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.
Highlights
Juglans rejia L., known as walnut or Qiang peach, is nutritious and has high economic value[1]
The results showed that low-field nuclear magnetic resonance (LFNMR) led to a rapid detection of moisture and oil contents in walnuts, while cluster analysis classified different walnuts varieties based on these parameters
Information on the transverse relaxation attenuation curves of the samples was stored in the file, i.e. the transverse relaxation time of hydrogen protons and the corresponding signal amplitude of hydrogen protons in samples stimulated by a 180° reversed-phase pulse sequence of Carr-Purcell-Meiboom-Gill sequence (CPMG) pulses
Summary
Juglans rejia L., known as walnut or Qiang peach, is nutritious and has high economic value[1]. Walnut classification is based on multiple factors, such as its phenotypic traits, and the sensory and nutritional qualities, which are used as the reference standard for germplasm evaluation and selective breeding[2,3,4,5,6,7]. The storage characteristics of walnuts are closely related to its moisture content. The activity of walnut lipase and acid value of oil is affected by different storage conditions and temperature. Different storage conditions should be adopted for walnuts with different moisture content; a rapid method for detecting the moisture and oil contents of walnuts (and thereby facilitating their classification based on these characteristics) is absent
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