Performance Assessment in Fingerprinting and Multi Component Quantitative NMR Analyses.

An interlaboratory comparison (ILC) was organized with the aim to set up quality control indicators suitable for multicomponent quantitative analysis by nuclear magnetic resonance (NMR) spectroscopy. A total of 36 NMR data sets (corresponding to 1260 NMR spectra) were produced by 30 participants using 34 NMR spectrometers. The calibration line method was chosen for the quantification of a five-component model mixture. Results show that quantitative NMR is a robust quantification tool and that 26 out of 36 data sets resulted in statistically equivalent calibration lines for all considered NMR signals. The performance of each laboratory was assessed by means of a new performance index (named Qp-score) which is related to the difference between the experimental and the consensus values of the slope of the calibration lines. Laboratories endowed with a Qp-score falling within the suitable acceptability range are qualified to produce NMR spectra that can be considered statistically equivalent in terms of relative intensities of the signals. In addition, the specific response of nuclei to the experimental excitation/relaxation conditions was addressed by means of the parameter named NR. NR is related to the difference between the theoretical and the consensus slopes of the calibration lines and is specific for each signal produced by a well-defined set of acquisition parameters.

Development of a food class-discrimination system by non-targeted NMR analyses using different magnetic field strengths.

Non-targeted NMR-based approach has received great attention as a rapid method for food product authenticity assessment. The availability of a database containing many comparable NMR spectra produced by different spectrometers is crucial to develop functional classifiers able to discriminate rapidly the commodity class of a given food product. Nevertheless, variability in spectrometer features may hamper the production of comparable spectra due to inherent variations in signal resolution. In this paper, we report on the development of a class-discrimination model for grape juice authentication by application of non-targeted NMR spectroscopy. Different approaches for the pre-treatment of data will be described along with details about the model validation. The developed model performed excellently (95.4-100% correct predictions) even when it was tested against 650 spectra produced by 65 spectrometers with different configurations (magnetic field strength, manufacturer, age). This study may boost the use of non-targeted NMR methods for food control.

A community-built calibration system: The case study of quantification of metabolites in grape juice by qNMR spectroscopy.

Nuclear Magnetic Resonance (NMR) is an analytical technique extensively used in almost every chemical laboratory for structural identification. This technique provides statistically equivalent signals in spite of using spectrometer with different hardware features and is successfully used for the traceability and quantification of analytes in food samples. Nevertheless, to date only a few internationally agreed guidelines have been reported on the use of NMR for quantitative analysis. The main goal of the present study is to provide a methodological pipeline to assess the reproducibility of NMR data produced for a given matrix by spectrometers from different manufacturers, with different magnetic field strengths, age and hardware configurations. The results have been analyzed through a sequence of chemometric tests to generate a community-built calibration system which was used to verify the performance of the spectrometers and the reproducibility of the predicted sample concentrations.