On-Site Measurement of Fat and Protein Contents in Milk Using Mobile NMR Technology.

Robust and easy-to-use NMR sensor technology is proposed for accurate, on-site determination of fat and protein contents in milk. The two parameters are determined using fast consecutive 1H and 35Cl low-field NMR experiments on milk samples upon the 1:1 addition of a low-cost contrast solution. Reliable and accurate measurements are obtained without tedious calibrations and the need for extensive database information and may readily be conducted by non-experts in production site environments. This enables on-site application at farms or dairies, or use in laboratories harvesting significant reductions in costs and time per analysis as compared to wet-chemistry analysis. The performance is demonstrated for calibration samples, various supermarket milk products, and raw milk samples, of which some were analyzed directly in the milking room. To illustrate the wide application range, the supermarket milk products included both conventionally/organically produced, lactose-free milk, cow's, sheep's and goat's milk, homogenized and unhomogenized milk, and a broad nutrient range (0.1-9% fat, 1-6% protein). Excellent agreement between NMR measurements and reference values, without corrections or changes in calibration for various products and during extensive periods of experiment conduction (4 months) demonstrates the robustness of the procedure and instrumentation. For the raw milk samples, correlations between NMR and IR, NMR and wet-chemistry, as well as IR and wet-chemistry results, show that NMR, in terms of accuracy, compares favorably with the other methods.

Fast Wide-Line Solid-State NMR on a Low-Cost Benchtop Spectrometer.

Solid-state NMR may provide access to a wealth of information on molecular structure and dynamics. However, for many applications, the acquisition is challenged by broad resonances implying large spectral linewidths and low sensitivity. Conventionally, this is tackled by using costly and laboratory-fixed spectrometers based on large high-field superconducting magnets. In this Communication, we demonstrate that a range of challenging wide-line solid-state NMR spectra can be acquired on a robust, maintenance-free, low-cost benchtop/mobile NMR spectrometer with a sensitivity comparable to common high-field instruments. The performance and versatility for recording sensitive wide-line spectra is demonstrated through acquisition of 31 P NMR of paramagnetic FePO4 and full quadupolar lineshapes of Al2 O3 (27 Al) and KNO3 (14 N). Also, we introduce interleaved acquisition of frequency-stepped slices providing a dramatic reduction of the required experiment time.

High Throughput Identification and Quantification of Phospholipids in Complex Mixtures.

We present a fully automatic method, autoP, for identification and quantification of lipids in complex lipid mixtures from 1D (31)P and 2D (1)H-(31)P NMR spectra. The (31)P chemical shifts in lipids are highly sensitive to experimental conditions such as pH and temperature, so the present method uses the much more unambiguous (1)H chemical shifts for assignment and (31)P intensities for quantification. By using 2D (1)H-(31)P total correlation spectroscopy (TOCSY) correlation experiments, we demonstrate that approximately 20 different lipids can be automatically and unambiguously assigned and quantified by this automatic method.

Fast Forward Maximum entropy reconstruction of sparsely sampled data.

We present an analytical algorithm using fast Fourier transformations (FTs) for deriving the gradient needed as part of the iterative reconstruction of sparsely sampled datasets using the forward maximum entropy reconstruction (FM) procedure by Hyberts and Wagner [J. Am. Chem. Soc. 129 (2007) 5108]. The major drawback of the original algorithm is that it required one FT and one evaluation of the entropy per missing datapoint to establish the gradient. In the present study, we demonstrate that the entire gradient may be obtained using only two FT's and one evaluation of the entropy derivative, thus achieving impressive time savings compared to the original procedure. An example: A 2D dataset with sparse sampling of the indirect dimension, with sampling of only 75 out of 512 complex points (15% sampling) would lack (512-75)×2=874 points per ν(2) slice. The original FM algorithm would require 874 FT's and entropy function evaluations to setup the gradient, while the present algorithm is ∼450 times faster in this case, since it requires only two FT's. This allows reduction of the computational time from several hours to less than a minute. Even more impressive time savings may be achieved with 2D reconstructions of 3D datasets, where the original algorithm required days of CPU time on high-performance computing clusters only require few minutes of calculation on regular laptop computers with the new algorithm.