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.

Walnut (Juglans regia) is an economically significant woody oil tree species widely cultivated in China. However, its production is increasingly threatened by extreme low-temperature events, such as unseasonal frosts and late-spring cold. Salicylic acid (SA) is a key phytohormone known to enhance cold tolerance in plants, yet its underlying mechanism in walnut remains unclear. In this study, we present the first integrated analysis combining physiological measurements, transcriptomics, and metabolomics to investigate how exogenous SA improves cold tolerance in walnut leaves. Our results showed that SA treatment significantly increased the accumulation of soluble sugars, chlorophyll, and proline, enhanced peroxidase (POD) activity, and reduced malondialdehyde (MDA) levels under cold stress. Multi-omics analysis revealed that SA modulated the expression of genes involved in multiple hormone signaling pathways, including those of SA, auxin, jasmonic acid, and abscisic acid, and altered corresponding hormone levels. Notably, carbohydrate metabolism emerged as a central pathway mediating SA-induced cold adaptation. Weighted gene co-expression network analysis (WGCNA) further identified several core candidate genes, such as JrTGA, JrPP2C, JrTPS, and JrBAM, which may play key roles in this process. Collectively, this study provides the first multi-omics perspective on the regulatory network underlying SA-enhanced cold tolerance in walnut. These findings offer both a theoretical and technical foundation for applying SA in cold-resistant walnut cultivation and contribute to the development of stress-resilient production strategies.