Application of NMR Spectroscopy for Foods and Lipids

Hwang, Hong‐Sik

doi:10.1007/978-3-319-54196-9_2

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…The oxidation stability of edible oils and lipid-rich foods is a very important issue, especially with foods that contain polyunsaturated fatty acids (PUFAs), an important nutritional component for health such as cardiovascular and neurological health [ 1 , 2 , 3 ]. Paradoxically, however, these lipids are highly susceptible to oxidation into toxic products during food preparation, transportation, storage, and cooking [ 4 , 5 , 6 ], affecting the food’s texture, taste, aroma, and shelf life [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Numerous chemical and physical analytical methods have been developed to assess lipid oxidation such as conjugated diene values, peroxide values (PV), alcohols, epoxides, p -anisidine value ( p -AV) assay, HBR titration, iodometric titration, xylenol orange, and total polar components (TPC) by analytical systems, e.g., HPLC, GC-MS, FTIR, SEC, ESR, and high-field (HF) NMR [ 6 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…Many of these technologies are based on methods with chemical reactions requiring relatively long reaction times and extensive labor, which cannot be automated; measure only one kind of oxidation product; only determine the concentration of an oxidation product that reaches a peak in a short time; and/or have inconsistencies due to multiple variations in the procedure [ 6 ]. Overcoming current analytical limitations requires analytical instruments for measuring multiple samples in a short time and detecting many oxidation products in a given sample during the oxidation processes.…”

Section: Introductionmentioning

confidence: 99%

“…The proton LF-NMR systems can potentially meet these needs for the analysis of lipid components in foods. More conveniently, user-friendly NMR instruments such as small benchtop LF-NMR instruments and software programs such as an automated analysis program for the fatty acid composition of vegetable oils have been developed [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…The low-field NMR relaxation sensor is a rapid, versatile, non-destructive analytical tool extensively used in the food, polymer, petroleum, and pharmaceutical industries [ 20 , 21 , 22 , 23 ], and is increasingly attracting the food industries’ attention for quality control without the need for chemicals with health or environmental effects [ 6 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Osheter

Campisi-Pinto

Randieri

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Osheter

Campisi-Pinto

Randieri

et al. 2023

Sensors

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

2022

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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.

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

et al. 2022

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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.

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Application of NMR Spectroscopy for Foods and Lipids

Cited by 3 publications

References 19 publications

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

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

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

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

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