Nondestructive grading test of rice seed activity using near infrared super-continuum laser spectrum

文献类型: 外文期刊

第一作者: Jin Wen-ling

作者: Jin Wen-ling;Cao Nai-liang;Chen Wei;Zhang Pei-guang;Zhao Qing-lei;Liang Jing-qiu;Lv Jin-guang;Kan Rui-feng;Jin Wen-ling;Zhu Ming-dong;Yu Ying-hong

作者机构:

关键词: near-infrared spectroscopy; seed vigor; partial least-squares discriminant analysis; principal component analysis

期刊名称:CHINESE OPTICS ( 影响因子:1.1; 五年影响因子:0.9 )

ISSN: 2095-1531

年卷期: 2020 年 13 卷 5 期

页码:

收录情况: SCI

摘要: In view of the urgent need for seed selection technology in agriculture and for grading detection of the vigor of three different years of unpeeled rice seeds, we proposed a new method of detecting the vigor of rice seeds based on near-infrared super-continuous laser spectrum to overcome the significant issues in preexisting universal brown rice detection technology. Firstly, we design a near-infrared absorption spectroscopy system with which we detect seed viability and measure the NIR spectra of three different years of unpeeled rice seeds. The results show that the activity gradient of the rice seeds is correlated with the characteristic absorption peak of their NIR absorption spectrum. Then, the spectrum of seed is optimized with a pretreatment algorithm of normalization, second derivative correction and orthogonal signal correction. Finally, a Principal Component Analysis (PCA) model is established to reduce the dimension of the spectrum and determine the optimal number of principal components. A Partial Least Squares Discriminant Analysis (PLS-DA) model is established. The analysis results show that the transmission absorption spectrum detection system designed in this paper combined with the PLS-DA discrimination model can classify rice seeds of different vigor with an accuracy of 94.44% and 95.92%. After screening, the germination rate of rice seeds can reach 97.17%. The results show that it is feasible to achieve non-destructive classification of rice seed activity using near-infrared spectroscopy with high accuracy.

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