Application of Low-Field Nuclear Magnetic Resonance (LF-NMR) Technology in Food Processing

Overview of Low-Field Nuclear Magnetic Resonance (LF-NMR) Technology:

Nuclear Magnetic Resonance (NMR) is a physical phenomenon that arises from the interaction between an alternating magnetic field and a static strong magnetic field with matter. When nuclear magnetic moments in the low-energy state absorb energy from the alternating field, they transition to higher energy states and generate NMR signals. Today, 1H NMR is the most widely applied form, while 13C NMR has also seen significant advances in recent years. Because NMR is fast, efficient, accurate, non-destructive, environmentally friendly, requires minimal sample preparation and does not pose health risks to operators, it is increasingly replacing traditional analytical techniques across many fields. NMR now has three major application branches: liquid-state NMR, solid-state NMR and magnetic resonance imaging (MRI).

NMR can be classified by field strength: high-field NMR (>1.0 T), mid-field NMR (0.5–1.0 T) and low-field NMR (<0.5 T). Low-field NMR, often called low-resolution NMR, is widely used to measure physical properties of materials and in food science it is mainly applied to determine lipid content, moisture content and the states in which water exists within foods.

Applications of LF-NMR in Food Processing:

1. Application in Baking

Baking (roasting) dehydrates materials using dry heat below their ignition point. Products include bread, biscuits, pastries and muffins. With rising living standards, consumers demand premium baked goods. Moisture content, distribution and activity are crucial to texture, colour and shelf life. LF-NMR enables study of water distribution, migration and water–molecule interactions before and after baking, helping to identify causes of quality defects.

LF-NMR use in baking is still developing. For example, Li Dongsen et al. used LF-NMR to study water migration and retention in battered and fried oyster products during reheating. Li Juan applied LF-NMR to whole-wheat dough with different enzyme and gum additions to investigate causes of poor baking quality. In tobacco curing, LF-NMR has been applied more extensively: Wei Shuo and Liang Guohai showed LF-NMR offered superior repeatability and precision compared with oven methods. LF-NMR’s non-destructive, repeatable capability makes it ideal for developing online monitoring tools for dynamic visualisation during baking.

2. Application in Frying

Frying cooks foods quickly in hot oil, affecting microbiology, flavour, texture and shelf life. Process parameters strongly influence final quality. LF-NMR can monitor physicochemical changes during frying and study how frying temperature affects product attributes. For instance, Ding Yuanyuan et al. used LF-NMR to map water and oil distribution in fried glutinous rice cakes and identify optimal frying temperature. Chen et al. used LF-NMR to investigate how pullulan influences oil uptake in frying and demonstrated the technique’s accuracy in determining oil content in fried starch systems.

3. Application in Steaming and Boiling

Steaming and boiling (100–200 °C range) affect moisture migration and texture. LF-NMR can characterise these changes. Ling et al. studied water migration in fresh versus dried noodles during boiling and linked migration rates to starch gelatinisation. Wan et al. showed vacuum and sous-vide steaming better preserved largemouth bass quality by analysing water migration.

4. Application in Drying

Drying removes moisture to aid storage and transport. Heat-sensitive foods can develop surface hardening and uneven moisture. LF-NMR offers non-destructive monitoring of moisture distribution and migration during drying, enabling optimisation of processing to protect quality. Li et al. compared LF-NMR with DSC for apple drying and found LF-NMR better distinguished bound and free water. Chen et al. used LF-NMR parameters to predict garlic quality across drying methods, demonstrating strong correlations. LF-NMR can support dynamic industrial drying control, as shown in studies on microwave drying of wheat and maize drying temperature optimisation. Combining LF-NMR with other measured properties enables predictive models for process control.

5. Application in Freezing

Freezing preserves food but temperature fluctuations drive ice recrystallisation and dry-out. LF-NMR can study the effects of freezing conditions, storage time and vapour pressure on product quality for meat, seafood and produce. Zhu Yingchun et al. used LF-NMR to evaluate water retention in beef under different packaging and temperatures. Liang Zuanhao et al. examined oysters frozen by several methods and assessed post-thaw moisture distribution. Chen et al. monitored cooked potatoes under time–temperature cycles to guide cold-chain handling. LF-NMR also helps evaluate active packaging films for fruit preservation by tracking internal moisture states.

LF-NMR can also assess additive effects in frozen foods. Carneiro et al. studied sodium tripolyphosphate’s impact on frozen shrimp water states. Tan et al. used LF-NMR to rapidly monitor squid coated with preservation glazes during frozen storage.

6. Application in Microencapsulation

Microencapsulation encloses active cores in polymer or inorganic shells to protect, mask flavours, or control release. LF-NMR applications here are emerging: Bao Shasha et al. used LF-NMR (CPMG signals) and built predictive models to estimate encapsulation efficiency of Antarctic krill oil microcapsules, enabling rapid, non-destructive evaluation.

7. Application in 3D Food Printing

Food 3D printing builds shapes from digital models. The printability of gels correlates with LF-NMR relaxation characteristics, which can predict rheological and structural behaviour. Xu et al. used LF-NMR to probe protons related to protein, water and fat in heat-treated egg yolk to optimise thermal processing for 3D printing. Phuhongsung et al. showed correlations between LF-NMR main peak T2 (T2(MP)), peak area (A22) and rheology for soy protein isolate gels, linking these metrics to printability. Thus, LF-NMR can be a rapid screening tool for materials suited to extrusion-based 3D food printing.

Rapid Testing

Because LF-NMR is fast, accurate and non-destructive for quality assessment, it holds great potential for rapid food testing. Current rapid-detection applications include monitoring solid-state fermentation, adulteration detection and lipid oxidation.

Li et al. used LF-NMR to monitor water dynamics during soaking, steaming and solid-state fermentation of glutinous rice, demonstrating LF-NMR’s utility for SSF monitoring. Miaw et al. combined LF-NMR with multivariate classification to detect adulteration in grape nectar. Wang Shengwei used LF-NMR plus PCA to distinguish injected or gel-added mutton. Dong Haisheng applied LF-NMR to quantify moisture and peroxide values in mooncakes, establishing non-destructive quality models. LF-NMR’s advantages make it increasingly valuable for food quality and authenticity testing.

References

[1] Guo Q., Li Y., Ren S., et al. Application of low-field NMR in rapid food safety detection. Journal of Food Safety and Quality Testing, 2019, 10(2): 380-384.

[2] Wang H. Principles and applications of low-field NMR analysis and testing. Proceedings of the 2010 Cross-Strait Biomedical Engineering Conference. Fujian: China Instrument and Control Society, 2010: 10.

[3] Zhang C. Progress in LF-NMR and MRI applications in food. Proceedings of “Healthy China 2030: Food Safety & Innovation” (Guangdong), 2018: 7.

[4] Li D., Du H., Hu Y., et al. Study of moisture changes during reheating of pre-fried products. Food Research and Development, 2017, 38(14): 4-7.

[5] Li J. Improving baking quality and mechanisms of moisture migration in wholegrain soda crackers. Dissertation, Jiangnan University, 2013.

[6] Wei S., Wang D., Su J., et al. Determination of moisture in tobacco stalks by LF-NMR. Tobacco Science & Technology, 2016, 49(10): 31-35.

[7] Liang G., Liu B., Zhu Z., et al. Analysis of tobacco moisture using LF-NMR. China Tobacco Journal, 2014, 20(5): 6-11.

[8] Ding Y., Zhou Y., Wu Y. Water and oil distribution in fried glutinous rice cakes at different frying temperatures. Food Industry Science and Technology, 2018, 39(13): 56-61.

[9] Chen L., McClements D. J., Zhang Z. P., et al. Effect of pullulan on oil absorption and structural organisation of native maize starch during frying. Food Chemistry, 2020, 309.

[10] Ling X., Tang N., Zhao B., et al. Water state, mobility and textural property of Chinese noodles during boiling. Food Science and Technology, 2019.

[11] Wan J., Cao A., Cai L. Effects of vacuum or sous-vide cooking on largemouth bass quality. Int. J. Gastronomy and Food Science, 2019, 18: 100181.

[12] Li X., Bi J., Jin X., et al. Characterization of apple pectin water-binding by LF-NMR and DSC. Food Bioprocess Technology, 2020, 13(2): 265-274.

[13] Chen Y., Li M., Dharmasiri T. S. K., et al. Ultrasonic-assisted vacuum drying for garlic: quality prediction by LF-NMR. Food Chemistry, 2020, 306: 125625.

[14] Qu C., Liu C., Wang F., et al. Microwave drying effects on wheat moisture migration. Food Industry, 2017, 38(2): 91-93.

[15] Ke Y., Xu X., Li L., et al. LF-NMR analysis of moisture states during maize drying. Food Industry, 2018, 39(11): 62-64.

[16] Li L., Zhang M., Yang P. Suitability of LF-NMR to analyse water state and predict dielectric properties of Chinese yam during microwave vacuum drying. LWT – Food Science and Technology, 2019, 105: 257-264.

[17] Zhu Y., Li Q., Ma L., et al. Effects of packaging and storage temperature on beef water retention. Food Research & Development, 2016, 37(22): 15-19.

[18] Liang Z., Chen H., Liang F., et al. LF-NMR study of immersion frozen oysters. Food Science, 2020.

[19] Chen Y., Singh J., Midgley J., et al. Influence of time–temperature cycles on potato starch retrogradation and digestion. Food Hydrocolloids, 2020.

[20] Li Y., Song W., Gao H., et al. PLA-P3,4HB active films with essential oils for peach preservation. Food Chemistry, 2019, 313: 126134.

[21] Carneiro C. D. S., Mársico E. T., Ribeiro R. D. O. R., et al. Study of sodium tripolyphosphate effects on frozen shrimp using LF-NMR. LWT – Food Science and Technology, 2013, 50(2).

[22] Tan M., Li P., Yu W., et al. Effects of glazing with preservatives on squid quality during frozen storage. Applied Sciences, 2019, 9(18): 3847.

[23] Bao S., Zhang J., Tan M., et al. Microencapsulation of Antarctic krill oil and an LF-NMR based method to estimate encapsulation efficiency. Journal of Chinese Food Science, 2018, 18(2): 272-279.

[24] Xu L., Gu L., Su Y., et al. Thermal treatment impact on egg yolk rheology and 3D printing characteristics. Food Hydrocolloids, 2020, 100.

[25] Phuhongsung P., Zhang M., Devahastin S. Investigation of 3D printing ability of soy protein isolate gels via LF-NMR. LWT, 2020, 122.

[26] Li T., Tu C., Rui X., et al. Water dynamics in glutinous rice processing by LF-NMR. Journal of Agricultural and Food Chemistry, 2015, 63(12): 3261-3270.

[27] Miaw C. S. W., Santos P. M., Silva A. R. C. S., et al. Multivariate classification for detection of grape nectar adulteration by LF-NMR. Food Analytical Methods, 2020.

[28] Wang S., Mu Y., Zhao X., et al. Distinguishing injected or gel-added mutton by LF-NMR relaxation traits. Food Industry, 2015, 36(6): 184-188.

[29] Dong H., Zang P., Zhao W., et al. Non-destructive detection of mooncake moisture and fats by LF-NMR. Journal of Hebei University of Science & Technology, 2018, 32(1): 50-57.

 

This article is reprinted from the WeChat public account “Zhongyuan Food Safety & Agri-Innovation Interest Group”, original article: “Food Safety 101 — What is low-field NMR? Basic principles and process applications of LF-NMR”.