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

• Published in: International journal of agricultural and biological engineering Vol. 12; no. 6; pp. 116 - 121
• Main Authors: Song, Ping, Gao, He, Zhao, Baojun, Zhang, Aiguo, Liu, Feng
• Format: Journal Article
• Language: English
• Published: Beijing International Journal of Agricultural and Biological Engineering (IJABE) 01.11.2019
• Subjects: Analytic hierarchy process; Bound water; Chemical indicators; Classification; Cluster analysis; Edible oils; Magnetic fields; Methods; Moisture content; NMR; Nuclear magnetic resonance; Oil; Organic chemistry; Parameters; Principal components analysis; Relaxation time; Resonance; Signal processing; Software; Spectrum analysis; Statistical methods; Storage; Storage conditions; Technology assessment; Walnuts; Water content
• DOI: 10.25165/j.ijabe.20191206.5064
• ISSN: 1934-6344

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.