Towards common useable spectra in non-targeted analysis - A feasibility study by mid-infrared spectroscopy, transfer and correction approaches

Abstract

Spectroscopy-based methods are often used to verify the authenticity of food and feed. In the scientific environment, especially non-targeted approaches are becoming increasingly popular here, although their routine applicability, e.g., in official food control, is currently limited. The comparability of spectra acquired by different spectroscopic instruments using the same measurement principle is essential for the application of a common spectral database in laboratories and represents a current challenge in this field. In order to investigate possible approaches towards improved comparability of these spectra, a sample set of rapeseed (Brassica napus) and pumpkin seed (Cucurbita maxima) oils was analyzed using three Fourier-transform-mid-infrared spectrometers following the same procedure for sample preparation and measuring conditions. Depending on the instrument, the obtained spectra exhibited differences in absorbance, but not in wavelengths of bands position and maxima. As a result, the mutual prediction of original data (without mathematical correction) from different instruments using a partial least squares discriminant analysis model built and optimized with spectral data from only one instrument provided a sensitivity of 0% for the two-class model of rapeseed in pumpkin seed oil, indicating no discrimination at all. In order to minimize the spectral differences and thus to enhance the performance parameters of the prediction models, different mathematical correction approaches were investigated: (i) instrument-specific correction factors, (ii) combinations of different pre-processing steps and (iii) piecewise direct standardization (partial least squares-based regression). All investigated approaches achieved classification results with 100% sensitivity for the mathematically corrected data set of the respective instrument. The outcome of this study indicates that the correction approaches can be used to optimize the comparability of spectral data from different instruments, which is a first step towards harmonization in non-targeted analysis.