Classification of different walnut varieties using low-field nuclear magnetic resonance technology and cluster analysis

Ping Song, He Gao, Baojun Zhao, Aiguo Zhang, Feng Liu

Abstract


To classify different walnut varieties based on water and oil content of walnut, and determine their storage conditions, the low-field nuclear magnetic resonance (LFNMR) technology was used to obtain the NMR transverse relaxation time (T2) of the samples based on the physical and chemical indicators of the walnut quality. The relationship between the relaxation time and phase state of the internal material of the sample was investigated, and the characteristic parameters of the NMR spectrum signals were statistically analyzed using cluster analysis to determine the different walnut varieties, and three different components, as well as their contents, were detected by a LFNMR spectrometer: firmly bound water, weakly bound water, and weakly bound oil. Test results indicated that the oil peak was dominant in the overall signal intensity compared to the water peaks, in which the firmly bound water phase contributed more to the overall water signal between the water peaks. Using the analytic hierarchy process of cluster analysis, 21 walnut samples were classified into three different classes, based on the characteristic parameters of the water-content and oil-content spectrum signals. The first class contains four walnut varieties characterized by least water and highest oil contents; the third class contains two walnut varieties, with the highest water content and least oil content; whereas, the second class contains 15 walnut varieties, with both water and oil contents at medium levels. The results showed that LFNMR led to a rapid detection of moisture and oil contents in walnuts, while cluster analysis classified different walnuts varieties based on these parameters. This study also provided the basis for optimizing the storage methods and storage conditions of walnuts.
Keywords: low-field nuclear magnetic resonance technology, transverse relaxation time, walnut, cluster analysis, decay curves
DOI: 10.25165/j.ijabe.20191206.5064

Citation: Song P, Gao H, Zhao B J, Zhang A G, Liu F. Classification of different walnut varieties using low-field nuclear magnetic resonance technology and cluster analysis. Int J Agric & Biol Eng, 2019; 12(6): 116–121.

Keywords


low-field nuclear magnetic resonance technology, transverse relaxation time, walnut, cluster analysis, decay curves

Full Text:

PDF

References


Zhang Y X. Fruit tree cultivation theory. Beijing: China Agricultural Press, 2008. (in Chinese )

Vahdati K, Pourtaklu S M, Karimi R, Barzehkar R, Amiri R, Mozaffari M, et al. Genetic diversity and gene flow of some Persian walnut populations in southeast of Iran revealed by SSR markers. Plant Systematics & Evolution, 2015; 301(2): 691–699.

Amiri R, Vahdati K, Mohsenipoor S, Mozaffari M R, Leslie C. Correlations between Some Horticultural Traits in Walnut. Hortscience A Publication of the American Society for Horticultural Science, 2010; 45(11): 361–365.

Pop I F, Vicol A C, Botu M, Raica P A, Vahdati K, Pamfil D. Relationships of walnut cultivars in a germplasm collection: Comparative analysis of phenotypic and molecular data. Scientia Horticulturae, 2013; 153(8): 124–135.

Li Z, Shun L, Gao F, Qian Y C, Wang X J. Research progress on nutritional components and activity of walnut kernel. Pharmaceutical Biotechnology, 2016; 23(5): 467–470. (in Chinese)

Pan X J, Zhang W E, Li Q Q, Wang J M, Zhang Z. Principal cornponent analysis and cluster analysis of sensory and nutritional quality of walnut. Food Science，2013; 34(8): 195–198. (in Chinese)

Deng F B, Luo L X, Hu H F, Ouyang Y Q, Yuan Y T, Zhang R. Analysis of phenotypic diversity of nuts in wild walnut (Juglans cathayensis Dode) in Xinjiang. Journal of Fruit Science, 2018; 35(3): 275–284. (in Chinese)

Song P, Peng Y F, Wang G H,Song P, Wang K T, Yang T. Detection of internal water flow in germinating corn seeds based on low field nuclear magnetic resonance. Transactions of the CSAE, 2018; 34(10): 274–281. (in Chinese)

Li Y R, Gao X S, Wu J. A fast identification method of sunflower-seed quality based on low field-nuclear magnetic resonance technology. Fenxi Shiyanshi, 2016; 35(7): 796–799.

Liu T, Wang T, Yang Y, Wang Z J, Zhuang Y P, Chu J, et al. Low field nuclear magnetic resonance for rapid quantitation of microalgae lipid and its application in high throughput screening. Chinese Journal of Biotechnology, 2016; 32(3): 385–396.

Li N, Li Y. Analysis of internal moisture changes of Benincasa hispida during vacuum drying using low-field NMR. Food Science, 2016; 37(23): 84–88. (in Chinese)

Song P, Xu J, Ma H N, Wang C, Yang T, Gao H. Moisture phase state and distribution characteristics of seed during rice seed soaking process by low field nuclear magnetic resonance. Transactions of the CSAE, 2016; 32(6): 204–210. (in Chinese)

Shao X L, Song W, Li Y F. Research progress of low field NMR detection technology for cereals, Oils and foodstuffs. Journal of the Chinese Cereals and Oils Association, 2013; 28(7): 114–118. (in Chinese)

Greer M, Cheng C, Mandal S. Automated classification of food products using 2D low-field NMR. Journal of Magnetic Resonance, 2018; 294(9): 44–58.

Zhang H, Ma L, Wu J, Xu J. Differentiation of different brands of sauced beef based on low field nuclear magnetic resonance technique. Farm Products Processing, 2018; 451(3): 10–12.

Xia A L, Xia X M, Ji L L, Zhao L Z. Distinction of leisure dried tofu brands by using chemical pattern recognition combined with low field nuclear magnetic resonance. Transactions of the CSAE, 2018; 34(10): 282–288.

Sze-Tao K W C, Sathe S K. Walnuts (Juglans regia L): proximate composition, protein solubility, protein amino acid composition and protein in vitro digestibility. Journal of the Science of Food & Agriculture, 2000; 80(9): 1393–1401.

Tapia M I, Sánchez Morgado J, García-Parra J, Ramírez R, González-Gómez D. Comparative study of the nutritional and bioactive compounds content of four walnut (Juglans regia L.) cultivars. Journal of Food Composition & Analysis, 2013; 31(2): 232–237.

Micklander E, Peshlov B, Purslow P P, Søren B E. NMR-cooking: monitoring the changes in meat during cooking by low-field 1 H-NMR. Trends in Food Science & Technology, 2002; 13(9): 341–346.

Li Y, Li X, Chen C, Zhao D W, Su Z G, Ma G H, et al. A rapid, non-invasive and non-destructive method for studying swelling behavior and microstructure variations of hydrogels. Carbohydrate Polymers, 2016; 151(10): 1251–1260.

Duan Y X, Zhao Y, Chi Y J. Low field nuclear magnetic resonance analysis of moisture distribution and quality variation in boiled eggs under different storage conditions. Food science, 2018; 39(9): 26–32. (in Chinese)

Krause A, Wu Y, Tian R, Van Beek T. Is low-field NMR a complementary tool to GC-MS in quality control of essential oils? A case study: patchouli essential oil. Planta Medica, 2018; 84(12/13): 953–963.

Kim J H, Lee H J, Kwon K, Chun H S, Ahn S, Kim B H. A 43 MHz low-field benchtop 1H nuclear magnetic resonance method to discriminate perilla oil Authenticity. Journal of Oleo Science, 2018; 67(5): 507–513.

Fan D, Ma S, Wang L, Zhao H F, Zhao J X, Zhang H, et al. 1H NMR studies of starch–water interactions during microwave heating. Carbohydrate Polymers, 2013; 97(2): 406–412.

Nie J Y, Zhang H J, Ma Z Y, Yang Z F, Li J. The application of cluster analysis in the fruit research in China and its problems .Journal of Fruit Science, 2000; 17(2): 128–130. (in Chinese)

Sturn A, Quackenbush J, Trajanoski Z. Genesis: cluster analysis of microarray data. Bioinformatics, 2002; 18(1): 207–208.

Liang X, Deng G, Yan B. Fruit and vegetable nutrition value assessment and replacement based on the principal component analysis and cluster analysis. Applied Mathematics, 2015; 6(9): 1620–1629.

Gong L Y, Meng X J, Liu N Q, Bi J F,. Evaluation of apple quality based on principal component and hierarchical cluster analysis. Transactions of the Chinese Society of Agricultural Engineering, 2014; 30(13): 276–285. (in Chinese)

Moore W C, Meyers D A, Wenzel S E, Teague W G, Li H S, Li X N, et al. Identification of asthma phenotypes using cluster analysis in the severe asthma research program. American Journal of Respiratory & Critical Care Medicine, 2010; 181(4): 315.

Fitzpatrick A M, Teague W G, Meyers D A, Peters S P, Li X N, Li H S, et al. Heterogeneity of severe asthma in childhood: confirmation by cluster analysis of children in the National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program. Journal of Allergy & Clinical Immunology, 2011; 127(2): 382–389.e13.




Copyright (c) 2019 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office