Identification technology for rice origins via tracking trace elements in Northeast China

Abstract: Northeast Rice is mainly grown in the plain areas of Heilongjiang, Jilin, and Liaoning provinces of China. The unique quality of Northeast rice can be attributed to the environmental advantages, including the fertile soil, sufficient sunshine, excellent water quality, long accumulated temperature, and large temperature difference between day and night. However, it is difficult to identify the Northeast rice in the market for the protection of regional special products. An accurate and rapid identification technology is of great significance to the Northeast rice origin. In this study, a total of 10 sampling areas were prepared in Heilongjiang, Jilin, and Liaoning provinces. 90 soil surface and rice samples were then collected. Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the content of 23 trace elements (such as Li, B, and Be) in 90 soil-crop seeds from the main rice-producing areas. The SPSS and SIMCA statistical analysis software was also used to analyze the distribution of trace elements in rice and soil from different producing areas. Correlation analysis showed that the contents of Mo and Zn in rice were positively correlated with the contents of Mo and Zn in soil. The analysis of variance showed that there was a consistent distribution of Ga, Pb, Sr, Zr, and Ba in rice from the three provinces, whereas, the rest 18 elements showed significant differences. Principal component analysis (PCA), partial least squares regression analysis (PLS-DA), orthogonal partial least squares regression analysis (OPLS-DA), fisher discriminant analysis (FDA), and multi-layer perceptron neural network (MLP-NN) were performed on the 18 elements with significant differences in rice. Furthermore, the cumulative variance of the first principal component and the second principal component was 46.39%, indicating only a little original variable information. There was no aggregate for the rice from the different provinces in two-dimensional space in the projection of the principal component score. By contrast, there was a small difference in rice element characteristics in the PLS-DA score chart, due to the geographical proximity. Meanwhile, confusion and cross phenomenon were found among rice samples from different producing areas. OPLS-DA, FDA, and MLP-NN were utilized to distinguish the rice from different producing areas. The OPLS-DA scores performed better to distinguish the rice from the Heilongjiang and Jilin provinces. There were a few overlaps in the samples between Jilin and Liaoning provinces, or between Heilongjiang and Liaoning provinces. The result of permutation test shows that the model established by orthogonal partial least squares regression analysis is reliable. In the FDA, the elements that were introduced into the Fisher discriminant model were B, Cr, Ni, Cu, Ge, Mo, and W in the order of stepwise discriminant analysis. The accuracy of the discriminant function was 93.8% for the original grouped cases, and 92.6% for the cross-validation of the rest. The multi-layer perceptron neural network was used to analyze 63 actual training samples, and 27 verification samples, with the group as the dependent variable, and 18 elements content as the covariable. The correct discrimination rate of training samples was 100%, and the comprehensive correct discrimination rate of the overall test group was 96.3%. Consequently, the different discrimination models, the content of trace elements in rice, and the characteristic elements can be expected to effectively distinguish the rice-producing areas of the three provinces in Northeast China.