original calibration count) is required to account for cross-platform spectral variance.
• Time and resource efficiency: FT scans were short (~25 s per inoculation standard), and file importation into TQ Analyst is effectively automated, enabling rapid method migration and reduced downtime.
• Robust predictive performance: Transfer achieved near-identical predictions and improved or comparable prediction precision (RMSEP) relative to the original dispersive calibration, making FT adoption feasible without loss of quantitative capability.

Future trends and applications

• Broader platform migrations: As dispersive NIR instruments age and FT-NIR becomes more prevalent, validated transfer protocols like this will be increasingly important for industrial QC, PAT implementations, and multi-site harmonization.
• Standardized inoculation strategies: Formalizing strategies for selecting minimal, representative inoculation sets (including guidelines for coverage of concentration ranges and sample matrices) will improve reproducibility across use cases.
• Software-driven harmonization: Enhanced chemometric tools that automate interpolation, artifact detection, preprocessing adjustments, and transfer optimization will further reduce manual effort and subjectivity in method transfers.
• Cross-vendor compatibility: Development of vendor-neutral transfer workflows and data-exchange formats will facilitate migration between diverse hardware and software ecosystems.

Conclusion

The study demonstrates a practical, validated procedure to transfer an existing dispersive NIR quantitative method for a food powder to an Antaris FT-NIR analyzer with minimal effort and without rescanning the full calibration set. By converting spectral files to a baseline TQ Analyst calibration and incorporating a small number of FT-run inoculation standards, the transfer method matched baseline predictions within ~1% and improved prediction precision (RMSEP) substantially for several components. This approach supports rapid migration of NIR methods to FT platforms while preserving analytical performance.

Reference

• Thermo Fisher Scientific, Application Note 50696: Validated Transfer of a Working Food Method from a Dispersive Instrument to the Antaris FT-NIR Analyzer; authors Jeffrey Hirsch, Mike Bradley, Carla S. Draper, Garry L. Ritter; Madison, WI, USA; 2007.