Successful combination of benchtop nuclear magnetic resonance spectroscopy and chemometric tools: A review.

Low-field nuclear magnetic resonance (NMR) has three general modalities: spectroscopy, imaging, and relaxometry. In the last twelve years, the modality of spectroscopy, also known as benchtop NMR, compact NMR, or just low-field NMR, has undergone instrumental development due to new permanent magnetic materials and design. As a result, benchtop NMR has emerged as a powerful analytical tool for use in process analytical control (PAC). Nevertheless, the successful application of NMR devices as an analytical tool in several areas is intrinsically linked to its coupling with different chemometric methods. This review focuses on the evolution of benchtop NMR and chemometrics in chemical analysis, including applications in fuels, foods, pharmaceuticals, biochemicals, drugs, metabolomics, and polymers. The review also presents different low-resolution NMR methods for spectrum acquisition and chemometric techniques for calibration, classification, discrimination, data fusion, calibration transfer, multi-block and multi-way.

Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration.

Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.

Compact low-field NMR spectroscopy and chemometrics applied to the analysis of edible oils.

Compact nuclear magnetic resonance (NMR) spectroscopy combined with chemometric tools opens new perspectives for NMR use. This work compares the potential of 43, 60 and 400 MHz NMR spectroscopy for quality control of edible oils. Partial least squares regression (PLSR) and support vector regression (SVR) models built on the three NMR devices had equivalent performances for fatty acids and iodine value, and the models built with the low field spectra were equivalent to the high field. Moreover, performances for calibration indicated that most of the models built with medium/or high-resolution fields presented reproducibility values lower than the minimum accepted by the American Oil Chemists' Society (AOCS). Compared to classical methods, this new approach allows the application of medium resolution devices as a sample screening tool in analytical laboratories since it allows the spectrum obtention in a few seconds, without the need for sample preparation or the use of deuterated solvents.

Increasing the detection distance of remote NMR using wireless inductive coupling coil.

Unilateral nuclear magnetic resonance (UNMR) spectrometers have been applied in a variety of fields such as petrochemistry, materials science, and process control 1 . In UNMR measurements the sample is placed outside of the UNMR sensor and the signal intensity is reduced almost exponentially as the sample-to-sensor distances increases. To expand the detection limits of remote UNMR sensors, wireless inductive coupling was proposed and tested. This strategy was proved to reduce signal attenuation due to sample detachment from sensor, resulting in an increase in detection distance by one order of magnitude (i.e., from few millimeters to few centimeters). This novel approach broadens the potential applications of UNMR sensors and opens new opportunities in several areas, from chemical to biomedical applications.