用微量元素对东北大米产地识别的技术

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

为探讨元素指纹分析技术对东北三省大米产地识别的可行性,筛选出可以区分不同产地大米的标志元素,该研究采用电感耦合等离子体质谱(Inductively Coupled Plasma Mass Spectrometry,ICP-MS)测定东北三省主要水稻产区土壤-作物籽实中Li、B、Be等23种微量元素含量,利用相关分析、方差分析、偏最小二乘回归分析等多种分析方法对不同产地大米及土壤中微量元素含量进行分析,建立识别东北三省大米产地的判别模型。结果表明:大米中Mo、Zn含量与土壤中Mo、Zn含量呈显著正相关(P<0.01);3个省份大米中Ga、Pb、Sr、Zr、Ba元素分布表现出一致性,而另外18种元素表现出显著差异性(P<0.05)。对18种显著差异元素建立产地识别模型,发现正交偏最小二乘回归分析和多层感知器神经网络分析建立的判别模型能较好地对东北三省大米进行有效区分和识别,多层感知器神经网络分析中整体检验组的综合正确判别率为96.3%;在Fisher判别分析中利用逐步判别法筛选出的7种元素建立的判别模型能有效识别东北三省大米产地,判别正确率为93.8%。研究表明基于微量元素含量特征能够对东北三省大米产地进行有效识别,可为保护地区特色产品提供技术参考。

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.