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Journal of Biomedical Physics & Engineering logoLink to Journal of Biomedical Physics & Engineering
. 2018 Mar 1;8(1):127–132.

Use of Magnetic Resonance Imaging in Food Quality Control: A Review

Hamed Ebrahimnejad 1*, Hadi Ebrahimnejad 2, A Salajegheh 3, H Barghi 4
PMCID: PMC5928302  PMID: 29732347

Abstract

Modern challenges of food science require a new understanding of the determinants of food quality and safety. Application of advanced imaging modalities such as magnetic resonance imaging (MRI) has seen impressive successes and fast growth over the past decade. Since MRI does not have any harmful ionizing radiation, it can be considered as a magnificent tool for the quality control of food products. MRI allows the structure of foods to be imaged noninvasively and nondestructively. Magnetic resonance images can present information about several processes and material properties in foods. This review will provide an overview of the most prominent applications of MRI in food research.

Keywords: Food Quality , Food Technology , Food Analysis , Food Industry , Magnetic Resonance Imaging

Introduction

With everyday increasing importance of food quality control and wise use of nondestructive methods, corresponding technologies are increasingly being promoted [1]. Traditional assessment procedures such as mechanical or chemical methods are destructive and time-consuming that cannot respond to the contemporary food industry demands. Over the last few decades, image-processing techniques such as magnetic resonance imaging (MRI), optical imaging, ultrasound, computed tomography (CT) and thermal imaging have been utilized for food quality evaluation [2].

MRI is a non-ionizing, non-invasive technology that is widely used in diagnostic radiology. In MR imaging, atomic particles interact with an external magnetic field and emit energy at specific frequencies. Therefore, the emitted signal intensity is representative of the imaged tissue structure [3]. Food science can also benefit from the recent vast developments in MRI. Inspection of food products, from the harvesting to the marketing, can be readily accomplished by MRI. It provides an unrivaled opportunity to better study foods and understand the dynamic interactions that occur during processing and storage [4]. Today, MRI is used at online quality control systems for meat, fruits and vegetables. The applications of MRI in food quality control are described below.

Body Composition and Fat Distribution

MRI has been extensively used for assessing meat and meat products [5-8]. The fat content and its distribution are important elements in tastiness, texture and smell of meat product. This is helpful at the online sorting systems [9]. MRI has been employed as an exquisite method for imaging the distribution of muscle and fat [10]; this is done by implementing specific MRI sequences in order to increase the contrast between muscle and fat tissues [11].

Different diffusion coefficients of protons in water and lipid molecules constitute the principles of diffusion-weighted MRI (DW-MRI) for imaging fat distribution. This type of imaging can be used to determine the amount of oil uptake in the meat frying process [12-14]. In another study, MRI and gas chromatography (GC) were used to evaluate the content of fat and visualize its distribution in Atlantic mackerel in two different conditions (most starved and well-fed). For starved fish, fat content (40±23 mg/g) measured by MRI had a correlation with GC (39±16 mg/g). However, no agreement was noticed for well-fed fish. This might be due to the production of non-triglyceride lipids in well-fed fish and different sensitivity of MRI and GC [15]. This survey demonstrated that MRI could depict fat content more accurately than traditional methods. MRI is helpful in determining the distribution, size, volume and shape of the adipose tissues in intact and live fish [16]. Besides fish, composition of other meat types have also been assessed using MRI [17,18].

Salt and Water Distribution

MRI can well illustrate salt diffusion and water mobility in meat during brine curing [19-26]. During curing, the meat microstructure greatly changes and the diffusion coefficient increases. The diffusion behavior was explicitly different in various meat tissues. For instance, the diffusion pattern in meat with connective tissue or fat obviously differs from that with pure myofilament. Moreover, MRI is also applied in fish products in order to monitor and optimize brining [27-30]. These studies proved that salt diffusion is higher in the low-density structured tissues.

Muscle Structure

MRI has the ability to provide structural information about muscle tissue [8,31-33]. Diffusion tensor imaging (DTI) is a kind of diffusion-weighted MRI that measures diffusion coefficients in at least 6 directions. DTI has the ability to delineate muscle fiber orientation and distribution [34]. It is asserted that DTI can exhibit complex structural information such as fiber type and diameter. These structural details have a correlation with muscle metabolic characteristics [35].

Cooking Process

MRI can be utilized to monitor the structural events occuring during cooking, proofing of dough and baking in order to enhance the process [36,37]. During cooking, structural and physical properties of meat would change. Dynamic MRI and thermal simulation were used to monitor deformations and water transfer in meat [20]. MRI was as efficient as traditional destructive methods. These surveys revealed that in contrast with water content, deformation increases with temperature depending on the tissue composition [21]. MRI has also been regarded as a fruitful method for the evaluation of texture and structure of lasagna, pasta, potato and noodles during and after cooking [38-40].

Freezing Process

The MRI technique can be considered as a useful tool for monitoring the freezing process. In an MRI study, the redistribution of water during drying and freezing of apple tissue was assessed [41]. Some researchers applied MRI to visualize the freezing mechanism of sucrose solution [42]. In a similar study, MRI was used to evaluate ice formation and crystallization of a sucrose solution during the freezing process [43]. The results proved that MRI could appropriately image the behavior of sucrose solutions during freezing.

Diaries, Cereals and Cookies

MRI has the benefit of being a non-destructive modality for assessing ripening kinetics and quantitative mapping of moisture or fat in dairy products such as cheese. MRI has been proposed to quantify the separation of cream from milk [44,45].

Water mobility and moisture migration in cereals and cookies such as rice kernel, corn flakes or caramel candies have been investigated by MRI [46,47]. It has been reported that water mobility changes due to various chemical interactions. MRI can also monitor the diffusion process of lipids in confectionery products like chocolate [48,49].

Fruits and Vegetables

There is a plethora of literature reporting the use of MRI as a non-destructive method for the evaluation of agricultural products and postharvest sorting and processing [50,51]. MRI allows studying soil, root, stem and leaf water content and transport [52].

Internal quality assessment and monitoring of ripening of a wide variety of fruits have been accomplished using MRI: apple, avocado, blueberry, cucumber, durian, kiwifruit, mandarin, mango, melon, nectarine, olive, onion, orange, papaya, pear, peach, pineapple, potato, persimmon, pomegranate, tangerine, tomato, strawberry, melon, watermelon and oil palm fruit [53-58]. MR images could provide useful information on the effect of chitosan on the maturity and conservation of citrus [59]. Usefulness of MRI in distinguishing mealy from fresh fruits such as apple and peach has been confirmed [60]. MRI was utilized to survey the extent of damage caused by low pressure in strawberry [61]. Infestation of apple fruits by the peach fruit moth was studied using MRI, and discrimination between sound and infested fruit was successful [62].

MRI is a technique which can detect and follow up the development of storage disorders over time. It has been applied to recognize core breakdown in pears, worm damage and bruises in different fruits [63,64]. Some authors investigated water status in kiwi fruits and announced that the water loss rate depends on the initial water status of the kiwi fruit [65].

Conclusion

MRI is an effective non-invasive technique for quality assessment in a wide variety of food products. In this review, we have discussed recent applications of MRI in the evaluation of body composition and fat distribution, salt and water distribution, muscle structure, cooking and freezing processes, diaries, cereals and cookies, fruits and vegetables. Magnetic resonance imaging is a non-destructive, precise and fast method which has many advantages over the traditional food quality control procedures.

Footnotes

Conflict of interests: None declared.

References

  • 1.Grunert KG. Food quality and safety: consumer perception and demand. European Review of Agricultural Economics. 2005;32:369–91. doi: 10.1093/eurrag/jbi011. [DOI] [Google Scholar]
  • 2.Xiong Z, Sun DW, Pu H, Gao W, Dai Q. Applications of Emerging Imaging Techniques for Meat Quality and Safety Detection and Evaluation: A Review. Crit Rev Food Sci Nutr. 2015;0 doi: 10.1080/10408398.2014.954282. [DOI] [PubMed] [Google Scholar]
  • 3.Bushong SC. Magnetic resonance imaging: physical and biological principles. 3rd ed. United States of America: Mosby; 2003. [Google Scholar]
  • 4.Van As H, van Duynhoven J. MRI of plants and foods. J Magn Reson. 2013;229:25–34. doi: 10.1016/j.jmr.2012.12.019. [DOI] [PubMed] [Google Scholar]
  • 5.Collewet G, Bogner P, Allen P, Busk H, Dobrowolski A, Olsen E, et al. Determination of the lean meat percentage of pig carcasses using magnetic resonance imaging. Meat Sci. 2005;70:563–72. doi: 10.1016/j.meatsci.2005.02.005. [DOI] [PubMed] [Google Scholar]
  • 6.Kremer PV, Forster M, Scholz AM. Use of magnetic resonance imaging to predict the body composition of pigs in vivo. Animal. 2013;7:879–84. doi: 10.1017/S1751731112002340. [DOI] [PubMed] [Google Scholar]
  • 7.Mohrmann M, Roehe R, Susenbeth A, Baulain U, Knap PW, Looft H, et al. Association between body composition of growing pigs determined by magnetic resonance imaging, deuterium dilution technique, and chemical analysis. Meat Sci. 2006;72:518–31. doi: 10.1016/j.meatsci.2005.08.020. [DOI] [PubMed] [Google Scholar]
  • 8.Monziols M, Collewet G, Bonneau M, Mariette F, Davenel A, Kouba M. Quantification of muscle, subcutaneous fat and intermuscular fat in pig carcasses and cuts by magnetic resonance imaging. Meat Sci. 2006;72:146–54. doi: 10.1016/j.meatsci.2005.06.018. [DOI] [PubMed] [Google Scholar]
  • 9.Dransfield E. The taste of fat. Meat Sci. 2008;80:37–42. doi: 10.1016/j.meatsci.2008.05.030. [DOI] [PubMed] [Google Scholar]
  • 10.Collewett G, Toussaint C, Davenel A, Akoka S, Médale F, Fauconneau B, et al. Magnetic resonance imaging as a tool to quantify the adiposity distribution in fish. Special Publication-Royal Society of Chemistry. 2001;262:252–8. doi: 10.1039/9781847551252-00252. [DOI] [Google Scholar]
  • 11.Davenel A, Bazin C, Quellec S, Challois S, Gispert M, Mercat M, et al. High throughput determination of intramuscular fat content by magnetic resonance imaging. Journees de la recherche porcine en France. 2012;44:53–4. [Google Scholar]
  • 12.Clerjon S, Bonny JM. Diffusion-weighted NMR micro-imaging of lipids: Application to food products. 2011. [Google Scholar]
  • 13.Clerjon S, Kondjoyan A, Bonny JM, Portanguen S, Chevarin C, Thomas A, et al. Oil uptake by beef during pan frying: impact on fatty acid composition. Meat Sci. 2012;91:79–87. doi: 10.1016/j.meatsci.2011.12.009. [DOI] [PubMed] [Google Scholar]
  • 14.Horigane A, Motoi H, Irie K, Yoshida M. Observation of the structure, moisture distribution, and oil distribution in the coating of tempura by NMR micro imaging. Journal of food science. 2003;68:2034–9. doi: 10.1111/j.1365-2621.2003.tb07014.x. [DOI] [Google Scholar]
  • 15.Brix O, Apablaza P, Baker A, Taxt T, Grüner R. Chemical shift based MR imaging and gas chromatography for quantification and localization of fat in Atlantic mackerel. Journal of experimental marine biology and ecology. 2009;376:68–75. doi: 10.1016/j.jembe.2009.06.006. [DOI] [Google Scholar]
  • 16.Wu J-L, Zhang J-L, Du X-X, Shen Y-J, Lao X, Zhang M-L, et al. Evaluation of the distribution of adipose tissues in fish using magnetic resonance imaging (MRI) Aquaculture. 2015;448:112–22. doi: 10.1016/j.aquaculture.2015.06.002. [DOI] [Google Scholar]
  • 17.Ballerini L, Hogberg A, Borgefors G, Bylund A-C, Lindgard A, Lundstrom K, et al. A segmentation technique to determine fat content in NMR images of beef meat. IEEE transactions on Nuclear Science. 2002;49:195–9. doi: 10.1109/TNS.2002.998751. [DOI] [Google Scholar]
  • 18.Toussaint C, Fauconneau B, Médale F, Collewet G, Akoka S, Haffray P, et al. Description of the heterogeneity of lipid distribution in the flesh of brown trout (Salmo trutta) by MR imaging. Aquaculture. 2005;243:255–67. doi: 10.1016/j.aquaculture.2004.09.029. [DOI] [Google Scholar]
  • 19.Aaslyng MD, Bejerholm C, Ertbjerg P, Bertram HC, Andersen HJ. Cooking loss and juiciness of pork in relation to raw meat quality and cooking procedure. Food quality and preference. 2003;14:277–88. doi: 10.1016/S0950-3293(02)00086-1. [DOI] [Google Scholar]
  • 20.Bouhrara M, Clerjon S, Damez JL, Chevarin C, Portanguen S, Kondjoyan A, et al. Dynamic MRI and thermal simulation to interpret deformation and water transfer in meat during heating. J Agric Food Chem. 2011;59:1229–35. doi: 10.1021/jf103384d. [DOI] [PubMed] [Google Scholar]
  • 21.Bouhrara M, Lehallier B, Clerjon S, Damez JL, Bonny JM. Mapping of muscle deformation during heating: in situ dynamic MRI and nonlinear registration. Magn Reson Imaging. 2012;30:422–30. doi: 10.1016/j.mri.2011.10.002. [DOI] [PubMed] [Google Scholar]
  • 22.Hansen CL, van der Berg F, Ringgaard S, Stodkilde-Jorgensen H, Karlsson AH. Diffusion of NaCl in meat studied by (1)H and (23)Na magnetic resonance imaging. Meat Sci. 2008;80:851–6. doi: 10.1016/j.meatsci.2008.04.003. [DOI] [PubMed] [Google Scholar]
  • 23.Ruiz-Cabrera MA, Gou P, Foucat L, Renou JP, Daudin JD. Water transfer analysis in pork meat supported by NMR imaging. Meat Sci. 2004;67:169–78. doi: 10.1016/j.meatsci.2003.10.005. [DOI] [PubMed] [Google Scholar]
  • 24.Veliyulin E, Egelandsdal B, Marica F, Balcom BJ. Quantitative 23Na magnetic resonance imaging of model foods. J Agric Food Chem. 2009;57:4091–5. doi: 10.1021/jf9000605. [DOI] [PubMed] [Google Scholar]
  • 25.Vestergaard C, Risum J, Adler-Nissen J. Quantification of salt concentrations in cured pork by computed tomography. Meat Sci. 2004;68:107–13. doi: 10.1016/j.meatsci.2004.02.011. [DOI] [PubMed] [Google Scholar]
  • 26.Vestergaard C, Risum J, Adler-Nissen J. (23)Na-MRI quantification of sodium and water mobility in pork during brine curing. Meat Sci. 2005;69:663–72. doi: 10.1016/j.meatsci.2004.11.001. [DOI] [PubMed] [Google Scholar]
  • 27.Aursand IG, Erikson U, Veliyulin E. Water properties and salt uptake in Atlantic salmon fillets as affected by ante-mortem stress, rigor mortis, and brine salting: a low-field 1 H NMR and 1 H/23 Na MRI study. Food chemistry. 2010;120:482–9. doi: 10.1016/j.foodchem.2009.10.041. [DOI] [Google Scholar]
  • 28.Aursand IG, Veliyulin E, Bocker U, Ofstad R, Rustad T, Erikson U. Water and salt distribution in Atlantic salmon (Salmo salar) studied by low-field 1H NMR, 1H and 23Na MRI and light microscopy: effects of raw material quality and brine salting. J Agric Food Chem. 2009;57:46–54. doi: 10.1021/jf802158u. [DOI] [PubMed] [Google Scholar]
  • 29.Erikson U, Veliyulin E, Singstad T, Aursand M. Salting and Desalting of Fresh and Frozen-thawed Cod (Gadus morhua) Fillets: A Comparative Study Using 23Na NMR, 23Na MRI, Low-field 1H NMR, and Physicochemical Analytical Methods. Journal of food science. 2004;69:FEP107–FEP14. doi: 10.1111/j.1365-2621.2004.tb13362.x. [DOI] [Google Scholar]
  • 30.Veliyulin E, Aursand IG. (1)H and (23)Na MRI studies of Atlantic salmon (Salmo salar) and Atlantic cod (Gadus morhua) fillet pieces salted in different brine concentrations. J Sci Food Agric. 2007;87(14):2676–83. doi: 10.1002/jsfa.3030. [DOI] [PubMed] [Google Scholar]
  • 31.Bonny J, Laurent W, Renou J. Characterisation of meat structure by NMR imaging at high field. Special publication-royal society of chemistry. 2001;262:17–21. doi: 10.1039/9781847551252-00017. [DOI] [Google Scholar]
  • 32.Laurent W, Bonny J, Renou J. Muscle characterisation by NMR imaging and spectroscopic techniques. Food chemistry. 2000;69:419–26. doi: 10.1016/S0308-8146(00)00051-0. [DOI] [Google Scholar]
  • 33.Pérez-Palacios T, Antequera T, Durán ML, Caro A, Rodríguez PG, Palacios R. MRI-based analysis of feeding background effect on fresh Iberian ham. Food chemistry. 2011;126:1366–72. doi: 10.1016/j.foodchem.2010.11.101. [DOI] [Google Scholar]
  • 34.Bonny JM, Renou JP. Water diffusion features as indicators of muscle structure ex vivo. Magn Reson Imaging. 2002;20:395–400. doi: 10.1016/S0730-725X(02)00515-5. [DOI] [PubMed] [Google Scholar]
  • 35.Amez J, Clerjon S, Labas R, Danon J, Peyrin F, Bonny J, editors . Microstructure characterization of meat by quantitative MRI. 12-17 August 2012. Montreal: 58th International Congress of Meat Science and Technology; 2012. [Google Scholar]
  • 36.Shaarani SM, Nott KP, Hall LD. Combination of NMR and MRI quantitation of moisture and structure changes for convection cooking of fresh chicken meat. Meat Sci. 2006;72:398–403. doi: 10.1016/j.meatsci.2005.07.017. [DOI] [PubMed] [Google Scholar]
  • 37.Van Duynhoven J, Goudappel GW, Weglarz W. Noninvasive assessment of moisture migration in food products by MRI. In: Codd S, Seymour JD et al., eds. Magnetic resonance microscopy. Weinheim: Wiley-VCH; 2009. pp. 331–351. [Google Scholar]
  • 38.Gonzalez J, McCarthy K, McCarthy M. Textural and structural changes in lasagna after cooking. Journal of texture studies. 2000;31:93–108. doi: 10.1111/j.1745-4603.2000.tb00286.x. [DOI] [Google Scholar]
  • 39.Thybo AK, Szczypiński PM, Karlsson AH, Dønstrup S, Stødkilde-Jørgensen HS, Andersen HJ. Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods. Journal of Food Engineering. 2004;61:91–100. doi: 10.1016/S0260-8774(03)00190-0. [DOI] [Google Scholar]
  • 40.Lai H-M, Hwang S-C. Water status of cooked white salted noodles evaluated by MRI. Food research international. 2004;37:957–66. doi: 10.1016/j.foodres.2004.06.008. [DOI] [Google Scholar]
  • 41.Hills BP, Remigereau B. NMR studies of changes in subcellular water compartmentation in parenchyma apple tissue during drying and freezing. International Journal of Food Science & Technology. 1997;32:51–61. doi: 10.1046/j.1365-2621.1997.00381.x. [DOI] [Google Scholar]
  • 42.Hindmarsh J, Buckley C, Russell A, Chen X, Gladden L, Wilson D, et al. Imaging droplet freezing using MRI. Chemical engineering science. 2004;59:2113–22. doi: 10.1016/j.ces.2003.12.031. [DOI] [Google Scholar]
  • 43.Mahdjoub R, Chouvenc P, Seurin MJ, Andrieu J, Briguet A. Sucrose solution freezing studied by magnetic resonance imaging. Carbohydr Res. 2006;341:492–8. doi: 10.1016/j.carres.2006.01.005. [DOI] [PubMed] [Google Scholar]
  • 44.Chaland B, Mariette F, Marchal P, De Certaines J. 1H nuclear magnetic resonance relaxometric characterization of fat and water states in soft and hard cheese. J Dairy Res. 2000;67:609–18. doi: 10.1017/S0022029900004398. [DOI] [PubMed] [Google Scholar]
  • 45.Mahdjoub R, Molegnana J, Seurin MJ, Briguet A. High resolution magnetic resonance imaging evaluation of cheese. Journal of food science. 2003;68:1982–4. doi: 10.1111/j.1365-2621.2003.tb07005.x. [DOI] [Google Scholar]
  • 46.Hwang S-S, Cheng Y-C, Chang C, Lur H-S, Lin T-T. Magnetic resonance imaging and analyses of tempering processes in rice kernels. Journal of Cereal Science. 2009;50:36–42. doi: 10.1016/j.jcs.2008.10.012. [DOI] [Google Scholar]
  • 47.Cornillon P, Salim LC. Characterization of water mobility and distribution in low- and intermediate-moisture food systems. Magn Reson Imaging. 2000;18:335–41. doi: 10.1016/S0730-725X(99)00139-3. [DOI] [PubMed] [Google Scholar]
  • 48.Mariette F. Investigations of food colloids by NMR and MRI. Current Opinion in Colloid & Interface Science. 2009;14:203–11. doi: 10.1016/j.cocis.2008.10.006. [DOI] [Google Scholar]
  • 49.Miquel ME, Hall LD. Measurement by MRI of storage changes in commercial chocolate confectionery products. Food research international. 2002;35:993–8. doi: 10.1016/S0963-9969(02)00160-6. [DOI] [Google Scholar]
  • 50.JHA SN, MATSUOKA T. Non-Destructive Techniques for Quality Evaluation of Intact Fruits and Vegetables. Food Science and Technology Research. 2000;6:248–51. doi: 10.3136/fstr.6.248. [DOI] [Google Scholar]
  • 51.Milczarek RR, McCarthy MJ. Low-field MR Sensors for Fruit Inspection. In: Codd SL, Seymour JD, eds. Magnetic Resonance Microscopy Weinheim, Wiley-VCH [Google Scholar]
  • 52.Van As H. Intact plant MRI for the study of cell water relations, membrane permeability, cell-to-cell and long distance water transport. J Exp Bot. 2007;58:743–56. doi: 10.1093/jxb/erl157. [DOI] [PubMed] [Google Scholar]
  • 53.Hills B, Clark C. Quality assessment of horticultural products by NMR. Annual Reports on NMR spectroscopy. 2003;50:75–120. doi: 10.1016/S0066-4103(03)50002-3. [DOI] [Google Scholar]
  • 54.Clark CJ, MacFall JS. Quantitative magnetic resonance imaging of ‘Fuyu’ persimmon fruit during development and ripening. Magn Reson Imaging. 2003;21:679–85. doi: 10.1016/S0730-725X(03)00082-1. [DOI] [PubMed] [Google Scholar]
  • 55.Musse M, Quellec S, Cambert M, Devaux MF, Lahaye M, Mariette F. Monitoring the postharvest ripening of tomato fruit using quantitative MRI and NMR relaxometry. Postharvest Biology and Technology. 2009;53:22–35. doi: 10.1016/j.postharvbio.2009.02.004. [DOI] [Google Scholar]
  • 56.Joyce DC, Hockings PD, Mazucco RA, Shorter AJ. 1H-Nuclear magnetic resonance imaging of ripening’Kensington Pride’mango fruit. Functional plant biology. 2002;29:873–9. doi: 10.1071/PP01150. [DOI] [PubMed] [Google Scholar]
  • 57.Shaarani SM, Cardenas-Blanco A, Amin MG, Soon NG, Hall LD. Monitoring development and ripeness of oil palm fruit (Elaeis guneensis) by MRI and bulk NMR. International Journal of Agriculture and Biology (Pakistan) 2010;12:101–105. [Google Scholar]
  • 58.Khoshroo A, Keyhani A, Zoroofi R, Yaghoobi G, editors . Nondestructive inspection of pomegranate maturity using magnetic resonance imaging and neural networks. April; 2011. France: CIGR Section VI International Symposium on Towards a Sustainable Food Chain Food Process, Bioprocess Food Qual Manag Nantes; 2011. [Google Scholar]
  • 59.Galed G, Fernandez-Valle ME, Martinez A, Heras A. Application of MRI to monitor the process of ripening and decay in citrus treated with chitosan solutions. Magn Reson Imaging. 2004;22:127–37. doi: 10.1016/j.mri.2003.05.006. [DOI] [PubMed] [Google Scholar]
  • 60.Barreiro P, Ortiz C, Ruiz-Altisent M, Ruiz-Cabello J, Fernandez-Valle ME, Recasens I, et al. Mealiness assessment in apples and peaches using MRI techniques. Magn Reson Imaging. 2000;18:1175–81. doi: 10.1016/S0730-725X(00)00179-X. [DOI] [PubMed] [Google Scholar]
  • 61.Otero L, Préstamo G. Effects of pressure processing on strawberry studied by nuclear magnetic resonance. Innovative Food Science & Emerging Technologies. 2009;10:434–40. doi: 10.1016/j.ifset.2009.04.004. [DOI] [Google Scholar]
  • 62.Haishi T, Koizumi H, Arai T, Koizumi M, Kano H. Rapid Detection of Infestation of Apple Fruits by the Peach Fruit Moth, Carposina sasakii Matsumura, Larvae Using a 0. 2-T Dedicated Magnetic Resonance Imaging Apparatus. Appl Magn Reson 2011;41:1–18. doi: 10.1007/s00723-011-0222-8. [ PMC Free Article] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Lammertyn J, Dresselaers T, Van Hecke P, Jancsok P, Wevers M, Nicolai BM. MRI and x-ray CT study of spatial distribution of core breakdown in ‘Conference’ pears. Magn Reson Imaging. 2003;21:805–15. doi: 10.1016/S0730-725X(03)00105-X. [DOI] [PubMed] [Google Scholar]
  • 64.Létal J, Jirak D, Šuderlová L, Hájek M. MRI ‘texture’analysis of MR images of apples during ripening and storage. LWT-Food Science and Technology. 2003;36:719–27. doi: 10.1016/S0023-6438(03)00099-9. [DOI] [Google Scholar]
  • 65.Burdon J, Clark C. Effect of postharvest water loss on ‘Hayward’kiwifruit water status. Postharvest Biology and Technology. 2001;22:215–25. doi: 10.1016/S0925-5214(01)00095-3. [DOI] [Google Scholar]

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