Mitigating instrument effects in 60 MHz 1H NMR spectroscopy for authenticity screening of edible oils

Low field (60 MHz) 1H NMR spectroscopy was used to analyse a large (n = 410) collection of edible oils, including olive and argan, in an authenticity screening scenario. Experimental work was carried out on multiple spectrometers at two different laboratories, aiming to explore multivariate model stability and transfer between instruments. Three modelling methods were employed: Partial Least Squares Discriminant Analysis, Random Forests, and a One Class Classification approach. Clear inter-instrument differences were observed between replicated data collections, sufficient to compromise effective transfer of models based on raw data between instruments. As mitigations to this issue, various data pre-treatments were investigated: Piecewise Direct Standardisation, Standard Normal Variates, and Rank Transformation. Datasets comprised both phase corrected and magnitude spectra, and it was found that that the latter spectral form may offer some advantages in the context of pattern recognition and classification modelling, particularly when used in combination with the Rank Transformation pre-treatment.

Automated summary

Low field 1H NMR spectroscopy was used to analyse a large collection of edible oils, revealing clear inter-instrument differences that hinder effective model transfer. Various data pre-treatments were investigated, with magnitude spectra showing potential advantages in pattern recognition and classification when used in combination with Rank Transformation.