A new method to predict the seed vigor of rice was developed to control adulteration during the seed trading process and to address the deficiencies of traditional manual detection methods. Low-field nuclear magnetic resonance (LF-NMR) technique was used to detect the vigor of rice seeds. Four varieties (Beijing-1, Qianchonglang-2, Yanfeng-47 and Shennong-265) of rice seeds from the Rice Research Institute of Shenyang Agricultural University were chosen for the experiment. The transverse relaxation time T2, T21 and T22 were observed in the experiment. The peak start time of free water (transverse relaxation time T22), signal amplitude of bound water (transverse relaxation time T21), and moisture content decreased with the decrease in the vigor of the seeds. There were no obvious trends observed for the top of the peak and the end point of the transverse relaxation time T22. In addition, the start, top, and end time of the peak (transverse relaxation time T21), and the signal amplitude of bound water showed no consistent changes. The results indicated that LF-NMR could be used as a method to distinguish the vigor of rice seeds rapidly. This study provided theoretical basis and technical support for the rapid detection of rice seed vigor. Keywords: nondestructive detection, nuclear magnetic resonance, transverse relaxation time, signal amplitude, rice, seeds vigor DOI: 10.25165/j.ijabe.20181106.4323 Citation: Song P, Song P, Yang H W, Yang T, Xu J, Wang K T. Detection of rice seed vigor by low-field nuclear magnetic resonance. Int J Agric & Biol Eng, 2018; 11(6): 195–200. (6): 195–200.

Micro-nondestructive detection of the moisture and ion of rice seeds during germination under salt stress

The change in the maize moisture content during different growth stages is an important indicator to evaluate the growth status of maize. In particular, the moisture content during the grain-filling stage reflects the grain quality and maturity and it can also be used as an important indicator for breeding and seed selection. At present, the drying method is usually used to calculate the moisture content and the dehydration rate at the grain-filling stage, however, it requires large sample size and long test time. In order to monitor the change in the moisture content at the maize grain-filling stage using small sample set, the Bootstrap re-sampling strategy-sample set partitioning based on joint x-y distances-partial least squares (Bootstrap-SPXY-PLS) moisture content monitoring model and near-infrared spectroscopy for small sample sizes of 10, 20, and 50 were used. To improve the prediction accuracy of the model, the optimal number of factors of the model was determined and the comprehensive evaluation thresholds RVP (coefficient of determination (R2), the root mean square error of cross-validation (RMSECV) and the root mean square error of prediction (RMSEP)) was proposed for sub-model screening. The model exhibited a good performance for predicting the moisture content of the maize grain at the filling stage for small sample set. For the sample sizes of 20 and 50, the R2 values were greater than 0.99. The average deviations of the predicted and reference values of the model were 0.1078%, 0.057%, and 0.0918%, respectively. Therefore, the model was effective for monitoring the moisture content at the grain-filling stage for a small sample size. The method is also suitable for the quantitative analysis of different concentrations using near-infrared spectroscopy and small sample size. Keywords: moisture content monitoring, maize, growth stage, near-infrared spectroscopy (NIRS), small sample set, model screening, optimal factor number, Bootstrap-SPXY-PLS DOI: 10.25165/j.ijabe.20191202.4708 Citation: Wang X, Ma T M, Yang T, Song P, Chen Z G, Xie H. Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size. Int J Agric & Biol Eng, 2019; 12(2): 132–140.

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.