1 H LF‐NMR Energy Relaxation Time Characterization of the Chemical and Morphological Structure of PUFA‐Rich Linseed Oil During Oxidation With and Without Antioxidants

Chemical Characteristics and Thermal Oxidative Stability of Novel Cold-Pressed Oil Blends: GC, LF NMR, and DSC Studies

Plant oils contain a high content of unsaturated fatty acids. Studies of food products have revealed a considerable disproportion in the ratio of ω6 to ω3. This article presents information on the healthful qualities of eight new oil blends that contain a beneficial proportion of ω6 to ω3 fatty acids (5:1), as well as their degradation during heating at 170 and 200 °C. The fatty acid profile was analyzed by gas chromatography (GC), content of polar compounds and polymers of triacylglycerols by liquid chromatography (LC), water content was measured by the Karl Fischer method, and oxidative stability was measured by differential scanning calorimetry (DSC) and low-field nuclear magnetic resonance (LF NMR) methods. The results showed that during heating, the polar fraction content increased in samples heated at both analyzed temperatures compared to unheated oils. This was mainly due to the polymerization of triacylglycerols forming dimers. In some samples that were heated, particularly those heated to 200 °C, trimers were detected, however, even with the changes that were observed, the polar fraction content of the blends did not go beyond the limit. Despite the high content of unsaturated fatty acids, the analyzed blends of oils are characterized by high oxidative stability, confirmed by thermoanalytical and nuclear magnetic resonance methods. The high nutritional value as well as the oxidative stability of the developed oil blends allow them to be used in the production of food, in particular products that ensure an adequate supply of ω3 fatty acids.

Semi-Autonomic AI LF-NMR Sensor for Industrial Prediction of Edible Oil Oxidation Status

The evaluation of an oil's oxidation status during industrial production is highly important for monitoring the oil's purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil's material characterization. Towards this goal, a ¹H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a ¹H LF-NMR relaxation curve, and its reconstruction into T₁ and T₂ spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T₂ relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T₂ signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications.

1H LF-NMR Self-Diffusion Measurements for Rapid Monitoring of an Edible Oil's Food Quality with Respect to Its Oxidation Status

The food quality of edible oils is dependent on basic chemical and structural changes that can occur by oxidation during preparation and storage. A rapid and efficient analytical method of the different steps of oil oxidation is described using a time-domain nuclear magnetic resonance (TD-NMR) sensor for measuring signals related to the chemical and physical properties of the oil. The degree of thermal oxidation of edible oils at 80 °C was measured by the conventional methodologies of peroxide and aldehyde analysis. Intact non-modified samples of the same oils were more rapidly analyzed for oxidation using a TD-NMR sensor for 2D T₁-T₂ and self-diffusion (D) measurements. A good linear correlation between the D values and the conventional chemical analysis was achieved, with the highest correlation of R² = 0.8536 for the D vs. the aldehyde concentrations during the thermal oxidation of poly-unsaturated linseed oils, the oil most susceptible to oxidation. A good correlation between the D and aldehyde levels was also achieved for all the other oils. The possibility to simplify and minimize the time of oxidative analysis using the TD NMR sensors D values is discussed as an indicator of the oil’s oxidation quality, as a rapid and accurate methodology for the oil industry.

Time Domain (TD) Proton NMR Analysis of the Oxidative Safety and Quality of Lipid-Rich Foods

Food safety monitoring is highly important due to the generation of unhealthy components within many food products during harvesting, processing, storage, transportation and cooking. Current technologies for food safety analysis often require sample extraction and the modification of the complex chemical and morphological structures of foods, and are either time consuming, have insufficient component resolution or require costly and complex instrumentation. In addition to the detection of unhealthy chemical toxins and microbes, food safety needs further developments in (a) monitoring the optimal nutritional compositions in many different food categories and (b) minimizing the potential chemical changes of food components into unhealthy products at different stages from food production until digestion. Here, we review an efficient methodology for overcoming the present analytical limitations of monitoring a food's composition, with an emphasis on oxidized food components, such as polyunsaturated fatty acids, in complex structures, including food emulsions, using compact instruments for simple real-time analysis. An intelligent low-field proton NMR as a time domain (TD) NMR relaxation sensor technology for the monitoring of T₂ (spin-spin) and T₁ (spin-lattice) energy relaxation times is reviewed to support decision-making by producers, retailers and consumers in regard to food safety and nutritional value during production, shipping, storage and consumption.

Sensitivity of Proton NMR Relaxation and Proton NMR Diffusion Measurements to Olive Oil Adulterations with Vegetable Oils

Olive oils and, in particular, extra-virgin olive oils (EVOOs) are one of the most frauded food. Among the different adulterations of EVOOs, the mixture of high-quality olive oils with vegetable oils is one of the most common in the market. The need for fast and cheap techniques able to detect extra-virgin olive oil adulterations was the main motivation for the present research work based on ¹H NMR relaxation and diffusion measurements. In particular, the ¹H NMR relaxation times, T₁ and T₂, measured at 2 and 100 MHz on about 60 EVOO samples produced in Italy are compared with those measured on four different vegetable oils, produced from macadamia nuts, linseeds, sunflower seeds, and soybeans. Self-diffusion coefficients on this set of olive oils and vegetable oil samples were measured by means of the ¹H NMR diffusion ordered spectroscopy (DOSY) technique, showing that, except for the macadamia oil, other vegetable oils are characterized by an average diffusion coefficient sensibly different from extra-virgin olive oils. Preliminary tests based on both NMR relaxation and diffusometry methods indicate that eventual adulterations of EVOO with linseed oil and macadamia oil are the easiest and the most difficult frauds to be detected, respectively.

Proton Low Field NMR Relaxation Time Domain Sensor for Monitoring of Oxidation Stability of PUFA-Rich Oils and Emulsion Products

The nutritional characteristics of fatty acid (FA) containing foods are strongly dependent on the FA’s chemical/morphological arrangements. Paradoxically the nutritional, health enhancing FA polyunsaturated fatty acids (PUFAs) are highly susceptible to oxidation into harmful toxic side products during food preparation and storage. Current analytical technologies are not effective in the facile characterization of both the morphological and chemical structures of PUFA domains within materials for monitoring the parameters affecting their oxidation and antioxidant efficacy. The present paper is a review of our work on the development and application of a proton low field NMR relaxation sensor (¹H LF NMR) and signal to time domain (TD) spectra reconstruction for chemical and morphological characterization of PUFA-rich oils and their oil in water emulsions, for assessing their degree and susceptibility to oxidation and the efficacy of antioxidants. The NMR signals are energy relaxation signals generated by spin–lattice interactions (T₁) and spin–spin interactions (T₂). These signals are reconstructed into 1D (T₁ or T₂) and 2D graphics (T₁ vs. T₂) by an optimal primal-dual interior method using a convex objectives (PDCO) solver. This is a direct measurement on non-modified samples where the individual graph peaks correlate to structural domains within the bulk oil or its emulsions. The emulsions of this review include relatively complex PUFA-rich oleosome-oil bodies based on the aqueous extraction from linseed seeds with and without encapsulation of externally added oils such as fish oil. Potential applications are shown in identifying optimal health enhancing PUFA-rich food formulations with maximal stability against oxidation and the potential for on-line quality control during preparation and storage.

Low field, time domain NMR in the agriculture and agrifood sectors: An overview of applications in plants, foods and biofuels

In this contribution, a selective overview of low field, time-domain NMR (TD-NMR) applications in the agriculture and agrifood sectors is presented. The first applications of commercial TD-NMR instruments were in food and agriculture domains. Many of these earlier methods have now been recognized as standard methods by several international agencies. Since 2000, several new applications have been developed, using state of the art instruments, new pulse sequences and new signal processing methods. TD-NMR is expected, in the coming years, to become even more important in quality control of fresh food and agricultural products, as well as for a wide range of food-processed products. TD-NMR systems provide excellent means to collect data relevant for use in the agricultural environment and the bioenergy industry. Data and information collected by TD-NMR systems thus may support decision makers in business and public organizations.