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

Importance of the topic

The reliable transfer of established near-infrared (NIR) calibration models between instruments is critical for routine quality control and for scaling analytical methods from R&D to production. When spectral methods do not transfer cleanly between hardware platforms, re-collecting large calibration sets is time-consuming and costly, undermining the value of NIR as a process-analytical tool. This study addresses a practical and increasingly common need: transferring a working food-analysis method developed on a dispersive NIR spectrometer to a modern Fourier-transform NIR (FT-NIR) platform with minimal rework while preserving predictive performance.

Goals and overview of the study

The study aimed to demonstrate a validated, low-effort protocol to migrate a quantitative food powder method from a dispersive NIR instrument to the Thermo Scientific Antaris FT-NIR analyzer. Specific objectives were:

• To reproduce the dispersive-method predictive performance in TQ Analyst software (baseline method) using the original spectral and concentration files.
• To create a transfer calibration by adding a small set of spectra measured on the FT instrument (inoculation standards) to the baseline calibration.
• To validate and quantify differences in predictions, precision (RMSEC), and external prediction error (RMSEP) across the original dispersive method, the baseline TQ Analyst method, and the transfer method.

Used instrumentation

• Original dispersive spectrometer: FOSS NIR dispersive instrument (customer’s device), original spectra provided in wavelength (nm).
• Target FT instrument: Thermo Scientific Antaris FT-NIR analyzer using diffuse reflectance Sample Cup Spinner.
• Chemometric software: TQ Analyst (Thermo Fisher) for baseline, transfer calibration, and predictions.

Methodology

Key methodological steps and processing choices:

1. Baseline construction: Customer-provided spectral (text) and concentration files for 307 food-powder calibration standards were converted to JCAMP-DX and interpolated from wavelength (nm) to evenly spaced wavenumber (cm-1) data and imported into TQ Analyst. The baseline used most original preprocessing parameters, with two controlled changes: spectral region truncated to 8800–4100 cm-1 (original 9090–4000 cm-1) to remove a dispersive-artifact region, and Norris smoothing adjusted from 4,4 to 5,4. Partial least squares (PLS) regression and SNV plus first derivative pretreatment were retained.
2. Transfer calibration (inoculation): 25 standards were measured on the Antaris (32 scans, ~25 s, resolution 2.0 cm-1, no zero-filling, Norton–Beer medium apodization). Ten of these FT spectra were incorporated into the baseline as inoculation standards; the other 15 served as validation of the transfer. Reprocessing at 8 cm-1 resolution was evaluated and showed no effect on transfer performance.
3. Validation testing: The same 25 samples measured on the Antaris were returned to the customer and re-measured on the dispersive instrument. Predictions from: (a) the original dispersive method, (b) the baseline TQ Analyst method, and (c) the transfer method were compared to each other and to laboratory primary values. Missing concentration entries in the calibration (for several components across some standards) were handled using TQ Analyst’s missing-data algorithm, allowing partial use of standards rather than full exclusion.

Main results and discussion

Reproduction of original dispersive performance:

• The baseline TQ Analyst method reproduced the customer’s dispersive-method predictions to within an average of 2.7% across four components when the 25 validation standards (measured on dispersive) were compared (absolute difference relative to dispersive predictions).
• Calibration statistics (R2 and RMSEC) for the dispersive, baseline, and transfer calibrations were very similar, indicating comparable model fits across platforms.

Effectiveness of inoculation and transfer performance:

• Incorporating only 10 FT-measured inoculation standards (≈3% of the original 307-point calibration) produced a transfer method whose predicted concentrations for the 25 dispersive validation samples were within 1.0% of the baseline method on average.
• RMSEP (external prediction error) improved substantially after inoculation when evaluated on FT-measured validation subsets. Percent RMSEP improvement versus the non-inoculated baseline for components A–D were approximately 41%, 66%, 34%, and 10%, respectively. This demonstrates that a small set of inoculation spectra can model instrument-specific variance and materially improve prediction precision on the target platform.
• Resolution change (2.0 cm-1 vs reprocessed 8 cm-1) did not materially affect transfer performance in this case, indicating robustness to modest resolution differences between instruments.

Practical observations:

• Interpolation from wavelength to wavenumber and conversion to JCAMP-DX enabled direct import of legacy dispersive spectra into TQ Analyst, facilitating repro-duction of the original calibration without full re-measurement of the 307 standards on the FT instrument.
• The missing-data handling in TQ Analyst allowed retention of standards that had some unspecified component values, maximizing calibration utility.

Benefits and practical applications of the method

The protocol yields several practical advantages for laboratories and manufacturers migrating to FT-NIR:

• Time and resource savings: Avoids re-scanning large legacy calibration sets on new instruments; inoculation requires only a small number of target-instrument measurements.
• Fast deployment: In this study, each inoculation scan took less than one minute and file operations were automated within Thermo Fisher software workflows.
• Maintained analytical performance: Predictions and calibration statistics on the FT platform matched or improved on the dispersive instrument, enabling confident use of the transferred method in QC and PAT contexts.

Future trends and potential applications

Anticipated directions and opportunities include:

• Broader adoption of FT-NIR across industries as dispersive fleets age; method-transfer protocols will be essential for scalable NIR implementations across sites.
• Automation and standardization of inoculation selection guided by active-sampling or design-of-experiment strategies to optimize the minimal subset of transfer standards needed for diverse sample matrices.
• Advanced domain-adaptation and instrument-standardization algorithms (e.g., model updating, orthogonal signal correction, transfer with spectral standardization) combined with cloud-based spectral repositories could further reduce the need for physical transfer standards.
• Integration with PAT workflows: rapid transfer protocols support deployment of models to process-line analyzers and enable real-time feedback control across multiple plants.

Conclusion

The study demonstrates a validated, efficient pathway to transfer a working dispersive-NIR calibration for a food powder application to an Antaris FT-NIR analyzer with minimal loss of predictive accuracy. Key elements are conversion and reconstruction of the baseline calibration in TQ Analyst, plus the inclusion of a very small number of instrument-specific inoculation standards (about 3% of the original calibration count). The transfer method matched the baseline predictions within ~1% on average for the tested validation set and produced substantial RMSEP improvements for FT-measured samples. This protocol reduces downtime and preserves analytical integrity when migrating legacy NIR methods to FT platforms.

References

• Hirsch J., Bradley M., Draper C. S., Ritter G. L. Validated Transfer of a Working Food Method from a Dispersive Instrument to the Antaris FT-NIR Analyzer. Thermo Fisher Scientific Application Note 50696, 2007.