One of the most important quality parameters of a fat, is its solid fat content (SFC). The standard method to determine the SFC is pNMR using a f‐factor. This factor is determined with three standards. However, this contribution shows that SFC standards are not required when using deconvolution methods. At first, data acquisition is optimized. These experiments revealed that the deconvolution method worked better, if more sample is present in the detection zone of the NMR, due to a higher signal‐to‐noise ratio (SNR). Regarding deconvolution, a bi‐Gaussian model and a model combining a Gaussian and Abragamian function are compared. Both models are able to fit the free induction decay (FID) data. Furthermore, the corresponding SFC values are comparable with the SFC values of the f‐factor method when analyzing SFC standards or fats which are preprocessed using the AOCS tempering protocol. Upon evaluating the influence of the polymorphic states of cocoa butter, it became clear that the f‐factor standards resemble fats containing α‐polymorphs. As a further consequence, the f‐factor method fails when β‐polymorphs are present to a large extent. Overall this study shows that the deconvolution method is superior to the f‐factor method since it does not require any standards and is less affected by the polymorphic state.
Practical Applications: This work shows that the solid fat content (SFC) of a fat can be calculated without the use of calibration standards. If deconvolution would replace the standard used pNMR method, it could potentially reduce the preparation time for the measurements, because no calibration is necessary. Next to this, it also lowers the cost of SFC determination, because no standards should be bought. Deconvolution also gives insight in the behavior of the different components present in the sample, for example, T2‐values. There above, research toward deconvolution of pNMR signals is necessary as it could potentially also determine the presence of different fat crystal polymorphs present in samples.
One of the most important quality parameters of a fat, is its solid fat content (SFC). The standard method to determine the SFC is pNMR using a f‐factor. However, this contribution shows that SFC standards are not requires when using deconvolution methods. At first, data acquisition is optimized. These experiments revealed that the deconvolution method worked better, if more sample is present in the detection zone of the NMR, due to a higher signal‐to‐noise ratio (SNR). Regarding deconvolution, a bi‐Gaussian model and a model combining a Gaussian and Abragamian function are compared. Both models are able to fit the free induction decay (FID) data.
Water‐in‐oil‐in‐water (W/O/W) double emulsions are a promising technology for encapsulation applications of water soluble compounds with respect to functional food systems. Yet molecular transport through the oil phase is a well‐known problem for liquid oil‐based double emulsions. The influence of network crystallization in the oil phase of W/O/W globules was evaluated by NMR and laser light scattering experiments on both a liquid oil‐based double emulsion and a solid fat‐based double emulsion. Water transport was assessed by low‐resolution NMR diffusometry and by an osmotically induced swelling or shrinking experiment, whereas manganese ion permeation was followed by means of T2‐relaxometry. The solid fat‐based W/O/W globules contained a crystal network with about 80% solid fat. This W/O/W emulsion showed a reduced molecular water exchange and a slower manganese ion influx in the considered time frame, whereas its globule size remained stable under the applied osmotic gradients. The reduced permeability of the oil phase is assumed to be caused by the increased tortuosity of the diffusive path imposed by the crystal network. This solid network also provided mechanical strength to the W/O/W globules to counteract the applied osmotic forces.
Using the water diffusion data, the use of a low measurement temperature and diffusion delay Δ could reduce the droplet size overestimation resulting from extra-droplet water diffusion, but this undesirable effect was inevitable. Detailed analysis of the diffusion data revealed that the extra-droplet diffusion effect was due to an exchange between the inner water phase and the oil phase, rather than by exchange between the internal and external aqueous phase. A promising data analysis procedure for retrieving reliable size data consisted of the application of Einstein's diffusion law to the experimentally determined diffusion distances. This simple procedure allowed determining the inner water droplet size of W/O/W emulsions upon measurement of water diffusion by low-resolution NMR at or even above room temperature.
A time‐domain 1H nuclear magnetic resonance relaxometry method was elaborated for the rapid microstructural characterization of mozzarella cheese. For this purpose, there is a strong need to know how the experimentally determined T2 relaxation time distribution can be related to specific constituents in mozzarella. In this study, a detailed investigation is offered for fresh and aged low‐moisture mozzarella cheese, often applied as a pizza cheese, by application of both a conventional Carr–Purcell–Meiboom–Gill (CPMG) sequence and a free‐induction decay CPMG (FID‐CPMG) sequence. The relaxation behavior was further elucidated by addition of deuterium oxide and by mild heat treatment of samples. The relaxation times of water protons in mozzarella were found to range from a few microseconds to some tens of milliseconds (in aged mozzarella) or to about hundred milliseconds (in fresh mozzarella). The upper limit of the T2 distribution can even be extended to the seconds range upon releasing water protons from the mozzarella matrix using a mild heat treatment or upon addition of deuterated water. Both stimuli also provided evidence for the absorption of water into the cheese matrix. The potential release and uptake of water demonstrated that mozzarella acts as a very dynamic system during production and storage. The detected differences in the behavior of the water fraction between fresh and aged low‐moisture mozzarella might be utilized to study the influence of either production and/or storage conditions on the cheese ripening process.
The polymorphic state of edible fats is an important quality parameter in fat research as well as in industrial applications. Nowadays, X-ray diffraction (XRD) is the most commonly used method to determine the polymorphic state. However, quantification of the different polymorphic forms present in a sample is not straightforward. Differential Scanning Calorimetry (DSC) is another method which provides information about fat crystallization processes: the different peaks in the DSC spectrum can be coupled to the melting/crystallisation of certain polymorphs. During the last decade, nuclear magnetic resonance (NMR) has been proposed as a method to determine, qualitatively and/or quantitatively, the polymorphic forms present in fat samples. In this work, DSC- and NMR-deconvolution methods were evaluated on their ability to determine the polymorphic state of cocoa butter, with XRD as a reference method. Cocoa butter was subjected to two different temperature profiles, which enforced cocoa butter crystallization in different polymorphic forms. It was found that XRD remains the best method to qualitatively determine the polymorphic state of the fat. Whereas the quantitative NMR and DSC deconvolution results were not fully in line with the XRD results in all cases, NMR deconvolution showed great promise both in a qualitative and quantitative way.
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